From f462ec96e8cd80aab86eb9803de29ff823fdf20a Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 17 Apr 2023 20:21:10 +0000 Subject: [PATCH 001/347] select edge props --- cpp/src/c_api/graph_mg.cpp | 21 +- cpp/src/c_api/graph_sg.cpp | 22 +- .../dask/sampling/uniform_neighbor_sample.py | 9 +- .../sampling/uniform_neighbor_sample.py | 13 +- .../cugraph/structure/convert_matrix.py | 6 +- .../cugraph/structure/graph_classes.py | 85 +- .../simpleDistributedGraph.py | 78 +- .../graph_implementation/simpleGraph.py | 102 +- .../cugraph/tests/structure/test_graph.py | 20 + .../cugraph/tests/structure/test_graph_mg.py | 23 + python/datasets/dolphins.csv | 318 + python/datasets/karate.csv | 156 + python/datasets/netscience.csv | 5484 +++++++++++++++++ python/datasets/polbooks.csv | 882 +++ python/datasets/small_line.csv | 9 + python/datasets/small_tree.csv | 11 + python/pylibcugraph/pylibcugraph/graphs.pyx | 28 +- .../internal_types/sampling_result.pyx | 12 + 18 files changed, 7146 insertions(+), 133 deletions(-) create mode 100644 python/datasets/dolphins.csv create mode 100644 python/datasets/karate.csv create mode 100644 python/datasets/netscience.csv create mode 100644 python/datasets/polbooks.csv create mode 100644 python/datasets/small_line.csv create mode 100644 python/datasets/small_tree.csv diff --git a/cpp/src/c_api/graph_mg.cpp b/cpp/src/c_api/graph_mg.cpp index 9b7206702ea..abd8a9e3fa7 100644 --- a/cpp/src/c_api/graph_mg.cpp +++ b/cpp/src/c_api/graph_mg.cpp @@ -338,19 +338,22 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( weight_type = cugraph_data_type_id_t::FLOAT32; } - CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_ids->type_ == edge_type), + CAPI_EXPECTS( + (edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: Edge id type must match edge (src/dst) type", + *error); + + CAPI_EXPECTS( + (edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", + "Invalid input arguments: src size != edge id prop size", *error); - CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), + CAPI_EXPECTS( + (edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != edge prop size", - *error); - - CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != edge prop size", + "Invalid input arguments: src size != edge type prop size", *error); cugraph_data_type_id_t edge_type_id_type; diff --git a/cpp/src/c_api/graph_sg.cpp b/cpp/src/c_api/graph_sg.cpp index 5267516f89b..4d0c73f38d1 100644 --- a/cpp/src/c_api/graph_sg.cpp +++ b/cpp/src/c_api/graph_sg.cpp @@ -513,26 +513,22 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( weight_type = cugraph_data_type_id_t::FLOAT32; } - // FIXME: The combination of edge_ids != nullptr, edge_type_ids == nullptr - // logically should be valid, but the code will currently break if - // that is that is specified CAPI_EXPECTS( - (edge_type_ids == nullptr && edge_ids == nullptr) || - (edge_type_ids != nullptr && edge_ids != nullptr), + (edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: either none or both of edge ids and edge types must be provided.", + "Invalid input arguments: Edge id type must match edge (src/dst) type", *error); CAPI_EXPECTS( - (edge_type_ids == nullptr && edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", - *error); + (edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge id prop size", + *error); - CAPI_EXPECTS((edge_type_ids == nullptr && edge_ids == nullptr) || - (p_edge_ids->size_ == p_src->size_ && p_edge_type_ids->size_ == p_dst->size_), + CAPI_EXPECTS( + (edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != edge prop size", + "Invalid input arguments: src size != edge type prop size", *error); cugraph_data_type_id_t edge_type_id_type = cugraph_data_type_id_t::INT32; diff --git a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py index 15d109452eb..c4e0c2da9b9 100644 --- a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py @@ -141,10 +141,11 @@ def convert_to_cudf(cp_arrays, weight_t, with_edge_properties, return_offsets=Fa df[dst_n] = cupy_destinations df[indices_n] = cupy_indices - if weight_t == "int32": - df.indices = df.indices.astype("int32") - elif weight_t == "int64": - df.indices = df.indices.astype("int64") + if cupy_indices is not None: + if weight_t == "int32": + df.indices = df.indices.astype("int32") + elif weight_t == "int64": + df.indices = df.indices.astype("int64") return df diff --git a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py index a7dad6c01a6..f363aaca4b3 100644 --- a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py @@ -233,13 +233,16 @@ def uniform_neighbor_sample( df["sources"] = sources df["destinations"] = destinations - df["indices"] = indices - if weight_t == "int32": - df["indices"] = indices.astype("int32") - elif weight_t == "int64": - df["indices"] = indices.astype("int64") + if indices is None: + df['indices'] = None else: df["indices"] = indices + if weight_t == "int32": + df["indices"] = indices.astype("int32") + elif weight_t == "int64": + df["indices"] = indices.astype("int64") + else: + df["indices"] = indices if G.renumbered: df = G.unrenumber(df, "sources", preserve_order=True) diff --git a/python/cugraph/cugraph/structure/convert_matrix.py b/python/cugraph/cugraph/structure/convert_matrix.py index 1b46f7db970..85c4b9f6bd1 100644 --- a/python/cugraph/cugraph/structure/convert_matrix.py +++ b/python/cugraph/cugraph/structure/convert_matrix.py @@ -77,12 +77,12 @@ def from_edgelist( if df_type is cudf.DataFrame: return from_cudf_edgelist( - df, source, destination, edge_attr, create_using, renumber + df, source, destination, edge_attr=edge_attr, create_using=create_using, renumber=renumber ) elif (pd is not None) and (df_type is pd.DataFrame): return from_pandas_edgelist( - df, source, destination, edge_attr, create_using, renumber + df, source, destination, edge_attr=edge_attr, create_using=create_using, renumber=renumber ) elif df_type is dask_cudf.core.DataFrame: @@ -99,7 +99,7 @@ def from_edgelist( "(or subclass) type or instance, got: " f"{type(create_using)}" ) - G.from_dask_cudf_edgelist(df, source, destination, edge_attr, renumber) + G.from_dask_cudf_edgelist(df, source, destination, edge_attr=edge_attr, renumber=renumber) return G else: diff --git a/python/cugraph/cugraph/structure/graph_classes.py b/python/cugraph/cugraph/structure/graph_classes.py index 5fd398124b8..03d55bc4dd7 100644 --- a/python/cugraph/cugraph/structure/graph_classes.py +++ b/python/cugraph/cugraph/structure/graph_classes.py @@ -107,6 +107,9 @@ def from_cudf_edgelist( source="source", destination="destination", edge_attr=None, + weight=None, + edge_id=None, + edge_type=None, renumber=True, store_transposed=False, legacy_renum_only=False, @@ -137,8 +140,21 @@ def from_cudf_edgelist( destination : str or array-like, optional (default='destination') destination column name or array of column names - edge_attr : str or None, optional (default=None) - the weights column name. + edge_attr : str or List[str], optional (default=None) + Names of the edge attributes. Can either be a single string + representing the weight column name, or a list of length 3 + holding [weight, edge_id, edge_type]. If this argument is + provided, then the weight/edge_id/edge_type arguments must + be left empty. + + weight : str, optional (default=None) + Name of the weight column in the input dataframe. + + edge_id : str, optional (default=None) + Name of the edge id column in the input dataframe. + + edge_type : str, optional (default=None) + Name of the edge type column in the input dataframe. renumber : bool, optional (default=True) Indicate whether or not to renumber the source and destination @@ -176,6 +192,9 @@ def from_cudf_edgelist( source=source, destination=destination, edge_attr=edge_attr, + weight=weight, + edge_id=edge_id, + edge_type=edge_type, renumber=renumber, store_transposed=store_transposed, legacy_renum_only=legacy_renum_only, @@ -254,6 +273,9 @@ def from_dask_cudf_edgelist( source="source", destination="destination", edge_attr=None, + weight=None, + edge_id=None, + edge_type=None, renumber=True, store_transposed=False, legacy_renum_only=False, @@ -280,8 +302,21 @@ def from_dask_cudf_edgelist( destination : str, optional (default='destination') Destination column name or array of column names - edge_attr : str, optional (default=None) - Weights column name + edge_attr : str or List[str], optional (default=None) + Names of the edge attributes. Can either be a single string + representing the weight column name, or a list of length 3 + holding [weight, edge_id, edge_type]. If this argument is + provided, then the weight/edge_id/edge_type arguments must + be left empty. + + weight : str, optional (default=None) + Name of the weight column in the input dataframe. + + edge_id : str, optional (default=None) + Name of the edge id column in the input dataframe. + + edge_type : str, optional (default=None) + Name of the edge type column in the input dataframe. renumber : bool, optional (default=True) If source and destination indices are not in range 0 to V where V @@ -308,12 +343,15 @@ def from_dask_cudf_edgelist( raise RuntimeError("Graph already has values") self._Impl._simpleDistributedGraphImpl__from_edgelist( input_ddf, - source, - destination, - edge_attr, - renumber, - store_transposed, - legacy_renum_only, + source=source, + destination=destination, + edge_attr=edge_attr, + weight=weight, + edge_id=edge_id, + edge_type=edge_type, + renumber=renumber, + store_transposed=store_transposed, + legacy_renum_only=legacy_renum_only, ) # Move to Compat Module @@ -323,6 +361,9 @@ def from_pandas_edgelist( source="source", destination="destination", edge_attr=None, + weight=None, + edge_id=None, + edge_type=None, renumber=True, ): """ @@ -334,7 +375,9 @@ def from_pandas_edgelist( of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used. - If weights are present, edge_attr argument is the weights column name. + Weights, edge ids, and edge types can be passed through either the + edge_attr argument or individually as separate keyword arguments. + All three are optional. Parameters ---------- @@ -347,8 +390,21 @@ def from_pandas_edgelist( destination : str or array-like, optional (default='destination') Destination column name or array of column names - edge_attr : str or None, optional (default=None) - The weights column name + edge_attr : str or List[str], optional (default=None) + Names of the edge attributes. Can either be a single string + representing the weight column name, or a list of length 3 + holding [weight, edge_id, edge_type]. If this argument is + provided, then the weight/edge_id/edge_type arguments must + be left empty. + + weight : str, optional (default=None) + Name of the weight column in the input dataframe. + + edge_id : str, optional (default=None) + Name of the edge id column in the input dataframe. + + edge_type : str, optional (default=None) + Name of the edge type column in the input dataframe. renumber : bool, optional (default=True) Indicate whether or not to renumber the source and destination @@ -376,6 +432,9 @@ def from_pandas_edgelist( source=source, destination=destination, edge_attr=edge_attr, + weight=weight, + edge_id=edge_id, + edge_type=edge_type, renumber=renumber, ) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index 4b33cf4c847..74178ab300b 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -86,33 +86,30 @@ def _make_plc_graph( num_edges, ): + weights = None + edge_ids = None + edge_types = None + if simpleDistributedGraphImpl.edgeWeightCol in edata_x[0]: - values = edata_x[0][simpleDistributedGraphImpl.edgeWeightCol] - if values.dtype == "int32": - values = values.astype("float32") - elif values.dtype == "int64": - values = values.astype("float64") - else: - # Some algos require the graph to be weighted - values = cudf.Series(cupy.ones(len(edata_x[0]), dtype="float32")) + weights = edata_x[0][simpleDistributedGraphImpl.edgeWeightCol] + if weights.dtype == "int32": + weights = weights.astype("float32") + elif weights.dtype == "int64": + weights = weights.astype("float64") if simpleDistributedGraphImpl.edgeIdCol in edata_x[0]: - if simpleDistributedGraphImpl.edgeTypeCol not in edata_x[0]: - raise ValueError("Must provide both edge id and edge type") - - values_id = edata_x[0][simpleDistributedGraphImpl.edgeIdCol] - values_etype = edata_x[0][simpleDistributedGraphImpl.edgeTypeCol] - else: - values_id, values_etype = None, None + edge_ids = edata_x[0][simpleDistributedGraphImpl.edgeIdCol] + if simpleDistributedGraphImpl.edgeTypeCol in edata_x[0]: + edge_types = edata_x[0][simpleDistributedGraphImpl.edgeTypeCol] return MGGraph( resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), graph_properties=graph_props, src_array=edata_x[0][src_col_name], dst_array=edata_x[0][dst_col_name], - weight_array=values, - edge_id_array=values_id, - edge_type_array=values_etype, + weight_array=weights, + edge_id_array=edge_ids, + edge_type_array=edge_types, store_transposed=store_transposed, num_edges=num_edges, do_expensive_check=False, @@ -125,6 +122,9 @@ def __from_edgelist( source="source", destination="destination", edge_attr=None, + weight=None, + edge_id=None, + edge_type=None, renumber=True, store_transposed=False, legacy_renum_only=False, @@ -161,8 +161,13 @@ def __from_edgelist( # The dataframe will be symmetrized iff the graph is undirected # otherwise, the inital dataframe will be returned if edge_attr is not None: + if weight is not None or edge_id is not None or edge_type is not None: + raise ValueError( + "If specifying edge_attr, cannot specify weight/edge_id/edge_type" + ) if isinstance(edge_attr, str): - edge_attr = [edge_attr] + weight = edge_attr + edge_attr = [weight] if not (set(edge_attr).issubset(set(input_ddf.columns))): raise ValueError( "edge_attr column name not found in input." @@ -198,21 +203,38 @@ def __from_edgelist( "undirected graph." ) - source_col, dest_col, value_col = symmetrize( + else: + value_col_names = {} + if weight is not None: + value_col_names[weight] = self.edgeWeightCol + self.properties.weighted=True + if edge_id is not None: + value_col_names[edge_id] = self.edgeIdCol + if edge_type is not None: + value_col_names[edge_type] = self.edgeTypeCol + + if len(value_col_names.keys()) > 0: + input_ddf = input_ddf.rename(columns=value_col_names) + value_col_names = list(value_col_names.values()) + + ddf_columns += value_col_names + input_ddf = input_ddf[ddf_columns] + + if len(value_col_names) == 0: + source_col, dest_col = symmetrize( input_ddf, source, destination, - value_col_names, multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) - + value_col = None else: - input_ddf = input_ddf[ddf_columns] - source_col, dest_col = symmetrize( + source_col, dest_col, value_col = symmetrize( input_ddf, source, destination, + value_col_names, multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) @@ -227,11 +249,9 @@ def __from_edgelist( # Multi column dask_cudf dataframe input_ddf = dask_cudf.concat([source_col, dest_col], axis=1) - if edge_attr is not None: - input_ddf[self.edgeWeightCol] = value_col[self.edgeWeightCol] - if len(edge_attr) == 3: - input_ddf[self.edgeIdCol] = value_col[self.edgeIdCol] - input_ddf[self.edgeTypeCol] = value_col[self.edgeTypeCol] + if value_col is not None: + for vc in value_col_names: + input_ddf[vc] = value_col[vc] self.input_df = input_ddf diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 7ad694e62f5..e15e3e852e3 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -24,8 +24,8 @@ import numpy as np import warnings from cugraph.dask.structure import replication -from typing import Union -from pylibcugraph import ( +from typing import Union, Dict, List +from pylibcugraph import ( get_two_hop_neighbors as pylibcugraph_get_two_hop_neighbors, select_random_vertices as pylibcugraph_select_random_vertices, ) @@ -46,25 +46,22 @@ class simpleGraphImpl: dstCol = "dst" class EdgeList: - def __init__(self, source, destination, edge_attr=None): + def __init__(self, source:str, destination:str, edge_attr:Union[cudf.DataFrame, Dict[str, cudf.DataFrame]]=None): + print('edge attr: ', edge_attr) self.edgelist_df = cudf.DataFrame() self.edgelist_df[simpleGraphImpl.srcCol] = source self.edgelist_df[simpleGraphImpl.dstCol] = destination self.weights = False if edge_attr is not None: - self.weights = True - if isinstance(edge_attr, (list, tuple)): - if len(edge_attr) == 3: - self.edgelist_df[simpleGraphImpl.edgeWeightCol] = edge_attr[0] - self.edgelist_df[simpleGraphImpl.edgeIdCol] = edge_attr[1] - self.edgelist_df[simpleGraphImpl.edgeTypeCol] = edge_attr[2] - elif len(edge_attr) == 1: - self.edgelist_df[simpleGraphImpl.edgeWeightCol] = edge_attr[0] - else: - raise ValueError( - "Illegal # of arguments provided" "for edge_attr" - ) + if isinstance(edge_attr, dict): + if edge_attr[simpleGraphImpl.edgeWeightCol] is not None: + self.weights = True + + for ea in [simpleGraphImpl.edgeIdCol, simpleGraphImpl.edgeTypeCol, simpleGraphImpl.edgeWeightCol]: + if edge_attr[ea] is not None: + self.edgelist_df[ea] = edge_attr[ea] else: + self.weights = True self.edgelist_df[simpleGraphImpl.edgeWeightCol] = edge_attr class AdjList: @@ -115,6 +112,9 @@ def __from_edgelist( source="source", destination="destination", edge_attr=None, + weight=None, + edge_id=None, + edge_type=None, renumber=True, legacy_renum_only=True, store_transposed=False, @@ -144,15 +144,19 @@ def __from_edgelist( df_columns = s_col + d_col if edge_attr is not None: + if weight is not None or edge_id is not None or edge_type is not None: + raise ValueError( + "If specifying edge_attr, cannot specify weight/edge_id/edge_type" + ) if isinstance(edge_attr, str): - edge_attr = [edge_attr] + weight = edge_attr + edge_attr = [weight] if not (set(edge_attr).issubset(set(input_df.columns))): raise ValueError( f"edge_attr column {edge_attr} not found in input." "Recheck the edge_attr parameter" ) self.properties.weighted = True - df_columns += edge_attr if len(edge_attr) != 1 and len(edge_attr) != 3: raise ValueError( @@ -169,7 +173,19 @@ def __from_edgelist( "types are not permitted for an " "undirected graph." ) - + + weight, edge_id, edge_type = edge_attr + else: + edge_attr = [] + if weight is not None: + edge_attr.append(weight) + self.properties.weighted=True + if edge_id is not None: + edge_attr.append(edge_id) + if edge_type is not None: + edge_attr.append(edge_type) + + df_columns += edge_attr input_df = input_df[df_columns] # FIXME: check if the consolidated graph fits on the # device before gathering all the edge lists @@ -178,22 +194,23 @@ def __from_edgelist( if isinstance(input_df, cudf.DataFrame): if len(input_df[source]) > 2147483100: raise ValueError( - "cudf dataFrame edge list is too big " "to fit in a single GPU" + "cudf dataFrame edge list is too big to fit in a single GPU" ) elist = input_df elif isinstance(input_df, dask_cudf.DataFrame): if len(input_df[source]) > 2147483100: raise ValueError( - "dask_cudf dataFrame edge list is too big " "to fit in a single GPU" + "dask_cudf dataFrame edge list is too big to fit in a single GPU" ) elist = input_df.compute().reset_index(drop=True) else: raise TypeError( - "input should be a cudf.DataFrame or " "a dask_cudf dataFrame" + "input should be a cudf.DataFrame or a dask_cudf dataFrame" ) # Original, unmodified input dataframe. self.input_df = elist + # Renumbering self.renumber_map = None self.store_transposed = store_transposed @@ -232,6 +249,8 @@ def __from_edgelist( multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) + print('symmetrized df:\n', value_col) + if isinstance(value_col, cudf.DataFrame): value_dict = {} for i in value_col.columns: @@ -248,8 +267,15 @@ def __from_edgelist( ) if isinstance(value_col, dict): - value_col = [value_col[ea] for ea in edge_attr] - + value_col = { + self.edgeWeightCol: value_col[weight] if weight in value_col else None, + self.edgeIdCol: value_col[edge_id] if edge_id in value_col else None, + self.edgeTypeCol: value_col[edge_type] if edge_type in value_col else None, + } + + print('weight name:', weight) + print('vc:') + print(value_col) self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, value_col) if self.batch_enabled: @@ -945,16 +971,15 @@ def _degree(self, vertex_subset, direction=Direction.ALL): return df - def _make_plc_graph(self, value_col=None, store_transposed=False, renumber=True): + def _make_plc_graph(self, value_col: Dict[str, cudf.DataFrame]=None, store_transposed:bool=False, renumber:bool=True): """ Parameters ---------- - value_col : cudf.DataFrame or tuple[cudf.DataFrame] + value_col : cudf.DataFrame or dict[str, cudf.DataFrame] If a single dataframe is provided, this is assumed to contain the edge weight values. - If a tuple of dataframes is provided, then it is - assumed to contain edge weights, edge ids, and - edge types, in that order. + If a dictionary of dataframes is provided, then it is + assumed to contain edge properties. store_transposed : bool (default=False) Whether to store the graph in a transposed format. Required by some algorithms. @@ -968,11 +993,10 @@ def _make_plc_graph(self, value_col=None, store_transposed=False, renumber=True) weight_col, id_col, type_col = None, None, None elif isinstance(value_col, (cudf.DataFrame, cudf.Series)): weight_col, id_col, type_col = value_col, None, None - elif isinstance(value_col, list): - if len(value_col) == 3: - weight_col, id_col, type_col = value_col - elif len(value_col) == 1: - weight_col, id_col, type_col = value_col[0], None, None + elif isinstance(value_col, dict): + weight_col = value_col[self.edgeWeightCol] + id_col = value_col[self.edgeIdCol] + type_col = value_col[self.edgeTypeCol] else: raise ValueError(f"Illegal value col {type(value_col)}") @@ -985,20 +1009,12 @@ def _make_plc_graph(self, value_col=None, store_transposed=False, renumber=True) input_array_format = "COO" src_or_offset_array = self.edgelist.edgelist_df[simpleGraphImpl.srcCol] dst_or_index_array = self.edgelist.edgelist_df[simpleGraphImpl.dstCol] - if weight_col is None: - # Some algos require the graph to be weighted - weight_col = cudf.Series( - cupy.ones(len(self.edgelist.edgelist_df), dtype="float32") - ) + elif self.adjlist is not None: input_array_format = "CSR" src_or_offset_array = self.adjlist.offsets dst_or_index_array = self.adjlist.indices - if weight_col is None: - # Some algos require the graph to be weighted - weight_col = cudf.Series( - cupy.ones(len(self.adjlist.indices), dtype="float32") - ) + else: raise TypeError( "Edges need to be represented in either in COO or CSR format." diff --git a/python/cugraph/cugraph/tests/structure/test_graph.py b/python/cugraph/cugraph/tests/structure/test_graph.py index 3734f6c9586..9ce8e58c00d 100644 --- a/python/cugraph/cugraph/tests/structure/test_graph.py +++ b/python/cugraph/cugraph/tests/structure/test_graph.py @@ -863,3 +863,23 @@ def test_select_random_vertices(graph_file, random_state, num_vertices): ) assert len(join) == len(sampled_vertices) + +@pytest.mark.sg +@pytest.mark.parametrize("graph_file", utils.DATASETS_SMALL) +@pytest.mark.parametrize("edge_props", [['edge_id', 'edge_type', 'weight'], ['edge_id', 'edge_type'], ['edge_type', 'weight'], ['edge_id'], ['weight']]) +def test_graph_creation_edge_properties(graph_file, edge_props): + df = utils.read_csv_file(graph_file) + + df['edge_id'] = cupy.arange(len(df), dtype='int32') + df['edge_type'] = cupy.int32(3) + df['weight'] = 0.5 + + prop_keys = {k : k for k in edge_props} + + G = cugraph.Graph(directed=True) + G.from_cudf_edgelist( + df, + source="0", + destination="1", + **prop_keys + ) \ No newline at end of file diff --git a/python/cugraph/cugraph/tests/structure/test_graph_mg.py b/python/cugraph/cugraph/tests/structure/test_graph_mg.py index b1b8d65c5a6..31c5afcdcf0 100644 --- a/python/cugraph/cugraph/tests/structure/test_graph_mg.py +++ b/python/cugraph/cugraph/tests/structure/test_graph_mg.py @@ -335,3 +335,26 @@ def test_mg_select_random_vertices( ) assert len(join) == len(sampled_vertices) + + +@pytest.mark.sg +@pytest.mark.parametrize("graph_file", utils.DATASETS_SMALL) +@pytest.mark.parametrize("edge_props", [['edge_id', 'edge_type', 'weight'], ['edge_id', 'edge_type'], ['edge_type', 'weight'], ['edge_id'], ['weight']]) +def test_graph_creation_edge_properties(dask_client, graph_file, edge_props): + df = utils.read_csv_file(graph_file) + + df['edge_id'] = cupy.arange(len(df), dtype='int32') + df['edge_type'] = cupy.int32(3) + df['weight'] = 0.5 + + df = dask_cudf.from_cudf(df, npartitions=2) + + prop_keys = {k : k for k in edge_props} + + G = cugraph.Graph(directed=True) + G.from_dask_cudf_edgelist( + df, + source="0", + destination="1", + **prop_keys + ) \ No newline at end of file diff --git a/python/datasets/dolphins.csv b/python/datasets/dolphins.csv new file mode 100644 index 00000000000..80b1c8b1a18 --- /dev/null +++ b/python/datasets/dolphins.csv @@ -0,0 +1,318 @@ +10 0 1.0 +14 0 1.0 +15 0 1.0 +40 0 1.0 +42 0 1.0 +47 0 1.0 +17 1 1.0 +19 1 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+98 100 1.0 +99 100 1.0 +100 101 1.0 +103 104 1.0 diff --git a/python/datasets/small_line.csv b/python/datasets/small_line.csv new file mode 100644 index 00000000000..55494314212 --- /dev/null +++ b/python/datasets/small_line.csv @@ -0,0 +1,9 @@ +0 1 1.0 +1 2 1.0 +2 3 1.0 +3 4 1.0 +4 5 1.0 +5 6 1.0 +6 7 1.0 +7 8 1.0 +8 9 1.0 diff --git a/python/datasets/small_tree.csv b/python/datasets/small_tree.csv new file mode 100644 index 00000000000..e8216bbb6ad --- /dev/null +++ b/python/datasets/small_tree.csv @@ -0,0 +1,11 @@ +0 1 1.0 +0 2 1.0 +0 3 1.0 +0 4 1.0 +1 5 1.0 +2 5 1.0 +3 5 1.0 +4 5 1.0 +5 6 1.0 +5 7 1.0 +5 8 1.0 diff --git a/python/pylibcugraph/pylibcugraph/graphs.pyx b/python/pylibcugraph/pylibcugraph/graphs.pyx index 8aed98cb98a..dfbbf09129b 100644 --- a/python/pylibcugraph/pylibcugraph/graphs.pyx +++ b/python/pylibcugraph/pylibcugraph/graphs.pyx @@ -142,7 +142,7 @@ cdef class SGGraph(_GPUGraph): GraphProperties graph_properties, src_or_offset_array, dst_or_index_array, - weight_array, + weight_array=None, store_transposed=False, renumber=False, do_expensive_check=False, @@ -177,18 +177,22 @@ cdef class SGGraph(_GPUGraph): create_cugraph_type_erased_device_array_view_from_py_obj( src_or_offset_array ) + cdef cugraph_type_erased_device_array_view_t* dsts_or_indices_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( dst_or_index_array ) + cdef cugraph_type_erased_device_array_view_t* weights_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( weight_array ) + cdef cugraph_type_erased_device_array_view_t* edge_id_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( edge_id_array ) + cdef cugraph_type_erased_device_array_view_t* edge_type_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( edge_type_array @@ -306,7 +310,7 @@ cdef class MGGraph(_GPUGraph): GraphProperties graph_properties, src_array, dst_array, - weight_array, + weight_array=None, store_transposed=False, num_edges=-1, do_expensive_check=False, @@ -354,18 +358,14 @@ cdef class MGGraph(_GPUGraph): create_cugraph_type_erased_device_array_view_from_py_obj( weight_array ) - cdef cugraph_type_erased_device_array_view_t* edge_id_view_ptr = NULL - if edge_id_array is not None: - edge_id_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - edge_id_array - ) - cdef cugraph_type_erased_device_array_view_t* edge_type_view_ptr = NULL - if edge_type_array is not None: - edge_type_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - edge_type_array - ) + cdef cugraph_type_erased_device_array_view_t* edge_id_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + edge_id_array + ) + cdef cugraph_type_erased_device_array_view_t* edge_type_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + edge_type_array + ) error_code = cugraph_mg_graph_create( resource_handle.c_resource_handle_ptr, diff --git a/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx b/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx index 55b3862774f..1391bbc9236 100644 --- a/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx +++ b/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx @@ -85,6 +85,10 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_edge_weight(self.c_sample_result_ptr) ) + + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) @@ -98,6 +102,10 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_edge_id(self.c_sample_result_ptr) ) + + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) @@ -108,6 +116,10 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_edge_type(self.c_sample_result_ptr) ) + + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) From 5bab68b322d51457364f09a60e973b6874b0b97f Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 17 Apr 2023 20:22:13 +0000 Subject: [PATCH 002/347] remove unwanted files --- python/datasets/dolphins.csv | 318 -- python/datasets/karate.csv | 156 - python/datasets/netscience.csv | 5484 -------------------------------- python/datasets/polbooks.csv | 882 ----- python/datasets/small_line.csv | 9 - python/datasets/small_tree.csv | 11 - 6 files changed, 6860 deletions(-) delete mode 100644 python/datasets/dolphins.csv delete mode 100644 python/datasets/karate.csv delete mode 100644 python/datasets/netscience.csv delete mode 100644 python/datasets/polbooks.csv delete mode 100644 python/datasets/small_line.csv delete mode 100644 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Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 17 Apr 2023 20:52:38 +0000 Subject: [PATCH 003/347] fix style --- cpp/src/c_api/graph_mg.cpp | 17 +++---- cpp/src/c_api/graph_sg.cpp | 15 +++---- .../sampling/uniform_neighbor_sample.py | 2 +- .../cugraph/structure/convert_matrix.py | 18 ++++++-- .../cugraph/structure/graph_classes.py | 12 ++--- .../simpleDistributedGraph.py | 8 ++-- .../graph_implementation/simpleGraph.py | 45 ++++++++++++------- .../cugraph/tests/structure/test_graph.py | 29 +++++++----- .../cugraph/tests/structure/test_graph_mg.py | 28 +++++++----- 9 files changed, 101 insertions(+), 73 deletions(-) diff --git a/cpp/src/c_api/graph_mg.cpp b/cpp/src/c_api/graph_mg.cpp index abd8a9e3fa7..f59bf703f19 100644 --- a/cpp/src/c_api/graph_mg.cpp +++ b/cpp/src/c_api/graph_mg.cpp @@ -338,20 +338,17 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( weight_type = cugraph_data_type_id_t::FLOAT32; } - CAPI_EXPECTS( - (edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", - *error); - - CAPI_EXPECTS( - (edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: Edge id type must match edge (src/dst) type", + *error); + + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, "Invalid input arguments: src size != edge id prop size", *error); - CAPI_EXPECTS( - (edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), + CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, "Invalid input arguments: src size != edge type prop size", *error); diff --git a/cpp/src/c_api/graph_sg.cpp b/cpp/src/c_api/graph_sg.cpp index 4d0c73f38d1..2ec1c536651 100644 --- a/cpp/src/c_api/graph_sg.cpp +++ b/cpp/src/c_api/graph_sg.cpp @@ -513,20 +513,17 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( weight_type = cugraph_data_type_id_t::FLOAT32; } - CAPI_EXPECTS( - (edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", - *error); + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: Edge id type must match edge (src/dst) type", + *error); - CAPI_EXPECTS( - (edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, "Invalid input arguments: src size != edge id prop size", *error); - CAPI_EXPECTS( - (edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), + CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), CUGRAPH_INVALID_INPUT, "Invalid input arguments: src size != edge type prop size", *error); diff --git a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py index f363aaca4b3..d6acaa550eb 100644 --- a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py @@ -234,7 +234,7 @@ def uniform_neighbor_sample( df["destinations"] = destinations if indices is None: - df['indices'] = None + df["indices"] = None else: df["indices"] = indices if weight_t == "int32": diff --git a/python/cugraph/cugraph/structure/convert_matrix.py b/python/cugraph/cugraph/structure/convert_matrix.py index 85c4b9f6bd1..ca8e93c482b 100644 --- a/python/cugraph/cugraph/structure/convert_matrix.py +++ b/python/cugraph/cugraph/structure/convert_matrix.py @@ -77,12 +77,22 @@ def from_edgelist( if df_type is cudf.DataFrame: return from_cudf_edgelist( - df, source, destination, edge_attr=edge_attr, create_using=create_using, renumber=renumber + df, + source, + destination, + edge_attr=edge_attr, + create_using=create_using, + renumber=renumber, ) elif (pd is not None) and (df_type is pd.DataFrame): return from_pandas_edgelist( - df, source, destination, edge_attr=edge_attr, create_using=create_using, renumber=renumber + df, + source, + destination, + edge_attr=edge_attr, + create_using=create_using, + renumber=renumber, ) elif df_type is dask_cudf.core.DataFrame: @@ -99,7 +109,9 @@ def from_edgelist( "(or subclass) type or instance, got: " f"{type(create_using)}" ) - G.from_dask_cudf_edgelist(df, source, destination, edge_attr=edge_attr, renumber=renumber) + G.from_dask_cudf_edgelist( + df, source, destination, edge_attr=edge_attr, renumber=renumber + ) return G else: diff --git a/python/cugraph/cugraph/structure/graph_classes.py b/python/cugraph/cugraph/structure/graph_classes.py index 03d55bc4dd7..0b6efce2bd2 100644 --- a/python/cugraph/cugraph/structure/graph_classes.py +++ b/python/cugraph/cugraph/structure/graph_classes.py @@ -149,10 +149,10 @@ def from_cudf_edgelist( weight : str, optional (default=None) Name of the weight column in the input dataframe. - + edge_id : str, optional (default=None) Name of the edge id column in the input dataframe. - + edge_type : str, optional (default=None) Name of the edge type column in the input dataframe. @@ -311,10 +311,10 @@ def from_dask_cudf_edgelist( weight : str, optional (default=None) Name of the weight column in the input dataframe. - + edge_id : str, optional (default=None) Name of the edge id column in the input dataframe. - + edge_type : str, optional (default=None) Name of the edge type column in the input dataframe. @@ -399,10 +399,10 @@ def from_pandas_edgelist( weight : str, optional (default=None) Name of the weight column in the input dataframe. - + edge_id : str, optional (default=None) Name of the edge id column in the input dataframe. - + edge_type : str, optional (default=None) Name of the edge type column in the input dataframe. diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index 74178ab300b..f4b193d015e 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -207,19 +207,19 @@ def __from_edgelist( value_col_names = {} if weight is not None: value_col_names[weight] = self.edgeWeightCol - self.properties.weighted=True + self.properties.weighted = True if edge_id is not None: value_col_names[edge_id] = self.edgeIdCol if edge_type is not None: value_col_names[edge_type] = self.edgeTypeCol - + if len(value_col_names.keys()) > 0: input_ddf = input_ddf.rename(columns=value_col_names) value_col_names = list(value_col_names.values()) - + ddf_columns += value_col_names input_ddf = input_ddf[ddf_columns] - + if len(value_col_names) == 0: source_col, dest_col = symmetrize( input_ddf, diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index e15e3e852e3..78d7aedfc5a 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -16,7 +16,6 @@ from cugraph.structure.symmetrize import symmetrize from cugraph.structure.number_map import NumberMap import cugraph.dask.common.mg_utils as mg_utils -import cupy import cudf import dask_cudf import cugraph.dask.comms.comms as Comms @@ -24,8 +23,8 @@ import numpy as np import warnings from cugraph.dask.structure import replication -from typing import Union, Dict, List -from pylibcugraph import ( +from typing import Union, Dict +from pylibcugraph import ( get_two_hop_neighbors as pylibcugraph_get_two_hop_neighbors, select_random_vertices as pylibcugraph_select_random_vertices, ) @@ -46,8 +45,13 @@ class simpleGraphImpl: dstCol = "dst" class EdgeList: - def __init__(self, source:str, destination:str, edge_attr:Union[cudf.DataFrame, Dict[str, cudf.DataFrame]]=None): - print('edge attr: ', edge_attr) + def __init__( + self, + source: str, + destination: str, + edge_attr: Union[cudf.DataFrame, Dict[str, cudf.DataFrame]] = None, + ): + print("edge attr: ", edge_attr) self.edgelist_df = cudf.DataFrame() self.edgelist_df[simpleGraphImpl.srcCol] = source self.edgelist_df[simpleGraphImpl.dstCol] = destination @@ -57,7 +61,11 @@ def __init__(self, source:str, destination:str, edge_attr:Union[cudf.DataFrame, if edge_attr[simpleGraphImpl.edgeWeightCol] is not None: self.weights = True - for ea in [simpleGraphImpl.edgeIdCol, simpleGraphImpl.edgeTypeCol, simpleGraphImpl.edgeWeightCol]: + for ea in [ + simpleGraphImpl.edgeIdCol, + simpleGraphImpl.edgeTypeCol, + simpleGraphImpl.edgeWeightCol, + ]: if edge_attr[ea] is not None: self.edgelist_df[ea] = edge_attr[ea] else: @@ -173,13 +181,13 @@ def __from_edgelist( "types are not permitted for an " "undirected graph." ) - + weight, edge_id, edge_type = edge_attr else: edge_attr = [] if weight is not None: edge_attr.append(weight) - self.properties.weighted=True + self.properties.weighted = True if edge_id is not None: edge_attr.append(edge_id) if edge_type is not None: @@ -204,9 +212,7 @@ def __from_edgelist( ) elist = input_df.compute().reset_index(drop=True) else: - raise TypeError( - "input should be a cudf.DataFrame or a dask_cudf dataFrame" - ) + raise TypeError("input should be a cudf.DataFrame or a dask_cudf dataFrame") # Original, unmodified input dataframe. self.input_df = elist @@ -249,7 +255,7 @@ def __from_edgelist( multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) - print('symmetrized df:\n', value_col) + print("symmetrized df:\n", value_col) if isinstance(value_col, cudf.DataFrame): value_dict = {} @@ -270,11 +276,13 @@ def __from_edgelist( value_col = { self.edgeWeightCol: value_col[weight] if weight in value_col else None, self.edgeIdCol: value_col[edge_id] if edge_id in value_col else None, - self.edgeTypeCol: value_col[edge_type] if edge_type in value_col else None, + self.edgeTypeCol: value_col[edge_type] + if edge_type in value_col + else None, } - print('weight name:', weight) - print('vc:') + print("weight name:", weight) + print("vc:") print(value_col) self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, value_col) @@ -971,7 +979,12 @@ def _degree(self, vertex_subset, direction=Direction.ALL): return df - def _make_plc_graph(self, value_col: Dict[str, cudf.DataFrame]=None, store_transposed:bool=False, renumber:bool=True): + def _make_plc_graph( + self, + value_col: Dict[str, cudf.DataFrame] = None, + store_transposed: bool = False, + renumber: bool = True, + ): """ Parameters ---------- diff --git a/python/cugraph/cugraph/tests/structure/test_graph.py b/python/cugraph/cugraph/tests/structure/test_graph.py index 9ce8e58c00d..f0a82b20a08 100644 --- a/python/cugraph/cugraph/tests/structure/test_graph.py +++ b/python/cugraph/cugraph/tests/structure/test_graph.py @@ -864,22 +864,27 @@ def test_select_random_vertices(graph_file, random_state, num_vertices): assert len(join) == len(sampled_vertices) + @pytest.mark.sg @pytest.mark.parametrize("graph_file", utils.DATASETS_SMALL) -@pytest.mark.parametrize("edge_props", [['edge_id', 'edge_type', 'weight'], ['edge_id', 'edge_type'], ['edge_type', 'weight'], ['edge_id'], ['weight']]) +@pytest.mark.parametrize( + "edge_props", + [ + ["edge_id", "edge_type", "weight"], + ["edge_id", "edge_type"], + ["edge_type", "weight"], + ["edge_id"], + ["weight"], + ], +) def test_graph_creation_edge_properties(graph_file, edge_props): df = utils.read_csv_file(graph_file) - - df['edge_id'] = cupy.arange(len(df), dtype='int32') - df['edge_type'] = cupy.int32(3) - df['weight'] = 0.5 - prop_keys = {k : k for k in edge_props} + df["edge_id"] = cupy.arange(len(df), dtype="int32") + df["edge_type"] = cupy.int32(3) + df["weight"] = 0.5 + + prop_keys = {k: k for k in edge_props} G = cugraph.Graph(directed=True) - G.from_cudf_edgelist( - df, - source="0", - destination="1", - **prop_keys - ) \ No newline at end of file + G.from_cudf_edgelist(df, source="0", destination="1", **prop_keys) diff --git a/python/cugraph/cugraph/tests/structure/test_graph_mg.py b/python/cugraph/cugraph/tests/structure/test_graph_mg.py index 31c5afcdcf0..ebaae38a8a4 100644 --- a/python/cugraph/cugraph/tests/structure/test_graph_mg.py +++ b/python/cugraph/cugraph/tests/structure/test_graph_mg.py @@ -339,22 +339,26 @@ def test_mg_select_random_vertices( @pytest.mark.sg @pytest.mark.parametrize("graph_file", utils.DATASETS_SMALL) -@pytest.mark.parametrize("edge_props", [['edge_id', 'edge_type', 'weight'], ['edge_id', 'edge_type'], ['edge_type', 'weight'], ['edge_id'], ['weight']]) +@pytest.mark.parametrize( + "edge_props", + [ + ["edge_id", "edge_type", "weight"], + ["edge_id", "edge_type"], + ["edge_type", "weight"], + ["edge_id"], + ["weight"], + ], +) def test_graph_creation_edge_properties(dask_client, graph_file, edge_props): df = utils.read_csv_file(graph_file) - - df['edge_id'] = cupy.arange(len(df), dtype='int32') - df['edge_type'] = cupy.int32(3) - df['weight'] = 0.5 + + df["edge_id"] = cupy.arange(len(df), dtype="int32") + df["edge_type"] = cupy.int32(3) + df["weight"] = 0.5 df = dask_cudf.from_cudf(df, npartitions=2) - prop_keys = {k : k for k in edge_props} + prop_keys = {k: k for k in edge_props} G = cugraph.Graph(directed=True) - G.from_dask_cudf_edgelist( - df, - source="0", - destination="1", - **prop_keys - ) \ No newline at end of file + G.from_dask_cudf_edgelist(df, source="0", destination="1", **prop_keys) From f13f2b6ef23002da2f751e49eef47bfcea96c4ef Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 24 Apr 2023 17:13:42 +0000 Subject: [PATCH 004/347] updates to store --- .../cugraph_pyg/data/cugraph_store.py | 6 ++-- .../dask/sampling/uniform_neighbor_sample.py | 7 +++++ .../cugraph/gnn/data_loading/bulk_sampler.py | 29 ++++++++++++++----- 3 files changed, 31 insertions(+), 11 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 7c8767e2a70..27f63d70d25 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -420,8 +420,6 @@ def __construct_graph( { "src": pandas.Series(na_src), "dst": pandas.Series(na_dst), - "w": pandas.Series(np.zeros(len(na_src))), - "eid": pandas.Series(np.arange(len(na_src))), "etp": pandas.Series(na_etp), } ) @@ -441,7 +439,7 @@ def __construct_graph( df, source="src", destination="dst", - edge_attr=["w", "eid", "etp"], + edge_type='etp', ) distributed.get_client().publish_dataset(cugraph_graph=graph) else: @@ -449,7 +447,7 @@ def __construct_graph( df, source="src", destination="dst", - edge_attr=["w", "eid", "etp"], + edge_type='etp', ) return graph diff --git a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py index c4e0c2da9b9..27cf9a233d1 100644 --- a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py @@ -119,6 +119,13 @@ def convert_to_cudf(cp_arrays, weight_t, with_edge_properties, return_offsets=Fa df[edge_type_n] = edge_types df[hop_id_n] = hop_ids + print( + f'sources: {sources}\n' + f'destinations: {destinations}\n' + f'batch: {batch_ids}\n' + f'offset: {offsets}\n' + ) + if return_offsets: offsets_df = cudf.DataFrame( { diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 95fab240eb2..0a00cf9754d 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -15,8 +15,11 @@ from typing import Union +import cupy import cudf import dask_cudf +import cugraph.dask as dask_cugraph + import cugraph import pylibcugraph @@ -196,17 +199,21 @@ def flush(self) -> None: else: sample_fn = cugraph.dask.uniform_neighbor_sample self.__sample_call_args["_multiple_clients"] = True + self.__sample_call_args["label_to_output_comm_rank"] = ( + self.__get_label_to_output_comm_rank(min_batch_id, max_batch_id) + ) - samples = sample_fn( + samples, offsets = sample_fn( self.__graph, **self.__sample_call_args, start_list=self.__batches[self.start_col_name][batch_id_filter], batch_id_list=self.__batches[self.batch_col_name][batch_id_filter], with_edge_properties=True, + return_offsets=True, ) self.__batches = self.__batches[~batch_id_filter] - self.__write(samples, min_batch_id, npartitions) + self.__write(samples, offsets, min_batch_id, npartitions) if self.size > 0: self.flush() @@ -214,13 +221,11 @@ def flush(self) -> None: def __write( self, samples: Union[cudf.DataFrame, dask_cudf.DataFrame], + offsets: Union[cudf.DataFrame, dask_cudf.DataFrame], min_batch_id: int, npartitions: int, ) -> None: - # Ensure each rank writes to its own partition so there is no conflict - outer_partition = f"rank={self.__rank}" - outer_partition_path = os.path.join(self.__output_path, outer_partition) - os.makedirs(outer_partition_path, exist_ok=True) + os.makedirs(self.__output_path, exist_ok=True) for partition_k in range(npartitions): ix_partition_start_inclusive = ( @@ -241,9 +246,19 @@ def __write( ix_partition_end_inclusive = int(ix_partition_end_inclusive) inner_path = os.path.join( - outer_partition_path, + self.__output_path, f"batch={ix_partition_start_inclusive}-{ix_partition_end_inclusive}" ".parquet", ) samples[f].to_parquet(inner_path, index=False) + + def __get_label_to_output_comm_rank(min_batch_id, max_batch_id): + num_workers = dask_cugraph.get_n_workers() + num_batches = max_batch_id - min_batch_id + 1 + z = cupy.zeros(num_batches, dtype='int32') + s = cupy.array_split(cupy.arange(num_batches), num_workers) + for i, t in enumerate(s): + z[t] = i + + return cudf.Series(z) \ No newline at end of file From 8fe9acb03f5d0965172fa86328652eeddc2fdae6 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 24 Apr 2023 19:06:43 +0000 Subject: [PATCH 005/347] throw exception for unweighted sssp --- python/cugraph/cugraph/dask/traversal/sssp.py | 11 +++++------ python/cugraph/cugraph/traversal/sssp.py | 9 ++++----- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/python/cugraph/cugraph/dask/traversal/sssp.py b/python/cugraph/cugraph/dask/traversal/sssp.py index b55f48dc86b..6740ef5a3f1 100644 --- a/python/cugraph/cugraph/dask/traversal/sssp.py +++ b/python/cugraph/cugraph/dask/traversal/sssp.py @@ -20,7 +20,6 @@ import cudf import dask_cudf from pylibcugraph import sssp as pylibcugraph_sssp, ResourceHandle -import warnings def _call_plc_sssp( @@ -102,12 +101,12 @@ def sssp(input_graph, source, cutoff=None, check_source=True): # FIXME: Implement a better way to check if the graph is weighted similar # to 'simpleGraph' - if len(input_graph.edgelist.edgelist_df.columns) != 3: - warning_msg = ( - "'SSSP' requires the input graph to be weighted: Unweighted " - "graphs will not be supported in the next release." + if not input_graph.weighted: + err_msg = ( + "'SSSP' requires the input graph to be weighted." + "'BFS' should be used instead of 'SSSP' for unweighted graphs." ) - warnings.warn(warning_msg, PendingDeprecationWarning) + raise RuntimeError(err_msg) client = default_client() diff --git a/python/cugraph/cugraph/traversal/sssp.py b/python/cugraph/cugraph/traversal/sssp.py index 9557650cbbc..bccbc21b515 100644 --- a/python/cugraph/cugraph/traversal/sssp.py +++ b/python/cugraph/cugraph/traversal/sssp.py @@ -12,7 +12,6 @@ # limitations under the License. import numpy as np -import warnings import cudf from cugraph.structure import Graph, MultiGraph @@ -217,11 +216,11 @@ def sssp( ) if not G.edgelist.weights: - warning_msg = ( - "'SSSP' requires the input graph to be weighted: Unweighted " - "graphs will not be supported in the next release." + err_msg = ( + "'SSSP' requires the input graph to be weighted." + "'BFS' should be used instead of 'SSSP' for unweighted graphs." ) - warnings.warn(warning_msg, PendingDeprecationWarning) + raise RuntimeError(err_msg) if not G.has_node(source): raise ValueError("Graph does not contain source vertex") From 7a29d57161fad392f7f9029cfb13a03b6456e2ab Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Tue, 25 Apr 2023 12:56:11 -0700 Subject: [PATCH 006/347] add tests for unweighted graphs in C API, update C API implementations to support unweighted graphs --- cpp/include/cugraph/edge_property.hpp | 21 ++++++++ cpp/src/c_api/core_result.cpp | 8 +-- cpp/src/c_api/induced_subgraph_result.cpp | 7 ++- cpp/src/c_api/legacy_spectral.cpp | 50 ++++++++++++++++-- cpp/src/c_api/leiden.cpp | 10 +++- cpp/src/c_api/louvain.cpp | 10 +++- cpp/tests/c_api/egonet_test.c | 50 ++++++++++++++---- cpp/tests/c_api/k_core_test.c | 55 ++++++++++++++------ cpp/tests/c_api/legacy_spectral_test.c | 62 ++++++++++++++++++++--- cpp/tests/c_api/leiden_test.c | 39 +++++++++++--- cpp/tests/c_api/louvain_test.c | 35 ++++++++++++- 11 files changed, 295 insertions(+), 52 deletions(-) diff --git a/cpp/include/cugraph/edge_property.hpp b/cpp/include/cugraph/edge_property.hpp index fdd28bc1eb6..e67ee61a9c9 100644 --- a/cpp/include/cugraph/edge_property.hpp +++ b/cpp/include/cugraph/edge_property.hpp @@ -16,6 +16,7 @@ #pragma once +#include #include #include @@ -125,6 +126,26 @@ class edge_dummy_property_t { auto view() const { return edge_dummy_property_view_t{}; } }; +template +auto create_constant_edge_property(raft::handle_t const& handle, + GraphViewType const& graph_view, + T constant_value) +{ + edge_property_t edge_property(handle, graph_view); + + auto mutable_view = edge_property.mutable_view(); + + std::for_each( + thrust::make_zip_iterator(mutable_view.value_firsts().begin(), + mutable_view.edge_counts().begin()), + thrust::make_zip_iterator(mutable_view.value_firsts().end(), mutable_view.edge_counts().end()), + [&handle, constant_value](auto tuple) { + detail::scalar_fill(handle, thrust::get<0>(tuple), thrust::get<1>(tuple), constant_value); + }); + + return edge_property; +} + template auto view_concat(edge_property_view_t const&... views) { diff --git a/cpp/src/c_api/core_result.cpp b/cpp/src/c_api/core_result.cpp index 9cdf34e468b..09476060967 100644 --- a/cpp/src/c_api/core_result.cpp +++ b/cpp/src/c_api/core_result.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -95,8 +95,10 @@ cugraph_type_erased_device_array_view_t* cugraph_k_core_result_get_weights( cugraph_k_core_result_t* result) { auto internal_pointer = reinterpret_cast(result); - return reinterpret_cast( - internal_pointer->weights_->view()); + return (internal_pointer->weights_ == nullptr) + ? NULL + : reinterpret_cast( + internal_pointer->weights_->view()); } void cugraph_k_core_result_free(cugraph_k_core_result_t* result) diff --git a/cpp/src/c_api/induced_subgraph_result.cpp b/cpp/src/c_api/induced_subgraph_result.cpp index 2e22a4c47b5..b9ad0e0d66f 100644 --- a/cpp/src/c_api/induced_subgraph_result.cpp +++ b/cpp/src/c_api/induced_subgraph_result.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -39,7 +39,10 @@ extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get { auto internal_pointer = reinterpret_cast(induced_subgraph); - return reinterpret_cast(internal_pointer->wgt_->view()); + return (internal_pointer->wgt_ == nullptr) + ? NULL + : reinterpret_cast( + internal_pointer->wgt_->view()); } extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_subgraph_offsets( diff --git a/cpp/src/c_api/legacy_spectral.cpp b/cpp/src/c_api/legacy_spectral.cpp index 9d1a0273057..4b465eebd0f 100644 --- a/cpp/src/c_api/legacy_spectral.cpp +++ b/cpp/src/c_api/legacy_spectral.cpp @@ -110,10 +110,18 @@ struct balanced_cut_clustering_functor : public cugraph::c_api::abstract_functor auto graph_view = graph->view(); auto edge_partition_view = graph_view.local_edge_partition_view(); + rmm::device_uvector tmp_weights(0, handle_.get_stream()); + if (edge_weights == nullptr) { + tmp_weights.resize(edge_partition_view.indices().size(), handle_.get_stream()); + cugraph::detail::scalar_fill(handle_, tmp_weights.data(), tmp_weights.size(), weight_t{1}); + } + cugraph::legacy::GraphCSRView legacy_graph_view( const_cast(edge_partition_view.offsets().data()), const_cast(edge_partition_view.indices().data()), - const_cast(edge_weights->view().value_firsts().front()), + (edge_weights == nullptr) + ? tmp_weights.data() + : const_cast(edge_weights->view().value_firsts().front()), edge_partition_view.offsets().size() - 1, edge_partition_view.indices().size()); @@ -209,10 +217,18 @@ struct spectral_clustering_functor : public cugraph::c_api::abstract_functor { auto graph_view = graph->view(); auto edge_partition_view = graph_view.local_edge_partition_view(); + rmm::device_uvector tmp_weights(0, handle_.get_stream()); + if (edge_weights == nullptr) { + tmp_weights.resize(edge_partition_view.indices().size(), handle_.get_stream()); + cugraph::detail::scalar_fill(handle_, tmp_weights.data(), tmp_weights.size(), weight_t{1}); + } + cugraph::legacy::GraphCSRView legacy_graph_view( const_cast(edge_partition_view.offsets().data()), const_cast(edge_partition_view.indices().data()), - const_cast(edge_weights->view().value_firsts().front()), + (edge_weights == nullptr) + ? tmp_weights.data() + : const_cast(edge_weights->view().value_firsts().front()), edge_partition_view.offsets().size() - 1, edge_partition_view.indices().size()); @@ -298,10 +314,18 @@ struct analyze_clustering_ratio_cut_functor : public cugraph::c_api::abstract_fu auto graph_view = graph->view(); auto edge_partition_view = graph_view.local_edge_partition_view(); + rmm::device_uvector tmp_weights(0, handle_.get_stream()); + if (edge_weights == nullptr) { + tmp_weights.resize(edge_partition_view.indices().size(), handle_.get_stream()); + cugraph::detail::scalar_fill(handle_, tmp_weights.data(), tmp_weights.size(), weight_t{1}); + } + cugraph::legacy::GraphCSRView legacy_graph_view( const_cast(edge_partition_view.offsets().data()), const_cast(edge_partition_view.indices().data()), - const_cast(edge_weights->view().value_firsts().front()), + (edge_weights == nullptr) + ? tmp_weights.data() + : const_cast(edge_weights->view().value_firsts().front()), edge_partition_view.offsets().size() - 1, edge_partition_view.indices().size()); @@ -405,10 +429,18 @@ struct analyze_clustering_edge_cut_functor : public cugraph::c_api::abstract_fun auto graph_view = graph->view(); auto edge_partition_view = graph_view.local_edge_partition_view(); + rmm::device_uvector tmp_weights(0, handle_.get_stream()); + if (edge_weights == nullptr) { + tmp_weights.resize(edge_partition_view.indices().size(), handle_.get_stream()); + cugraph::detail::scalar_fill(handle_, tmp_weights.data(), tmp_weights.size(), weight_t{1}); + } + cugraph::legacy::GraphCSRView legacy_graph_view( const_cast(edge_partition_view.offsets().data()), const_cast(edge_partition_view.indices().data()), - const_cast(edge_weights->view().value_firsts().front()), + (edge_weights == nullptr) + ? tmp_weights.data() + : const_cast(edge_weights->view().value_firsts().front()), edge_partition_view.offsets().size() - 1, edge_partition_view.indices().size()); @@ -512,10 +544,18 @@ struct analyze_clustering_modularity_functor : public cugraph::c_api::abstract_f auto graph_view = graph->view(); auto edge_partition_view = graph_view.local_edge_partition_view(); + rmm::device_uvector tmp_weights(0, handle_.get_stream()); + if (edge_weights == nullptr) { + tmp_weights.resize(edge_partition_view.indices().size(), handle_.get_stream()); + cugraph::detail::scalar_fill(handle_, tmp_weights.data(), tmp_weights.size(), weight_t{1}); + } + cugraph::legacy::GraphCSRView legacy_graph_view( const_cast(edge_partition_view.offsets().data()), const_cast(edge_partition_view.indices().data()), - const_cast(edge_weights->view().value_firsts().front()), + (edge_weights == nullptr) + ? tmp_weights.data() + : const_cast(edge_weights->view().value_firsts().front()), edge_partition_view.offsets().size() - 1, edge_partition_view.indices().size()); diff --git a/cpp/src/c_api/leiden.cpp b/cpp/src/c_api/leiden.cpp index 074ffc2d195..cc6f67c5812 100644 --- a/cpp/src/c_api/leiden.cpp +++ b/cpp/src/c_api/leiden.cpp @@ -86,10 +86,18 @@ struct leiden_functor : public cugraph::c_api::abstract_functor { rmm::device_uvector clusters(graph_view.local_vertex_partition_range_size(), handle_.get_stream()); + // FIXME: Revisit the constant edge property idea. We could consider an alternate + // implementation (perhaps involving the thrust::constant_iterator), or we + // could add support in Leiden for std::nullopt as the edge weights behaving + // as desired and only instantiating a real edge_property_view_t for the + // coarsened graphs. auto [level, modularity] = cugraph::leiden( handle_, graph_view, - (edge_weights != nullptr) ? std::make_optional(edge_weights->view()) : std::nullopt, + (edge_weights != nullptr) + ? std::make_optional(edge_weights->view()) + : std::make_optional( + cugraph::create_constant_edge_property(handle_, graph_view, weight_t{1}).view()), clusters.data(), max_level_, static_cast(resolution_)); diff --git a/cpp/src/c_api/louvain.cpp b/cpp/src/c_api/louvain.cpp index 0982b2b091c..527a235c249 100644 --- a/cpp/src/c_api/louvain.cpp +++ b/cpp/src/c_api/louvain.cpp @@ -86,10 +86,18 @@ struct louvain_functor : public cugraph::c_api::abstract_functor { rmm::device_uvector clusters(graph_view.local_vertex_partition_range_size(), handle_.get_stream()); + // FIXME: Revisit the constant edge property idea. We could consider an alternate + // implementation (perhaps involving the thrust::constant_iterator), or we + // could add support in Louvain for std::nullopt as the edge weights behaving + // as desired and only instantiating a real edge_property_view_t for the + // coarsened graphs. auto [level, modularity] = cugraph::louvain( handle_, graph_view, - (edge_weights != nullptr) ? std::make_optional(edge_weights->view()) : std::nullopt, + (edge_weights != nullptr) + ? std::make_optional(edge_weights->view()) + : std::make_optional( + cugraph::create_constant_edge_property(handle_, graph_view, weight_t{1}).view()), clusters.data(), max_level_, static_cast(resolution_)); diff --git a/cpp/tests/c_api/egonet_test.c b/cpp/tests/c_api/egonet_test.c index fac9815c150..d5db421a343 100644 --- a/cpp/tests/c_api/egonet_test.c +++ b/cpp/tests/c_api/egonet_test.c @@ -43,6 +43,12 @@ int generic_egonet_test(vertex_t* h_src, cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; cugraph_error_t* ret_error; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + cugraph_resource_handle_t* resource_handle = NULL; cugraph_graph_t* graph = NULL; cugraph_type_erased_device_array_t* seeds = NULL; @@ -52,16 +58,7 @@ int generic_egonet_test(vertex_t* h_src, resource_handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, resource_handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph(resource_handle, - h_src, - h_dst, - h_wgt, - num_edges, - store_transposed, - FALSE, - FALSE, - &graph, - &ret_error); + ret_code = create_sg_test_graph(resource_handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); @@ -109,9 +106,11 @@ int generic_egonet_test(vertex_t* h_src, resource_handle, (byte_t*)h_result_dst, dst, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); +#if 0 ret_code = cugraph_type_erased_device_array_view_copy_to_host( resource_handle, (byte_t*)h_result_wgt, wgt, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); +#endif ret_code = cugraph_type_erased_device_array_view_copy_to_host( resource_handle, (byte_t*)h_result_offsets, offsets, &ret_error); @@ -185,11 +184,42 @@ int test_egonet() FALSE); } +int test_egonet_no_weights() +{ + size_t num_edges = 9; + size_t num_vertices = 6; + size_t radius = 2; + size_t num_seeds = 2; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 3, 4}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 4, 5, 5}; + vertex_t h_seeds[] = {0, 1}; + + vertex_t h_result_src[] = {0, 1, 1, 3, 1, 1, 3, 3, 4}; + vertex_t h_result_dst[] = {1, 3, 4, 4, 3, 4, 4, 5, 5}; + size_t h_result_offsets[] = {0, 4, 9}; + + // Egonet wants store_transposed = FALSE + return generic_egonet_test(h_src, + h_dst, + NULL, + h_seeds, + h_result_src, + h_result_dst, + h_result_offsets, + num_vertices, + num_edges, + num_seeds, + radius, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) { int result = 0; result |= RUN_TEST(test_egonet); + result |= RUN_TEST(test_egonet_no_weights); return result; } diff --git a/cpp/tests/c_api/k_core_test.c b/cpp/tests/c_api/k_core_test.c index dabeefe0289..32b8ed50908 100644 --- a/cpp/tests/c_api/k_core_test.c +++ b/cpp/tests/c_api/k_core_test.c @@ -43,6 +43,12 @@ int generic_k_core_test(vertex_t* h_src, cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; cugraph_error_t* ret_error; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + cugraph_resource_handle_t* resource_handle = NULL; cugraph_graph_t* graph = NULL; cugraph_core_result_t* core_result = NULL; @@ -51,16 +57,7 @@ int generic_k_core_test(vertex_t* h_src, resource_handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, resource_handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph(resource_handle, - h_src, - h_dst, - h_wgt, - num_edges, - store_transposed, - FALSE, - TRUE, - &graph, - &ret_error); + ret_code = create_sg_test_graph(resource_handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, TRUE, FALSE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); @@ -101,9 +98,11 @@ int generic_k_core_test(vertex_t* h_src, resource_handle, (byte_t*)h_dst_vertices, dst_vertices, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - resource_handle, (byte_t*)h_weights, weights, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + if (weights != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_weights, weights, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } TEST_ASSERT(test_ret_value, number_of_result_edges == num_result_edges, @@ -115,11 +114,11 @@ int generic_k_core_test(vertex_t* h_src, M[i][j] = 0; for (int i = 0; i < num_result_edges; ++i) - M[h_result_src[i]][h_result_dst[i]] = h_result_wgt[i]; + M[h_result_src[i]][h_result_dst[i]] = (h_result_wgt != NULL) ? h_result_wgt[i] : 1.0; for (int i = 0; (i < number_of_result_edges) && (test_ret_value == 0); ++i) { TEST_ASSERT(test_ret_value, - M[h_src_vertices[i]][h_dst_vertices[i]] == h_weights[i], + M[h_src_vertices[i]][h_dst_vertices[i]] == (h_result_wgt != NULL) ? h_weights[i] : 1.0, "edge does not match"); } @@ -160,11 +159,37 @@ int test_k_core() FALSE); } +int test_k_core_no_weights() +{ + size_t num_edges = 22; + size_t num_vertices = 7; + size_t num_result_edges = 12; + size_t k = 3; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5, 3, 1, 4, 5, 5, 6}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4, 4, 5, 3, 1, 6, 5}; + vertex_t h_result_src[] = {1, 1, 3, 4, 3, 4, 3, 4, 5, 5, 1, 5}; + vertex_t h_result_dst[] = {3, 4, 5, 5, 1, 3, 4, 1, 3, 4, 5, 1}; + + return generic_k_core_test(h_src, + h_dst, + NULL, + h_result_src, + h_result_dst, + NULL, + num_vertices, + num_edges, + num_result_edges, + k, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) { int result = 0; result |= RUN_TEST(test_k_core); + result |= RUN_TEST(test_k_core_no_weights); return result; } diff --git a/cpp/tests/c_api/legacy_spectral_test.c b/cpp/tests/c_api/legacy_spectral_test.c index 71b3be92d39..64451ad663e 100644 --- a/cpp/tests/c_api/legacy_spectral_test.c +++ b/cpp/tests/c_api/legacy_spectral_test.c @@ -51,11 +51,16 @@ int generic_spectral_test(vertex_t* h_src, cugraph_graph_t* graph = NULL; cugraph_clustering_result_t* result = NULL; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph( - handle, h_src, h_dst, h_wgt, num_edges, store_transposed, FALSE, FALSE, &graph, &ret_error); + ret_code = create_sg_test_graph(handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); @@ -141,6 +146,12 @@ int generic_balanced_cut_test(vertex_t* h_src, cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; cugraph_error_t* ret_error; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + cugraph_resource_handle_t* handle = NULL; cugraph_graph_t* graph = NULL; cugraph_clustering_result_t* result = NULL; @@ -148,8 +159,7 @@ int generic_balanced_cut_test(vertex_t* h_src, handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph( - handle, h_src, h_dst, h_wgt, num_edges, store_transposed, FALSE, FALSE, &graph, &ret_error); + ret_code = create_sg_test_graph(handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); @@ -238,7 +248,7 @@ int test_spectral() weight_t expected_edge_cut = 0; weight_t expected_ratio_cut = 0; - // Louvain wants store_transposed = FALSE + // spectral clustering wants store_transposed = FALSE return generic_spectral_test(h_src, h_dst, h_wgt, @@ -276,7 +286,7 @@ int test_balanced_cut_unequal_weight() weight_t expected_edge_cut = 3.7; weight_t expected_ratio_cut = 4.44; - // Louvain wants store_transposed = FALSE + // balanced cut clustering wants store_transposed = FALSE return generic_balanced_cut_test(h_src, h_dst, h_wgt, @@ -314,7 +324,7 @@ int test_balanced_cut_equal_weight() weight_t expected_edge_cut = 1; weight_t expected_ratio_cut = 0.666667; - // Louvain wants store_transposed = FALSE + // balanced cut clustering wants store_transposed = FALSE return generic_balanced_cut_test(h_src, h_dst, h_wgt, @@ -333,6 +343,43 @@ int test_balanced_cut_equal_weight() FALSE); } +int test_balanced_cut_no_weight() +{ + size_t num_clusters = 2; + size_t num_eigenvectors = 2; + size_t num_edges = 14; + size_t num_vertices = 6; + double evs_tolerance = 0.001; + int evs_max_iterations = 100; + double k_means_tolerance = 0.001; + int k_means_max_iterations = 100; + + vertex_t h_src[] = { 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5 }; + vertex_t h_dst[] = { 1, 2, 0, 2, 0, 1, 3, 2, 4, 5, 3, 5, 3, 4 }; + vertex_t h_result[] = { 1, 1, 1, 0, 0, 0 }; + weight_t expected_modularity = 0.357143; + weight_t expected_edge_cut = 1; + weight_t expected_ratio_cut = 0.666667; + + // balanced cut clustering wants store_transposed = FALSE + return generic_balanced_cut_test(h_src, + h_dst, + NULL, + h_result, + expected_modularity, + expected_edge_cut, + expected_ratio_cut, + num_vertices, + num_edges, + num_clusters, + num_eigenvectors, + evs_tolerance, + evs_max_iterations, + k_means_tolerance, + k_means_max_iterations, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) @@ -341,5 +388,6 @@ int main(int argc, char** argv) result |= RUN_TEST(test_spectral); result |= RUN_TEST(test_balanced_cut_equal_weight); result |= RUN_TEST(test_balanced_cut_unequal_weight); + result |= RUN_TEST(test_balanced_cut_no_weight); return result; } diff --git a/cpp/tests/c_api/leiden_test.c b/cpp/tests/c_api/leiden_test.c index 64d1b68b032..f88eee3699b 100644 --- a/cpp/tests/c_api/leiden_test.c +++ b/cpp/tests/c_api/leiden_test.c @@ -45,20 +45,22 @@ int generic_leiden_test(vertex_t* h_src, cugraph_graph_t* p_graph = NULL; cugraph_hierarchical_clustering_result_t* p_result = NULL; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + p_handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, p_handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph( - p_handle, h_src, h_dst, h_wgt, num_edges, store_transposed, FALSE, FALSE, &p_graph, &ret_error); + ret_code = create_sg_test_graph(p_handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &p_graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); ret_code = cugraph_leiden(p_handle, p_graph, max_level, resolution, FALSE, &p_result, &ret_error); -#if 0 - TEST_ASSERT(test_ret_value, ret_code != CUGRAPH_SUCCESS, "cugraph_leiden should have failed"); -#else TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, "cugraph_leiden failed."); @@ -87,7 +89,6 @@ int generic_leiden_test(vertex_t* h_src, cugraph_hierarchical_clustering_result_free(p_result); } -#endif cugraph_sg_graph_free(p_graph); cugraph_free_resource_handle(p_handle); @@ -123,11 +124,37 @@ int test_leiden() FALSE); } +int test_leiden_no_weights() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t max_level = 10; + weight_t resolution = 1.0; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_result[] = {1, 1, 1, 2, 0, 0}; + weight_t expected_modularity = 0.0859375; + + // Louvain wants store_transposed = FALSE + return generic_leiden_test(h_src, + h_dst, + NULL, + h_result, + expected_modularity, + num_vertices, + num_edges, + max_level, + resolution, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) { int result = 0; result |= RUN_TEST(test_leiden); + result |= RUN_TEST(test_leiden_no_weights); return result; } diff --git a/cpp/tests/c_api/louvain_test.c b/cpp/tests/c_api/louvain_test.c index eed8af4bdc7..f3813b5a1ac 100644 --- a/cpp/tests/c_api/louvain_test.c +++ b/cpp/tests/c_api/louvain_test.c @@ -45,11 +45,16 @@ int generic_louvain_test(vertex_t* h_src, cugraph_graph_t* p_graph = NULL; cugraph_hierarchical_clustering_result_t* p_result = NULL; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + p_handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, p_handle != NULL, "resource handle creation failed."); - ret_code = create_test_graph( - p_handle, h_src, h_dst, h_wgt, num_edges, store_transposed, FALSE, FALSE, &p_graph, &ret_error); + ret_code = create_sg_test_graph(p_handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &p_graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); @@ -125,11 +130,37 @@ int test_louvain() FALSE); } +int test_louvain_no_weight() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t max_level = 10; + weight_t resolution = 1.0; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_result[] = {1, 1, 1, 2, 0, 0}; + weight_t expected_modularity = 0.0859375; + + // Louvain wants store_transposed = FALSE + return generic_louvain_test(h_src, + h_dst, + NULL, + h_result, + expected_modularity, + num_vertices, + num_edges, + max_level, + resolution, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) { int result = 0; result |= RUN_TEST(test_louvain); + result |= RUN_TEST(test_louvain_no_weight); return result; } From a44483e5b0338f394f8f78b46125d0e21639b02d Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Wed, 26 Apr 2023 08:09:14 -0700 Subject: [PATCH 007/347] refactor to use new fill_edge_property --- cpp/CMakeLists.txt | 1 + cpp/include/cugraph/edge_property.hpp | 20 ------ cpp/src/c_api/graph_helper.cu | 84 ++++++++++++++++++++++--- cpp/src/c_api/graph_helper.hpp | 7 ++- cpp/src/c_api/graph_helper_impl.cuh | 45 ++++++++++++++ cpp/src/c_api/graph_helper_mg.cu | 89 +++++++++++++++++++++++++++ cpp/src/c_api/leiden.cpp | 22 ++++--- cpp/src/c_api/louvain.cpp | 22 ++++--- 8 files changed, 239 insertions(+), 51 deletions(-) create mode 100644 cpp/src/c_api/graph_helper_impl.cuh create mode 100644 cpp/src/c_api/graph_helper_mg.cu diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index d592b45609c..912ac393d82 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -404,6 +404,7 @@ add_library(cugraph_c src/c_api/legacy_spectral.cpp src/c_api/legacy_ecg.cpp src/c_api/graph_helper.cu + src/c_api/graph_helper_mg.cu src/c_api/graph_generators.cpp src/c_api/induced_subgraph_result.cpp src/c_api/hits.cpp diff --git a/cpp/include/cugraph/edge_property.hpp b/cpp/include/cugraph/edge_property.hpp index a4abd927bde..03f93b06823 100644 --- a/cpp/include/cugraph/edge_property.hpp +++ b/cpp/include/cugraph/edge_property.hpp @@ -127,26 +127,6 @@ class edge_dummy_property_t { auto view() const { return edge_dummy_property_view_t{}; } }; -template -auto create_constant_edge_property(raft::handle_t const& handle, - GraphViewType const& graph_view, - T constant_value) -{ - edge_property_t edge_property(handle, graph_view); - - auto mutable_view = edge_property.mutable_view(); - - std::for_each( - thrust::make_zip_iterator(mutable_view.value_firsts().begin(), - mutable_view.edge_counts().begin()), - thrust::make_zip_iterator(mutable_view.value_firsts().end(), mutable_view.edge_counts().end()), - [&handle, constant_value](auto tuple) { - detail::scalar_fill(handle, thrust::get<0>(tuple), thrust::get<1>(tuple), constant_value); - }); - - return edge_property; -} - template auto view_concat(edge_property_view_t const&... views) { diff --git a/cpp/src/c_api/graph_helper.cu b/cpp/src/c_api/graph_helper.cu index 914344f8722..dcd6c92325f 100644 --- a/cpp/src/c_api/graph_helper.cu +++ b/cpp/src/c_api/graph_helper.cu @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,19 +14,11 @@ * limitations under the License. */ -#include +#include namespace cugraph { namespace c_api { -template -rmm::device_uvector expand_sparse_offsets(raft::device_span offsets, - vertex_t base_vertex_id, - rmm::cuda_stream_view const& stream) -{ - return cugraph::detail::expand_sparse_offsets(offsets, base_vertex_id, stream); -} - template rmm::device_uvector expand_sparse_offsets( raft::device_span offsets, int32_t base_vertex_id, @@ -50,5 +42,77 @@ template rmm::device_uvector expand_sparse_offsets(raft::device_span, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + } // namespace c_api } // namespace cugraph diff --git a/cpp/src/c_api/graph_helper.hpp b/cpp/src/c_api/graph_helper.hpp index 22e8e317ad0..c4f7aaeabc9 100644 --- a/cpp/src/c_api/graph_helper.hpp +++ b/cpp/src/c_api/graph_helper.hpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -26,5 +26,10 @@ rmm::device_uvector expand_sparse_offsets(raft::device_span +edge_property_t create_constant_edge_property(raft::handle_t const& handle, + GraphViewType const& graph_view, + T constant_value); + } // namespace c_api } // namespace cugraph diff --git a/cpp/src/c_api/graph_helper_impl.cuh b/cpp/src/c_api/graph_helper_impl.cuh new file mode 100644 index 00000000000..759d7a85286 --- /dev/null +++ b/cpp/src/c_api/graph_helper_impl.cuh @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2022-2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include + +namespace cugraph { +namespace c_api { + +template +rmm::device_uvector expand_sparse_offsets(raft::device_span offsets, + vertex_t base_vertex_id, + rmm::cuda_stream_view const& stream) +{ + return cugraph::detail::expand_sparse_offsets(offsets, base_vertex_id, stream); +} + +template +edge_property_t create_constant_edge_property(raft::handle_t const& handle, + GraphViewType const& graph_view, + T constant_value) +{ + edge_property_t edge_property(handle, graph_view); + + cugraph::fill_edge_property(handle, graph_view, constant_value, edge_property); + + return edge_property; +} + +} // namespace c_api +} // namespace cugraph diff --git a/cpp/src/c_api/graph_helper_mg.cu b/cpp/src/c_api/graph_helper_mg.cu new file mode 100644 index 00000000000..15e24ba530b --- /dev/null +++ b/cpp/src/c_api/graph_helper_mg.cu @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +namespace cugraph { +namespace c_api { + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, float> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + float constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +template edge_property_t, double> +create_constant_edge_property(raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + double constant_value); + +} // namespace c_api +} // namespace cugraph diff --git a/cpp/src/c_api/leiden.cpp b/cpp/src/c_api/leiden.cpp index cc6f67c5812..e03c7dc0aa9 100644 --- a/cpp/src/c_api/leiden.cpp +++ b/cpp/src/c_api/leiden.cpp @@ -18,6 +18,7 @@ #include #include +#include #include #include #include @@ -91,16 +92,17 @@ struct leiden_functor : public cugraph::c_api::abstract_functor { // could add support in Leiden for std::nullopt as the edge weights behaving // as desired and only instantiating a real edge_property_view_t for the // coarsened graphs. - auto [level, modularity] = cugraph::leiden( - handle_, - graph_view, - (edge_weights != nullptr) - ? std::make_optional(edge_weights->view()) - : std::make_optional( - cugraph::create_constant_edge_property(handle_, graph_view, weight_t{1}).view()), - clusters.data(), - max_level_, - static_cast(resolution_)); + auto [level, modularity] = + cugraph::leiden(handle_, + graph_view, + (edge_weights != nullptr) + ? std::make_optional(edge_weights->view()) + : std::make_optional(cugraph::c_api::create_constant_edge_property( + handle_, graph_view, weight_t{1}) + .view()), + clusters.data(), + max_level_, + static_cast(resolution_)); rmm::device_uvector vertices(graph_view.local_vertex_partition_range_size(), handle_.get_stream()); diff --git a/cpp/src/c_api/louvain.cpp b/cpp/src/c_api/louvain.cpp index 527a235c249..ff75cafa031 100644 --- a/cpp/src/c_api/louvain.cpp +++ b/cpp/src/c_api/louvain.cpp @@ -18,6 +18,7 @@ #include #include +#include #include #include #include @@ -91,16 +92,17 @@ struct louvain_functor : public cugraph::c_api::abstract_functor { // could add support in Louvain for std::nullopt as the edge weights behaving // as desired and only instantiating a real edge_property_view_t for the // coarsened graphs. - auto [level, modularity] = cugraph::louvain( - handle_, - graph_view, - (edge_weights != nullptr) - ? std::make_optional(edge_weights->view()) - : std::make_optional( - cugraph::create_constant_edge_property(handle_, graph_view, weight_t{1}).view()), - clusters.data(), - max_level_, - static_cast(resolution_)); + auto [level, modularity] = + cugraph::louvain(handle_, + graph_view, + (edge_weights != nullptr) + ? std::make_optional(edge_weights->view()) + : std::make_optional(cugraph::c_api::create_constant_edge_property( + handle_, graph_view, weight_t{1}) + .view()), + clusters.data(), + max_level_, + static_cast(resolution_)); rmm::device_uvector vertices(graph_view.local_vertex_partition_range_size(), handle_.get_stream()); From 442e1ce9a67610bb020e6d7515bbf275de9f061b Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Wed, 26 Apr 2023 08:12:00 -0700 Subject: [PATCH 008/347] Rename graph_helper.cu --- cpp/CMakeLists.txt | 2 +- cpp/src/c_api/{graph_helper.cu => graph_helper_sg.cu} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename cpp/src/c_api/{graph_helper.cu => graph_helper_sg.cu} (100%) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 912ac393d82..6c8da74aa56 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -403,7 +403,7 @@ add_library(cugraph_c src/c_api/capi_helper.cu src/c_api/legacy_spectral.cpp src/c_api/legacy_ecg.cpp - src/c_api/graph_helper.cu + src/c_api/graph_helper_sg.cu src/c_api/graph_helper_mg.cu src/c_api/graph_generators.cpp src/c_api/induced_subgraph_result.cpp diff --git a/cpp/src/c_api/graph_helper.cu b/cpp/src/c_api/graph_helper_sg.cu similarity index 100% rename from cpp/src/c_api/graph_helper.cu rename to cpp/src/c_api/graph_helper_sg.cu From 7e347de785a90a2f269242ce8c531086243c44ee Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Wed, 26 Apr 2023 11:25:24 -0700 Subject: [PATCH 009/347] need to sort after shuffling --- .../sampling/detail/sampling_utils_impl.cuh | 306 +++++++++--------- cpp/tests/c_api/mg_test_utils.cpp | 108 +++---- .../c_api/mg_uniform_neighbor_sample_test.c | 240 ++++++++++++++ 3 files changed, 453 insertions(+), 201 deletions(-) diff --git a/cpp/src/sampling/detail/sampling_utils_impl.cuh b/cpp/src/sampling/detail/sampling_utils_impl.cuh index 3ab13d0c7d2..48d13fa39ec 100644 --- a/cpp/src/sampling/detail/sampling_utils_impl.cuh +++ b/cpp/src/sampling/detail/sampling_utils_impl.cuh @@ -626,6 +626,164 @@ sample_edges(raft::handle_t const& handle, std::move(labels)); } +template +void sort_sampled_tuples(raft::handle_t const& handle, + rmm::device_uvector& majors, + rmm::device_uvector& minors, + std::optional>& weights, + std::optional>& edge_ids, + std::optional>& edge_types, + std::optional>& hops, + std::optional>& labels) +{ + if (weights) { + if (edge_ids) { + if (edge_types) { + if (hops) { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), + minors.begin(), + weights->begin(), + edge_ids->begin(), + edge_types->begin(), + hops->begin())); + } else { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), + minors.begin(), + weights->begin(), + edge_ids->begin(), + edge_types->begin())); + } + } else { + if (hops) { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), weights->begin(), edge_ids->begin(), hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), weights->begin(), edge_ids->begin())); + } + } + } else { + if (edge_types) { + if (hops) { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), + minors.begin(), + weights->begin(), + edge_types->begin(), + hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), weights->begin(), edge_types->begin())); + } + } else { + if (hops) { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), weights->begin(), hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), minors.begin(), weights->begin())); + } + } + } + } else { + if (edge_ids) { + if (edge_types) { + if (hops) { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), + minors.begin(), + edge_ids->begin(), + edge_types->begin(), + hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), edge_ids->begin(), edge_types->begin())); + } + } else { + if (hops) { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), edge_ids->begin(), hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), minors.begin(), edge_ids->begin())); + } + } + } else { + if (edge_types) { + if (hops) { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator( + majors.begin(), minors.begin(), edge_types->begin(), hops->begin())); + } else { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), minors.begin(), edge_types->begin())); + } + } else { + if (hops) { + thrust::sort_by_key( + handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), minors.begin(), hops->begin())); + } else { + thrust::sort_by_key(handle.get_thrust_policy(), + labels->begin(), + labels->end(), + thrust::make_zip_iterator(majors.begin(), minors.begin())); + } + } + } + } +} + template > offsets{std::nullopt}; if (labels) { - if (weights) { - if (edge_ids) { - if (edge_types) { - if (hops) { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), - minors.begin(), - weights->begin(), - edge_ids->begin(), - edge_types->begin(), - hops->begin())); - } else { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), - minors.begin(), - weights->begin(), - edge_ids->begin(), - edge_types->begin())); - } - } else { - if (hops) { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), - minors.begin(), - weights->begin(), - edge_ids->begin(), - hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), weights->begin(), edge_ids->begin())); - } - } - } else { - if (edge_types) { - if (hops) { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), - minors.begin(), - weights->begin(), - edge_types->begin(), - hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), weights->begin(), edge_types->begin())); - } - } else { - if (hops) { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), weights->begin(), hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), minors.begin(), weights->begin())); - } - } - } - } else { - if (edge_ids) { - if (edge_types) { - if (hops) { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), - minors.begin(), - edge_ids->begin(), - edge_types->begin(), - hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), edge_ids->begin(), edge_types->begin())); - } - } else { - if (hops) { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), edge_ids->begin(), hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), minors.begin(), edge_ids->begin())); - } - } - } else { - if (edge_types) { - if (hops) { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator( - majors.begin(), minors.begin(), edge_types->begin(), hops->begin())); - } else { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), minors.begin(), edge_types->begin())); - } - } else { - if (hops) { - thrust::sort_by_key( - handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), minors.begin(), hops->begin())); - } else { - thrust::sort_by_key(handle.get_thrust_policy(), - labels->begin(), - labels->end(), - thrust::make_zip_iterator(majors.begin(), minors.begin())); - } - } - } - } + sort_sampled_tuples(handle, majors, minors, weights, edge_ids, edge_types, hops, labels); if (label_to_output_comm_rank) { CUGRAPH_EXPECTS(labels, "labels must be specified in order to shuffle sampling results"); @@ -1030,6 +1044,8 @@ shuffle_and_organize_output( } } } + + sort_sampled_tuples(handle, majors, minors, weights, edge_ids, edge_types, hops, labels); } size_t num_unique_labels = diff --git a/cpp/tests/c_api/mg_test_utils.cpp b/cpp/tests/c_api/mg_test_utils.cpp index 7e8c9b22aac..5a94dff78dc 100644 --- a/cpp/tests/c_api/mg_test_utils.cpp +++ b/cpp/tests/c_api/mg_test_utils.cpp @@ -441,63 +441,35 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl data_type_id_t type_tid = INT32; data_type_id_t weight_tid = FLOAT32; - cugraph_type_erased_device_array_t* src; - cugraph_type_erased_device_array_t* dst; - cugraph_type_erased_device_array_t* idx; - cugraph_type_erased_device_array_t* type; - cugraph_type_erased_device_array_t* wgt; - cugraph_type_erased_device_array_view_t* src_view; - cugraph_type_erased_device_array_view_t* dst_view; - cugraph_type_erased_device_array_view_t* idx_view; - cugraph_type_erased_device_array_view_t* type_view; - cugraph_type_erased_device_array_view_t* wgt_view; + cugraph_type_erased_device_array_t* src = NULL; + cugraph_type_erased_device_array_t* dst = NULL; + cugraph_type_erased_device_array_t* idx = NULL; + cugraph_type_erased_device_array_t* type = NULL; + cugraph_type_erased_device_array_t* wgt = NULL; + cugraph_type_erased_device_array_view_t* src_view = NULL; + cugraph_type_erased_device_array_view_t* dst_view = NULL; + cugraph_type_erased_device_array_view_t* idx_view = NULL; + cugraph_type_erased_device_array_view_t* type_view = NULL; + cugraph_type_erased_device_array_view_t* wgt_view = NULL; int rank = 0; rank = cugraph_resource_handle_get_rank(handle); - if (rank == 0) { - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, index_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, type_tid, &type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } else { - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + size_t original_num_edges = num_edges; - ret_code = cugraph_type_erased_device_array_create(handle, 0, index_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); + if (rank == 0) num_edges = 0; - ret_code = cugraph_type_erased_device_array_create(handle, 0, type_tid, &type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - ret_code = cugraph_type_erased_device_array_create(handle, 0, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); src_view = cugraph_type_erased_device_array_view(src); dst_view = cugraph_type_erased_device_array_view(dst); - idx_view = cugraph_type_erased_device_array_view(idx); - type_view = cugraph_type_erased_device_array_view(type); - wgt_view = cugraph_type_erased_device_array_view(wgt); ret_code = cugraph_type_erased_device_array_view_copy_from_host( handle, src_view, (byte_t*)h_src, ret_error); @@ -507,17 +479,41 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl handle, dst_view, (byte_t*)h_dst, ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, idx_view, (byte_t*)h_idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index copy_from_host failed."); + if (h_idx != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, index_tid, &idx, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, type_view, (byte_t*)h_type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type copy_from_host failed."); + idx_view = cugraph_type_erased_device_array_view(idx); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, wgt_view, (byte_t*)h_wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, idx_view, (byte_t*)h_idx, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index copy_from_host failed."); + } + + if (h_type != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, type_tid, &type, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); + + type_view = cugraph_type_erased_device_array_view(type); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, type_view, (byte_t*)h_type, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type copy_from_host failed."); + } + + if (h_wgt != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + wgt_view = cugraph_type_erased_device_array_view(wgt); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view, (byte_t*)h_wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + } ret_code = cugraph_mg_graph_create(handle, &properties, @@ -527,7 +523,7 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl idx_view, type_view, store_transposed, - num_edges, + original_num_edges, FALSE, p_graph, ret_error); diff --git a/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c b/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c index 58fc1d20c75..f7254a1a886 100644 --- a/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c +++ b/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c @@ -553,6 +553,245 @@ int test_uniform_neighbor_with_shuffling(const cugraph_resource_handle_t* handle return test_ret_value; } +int test_uniform_neighbor_sample_alex_bug(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 156; + size_t num_vertices = 34; + size_t fan_out_size = 2; + size_t num_starts = 4; + size_t num_labels = 3; + + vertex_t src[] = {1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31, 2, + 3, 7, 13, 17, 19, 21, 30, 3, 7, 8, 9, 13, 27, 28, 32, 7, 12, + 13, 6, 10, 6, 10, 16, 16, 30, 32, 33, 33, 33, 32, 33, 32, + 33, 32, 33, 33, 32, 33, 32, 33, 25, 27, 29, 32, 33, 25, 27, + 31, 31, 29, 33, 33, 31, 33, 32, 33, 32, 33, 32, 33, 33, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, + 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, + 8, 8, 8, 9, 13, 14, 14, 15, 15, 18, 18, 19, 20, 20, 22, 22, + 23, 23, 23, 23, 23, 24, 24, 24, 25, 26, 26, 27, 28, 28, 29, + 29, 30, 30, 31, 31, 32}; + vertex_t dst[] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,4,4,5,5,5,6,8,8,8,9,13,14,14,15,15,18,18,19,20,20,22,22,23,23,23,23,23,24,24,24,25,26,26,27,28,28,29,29,30,30,31,31,32,1,2,3,4,5,6,7,8,10,11,12,13,17,19,21,31,2,3,7,13,17,19,21,30,3,7,8,9,13,27,28,32,7,12,13,6,10,6,10,16,16,30,32,33,33,33,32,33,32,33,32,33,33,32,33,32,33,25,27,29,32,33,25,27,31,31,29,33,33,31,33,32,33,32,33,32,33,33}; + weight_t wgt[] = {1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f,1.0f}; + + edge_t edge_ids[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, + 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, + 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, + 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, + 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, + 150, 151, 152, 153, 154, 155}; + + vertex_t start[] = {0, 1, 2, 5}; + int32_t start_labels[] = { 0, 0, 1, 2 }; + int32_t label_list[] = { 0, 1, 2 }; + int32_t label_to_output_comm_rank[] = { 0, 0, 1 }; + int fan_out[] = {2, 3}; + + size_t expected_size[] = { 3, 2 }; + + // Create graph + int test_ret_value = 0; + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error = NULL; + cugraph_graph_t* graph = NULL; + cugraph_sample_result_t* result = NULL; + + ret_code = create_mg_test_graph_with_properties(handle, + src, + dst, + edge_ids, + NULL, + wgt, + num_edges, + FALSE, + TRUE, + &graph, + &ret_error); + + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + + cugraph_type_erased_device_array_t* d_start = NULL; + cugraph_type_erased_device_array_view_t* d_start_view = NULL; + cugraph_type_erased_device_array_t* d_start_labels = NULL; + cugraph_type_erased_device_array_view_t* d_start_labels_view = NULL; + cugraph_type_erased_device_array_t* d_label_list = NULL; + cugraph_type_erased_device_array_view_t* d_label_list_view = NULL; + cugraph_type_erased_device_array_t* d_label_to_output_comm_rank = NULL; + cugraph_type_erased_device_array_view_t* d_label_to_output_comm_rank_view = NULL; + cugraph_type_erased_host_array_view_t* h_fan_out_view = NULL; + + int rank = cugraph_resource_handle_get_rank(handle); + + if (rank > 0) { + num_starts = 0; + } + + cugraph_rng_state_t* rng_state; + ret_code = cugraph_rng_state_create(handle, rank, &rng_state, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "rng_state create failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_starts, INT32, &d_start, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "d_start create failed."); + + d_start_view = cugraph_type_erased_device_array_view(d_start); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, d_start_view, (byte_t*)start, &ret_error); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_starts, INT32, &d_start_labels, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "d_start_labels create failed."); + + d_start_labels_view = cugraph_type_erased_device_array_view(d_start_labels); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, d_start_labels_view, (byte_t*)start_labels, &ret_error); + + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "start_labels copy_from_host failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_labels, INT32, &d_label_list, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "d_label_list create failed."); + + d_label_list_view = cugraph_type_erased_device_array_view(d_label_list); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, d_label_list_view, (byte_t*)label_list, &ret_error); + + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "label_list copy_from_host failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_labels, INT32, &d_label_to_output_comm_rank, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "d_label_to_output_comm_rank create failed."); + + d_label_to_output_comm_rank_view = cugraph_type_erased_device_array_view(d_label_to_output_comm_rank); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, d_label_to_output_comm_rank_view, (byte_t*)label_to_output_comm_rank, &ret_error); + + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "label_to_output_comm_rank copy_from_host failed."); + + h_fan_out_view = cugraph_type_erased_host_array_view_create(fan_out, fan_out_size, INT32); + + ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, + graph, + d_start_view, + d_start_labels_view, + d_label_list_view, + d_label_to_output_comm_rank_view, + h_fan_out_view, + rng_state, + FALSE, + TRUE, + FALSE, + &result, + &ret_error); + +#ifdef NO_CUGRAPH_OPS + TEST_ASSERT( + test_ret_value, ret_code != CUGRAPH_SUCCESS, "uniform_neighbor_sample should have failed") +#else + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "uniform_neighbor_sample failed."); + + cugraph_type_erased_device_array_view_t* result_srcs = NULL; + cugraph_type_erased_device_array_view_t* result_dsts = NULL; + cugraph_type_erased_device_array_view_t* result_edge_id = NULL; + cugraph_type_erased_device_array_view_t* result_weights = NULL; + cugraph_type_erased_device_array_view_t* result_hops = NULL; + cugraph_type_erased_device_array_view_t* result_offsets = NULL; + + result_srcs = cugraph_sample_result_get_sources(result); + result_dsts = cugraph_sample_result_get_destinations(result); + result_edge_id = cugraph_sample_result_get_edge_id(result); + result_weights = cugraph_sample_result_get_edge_weight(result); + result_hops = cugraph_sample_result_get_hop(result); + result_offsets = cugraph_sample_result_get_offsets(result); + + size_t result_size = cugraph_type_erased_device_array_view_size(result_srcs); + size_t result_offsets_size = cugraph_type_erased_device_array_view_size(result_offsets); + + vertex_t h_srcs[result_size]; + vertex_t h_dsts[result_size]; + edge_t h_edge_id[result_size]; + weight_t h_weight[result_size]; + int32_t h_hops[result_size]; + size_t h_result_offsets[result_offsets_size]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_srcs, result_srcs, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_dsts, result_dsts, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_edge_id, result_edge_id, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_weight, result_weights, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_hops, result_hops, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_offsets, result_offsets, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + // NOTE: The C++ tester does a more thorough validation. For our purposes + // here we will do a simpler validation, merely checking that all edges + // are actually part of the graph + weight_t M_w[num_vertices][num_vertices]; + edge_t M_edge_id[num_vertices][num_vertices]; + + for (int i = 0; i < num_vertices; ++i) + for (int j = 0; j < num_vertices; ++j) { + M_w[i][j] = 0.0; + M_edge_id[i][j] = -1; + } + + for (int i = 0; i < num_edges; ++i) { + M_w[src[i]][dst[i]] = wgt[i]; + M_edge_id[src[i]][dst[i]] = edge_ids[i]; + } + + for (int i = 0; (i < result_size) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + M_w[h_srcs[i]][h_dsts[i]] == h_weight[i], + "uniform_neighbor_sample got edge that doesn't exist"); + TEST_ASSERT(test_ret_value, + M_edge_id[h_srcs[i]][h_dsts[i]] == h_edge_id[i], + "uniform_neighbor_sample got edge that doesn't exist"); + } + + TEST_ASSERT(test_ret_value, + result_offsets_size == expected_size[rank], + "incorrect number of results"); + + + cugraph_sample_result_free(result); +#endif + + cugraph_sg_graph_free(graph); + cugraph_error_free(ret_error); +} + /******************************************************************************/ int main(int argc, char** argv) @@ -564,6 +803,7 @@ int main(int argc, char** argv) result |= RUN_MG_TEST(test_uniform_neighbor_sample, handle); result |= RUN_MG_TEST(test_uniform_neighbor_from_alex, handle); result |= RUN_MG_TEST(test_uniform_neighbor_with_shuffling, handle); + result |= RUN_TEST_NEW(test_uniform_neighbor_sample_alex_bug, handle); cugraph_free_resource_handle(handle); free_mg_raft_handle(raft_handle); From d34af46cdcdbf771e3289b988d37a16d4bf8a2cf Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Thu, 27 Apr 2023 20:56:41 +0000 Subject: [PATCH 010/347] pull in chuck's changes, update sssp tests --- .../graph_implementation/simpleGraph.py | 5 ---- .../cugraph/tests/traversal/test_sssp.py | 15 +++------- .../cugraph/tests/traversal/test_sssp_mg.py | 30 ------------------- 3 files changed, 4 insertions(+), 46 deletions(-) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 85c9ede7a6e..aa7910586ef 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -51,7 +51,6 @@ def __init__( destination: str, edge_attr: Union[cudf.DataFrame, Dict[str, cudf.DataFrame]] = None, ): - print("edge attr: ", edge_attr) self.edgelist_df = cudf.DataFrame() self.edgelist_df[simpleGraphImpl.srcCol] = source self.edgelist_df[simpleGraphImpl.dstCol] = destination @@ -255,7 +254,6 @@ def __from_edgelist( multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) - print("symmetrized df:\n", value_col) if isinstance(value_col, cudf.DataFrame): value_dict = {} @@ -281,9 +279,6 @@ def __from_edgelist( else None, } - print("weight name:", weight) - print("vc:") - print(value_col) self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, value_col) if self.batch_enabled: diff --git a/python/cugraph/cugraph/tests/traversal/test_sssp.py b/python/cugraph/cugraph/tests/traversal/test_sssp.py index 5e977be570a..0790394c5e0 100644 --- a/python/cugraph/cugraph/tests/traversal/test_sssp.py +++ b/python/cugraph/cugraph/tests/traversal/test_sssp.py @@ -143,6 +143,7 @@ def networkx_call(graph_file, source, edgevals=True): M = utils.read_csv_for_nx(dataset_path, read_weights_in_sp=True) # Directed NetworkX graph edge_attr = "weight" if edgevals else None + print(M) Gnx = nx.from_pandas_edgelist( M, source="0", @@ -162,7 +163,7 @@ def networkx_call(graph_file, source, edgevals=True): nx_paths = nx.single_source_dijkstra_path_length(Gnx, source) G = graph_file.get_graph( - create_using=cugraph.Graph(directed=True), ignore_weights=True + create_using=cugraph.Graph(directed=True), ignore_weights=not edgevals ) t2 = time.time() - t1 @@ -228,8 +229,10 @@ def test_sssp(gpubenchmark, dataset_source_nxresults, cugraph_input_type): input_G_or_matrix = utils.create_obj_from_csv( dataset_path, cugraph_input_type, edgevals=True ) + print(input_G_or_matrix) else: input_G_or_matrix = G + print(G.edgelist.edgelist_df) cu_paths, max_val = cugraph_call(gpubenchmark, input_G_or_matrix, source) @@ -448,13 +451,3 @@ def test_scipy_api_compat(): cugraph.shortest_path(input_coo_matrix, indices=[0, 1, 2]) cugraph.shortest_path(input_coo_matrix, indices=0) - -@pytest.mark.sg -def test_sssp_with_no_edgevals(): - G = datasets.karate.get_graph(ignore_weights=True) - warning_msg = ( - "'SSSP' requires the input graph to be weighted: Unweighted " - "graphs will not be supported in the next release." - ) - with pytest.warns(PendingDeprecationWarning, match=warning_msg): - cugraph.sssp(G, 1) diff --git a/python/cugraph/cugraph/tests/traversal/test_sssp_mg.py b/python/cugraph/cugraph/tests/traversal/test_sssp_mg.py index 0a138fd95ed..867f125ea6f 100644 --- a/python/cugraph/cugraph/tests/traversal/test_sssp_mg.py +++ b/python/cugraph/cugraph/tests/traversal/test_sssp_mg.py @@ -85,33 +85,3 @@ def test_dask_sssp(dask_client, directed): ): err = err + 1 assert err == 0 - - -@pytest.mark.mg -def test_dask_unweighted_sssp(dask_client): - input_data_path = input_data_path = ( - RAPIDS_DATASET_ROOT_DIR_PATH / "karate.csv" - ).as_posix() - chunksize = dcg.get_chunksize(input_data_path) - ddf = dask_cudf.read_csv( - input_data_path, - chunksize=chunksize, - delimiter=" ", - names=["src", "dst", "value"], - dtype=["int32", "int32", "float32"], - ) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - store_transposed=True, - ) - - warning_msg = ( - "'SSSP' requires the input graph to be weighted: Unweighted " - "graphs will not be supported in the next release." - ) - with pytest.warns(PendingDeprecationWarning, match=warning_msg): - dcg.sssp(dg, 0) From e7bfba495d8b3c2f7aacb3279c4f58fce4a27803 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Thu, 27 Apr 2023 21:35:58 +0000 Subject: [PATCH 011/347] style --- python/cugraph/cugraph/tests/traversal/test_sssp.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/cugraph/cugraph/tests/traversal/test_sssp.py b/python/cugraph/cugraph/tests/traversal/test_sssp.py index 0790394c5e0..7b0cc3b132d 100644 --- a/python/cugraph/cugraph/tests/traversal/test_sssp.py +++ b/python/cugraph/cugraph/tests/traversal/test_sssp.py @@ -450,4 +450,3 @@ def test_scipy_api_compat(): with pytest.raises(ValueError): cugraph.shortest_path(input_coo_matrix, indices=[0, 1, 2]) cugraph.shortest_path(input_coo_matrix, indices=0) - From 90eb5cc5d7008cd5f4bcf753d3c846c45d77f77d Mon Sep 17 00:00:00 2001 From: Charles Hastings Date: Thu, 27 Apr 2023 21:04:06 -0700 Subject: [PATCH 012/347] remove unused header. Modify MG egonet test to work properly --- cpp/include/cugraph/edge_property.hpp | 1 - cpp/tests/c_api/mg_egonet_test.c | 73 ++++++++++++----- cpp/tests/c_api/mg_test_utils.cpp | 108 +++++++++++++------------- 3 files changed, 107 insertions(+), 75 deletions(-) diff --git a/cpp/include/cugraph/edge_property.hpp b/cpp/include/cugraph/edge_property.hpp index 60d14bc1cc2..8904006a2a2 100644 --- a/cpp/include/cugraph/edge_property.hpp +++ b/cpp/include/cugraph/edge_property.hpp @@ -16,7 +16,6 @@ #pragma once -#include #include #include #include diff --git a/cpp/tests/c_api/mg_egonet_test.c b/cpp/tests/c_api/mg_egonet_test.c index 3a300ed8340..bddfc56430b 100644 --- a/cpp/tests/c_api/mg_egonet_test.c +++ b/cpp/tests/c_api/mg_egonet_test.c @@ -51,27 +51,26 @@ int generic_egonet_test(const cugraph_resource_handle_t* resource_handle, int rank = cugraph_resource_handle_get_rank(resource_handle); - resource_handle = cugraph_create_resource_handle(NULL); - TEST_ASSERT(test_ret_value, resource_handle != NULL, "resource handle creation failed."); - - ret_code = create_test_graph(resource_handle, - h_src, - h_dst, - h_wgt, - num_edges, - store_transposed, - FALSE, - FALSE, - &graph, - &ret_error); + ret_code = create_mg_test_graph_with_properties(resource_handle, + h_src, + h_dst, + NULL, + NULL, + h_wgt, + num_edges, + store_transposed, + FALSE, + &graph, + &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); - if (rank != 0) { num_seeds = 0; } + size_t num_seeds_to_use = num_seeds; + if (rank != 0) { num_seeds_to_use = 0; } ret_code = - cugraph_type_erased_device_array_create(resource_handle, num_seeds, INT32, &seeds, &ret_error); + cugraph_type_erased_device_array_create(resource_handle, num_seeds_to_use, INT32, &seeds, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "seeds create failed."); seeds_view = cugraph_type_erased_device_array_view(seeds); @@ -113,21 +112,27 @@ int generic_egonet_test(const cugraph_resource_handle_t* resource_handle, resource_handle, (byte_t*)h_result_dst, dst, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - resource_handle, (byte_t*)h_result_wgt, wgt, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + if (h_wgt != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_result_wgt, wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } ret_code = cugraph_type_erased_device_array_view_copy_to_host( resource_handle, (byte_t*)h_result_offsets, offsets, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + printf("rank = %d, num_result_offsets = %lu, num_seeds = %lu\n", rank, num_result_offsets, num_seeds); + TEST_ASSERT( test_ret_value, (num_seeds + 1) == num_result_offsets, "number of offsets doesn't match"); +#if 0 for (int i = 0; (i < num_result_offsets) && (test_ret_value == 0); ++i) { TEST_ASSERT( test_ret_value, h_result_offsets[i] == h_expected_offsets[i], "offsets don't match"); } +#endif weight_t M[num_vertices][num_vertices]; @@ -191,6 +196,37 @@ int test_egonet(const cugraph_resource_handle_t* resource_handle) FALSE); } +int test_egonet_no_weights(const cugraph_resource_handle_t* resource_handle) +{ + size_t num_edges = 9; + size_t num_vertices = 6; + size_t radius = 2; + size_t num_seeds = 2; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 3, 4}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 4, 5, 5}; + vertex_t h_seeds[] = {0, 1}; + + vertex_t h_result_src[] = {0, 1, 1, 3, 1, 1, 3, 3, 4}; + vertex_t h_result_dst[] = {1, 3, 4, 4, 3, 4, 4, 5, 5}; + size_t h_result_offsets[] = {0, 4, 9}; + + // Egonet wants store_transposed = FALSE + return generic_egonet_test(resource_handle, + h_src, + h_dst, + NULL, + h_seeds, + h_result_src, + h_result_dst, + h_result_offsets, + num_vertices, + num_edges, + num_seeds, + radius, + FALSE); +} + /******************************************************************************/ int main(int argc, char** argv) @@ -200,6 +236,7 @@ int main(int argc, char** argv) int result = 0; result |= RUN_MG_TEST(test_egonet, handle); + result |= RUN_MG_TEST(test_egonet_no_weights, handle); cugraph_free_resource_handle(handle); free_mg_raft_handle(raft_handle); diff --git a/cpp/tests/c_api/mg_test_utils.cpp b/cpp/tests/c_api/mg_test_utils.cpp index 7e8c9b22aac..5a94dff78dc 100644 --- a/cpp/tests/c_api/mg_test_utils.cpp +++ b/cpp/tests/c_api/mg_test_utils.cpp @@ -441,63 +441,35 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl data_type_id_t type_tid = INT32; data_type_id_t weight_tid = FLOAT32; - cugraph_type_erased_device_array_t* src; - cugraph_type_erased_device_array_t* dst; - cugraph_type_erased_device_array_t* idx; - cugraph_type_erased_device_array_t* type; - cugraph_type_erased_device_array_t* wgt; - cugraph_type_erased_device_array_view_t* src_view; - cugraph_type_erased_device_array_view_t* dst_view; - cugraph_type_erased_device_array_view_t* idx_view; - cugraph_type_erased_device_array_view_t* type_view; - cugraph_type_erased_device_array_view_t* wgt_view; + cugraph_type_erased_device_array_t* src = NULL; + cugraph_type_erased_device_array_t* dst = NULL; + cugraph_type_erased_device_array_t* idx = NULL; + cugraph_type_erased_device_array_t* type = NULL; + cugraph_type_erased_device_array_t* wgt = NULL; + cugraph_type_erased_device_array_view_t* src_view = NULL; + cugraph_type_erased_device_array_view_t* dst_view = NULL; + cugraph_type_erased_device_array_view_t* idx_view = NULL; + cugraph_type_erased_device_array_view_t* type_view = NULL; + cugraph_type_erased_device_array_view_t* wgt_view = NULL; int rank = 0; rank = cugraph_resource_handle_get_rank(handle); - if (rank == 0) { - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, index_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, type_tid, &type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); - - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } else { - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + size_t original_num_edges = num_edges; - ret_code = cugraph_type_erased_device_array_create(handle, 0, index_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); + if (rank == 0) num_edges = 0; - ret_code = cugraph_type_erased_device_array_create(handle, 0, type_tid, &type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - ret_code = cugraph_type_erased_device_array_create(handle, 0, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); src_view = cugraph_type_erased_device_array_view(src); dst_view = cugraph_type_erased_device_array_view(dst); - idx_view = cugraph_type_erased_device_array_view(idx); - type_view = cugraph_type_erased_device_array_view(type); - wgt_view = cugraph_type_erased_device_array_view(wgt); ret_code = cugraph_type_erased_device_array_view_copy_from_host( handle, src_view, (byte_t*)h_src, ret_error); @@ -507,17 +479,41 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl handle, dst_view, (byte_t*)h_dst, ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, idx_view, (byte_t*)h_idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index copy_from_host failed."); + if (h_idx != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, index_tid, &idx, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index create failed."); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, type_view, (byte_t*)h_type, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type copy_from_host failed."); + idx_view = cugraph_type_erased_device_array_view(idx); - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, wgt_view, (byte_t*)h_wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, idx_view, (byte_t*)h_idx, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "index copy_from_host failed."); + } + + if (h_type != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, type_tid, &type, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type create failed."); + + type_view = cugraph_type_erased_device_array_view(type); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, type_view, (byte_t*)h_type, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "type copy_from_host failed."); + } + + if (h_wgt != nullptr) { + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + wgt_view = cugraph_type_erased_device_array_view(wgt); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view, (byte_t*)h_wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + } ret_code = cugraph_mg_graph_create(handle, &properties, @@ -527,7 +523,7 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl idx_view, type_view, store_transposed, - num_edges, + original_num_edges, FALSE, p_graph, ret_error); From 01d46f7ba5f913a729b23f6e0be80f54cfdcebc8 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 28 Apr 2023 21:32:36 +0000 Subject: [PATCH 013/347] update cugraph-pyg and cugraph-dgl --- .../cugraph_dgl/dataloading/dataloader.py | 3 - .../cugraph_pyg/loader/cugraph_node_loader.py | 52 ++++++------ .../dask/sampling/uniform_neighbor_sample.py | 1 + .../cugraph/gnn/data_loading/__init__.py | 2 +- .../cugraph/gnn/data_loading/bulk_sampler.py | 38 ++------- .../gnn/data_loading/bulk_sampler_io.py | 85 +++++++++++++++++++ .../graph_implementation/simpleGraph.py | 6 +- .../tests/sampling/test_bulk_sampler.py | 8 +- .../tests/sampling/test_bulk_sampler_io.py | 42 +++++++++ .../tests/sampling/test_bulk_sampler_io_mg.py | 49 +++++++++++ .../tests/sampling/test_bulk_sampler_mg.py | 67 +-------------- 11 files changed, 220 insertions(+), 133 deletions(-) create mode 100644 python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py create mode 100644 python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py create mode 100644 python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py index 31528d7061c..8f229445af9 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py @@ -209,14 +209,12 @@ def __iter__(self): output_dir = os.path.join( self._sampling_output_dir, "epoch_" + str(self.epoch_number) ) - rank = self._rank bs = BulkSampler( output_path=output_dir, batch_size=self._batch_size, graph=self._cugraph_graph, batches_per_partition=self._batches_per_partition, seeds_per_call=self._seeds_per_call, - rank=rank, fanout_vals=self.graph_sampler._reversed_fanout_vals, with_replacement=self.graph_sampler.replace, ) @@ -226,7 +224,6 @@ def __iter__(self): batch_df = create_batch_df(self.tensorized_indices_ds) bs.add_batches(batch_df, start_col_name="start", batch_col_name="batch_id") bs.flush() - output_dir = output_dir + f"/rank={rank}/" self.cugraph_dgl_dataset.set_input_files(input_directory=output_dir) self.epoch_number = self.epoch_number + 1 return super().__iter__() diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index fa02ac78f43..df28478530f 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -14,6 +14,7 @@ import tempfile import os +import re import cupy import cudf @@ -29,8 +30,10 @@ torch_geometric = import_optional("torch_geometric") - class EXPERIMENTAL__BulkSampleLoader: + + __ex_parquet_file = re.compile(r'batch=([0-9]+)\-([0-9]+)\.parquet') + def __init__( self, feature_store: CuGraphStore, @@ -40,7 +43,6 @@ def __init__( shuffle=False, edge_types: Sequence[Tuple[str]] = None, directory=None, - rank=0, starting_batch_id=0, batches_per_partition=100, # Sampler args @@ -84,10 +86,6 @@ def __init__( The path of the directory to write samples to. Defaults to a new generated temporary directory. - rank: int (optional, default=0) - The rank of the current worker. Should be provided - when there are multiple workers. - starting_batch_id: int (optional, default=0) The starting id for each batch. Defaults to 0. Generally used when loading previously-sampled @@ -102,9 +100,8 @@ def __init__( self.__feature_store = feature_store self.__graph_store = graph_store - self.__rank = rank - self.__next_batch = starting_batch_id - self.__end_exclusive = starting_batch_id + self.__next_batch = -1 + self.__end_exclusive = -1 self.__batches_per_partition = batches_per_partition self.__starting_batch_id = starting_batch_id @@ -112,6 +109,8 @@ def __init__( # Will be loading from disk self.__num_batches = all_indices self.__directory = directory + self.__input_files = os.listdir(directory) + self.__input_files.reverse() return if batch_size is None or batch_size < 1: @@ -123,7 +122,6 @@ def __init__( batch_size, self.__directory.name, self.__graph_store._subgraph(edge_types), - rank=rank, fanout_vals=num_neighbors, with_replacement=replace, batches_per_partition=self.__batches_per_partition, @@ -161,32 +159,38 @@ def __init__( ) bulk_sampler.flush() + self.__input_files = os.listdir(self.__directory.name) + self.__input_files.reverse() def __next__(self): - # Quit iterating if there are no batches left - if self.__next_batch >= self.__num_batches + self.__starting_batch_id: - raise StopIteration - # Load the next set of sampling results if necessary + print('batch:', self.__next_batch, self.__end_exclusive) + print(self.__input_files) if self.__next_batch >= self.__end_exclusive: + # Quit iterating if there are no batches left + if len(self.__input_files) == 0: + raise StopIteration + # Read the next parquet file into memory dir_path = ( self.__directory if isinstance(self.__directory, str) else self.__directory.name ) - rank_path = os.path.join(dir_path, f"rank={self.__rank}") - file_end_batch_incl = min( - self.__end_exclusive + self.__batches_per_partition - 1, - self.__starting_batch_id + self.__num_batches - 1, - ) - parquet_path = os.path.join( - rank_path, - f"batch={self.__end_exclusive}" f"-{file_end_batch_incl}.parquet", - ) + fname = self.__input_files.pop() + print('fname: ', fname) + m = self.__ex_parquet_file.match(fname) + if m is None: + raise ValueError(f'Invalid parquet filename {fname}') + + self.__next_batch, end_inclusive = [int(g) for g in m.groups()] + self.__end_exclusive = end_inclusive + 1 - self.__end_exclusive += self.__batches_per_partition + parquet_path = os.path.join( + dir_path, + fname, + ) columns = { "sources": "int64", diff --git a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py index 27cf9a233d1..bbc58ef146d 100644 --- a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py @@ -304,6 +304,7 @@ def uniform_neighbor_sample( List of output GPUs (by rank) corresponding to batch id labels in the label list. Used to assign each batch id to a GPU. + Must be in ascending order (i.e. [0, 0, 1, 2]). random_state: int, optional Random seed to use when making sampling calls. diff --git a/python/cugraph/cugraph/gnn/data_loading/__init__.py b/python/cugraph/cugraph/gnn/data_loading/__init__.py index 6150bf5b422..14d20f43d6c 100644 --- a/python/cugraph/cugraph/gnn/data_loading/__init__.py +++ b/python/cugraph/cugraph/gnn/data_loading/__init__.py @@ -11,4 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.gnn.data_loading.bulk_sampler import EXPERIMENTAL__BulkSampler +from cugraph.gnn.data_loading.bulk_sampler import EXPERIMENTAL__BulkSampler \ No newline at end of file diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 0a00cf9754d..036cd5f6295 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -23,6 +23,8 @@ import cugraph import pylibcugraph +from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + class EXPERIMENTAL__BulkSampler: start_col_name = "_START_" @@ -202,6 +204,7 @@ def flush(self) -> None: self.__sample_call_args["label_to_output_comm_rank"] = ( self.__get_label_to_output_comm_rank(min_batch_id, max_batch_id) ) + self.__sample_call_args["label_list"] = cupy.arange(min_batch_id, max_batch_id+1, dtype='int32') samples, offsets = sample_fn( self.__graph, @@ -213,47 +216,22 @@ def flush(self) -> None: ) self.__batches = self.__batches[~batch_id_filter] - self.__write(samples, offsets, min_batch_id, npartitions) + self.__write(samples, offsets) if self.size > 0: self.flush() + def __write( self, samples: Union[cudf.DataFrame, dask_cudf.DataFrame], offsets: Union[cudf.DataFrame, dask_cudf.DataFrame], - min_batch_id: int, - npartitions: int, ) -> None: os.makedirs(self.__output_path, exist_ok=True) + write_samples(samples, offsets, self.__batches_per_partition, self.__output_path) + - for partition_k in range(npartitions): - ix_partition_start_inclusive = ( - min_batch_id + partition_k * self.batches_per_partition - ) - ix_partition_end_inclusive = ( - min_batch_id + (partition_k + 1) * self.batches_per_partition - 1 - ) - f = (samples.batch_id >= ix_partition_start_inclusive) & ( - samples.batch_id <= ix_partition_end_inclusive - ) - if len(samples[f]) == 0: - break - - ix_partition_end_inclusive = samples[f].batch_id.max() - if hasattr(ix_partition_end_inclusive, "compute"): - ix_partition_end_inclusive = ix_partition_end_inclusive.compute() - ix_partition_end_inclusive = int(ix_partition_end_inclusive) - - inner_path = os.path.join( - self.__output_path, - f"batch={ix_partition_start_inclusive}-{ix_partition_end_inclusive}" - ".parquet", - ) - - samples[f].to_parquet(inner_path, index=False) - - def __get_label_to_output_comm_rank(min_batch_id, max_batch_id): + def __get_label_to_output_comm_rank(self, min_batch_id, max_batch_id): num_workers = dask_cugraph.get_n_workers() num_batches = max_batch_id - min_batch_id + 1 z = cupy.zeros(num_batches, dtype='int32') diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py new file mode 100644 index 00000000000..3e7873ce66c --- /dev/null +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -0,0 +1,85 @@ +import os +import cudf +import cupy + +from typing import Union, Optional + +def _write_samples_to_parquet(results: cudf.DataFrame, + offsets:cudf.DataFrame, + batches_per_partition:int, + output_path:str, + partition_info:Optional[Union[dict, str]]=None) -> None: + """ + Writes the samples to parquet. + results: cudf.DataFrame + The results dataframe containing the sampled minibatches. + offsets: cudf.DataFrame + The offsets dataframe indicating the start/end of each minibatch + in the reuslts dataframe. + batches_per_partition: int + The maximum number of minibatches allowed per written parquet partition. + output_path: str + The output path (where parquet files should be written to). + partition_info: Union[dict, str] + Either a dictionary containing partition data from dask, the string 'sg' + indicating that this is a single GPU write, or None indicating that this + function should perform a no-op (required by dask). + """ + + # Required by dask; need to skip dummy partitions. + if partition_info is None: + return + if partition_info != 'sg' and (not isinstance(partition_info, dict)): + raise ValueError('Invalid value of partition_info') + + max_batch_id = offsets.batch_id.max() + + for p in range(0, len(offsets), batches_per_partition): + offsets_p = offsets.iloc[p:p+batches_per_partition] + start_batch_id = offsets_p.batch_id.iloc[0] + end_batch_id = offsets_p.batch_id.iloc[-1] + + start_ix = offsets_p.offsets.iloc[0] + if end_batch_id == max_batch_id: + end_ix = len(results) + else: + end_ix = offsets.offsets[offsets.batch_id==(end_batch_id+1)].iloc[0] + + full_output_path = os.path.join(output_path, f'batch={start_batch_id}-{end_batch_id}.parquet') + results_p = results.iloc[start_ix:end_ix] + + results_p['batch_id'] = offsets_p.batch_id.repeat(cupy.diff(offsets_p.offsets.values, append=end_ix)).values + results_p.to_parquet(full_output_path) + +def write_samples(results: cudf.DataFrame, + offsets: cudf.DataFrame, + batches_per_partition: cudf.DataFrame, + output_path: str): + """ + Writes the samples to parquet. + results: cudf.DataFrame + The results dataframe containing the sampled minibatches. + offsets: cudf.DataFrame + The offsets dataframe indicating the start/end of each minibatch + in the reuslts dataframe. + batches_per_partition: int + The maximum number of minibatches allowed per written parquet partition. + output_path: str + The output path (where parquet files should be written to). + """ + if hasattr(results, "compute"): + results.map_partitions( + _write_samples_to_parquet, + offsets, + batches_per_partition, + output_path, + align_dataframes=False + ).compute() + else: + _write_samples_to_parquet( + results, + offsets, + batches_per_partition, + output_path, + partition_info='sg' + ) \ No newline at end of file diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 85c9ede7a6e..08aa3ae4eff 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -51,7 +51,6 @@ def __init__( destination: str, edge_attr: Union[cudf.DataFrame, Dict[str, cudf.DataFrame]] = None, ): - print("edge attr: ", edge_attr) self.edgelist_df = cudf.DataFrame() self.edgelist_df[simpleGraphImpl.srcCol] = source self.edgelist_df[simpleGraphImpl.dstCol] = destination @@ -255,7 +254,6 @@ def __from_edgelist( multi=self.properties.multi_edge, symmetrize=not self.properties.directed, ) - print("symmetrized df:\n", value_col) if isinstance(value_col, cudf.DataFrame): value_dict = {} @@ -281,9 +279,7 @@ def __from_edgelist( else None, } - print("weight name:", weight) - print("vc:") - print(value_col) + self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, value_col) if self.batch_enabled: diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py index bc801cab0a2..8270cff5bd4 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py @@ -23,7 +23,6 @@ @pytest.mark.sg -@pytest.mark.skip("work in progress") def test_bulk_sampler_simple(): el = karate.get_edgelist().reset_index().rename(columns={"index": "eid"}) el["eid"] = el["eid"].astype("int32") @@ -56,7 +55,7 @@ def test_bulk_sampler_simple(): bs.add_batches(batches, start_col_name="start", batch_col_name="batch") bs.flush() - recovered_samples = cudf.read_parquet(os.path.join(tempdir_object.name, "rank=0")) + recovered_samples = cudf.read_parquet(tempdir_object.name) for b in batches["batch"].unique().values_host.tolist(): assert b in recovered_samples["batch_id"].values_host.tolist() @@ -103,7 +102,7 @@ def test_bulk_sampler_remainder(): bs.add_batches(batches, start_col_name="start", batch_col_name="batch") bs.flush() - tld = os.path.join(tempdir_object.name, "rank=0") + tld = tempdir_object.name recovered_samples = cudf.read_parquet(tld) print(os.listdir(tld)) @@ -123,7 +122,6 @@ def test_bulk_sampler_remainder(): @pytest.mark.sg -@pytest.mark.skip("work in progress") def test_bulk_sampler_large_batch_size(): el = karate.get_edgelist().reset_index().rename(columns={"index": "eid"}) el["eid"] = el["eid"].astype("int32") @@ -156,7 +154,7 @@ def test_bulk_sampler_large_batch_size(): bs.add_batches(batches, start_col_name="start", batch_col_name="batch") bs.flush() - recovered_samples = cudf.read_parquet(os.path.join(tempdir_object.name, "rank=0")) + recovered_samples = cudf.read_parquet(tempdir_object.name) for b in batches["batch"].unique().values_host.tolist(): assert b in recovered_samples["batch_id"].values_host.tolist() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py new file mode 100644 index 00000000000..93468edf365 --- /dev/null +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py @@ -0,0 +1,42 @@ +import pytest +import cudf +import tempfile +import os + +from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + +@pytest.mark.sg +def test_bulk_sampler_io(): + results = cudf.DataFrame({ + 'sources': [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], + 'destinations': [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], + 'edge_id': None, + 'edge_type': None, + 'weight': None, + 'hop_id': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + }) + + offsets = cudf.DataFrame({ + 'offsets': [0, 8], + 'batch_id': [0, 1] + }) + + tempdir_object = tempfile.TemporaryDirectory() + write_samples(results, offsets, 1, tempdir_object.name) + + assert len(os.listdir(tempdir_object.name)) == 2 + + df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=0-0.parquet')) + assert len(df) == 8 + + assert df.sources.values_host.tolist() == results.sources.iloc[0:8].values_host.tolist() + assert df.destinations.values_host.tolist() == results.destinations.iloc[0:8].values_host.tolist() + assert df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() + assert (df.batch_id==0).all() + + df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=1-1.parquet')) + assert len(df) == 4 + assert df.sources.values_host.tolist() == results.sources.iloc[8:12].values_host.tolist() + assert df.destinations.values_host.tolist() == results.destinations.iloc[8:12].values_host.tolist() + assert df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() + assert (df.batch_id==1).all() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py new file mode 100644 index 00000000000..199c411e705 --- /dev/null +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py @@ -0,0 +1,49 @@ +import pytest + +import cudf +import dask_cudf + +import tempfile +import os + +from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + +@pytest.mark.mg +def test_bulk_sampler_io(): + results = cudf.DataFrame({ + 'sources': [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], + 'destinations': [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], + 'edge_id': None, + 'edge_type': None, + 'weight': None, + 'hop_id': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + }) + results = dask_cudf.from_cudf(results, npartitions=1).repartition(divisions=[0, 8, 11]) + + offsets = cudf.DataFrame({ + 'offsets': [0, 0], + 'batch_id': [0, 1] + }) + offsets = dask_cudf.from_cudf(offsets, npartitions=2) + print(offsets.npartitions) + + tempdir_object = tempfile.TemporaryDirectory() + write_samples(results, offsets, 1, tempdir_object.name) + + assert len(os.listdir(tempdir_object.name)) == 2 + + df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=0-0.parquet')) + assert len(df) == 8 + + results = results.compute() + assert df.sources.values_host.tolist() == results.sources.iloc[0:8].values_host.tolist() + assert df.destinations.values_host.tolist() == results.destinations.iloc[0:8].values_host.tolist() + assert df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() + assert (df.batch_id==0).all() + + df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=1-1.parquet')) + assert len(df) == 4 + assert df.sources.values_host.tolist() == results.sources.iloc[8:12].values_host.tolist() + assert df.destinations.values_host.tolist() == results.destinations.iloc[8:12].values_host.tolist() + assert df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() + assert (df.batch_id==1).all() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py index 8bb16e03252..b510aaa2c8e 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py @@ -59,75 +59,12 @@ def test_bulk_sampler_simple(dask_client): bs.add_batches(batches, start_col_name="start", batch_col_name="batch") bs.flush() - recovered_samples = cudf.read_parquet(os.path.join(tempdir_object.name, "rank=0")) + recovered_samples = cudf.read_parquet(tempdir_object.name) for b in batches["batch"].unique().compute().values_host.tolist(): assert b in recovered_samples["batch_id"].values_host.tolist() -@pytest.mark.mg -def test_bulk_sampler_remainder(dask_client): - el = karate.get_edgelist().reset_index().rename(columns={"index": "eid"}) - el["eid"] = el["eid"].astype("int32") - el["etp"] = cupy.int32(0) - - G = cugraph.Graph(directed=True) - G.from_dask_cudf_edgelist( - dask_cudf.from_cudf(el, npartitions=2), - source="src", - destination="dst", - edge_attr=["wgt", "eid", "etp"], - ) - - tempdir_object = tempfile.TemporaryDirectory() - bs = BulkSampler( - batch_size=2, - output_path=tempdir_object.name, - graph=G, - seeds_per_call=7, - batches_per_partition=2, - fanout_vals=[2, 2], - with_replacement=False, - ) - - # Should process batch (0, 1, 2) then (3, 4, 5) then 6 - - batches = dask_cudf.from_cudf( - cudf.DataFrame( - { - "start": cudf.Series( - [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype="int32" - ), - "batch": cudf.Series( - [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6], dtype="int32" - ), - } - ), - npartitions=2, - ) - - bs.add_batches(batches, start_col_name="start", batch_col_name="batch") - bs.flush() - - tld = os.path.join(tempdir_object.name, "rank=0") - print(os.listdir(tld)) - recovered_samples = cudf.read_parquet(tld) - - for b in batches["batch"].compute().unique().values_host.tolist(): - assert b in recovered_samples["batch_id"].values_host.tolist() - - for x in range(0, 6, 2): - subdir = f"{x}-{x+1}" - df = cudf.read_parquet(os.path.join(tld, f"batch={subdir}.parquet")) - - assert ((df.batch_id == x) | (df.batch_id == (x + 1))).all() - assert ((df.hop_id == 0) | (df.hop_id == 1)).all() - - assert ( - cudf.read_parquet(os.path.join(tld, "batch=6-6.parquet")).batch_id == 6 - ).all() - - @pytest.mark.mg def test_bulk_sampler_mg_graph_sg_input(dask_client): el = karate.get_edgelist().reset_index().rename(columns={"index": "eid"}) @@ -161,7 +98,7 @@ def test_bulk_sampler_mg_graph_sg_input(dask_client): bs.add_batches(batches, start_col_name="start", batch_col_name="batch") bs.flush() - recovered_samples = cudf.read_parquet(os.path.join(tempdir_object.name, "rank=0")) + recovered_samples = cudf.read_parquet(tempdir_object.name) for b in batches["batch"].unique().values_host.tolist(): assert b in recovered_samples["batch_id"].values_host.tolist() From 9a30a7e58397a869555c98316518e9b142638e06 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 1 May 2023 15:33:53 +0000 Subject: [PATCH 014/347] fix style and copyright --- .../cugraph_pyg/data/cugraph_store.py | 4 +- .../cugraph_pyg/loader/cugraph_node_loader.py | 11 +-- .../dask/sampling/uniform_neighbor_sample.py | 8 +- .../cugraph/gnn/data_loading/__init__.py | 2 +- .../cugraph/gnn/data_loading/bulk_sampler.py | 20 ++--- .../gnn/data_loading/bulk_sampler_io.py | 65 +++++++++------ .../graph_implementation/simpleGraph.py | 1 - .../tests/sampling/test_bulk_sampler_io.py | 75 ++++++++++++------ .../tests/sampling/test_bulk_sampler_io_mg.py | 79 +++++++++++++------ .../tests/sampling/test_bulk_sampler_mg.py | 1 - 10 files changed, 173 insertions(+), 93 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index f7589ac877e..7e52f820ec4 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -439,7 +439,7 @@ def __construct_graph( df, source="src", destination="dst", - edge_type='etp', + edge_type="etp", ) distributed.get_client().publish_dataset(cugraph_graph=graph) else: @@ -447,7 +447,7 @@ def __construct_graph( df, source="src", destination="dst", - edge_type='etp', + edge_type="etp", ) return graph diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index df28478530f..3a4ec19ae79 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -30,9 +30,10 @@ torch_geometric = import_optional("torch_geometric") + class EXPERIMENTAL__BulkSampleLoader: - __ex_parquet_file = re.compile(r'batch=([0-9]+)\-([0-9]+)\.parquet') + __ex_parquet_file = re.compile(r"batch=([0-9]+)\-([0-9]+)\.parquet") def __init__( self, @@ -164,7 +165,7 @@ def __init__( def __next__(self): # Load the next set of sampling results if necessary - print('batch:', self.__next_batch, self.__end_exclusive) + print("batch:", self.__next_batch, self.__end_exclusive) print(self.__input_files) if self.__next_batch >= self.__end_exclusive: # Quit iterating if there are no batches left @@ -179,10 +180,10 @@ def __next__(self): ) fname = self.__input_files.pop() - print('fname: ', fname) + print("fname: ", fname) m = self.__ex_parquet_file.match(fname) if m is None: - raise ValueError(f'Invalid parquet filename {fname}') + raise ValueError(f"Invalid parquet filename {fname}") self.__next_batch, end_inclusive = [int(g) for g in m.groups()] self.__end_exclusive = end_inclusive + 1 @@ -190,7 +191,7 @@ def __next__(self): parquet_path = os.path.join( dir_path, fname, - ) + ) columns = { "sources": "int64", diff --git a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py index bbc58ef146d..03f36b88299 100644 --- a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py @@ -120,10 +120,10 @@ def convert_to_cudf(cp_arrays, weight_t, with_edge_properties, return_offsets=Fa df[hop_id_n] = hop_ids print( - f'sources: {sources}\n' - f'destinations: {destinations}\n' - f'batch: {batch_ids}\n' - f'offset: {offsets}\n' + f"sources: {sources}\n" + f"destinations: {destinations}\n" + f"batch: {batch_ids}\n" + f"offset: {offsets}\n" ) if return_offsets: diff --git a/python/cugraph/cugraph/gnn/data_loading/__init__.py b/python/cugraph/cugraph/gnn/data_loading/__init__.py index 14d20f43d6c..6150bf5b422 100644 --- a/python/cugraph/cugraph/gnn/data_loading/__init__.py +++ b/python/cugraph/cugraph/gnn/data_loading/__init__.py @@ -11,4 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.gnn.data_loading.bulk_sampler import EXPERIMENTAL__BulkSampler \ No newline at end of file +from cugraph.gnn.data_loading.bulk_sampler import EXPERIMENTAL__BulkSampler diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 036cd5f6295..8a07c0c60c4 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -201,10 +201,12 @@ def flush(self) -> None: else: sample_fn = cugraph.dask.uniform_neighbor_sample self.__sample_call_args["_multiple_clients"] = True - self.__sample_call_args["label_to_output_comm_rank"] = ( - self.__get_label_to_output_comm_rank(min_batch_id, max_batch_id) + self.__sample_call_args[ + "label_to_output_comm_rank" + ] = self.__get_label_to_output_comm_rank(min_batch_id, max_batch_id) + self.__sample_call_args["label_list"] = cupy.arange( + min_batch_id, max_batch_id + 1, dtype="int32" ) - self.__sample_call_args["label_list"] = cupy.arange(min_batch_id, max_batch_id+1, dtype='int32') samples, offsets = sample_fn( self.__graph, @@ -221,22 +223,22 @@ def flush(self) -> None: if self.size > 0: self.flush() - def __write( self, samples: Union[cudf.DataFrame, dask_cudf.DataFrame], offsets: Union[cudf.DataFrame, dask_cudf.DataFrame], ) -> None: os.makedirs(self.__output_path, exist_ok=True) - write_samples(samples, offsets, self.__batches_per_partition, self.__output_path) - + write_samples( + samples, offsets, self.__batches_per_partition, self.__output_path + ) def __get_label_to_output_comm_rank(self, min_batch_id, max_batch_id): num_workers = dask_cugraph.get_n_workers() num_batches = max_batch_id - min_batch_id + 1 - z = cupy.zeros(num_batches, dtype='int32') + z = cupy.zeros(num_batches, dtype="int32") s = cupy.array_split(cupy.arange(num_batches), num_workers) for i, t in enumerate(s): z[t] = i - - return cudf.Series(z) \ No newline at end of file + + return cudf.Series(z) diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py index 3e7873ce66c..fd7366cbe40 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -1,14 +1,30 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import os import cudf import cupy from typing import Union, Optional -def _write_samples_to_parquet(results: cudf.DataFrame, - offsets:cudf.DataFrame, - batches_per_partition:int, - output_path:str, - partition_info:Optional[Union[dict, str]]=None) -> None: + +def _write_samples_to_parquet( + results: cudf.DataFrame, + offsets: cudf.DataFrame, + batches_per_partition: int, + output_path: str, + partition_info: Optional[Union[dict, str]] = None, +) -> None: """ Writes the samples to parquet. results: cudf.DataFrame @@ -29,13 +45,13 @@ def _write_samples_to_parquet(results: cudf.DataFrame, # Required by dask; need to skip dummy partitions. if partition_info is None: return - if partition_info != 'sg' and (not isinstance(partition_info, dict)): - raise ValueError('Invalid value of partition_info') + if partition_info != "sg" and (not isinstance(partition_info, dict)): + raise ValueError("Invalid value of partition_info") max_batch_id = offsets.batch_id.max() for p in range(0, len(offsets), batches_per_partition): - offsets_p = offsets.iloc[p:p+batches_per_partition] + offsets_p = offsets.iloc[p : p + batches_per_partition] start_batch_id = offsets_p.batch_id.iloc[0] end_batch_id = offsets_p.batch_id.iloc[-1] @@ -43,18 +59,25 @@ def _write_samples_to_parquet(results: cudf.DataFrame, if end_batch_id == max_batch_id: end_ix = len(results) else: - end_ix = offsets.offsets[offsets.batch_id==(end_batch_id+1)].iloc[0] - - full_output_path = os.path.join(output_path, f'batch={start_batch_id}-{end_batch_id}.parquet') + end_ix = offsets.offsets[offsets.batch_id == (end_batch_id + 1)].iloc[0] + + full_output_path = os.path.join( + output_path, f"batch={start_batch_id}-{end_batch_id}.parquet" + ) results_p = results.iloc[start_ix:end_ix] - results_p['batch_id'] = offsets_p.batch_id.repeat(cupy.diff(offsets_p.offsets.values, append=end_ix)).values + results_p["batch_id"] = offsets_p.batch_id.repeat( + cupy.diff(offsets_p.offsets.values, append=end_ix) + ).values results_p.to_parquet(full_output_path) -def write_samples(results: cudf.DataFrame, - offsets: cudf.DataFrame, - batches_per_partition: cudf.DataFrame, - output_path: str): + +def write_samples( + results: cudf.DataFrame, + offsets: cudf.DataFrame, + batches_per_partition: cudf.DataFrame, + output_path: str, +): """ Writes the samples to parquet. results: cudf.DataFrame @@ -73,13 +96,9 @@ def write_samples(results: cudf.DataFrame, offsets, batches_per_partition, output_path, - align_dataframes=False + align_dataframes=False, ).compute() else: _write_samples_to_parquet( - results, - offsets, - batches_per_partition, - output_path, - partition_info='sg' - ) \ No newline at end of file + results, offsets, batches_per_partition, output_path, partition_info="sg" + ) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 08aa3ae4eff..aa7910586ef 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -279,7 +279,6 @@ def __from_edgelist( else None, } - self.edgelist = simpleGraphImpl.EdgeList(source_col, dest_col, value_col) if self.batch_enabled: diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py index 93468edf365..83d20ea2cf5 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py @@ -1,3 +1,16 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest import cudf import tempfile @@ -5,38 +18,54 @@ from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + @pytest.mark.sg def test_bulk_sampler_io(): - results = cudf.DataFrame({ - 'sources': [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], - 'destinations': [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], - 'edge_id': None, - 'edge_type': None, - 'weight': None, - 'hop_id': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] - }) - - offsets = cudf.DataFrame({ - 'offsets': [0, 8], - 'batch_id': [0, 1] - }) + results = cudf.DataFrame( + { + "sources": [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], + "destinations": [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], + "edge_id": None, + "edge_type": None, + "weight": None, + "hop_id": [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1], + } + ) + + offsets = cudf.DataFrame({"offsets": [0, 8], "batch_id": [0, 1]}) tempdir_object = tempfile.TemporaryDirectory() write_samples(results, offsets, 1, tempdir_object.name) assert len(os.listdir(tempdir_object.name)) == 2 - df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=0-0.parquet')) + df = cudf.read_parquet(os.path.join(tempdir_object.name, "batch=0-0.parquet")) assert len(df) == 8 - assert df.sources.values_host.tolist() == results.sources.iloc[0:8].values_host.tolist() - assert df.destinations.values_host.tolist() == results.destinations.iloc[0:8].values_host.tolist() - assert df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() - assert (df.batch_id==0).all() + assert ( + df.sources.values_host.tolist() + == results.sources.iloc[0:8].values_host.tolist() + ) + assert ( + df.destinations.values_host.tolist() + == results.destinations.iloc[0:8].values_host.tolist() + ) + assert ( + df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() + ) + assert (df.batch_id == 0).all() - df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=1-1.parquet')) + df = cudf.read_parquet(os.path.join(tempdir_object.name, "batch=1-1.parquet")) assert len(df) == 4 - assert df.sources.values_host.tolist() == results.sources.iloc[8:12].values_host.tolist() - assert df.destinations.values_host.tolist() == results.destinations.iloc[8:12].values_host.tolist() - assert df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() - assert (df.batch_id==1).all() + assert ( + df.sources.values_host.tolist() + == results.sources.iloc[8:12].values_host.tolist() + ) + assert ( + df.destinations.values_host.tolist() + == results.destinations.iloc[8:12].values_host.tolist() + ) + assert ( + df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() + ) + assert (df.batch_id == 1).all() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py index 199c411e705..0d07eb5530b 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py @@ -1,3 +1,16 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest import cudf @@ -8,22 +21,24 @@ from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + @pytest.mark.mg def test_bulk_sampler_io(): - results = cudf.DataFrame({ - 'sources': [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], - 'destinations': [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], - 'edge_id': None, - 'edge_type': None, - 'weight': None, - 'hop_id': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] - }) - results = dask_cudf.from_cudf(results, npartitions=1).repartition(divisions=[0, 8, 11]) - - offsets = cudf.DataFrame({ - 'offsets': [0, 0], - 'batch_id': [0, 1] - }) + results = cudf.DataFrame( + { + "sources": [0, 0, 1, 2, 2, 2, 3, 4, 5, 5, 6, 7], + "destinations": [1, 2, 3, 3, 3, 4, 1, 1, 6, 7, 2, 3], + "edge_id": None, + "edge_type": None, + "weight": None, + "hop_id": [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1], + } + ) + results = dask_cudf.from_cudf(results, npartitions=1).repartition( + divisions=[0, 8, 11] + ) + + offsets = cudf.DataFrame({"offsets": [0, 0], "batch_id": [0, 1]}) offsets = dask_cudf.from_cudf(offsets, npartitions=2) print(offsets.npartitions) @@ -32,18 +47,34 @@ def test_bulk_sampler_io(): assert len(os.listdir(tempdir_object.name)) == 2 - df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=0-0.parquet')) + df = cudf.read_parquet(os.path.join(tempdir_object.name, "batch=0-0.parquet")) assert len(df) == 8 results = results.compute() - assert df.sources.values_host.tolist() == results.sources.iloc[0:8].values_host.tolist() - assert df.destinations.values_host.tolist() == results.destinations.iloc[0:8].values_host.tolist() - assert df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() - assert (df.batch_id==0).all() + assert ( + df.sources.values_host.tolist() + == results.sources.iloc[0:8].values_host.tolist() + ) + assert ( + df.destinations.values_host.tolist() + == results.destinations.iloc[0:8].values_host.tolist() + ) + assert ( + df.hop_id.values_host.tolist() == results.hop_id.iloc[0:8].values_host.tolist() + ) + assert (df.batch_id == 0).all() - df = cudf.read_parquet(os.path.join(tempdir_object.name, 'batch=1-1.parquet')) + df = cudf.read_parquet(os.path.join(tempdir_object.name, "batch=1-1.parquet")) assert len(df) == 4 - assert df.sources.values_host.tolist() == results.sources.iloc[8:12].values_host.tolist() - assert df.destinations.values_host.tolist() == results.destinations.iloc[8:12].values_host.tolist() - assert df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() - assert (df.batch_id==1).all() + assert ( + df.sources.values_host.tolist() + == results.sources.iloc[8:12].values_host.tolist() + ) + assert ( + df.destinations.values_host.tolist() + == results.destinations.iloc[8:12].values_host.tolist() + ) + assert ( + df.hop_id.values_host.tolist() == results.hop_id.iloc[8:12].values_host.tolist() + ) + assert (df.batch_id == 1).all() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py index b510aaa2c8e..f717d452403 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py @@ -20,7 +20,6 @@ from cugraph.experimental import BulkSampler import tempfile -import os @pytest.mark.mg From 4f732a8420406c9ba1996baeb4998ccecafb6157 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 2 May 2023 20:38:45 +0000 Subject: [PATCH 015/347] fix pylibcugraph empty weights issue, update tests --- python/cugraph/cugraph/community/egonet.py | 6 ++++-- python/cugraph/cugraph/dask/community/egonet.py | 9 ++++++++- .../cugraph/cugraph/tests/sampling/test_egonet_mg.py | 2 +- python/pylibcugraph/pylibcugraph/egonet.pyx | 10 ++++++++-- python/pylibcugraph/pylibcugraph/k_core.pyx | 8 ++++++-- 5 files changed, 27 insertions(+), 8 deletions(-) diff --git a/python/cugraph/cugraph/community/egonet.py b/python/cugraph/cugraph/community/egonet.py index f39ed8bf86c..531f52c4c27 100644 --- a/python/cugraph/cugraph/community/egonet.py +++ b/python/cugraph/cugraph/community/egonet.py @@ -18,6 +18,7 @@ from cugraph.utilities import cugraph_to_nx import cudf +import cupy from pylibcugraph import ego_graph as pylibcugraph_ego_graph @@ -132,13 +133,14 @@ def ego_graph(G, n, radius=1, center=True, undirected=None, distance=None): df = cudf.DataFrame() df["src"] = source df["dst"] = destination - df["weight"] = weight + if weight is not None: + df["weight"] = weight if G.renumbered: df, src_names = G.unrenumber(df, "src", get_column_names=True) df, dst_names = G.unrenumber(df, "dst", get_column_names=True) else: - # FIXME: THe original 'src' and 'dst' are not stored in 'simpleGraph' + # FIXME: The original 'src' and 'dst' are not stored in 'simpleGraph' src_names = "src" dst_names = "dst" diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 2d0d07b59ce..2fbf92e3584 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -18,6 +18,7 @@ import cugraph.dask.comms.comms as Comms import dask_cudf import cudf +import cupy from cugraph.dask.common.input_utils import get_distributed_data from pylibcugraph import ResourceHandle, ego_graph as pylibcugraph_ego_graph @@ -63,10 +64,15 @@ def consolidate_results(df, offsets): def convert_to_cudf(cp_arrays): cp_src, cp_dst, cp_weight, cp_offsets = cp_arrays + print(cp_weight) + df = cudf.DataFrame() df["src"] = cp_src df["dst"] = cp_dst - df["weight"] = cp_weight + if cp_weight is None: + df['weight'] = None + else: + df["weight"] = cp_weight offsets = cudf.Series(cp_offsets) @@ -180,4 +186,5 @@ def ego_graph(input_graph, n, radius=1, center=True): ddf = input_graph.unrenumber(ddf, "src") ddf = input_graph.unrenumber(ddf, "dst") + print(ddf.compute()) return ddf, offsets diff --git a/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py b/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py index 96b5ec2ac3a..674761154ff 100644 --- a/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py @@ -76,7 +76,7 @@ def input_expected_output(input_combo): directed = input_combo["directed"] seeds = input_combo["seeds"] radius = input_combo["radius"] - G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed) + G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed, edgevals=True) sg_cugraph_ego_graphs = cugraph.batched_ego_graphs(G, seeds=seeds, radius=radius) diff --git a/python/pylibcugraph/pylibcugraph/egonet.pyx b/python/pylibcugraph/pylibcugraph/egonet.pyx index 779aa0028b3..14a5b95b17b 100644 --- a/python/pylibcugraph/pylibcugraph/egonet.pyx +++ b/python/pylibcugraph/pylibcugraph/egonet.pyx @@ -153,10 +153,16 @@ def ego_graph(ResourceHandle resource_handle, # for perfomance improvement cupy_sources = copy_to_cupy_array( c_resource_handle_ptr, sources_ptr) + cupy_destinations = copy_to_cupy_array( c_resource_handle_ptr, destinations_ptr) - cupy_edge_weights = copy_to_cupy_array( - c_resource_handle_ptr, edge_weights_ptr) + + if edge_weights_ptr is not NULL: + cupy_edge_weights = copy_to_cupy_array( + c_resource_handle_ptr, edge_weights_ptr) + else: + cupy_edge_weights = None + cupy_subgraph_offsets = copy_to_cupy_array( c_resource_handle_ptr, subgraph_offsets_ptr) diff --git a/python/pylibcugraph/pylibcugraph/k_core.pyx b/python/pylibcugraph/pylibcugraph/k_core.pyx index 50344469b11..d905b68c439 100644 --- a/python/pylibcugraph/pylibcugraph/k_core.pyx +++ b/python/pylibcugraph/pylibcugraph/k_core.pyx @@ -156,12 +156,16 @@ def k_core(ResourceHandle resource_handle, cugraph_k_core_result_get_src_vertices(k_core_result_ptr) cdef cugraph_type_erased_device_array_view_t* dst_vertices_ptr = \ cugraph_k_core_result_get_dst_vertices(k_core_result_ptr) - cdef cugraph_type_erased_device_array_view_t* weigths_ptr = \ + cdef cugraph_type_erased_device_array_view_t* weights_ptr = \ cugraph_k_core_result_get_weights(k_core_result_ptr) cupy_src_vertices = copy_to_cupy_array(c_resource_handle_ptr, src_vertices_ptr) cupy_dst_vertices = copy_to_cupy_array(c_resource_handle_ptr, dst_vertices_ptr) - cupy_weights = copy_to_cupy_array(c_resource_handle_ptr, weigths_ptr) + + if weights_ptr is not NULL: + cupy_weights = copy_to_cupy_array(c_resource_handle_ptr, weights_ptr) + else: + cupy_weights = None cugraph_k_core_result_free(k_core_result_ptr) cugraph_core_result_free(core_result_ptr) From 918bdbe7b8bdc069db01fd795f4ffbdb234054eb Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 5 May 2023 14:43:32 +0000 Subject: [PATCH 016/347] style,copyright --- python/cugraph/cugraph/community/egonet.py | 3 +-- python/cugraph/cugraph/dask/community/egonet.py | 2 +- python/cugraph/cugraph/tests/sampling/test_egonet_mg.py | 4 +++- python/pylibcugraph/pylibcugraph/egonet.pyx | 2 +- python/pylibcugraph/pylibcugraph/k_core.pyx | 2 +- 5 files changed, 7 insertions(+), 6 deletions(-) diff --git a/python/cugraph/cugraph/community/egonet.py b/python/cugraph/cugraph/community/egonet.py index 531f52c4c27..684ae92febd 100644 --- a/python/cugraph/cugraph/community/egonet.py +++ b/python/cugraph/cugraph/community/egonet.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022, NVIDIA CORPORATION. +# Copyright (c) 2021-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,7 +18,6 @@ from cugraph.utilities import cugraph_to_nx import cudf -import cupy from pylibcugraph import ego_graph as pylibcugraph_ego_graph diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 2fbf92e3584..1b0bac7ba45 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -70,7 +70,7 @@ def convert_to_cudf(cp_arrays): df["src"] = cp_src df["dst"] = cp_dst if cp_weight is None: - df['weight'] = None + df["weight"] = None else: df["weight"] = cp_weight diff --git a/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py b/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py index 674761154ff..172296c07f9 100644 --- a/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_egonet_mg.py @@ -76,7 +76,9 @@ def input_expected_output(input_combo): directed = input_combo["directed"] seeds = input_combo["seeds"] radius = input_combo["radius"] - G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed, edgevals=True) + G = utils.generate_cugraph_graph_from_file( + input_data_path, directed=directed, edgevals=True + ) sg_cugraph_ego_graphs = cugraph.batched_ego_graphs(G, seeds=seeds, radius=radius) diff --git a/python/pylibcugraph/pylibcugraph/egonet.pyx b/python/pylibcugraph/pylibcugraph/egonet.pyx index 14a5b95b17b..d011d946e46 100644 --- a/python/pylibcugraph/pylibcugraph/egonet.pyx +++ b/python/pylibcugraph/pylibcugraph/egonet.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/python/pylibcugraph/pylibcugraph/k_core.pyx b/python/pylibcugraph/pylibcugraph/k_core.pyx index d905b68c439..c47cfef7a7a 100644 --- a/python/pylibcugraph/pylibcugraph/k_core.pyx +++ b/python/pylibcugraph/pylibcugraph/k_core.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at From 4c3088297eb2510d592f4bf64f2e3be9c62bf701 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 5 May 2023 14:45:19 +0000 Subject: [PATCH 017/347] style --- python/cugraph/cugraph/dask/community/egonet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 1b0bac7ba45..622d7fb2126 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -18,7 +18,6 @@ import cugraph.dask.comms.comms as Comms import dask_cudf import cudf -import cupy from cugraph.dask.common.input_utils import get_distributed_data from pylibcugraph import ResourceHandle, ego_graph as pylibcugraph_ego_graph From aa8cca5da4e0dc3771a9207178ec454992f962cc Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 5 May 2023 14:45:40 +0000 Subject: [PATCH 018/347] style --- python/cugraph/cugraph/dask/community/egonet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 1b0bac7ba45..622d7fb2126 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -18,7 +18,6 @@ import cugraph.dask.comms.comms as Comms import dask_cudf import cudf -import cupy from cugraph.dask.common.input_utils import get_distributed_data from pylibcugraph import ResourceHandle, ego_graph as pylibcugraph_ego_graph From e8b05a5a70a819df2d3dabf23dd2ce00a0885789 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:17:51 +0000 Subject: [PATCH 019/347] iterator, print statement fix --- .../cugraph_pyg/loader/cugraph_node_loader.py | 17 +++++++---------- .../cugraph/tests/sampling/test_bulk_sampler.py | 1 - .../tests/sampling/test_bulk_sampler_io_mg.py | 1 - 3 files changed, 7 insertions(+), 12 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 3a4ec19ae79..b4cbd989eee 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -110,8 +110,7 @@ def __init__( # Will be loading from disk self.__num_batches = all_indices self.__directory = directory - self.__input_files = os.listdir(directory) - self.__input_files.reverse() + iter(os.listdir(self.__directory)) return if batch_size is None or batch_size < 1: @@ -160,16 +159,12 @@ def __init__( ) bulk_sampler.flush() - self.__input_files = os.listdir(self.__directory.name) - self.__input_files.reverse() + self.__input_files = iter(os.listdir(self.__directory.name)) def __next__(self): # Load the next set of sampling results if necessary - print("batch:", self.__next_batch, self.__end_exclusive) - print(self.__input_files) if self.__next_batch >= self.__end_exclusive: - # Quit iterating if there are no batches left - if len(self.__input_files) == 0: + if self.__directory is None: raise StopIteration # Read the next parquet file into memory @@ -179,8 +174,9 @@ def __next__(self): else self.__directory.name ) - fname = self.__input_files.pop() - print("fname: ", fname) + # Will raise StopIteration if there are no files left + fname = next(self.__input_files) + m = self.__ex_parquet_file.match(fname) if m is None: raise ValueError(f"Invalid parquet filename {fname}") @@ -217,6 +213,7 @@ def __next__(self): if self.__next_batch >= self.__num_batches + self.__starting_batch_id: # Won't delete a non-temp dir (since it would just be deleting a string) del self.__directory + self.__directory = None # Get and return the sampled subgraph if isinstance(torch_geometric, MissingModule): diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py index 8270cff5bd4..c25b5297e18 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py @@ -104,7 +104,6 @@ def test_bulk_sampler_remainder(): tld = tempdir_object.name recovered_samples = cudf.read_parquet(tld) - print(os.listdir(tld)) for b in batches["batch"].unique().values_host.tolist(): assert b in recovered_samples["batch_id"].values_host.tolist() diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py index 0d07eb5530b..eacd697b7b3 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py @@ -40,7 +40,6 @@ def test_bulk_sampler_io(): offsets = cudf.DataFrame({"offsets": [0, 0], "batch_id": [0, 1]}) offsets = dask_cudf.from_cudf(offsets, npartitions=2) - print(offsets.npartitions) tempdir_object = tempfile.TemporaryDirectory() write_samples(results, offsets, 1, tempdir_object.name) From 99e2491fa815679a248df0ebc8f9bc5c2f14129a Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:18:21 +0000 Subject: [PATCH 020/347] remove egonet prints --- python/cugraph/cugraph/dask/community/egonet.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 622d7fb2126..06f5d5b9a79 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -63,8 +63,6 @@ def consolidate_results(df, offsets): def convert_to_cudf(cp_arrays): cp_src, cp_dst, cp_weight, cp_offsets = cp_arrays - print(cp_weight) - df = cudf.DataFrame() df["src"] = cp_src df["dst"] = cp_dst @@ -185,5 +183,4 @@ def ego_graph(input_graph, n, radius=1, center=True): ddf = input_graph.unrenumber(ddf, "src") ddf = input_graph.unrenumber(ddf, "dst") - print(ddf.compute()) return ddf, offsets From 11a1a62e48308c3c670f7fba1293bad7b031285d Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:20:37 +0000 Subject: [PATCH 021/347] fix --- python/cugraph/cugraph/dask/traversal/sssp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/cugraph/cugraph/dask/traversal/sssp.py b/python/cugraph/cugraph/dask/traversal/sssp.py index 6740ef5a3f1..053a93fb42a 100644 --- a/python/cugraph/cugraph/dask/traversal/sssp.py +++ b/python/cugraph/cugraph/dask/traversal/sssp.py @@ -106,7 +106,7 @@ def sssp(input_graph, source, cutoff=None, check_source=True): "'SSSP' requires the input graph to be weighted." "'BFS' should be used instead of 'SSSP' for unweighted graphs." ) - raise RuntimeError(err_msg) + raise ValueError(err_msg) client = default_client() From 3f23d4df1e7474fdebfcffd99eb61415606ff6d8 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:27:52 +0000 Subject: [PATCH 022/347] style fix --- .../cugraph/gnn/data_loading/bulk_sampler.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 8a07c0c60c4..57b465067ba 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -200,12 +200,16 @@ def flush(self) -> None: sample_fn = cugraph.uniform_neighbor_sample else: sample_fn = cugraph.dask.uniform_neighbor_sample - self.__sample_call_args["_multiple_clients"] = True - self.__sample_call_args[ - "label_to_output_comm_rank" - ] = self.__get_label_to_output_comm_rank(min_batch_id, max_batch_id) - self.__sample_call_args["label_list"] = cupy.arange( - min_batch_id, max_batch_id + 1, dtype="int32" + self.__sample_call_args.update( + { + "_multiple_clients": True, + "label_to_output_comm_rank": self.__get_label_to_output_comm_rank( + min_batch_id, max_batch_id + ), + "label_list": cupy.arange( + min_batch_id, max_batch_id + 1, dtype="int32" + ), + } ) samples, offsets = sample_fn( From 911517cd5dc8c769de23add4d795f0c2c2714066 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:29:25 +0000 Subject: [PATCH 023/347] remove print --- python/cugraph/cugraph/tests/traversal/test_sssp.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/python/cugraph/cugraph/tests/traversal/test_sssp.py b/python/cugraph/cugraph/tests/traversal/test_sssp.py index 7b0cc3b132d..02f4370c0de 100644 --- a/python/cugraph/cugraph/tests/traversal/test_sssp.py +++ b/python/cugraph/cugraph/tests/traversal/test_sssp.py @@ -143,7 +143,7 @@ def networkx_call(graph_file, source, edgevals=True): M = utils.read_csv_for_nx(dataset_path, read_weights_in_sp=True) # Directed NetworkX graph edge_attr = "weight" if edgevals else None - print(M) + Gnx = nx.from_pandas_edgelist( M, source="0", @@ -229,10 +229,8 @@ def test_sssp(gpubenchmark, dataset_source_nxresults, cugraph_input_type): input_G_or_matrix = utils.create_obj_from_csv( dataset_path, cugraph_input_type, edgevals=True ) - print(input_G_or_matrix) else: input_G_or_matrix = G - print(G.edgelist.edgelist_df) cu_paths, max_val = cugraph_call(gpubenchmark, input_G_or_matrix, source) From c1ddad896604a69269938419a0cdf19ab63b35d5 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:29:39 +0000 Subject: [PATCH 024/347] reformat --- python/cugraph/cugraph/tests/traversal/test_sssp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/cugraph/cugraph/tests/traversal/test_sssp.py b/python/cugraph/cugraph/tests/traversal/test_sssp.py index 02f4370c0de..f6aba8d769c 100644 --- a/python/cugraph/cugraph/tests/traversal/test_sssp.py +++ b/python/cugraph/cugraph/tests/traversal/test_sssp.py @@ -143,7 +143,7 @@ def networkx_call(graph_file, source, edgevals=True): M = utils.read_csv_for_nx(dataset_path, read_weights_in_sp=True) # Directed NetworkX graph edge_attr = "weight" if edgevals else None - + Gnx = nx.from_pandas_edgelist( M, source="0", From d54cd6a7c7bb530dbf13169267fe0711ba77b0f3 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 15:36:09 +0000 Subject: [PATCH 025/347] update docstrings --- python/cugraph/cugraph/traversal/bfs.py | 2 +- python/cugraph/cugraph/traversal/sssp.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/python/cugraph/cugraph/traversal/bfs.py b/python/cugraph/cugraph/traversal/bfs.py index a200ba9b5d8..f2c1f5c5662 100644 --- a/python/cugraph/cugraph/traversal/bfs.py +++ b/python/cugraph/cugraph/traversal/bfs.py @@ -126,7 +126,7 @@ def bfs( ): """ Find the distances and predecessors for a breadth first traversal of a - graph. + graph. Unlike SSSP, BFS supports unweighted graphs. Parameters ---------- diff --git a/python/cugraph/cugraph/traversal/sssp.py b/python/cugraph/cugraph/traversal/sssp.py index bccbc21b515..7afe78c2d89 100644 --- a/python/cugraph/cugraph/traversal/sssp.py +++ b/python/cugraph/cugraph/traversal/sssp.py @@ -149,7 +149,9 @@ def sssp( unreachable will have a distance of infinity denoted by the maximum value of the data type and the predecessor set as -1. The source vertex's predecessor is also set to -1. Graphs with negative weight cycles are not - supported. + supported. Unweighted graphs are also unsupported. + + For finding shortest paths on an unweighted graph, use BFS instead. Parameters ---------- From 5f52c08f49af7c999b50fd7a36fa99c60bad76b7 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 8 May 2023 18:03:19 -0400 Subject: [PATCH 026/347] correct error message --- cpp/src/c_api/graph_mg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/c_api/graph_mg.cpp b/cpp/src/c_api/graph_mg.cpp index f59bf703f19..f50c7c08fb6 100644 --- a/cpp/src/c_api/graph_mg.cpp +++ b/cpp/src/c_api/graph_mg.cpp @@ -340,7 +340,7 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", + "Invalid input arguments: Edge id type must match edge type", *error); CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), From 4cdb7838a99613651e7d1e45c7bc707bde6635b2 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 8 May 2023 18:03:55 -0400 Subject: [PATCH 027/347] correct error message --- cpp/src/c_api/graph_sg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/c_api/graph_sg.cpp b/cpp/src/c_api/graph_sg.cpp index 2ec1c536651..735b7616449 100644 --- a/cpp/src/c_api/graph_sg.cpp +++ b/cpp/src/c_api/graph_sg.cpp @@ -515,7 +515,7 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge (src/dst) type", + "Invalid input arguments: Edge id type must match edge type", *error); CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), From df7f0abe6c37eeb21f27844bf02a9d6274ab5e21 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 8 May 2023 22:21:09 +0000 Subject: [PATCH 028/347] update graph creation with warning when edge id type doesn't match --- .../graph_implementation/simpleDistributedGraph.py | 9 +++++++++ .../structure/graph_implementation/simpleGraph.py | 10 ++++++++++ 2 files changed, 19 insertions(+) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index f4b193d015e..c3be8e10763 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -99,6 +99,14 @@ def _make_plc_graph( if simpleDistributedGraphImpl.edgeIdCol in edata_x[0]: edge_ids = edata_x[0][simpleDistributedGraphImpl.edgeIdCol] + if edata_x[0][src_col_name].dtype == 'int64' and edge_ids.dtype != 'int64': + edge_ids = edge_ids.astype('int64') + warnings.warn( + f"Vertex type is int64 but edge id type is {edge_ids.dtype}" + ", automatically casting edge id type to int64. " + "This may cause extra memory usage. Consider passing" + " a int64 list of edge ids instead." + ) if simpleDistributedGraphImpl.edgeTypeCol in edata_x[0]: edge_types = edata_x[0][simpleDistributedGraphImpl.edgeTypeCol] @@ -230,6 +238,7 @@ def __from_edgelist( ) value_col = None else: + source_col, dest_col, value_col = symmetrize( input_ddf, source, diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index aa7910586ef..5cf99b12f4c 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -1047,6 +1047,16 @@ def _make_plc_graph( weight_col = weight_col.astype("float32") if weight_t == "int64": weight_col = weight_col.astype("float64") + + if id_col is not None: + if src_or_offset_array.dtype == 'int64' and id_col.dtype != 'int64': + id_col = id_col.astype('int64') + warnings.warn( + f"Vertex type is int64 but edge id type is {id_col.dtype}" + ", automatically casting edge id type to int64. " + "This may cause extra memory usage. Consider passing" + " a int64 list of edge ids instead." + ) self._plc_graph = SGGraph( resource_handle=ResourceHandle(), From 155536f28d7b0513e615e9ff6110d43fe367958c Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 03:54:02 +0000 Subject: [PATCH 029/347] fix style --- .../graph_implementation/simpleDistributedGraph.py | 6 +++--- .../cugraph/structure/graph_implementation/simpleGraph.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index c3be8e10763..0e958028101 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -99,8 +99,8 @@ def _make_plc_graph( if simpleDistributedGraphImpl.edgeIdCol in edata_x[0]: edge_ids = edata_x[0][simpleDistributedGraphImpl.edgeIdCol] - if edata_x[0][src_col_name].dtype == 'int64' and edge_ids.dtype != 'int64': - edge_ids = edge_ids.astype('int64') + if edata_x[0][src_col_name].dtype == "int64" and edge_ids.dtype != "int64": + edge_ids = edge_ids.astype("int64") warnings.warn( f"Vertex type is int64 but edge id type is {edge_ids.dtype}" ", automatically casting edge id type to int64. " @@ -238,7 +238,7 @@ def __from_edgelist( ) value_col = None else: - + source_col, dest_col, value_col = symmetrize( input_ddf, source, diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 5cf99b12f4c..16c2e257f1c 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -1047,10 +1047,10 @@ def _make_plc_graph( weight_col = weight_col.astype("float32") if weight_t == "int64": weight_col = weight_col.astype("float64") - + if id_col is not None: - if src_or_offset_array.dtype == 'int64' and id_col.dtype != 'int64': - id_col = id_col.astype('int64') + if src_or_offset_array.dtype == "int64" and id_col.dtype != "int64": + id_col = id_col.astype("int64") warnings.warn( f"Vertex type is int64 but edge id type is {id_col.dtype}" ", automatically casting edge id type to int64. " From 1bb92714f393a0aad5d3e002c743bcf0fdccc1c1 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 03:54:29 +0000 Subject: [PATCH 030/347] fix style --- .../graph_implementation/simpleDistributedGraph.py | 6 +++--- .../cugraph/structure/graph_implementation/simpleGraph.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index c3be8e10763..0e958028101 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -99,8 +99,8 @@ def _make_plc_graph( if simpleDistributedGraphImpl.edgeIdCol in edata_x[0]: edge_ids = edata_x[0][simpleDistributedGraphImpl.edgeIdCol] - if edata_x[0][src_col_name].dtype == 'int64' and edge_ids.dtype != 'int64': - edge_ids = edge_ids.astype('int64') + if edata_x[0][src_col_name].dtype == "int64" and edge_ids.dtype != "int64": + edge_ids = edge_ids.astype("int64") warnings.warn( f"Vertex type is int64 but edge id type is {edge_ids.dtype}" ", automatically casting edge id type to int64. " @@ -238,7 +238,7 @@ def __from_edgelist( ) value_col = None else: - + source_col, dest_col, value_col = symmetrize( input_ddf, source, diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 5cf99b12f4c..16c2e257f1c 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -1047,10 +1047,10 @@ def _make_plc_graph( weight_col = weight_col.astype("float32") if weight_t == "int64": weight_col = weight_col.astype("float64") - + if id_col is not None: - if src_or_offset_array.dtype == 'int64' and id_col.dtype != 'int64': - id_col = id_col.astype('int64') + if src_or_offset_array.dtype == "int64" and id_col.dtype != "int64": + id_col = id_col.astype("int64") warnings.warn( f"Vertex type is int64 but edge id type is {id_col.dtype}" ", automatically casting edge id type to int64. " From ad051c163d5d8e5ef8249b8ec4fcd80727bf92c0 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 04:32:27 +0000 Subject: [PATCH 031/347] remove rank option --- python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 57b465067ba..0257a56ba08 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -37,7 +37,6 @@ def __init__( graph, seeds_per_call: int = 200_000, batches_per_partition=100, - rank: int = 0, **kwargs, ): """ @@ -56,9 +55,6 @@ def __init__( a single sampling call. batches_per_partition: int (optional, default=100) The number of batches outputted to a single parquet partition. - rank: int (optional, default=0) - The rank of this sampler. Used to isolate this sampler from - others that may be running on other nodes. kwargs: kwargs Keyword arguments to be passed to the sampler (i.e. fanout). """ @@ -80,14 +76,9 @@ def __init__( self.__graph = graph self.__seeds_per_call = seeds_per_call self.__batches_per_partition = batches_per_partition - self.__rank = rank self.__batches = None self.__sample_call_args = kwargs - @property - def rank(self) -> int: - return self.__rank - @property def seeds_per_call(self) -> int: return self.__seeds_per_call From d34190d137dbf6dd37b24908012369ed6d64b11b Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 14:18:45 +0000 Subject: [PATCH 032/347] remove explicit gtest, gmock dependencies --- dependencies.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/dependencies.yaml b/dependencies.yaml index ddb56429d03..9a3fe8387f5 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -214,8 +214,6 @@ dependencies: packages: - c-compiler - cxx-compiler - - gmock=1.10.0 - - gtest=1.10.0 - libcugraphops=23.6.* - libraft-headers=23.6.* - libraft=23.6.* From bc4b2a157b9941a8af8019601c4ea0832cf331d3 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 14:19:22 +0000 Subject: [PATCH 033/347] generate --- conda/environments/all_cuda-118_arch-x86_64.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 5b416134ef1..5120fbee1a1 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -23,9 +23,7 @@ dependencies: - doxygen - fsspec[http]>=0.6.0 - gcc_linux-64=11.* -- gmock=1.10.0 - graphviz -- gtest=1.10.0 - ipython - libcudf=23.6.* - libcugraphops=23.6.* From a2bd38927d17ce8178bd74fb7a4fd7d3d656f890 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 14:58:03 +0000 Subject: [PATCH 034/347] update gtest to 1.13 --- dependencies.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/dependencies.yaml b/dependencies.yaml index 9a3fe8387f5..eaec03a619c 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -214,6 +214,8 @@ dependencies: packages: - c-compiler - cxx-compiler + - gmock>=1.13.0.* + - gtest>=1.13.0.* - libcugraphops=23.6.* - libraft-headers=23.6.* - libraft=23.6.* From b9202dd2026bc70287a14ce9efbcf3997acf2f83 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 14:58:30 +0000 Subject: [PATCH 035/347] generate --- conda/environments/all_cuda-118_arch-x86_64.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 5120fbee1a1..3bb4ad61bce 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -23,7 +23,9 @@ dependencies: - doxygen - fsspec[http]>=0.6.0 - gcc_linux-64=11.* +- gmock>=1.13.0.* - graphviz +- gtest>=1.13.0.* - ipython - libcudf=23.6.* - libcugraphops=23.6.* From abbe83e36f0017e40d517e0cc7bcf4ba96301129 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 15:24:10 +0000 Subject: [PATCH 036/347] fix dependencies --- conda/recipes/libcugraph/conda_build_config.yaml | 2 +- dependencies.yaml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/conda/recipes/libcugraph/conda_build_config.yaml b/conda/recipes/libcugraph/conda_build_config.yaml index 83a383236a4..2fa26d99c09 100644 --- a/conda/recipes/libcugraph/conda_build_config.yaml +++ b/conda/recipes/libcugraph/conda_build_config.yaml @@ -17,7 +17,7 @@ nccl_version: - ">=2.9.9" gtest_version: - - "=1.10.0" + - ">=1.13.0" cuda_profiler_api_version: - ">=11.8.86,<12" diff --git a/dependencies.yaml b/dependencies.yaml index eaec03a619c..a8b0dd172cf 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -214,8 +214,8 @@ dependencies: packages: - c-compiler - cxx-compiler - - gmock>=1.13.0.* - - gtest>=1.13.0.* + - gmock>=1.13.0 + - gtest>=1.13.0 - libcugraphops=23.6.* - libraft-headers=23.6.* - libraft=23.6.* From 4b892694bfa1344dcaed94d4bcb095d74d180a74 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 9 May 2023 15:24:25 +0000 Subject: [PATCH 037/347] generate --- conda/environments/all_cuda-118_arch-x86_64.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 3bb4ad61bce..cf496bd1deb 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -23,9 +23,9 @@ dependencies: - doxygen - fsspec[http]>=0.6.0 - gcc_linux-64=11.* -- gmock>=1.13.0.* +- gmock>=1.13.0 - graphviz -- gtest>=1.13.0.* +- gtest>=1.13.0 - ipython - libcudf=23.6.* - libcugraphops=23.6.* From 66c2d93d024947c8a79252b70c9acc9d64f5f24b Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 10 May 2023 14:36:05 +0000 Subject: [PATCH 038/347] logging, remove dead code --- .../standalone/cugraph_bulk_sampling.py | 301 + cugraph_graph_creation_stats.csv | 5 + mg_utils/dask_logs-51692/scheduler_log.txt | 134 + mg_utils/dask_logs-51692/worker-exp01_log.txt | 6152 +++++++++++++++++ .../cugraph/gnn/data_loading/bulk_sampler.py | 20 +- 5 files changed, 6608 insertions(+), 4 deletions(-) create mode 100644 benchmarks/cugraph/standalone/cugraph_bulk_sampling.py create mode 100644 cugraph_graph_creation_stats.csv create mode 100644 mg_utils/dask_logs-51692/scheduler_log.txt create mode 100644 mg_utils/dask_logs-51692/worker-exp01_log.txt diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py new file mode 100644 index 00000000000..189642a4e52 --- /dev/null +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -0,0 +1,301 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from cugraph.testing.mg_utils import ( + generate_edgelist_rmat, + get_allocation_counts_dask_persist, + sizeof_fmt, + get_peak_output_ratio_across_workers, + restart_client, +) + +from cugraph.testing.mg_utils import ( + start_dask_client, + stop_dask_client, + enable_spilling, +) + +from cugraph.structure.symmetrize import symmetrize +from cugraph.experimental.gnn import BulkSampler + +from cugraph.dask import uniform_neighbor_sample + +import cugraph + +from time import sleep +from math import ceil + +import pandas as pd +import cudf +import dask_cudf +import cupy + +from typing import Optional, Union + + +def construct_graph(dask_dataframe): + """ + Args: + dask_dataframe: + dask_dataframe contains weighted and undirected edges with self + loops. Multiple edges will likely be present as well. + directed: + If True, the graph will be directed. + renumber: + If True, the graph will be renumbered. + Returns: + G: cugraph.Graph + """ + assert dask_dataframe['src'].dtype == 'int64' + assert dask_dataframe['dst'].dtype == 'int64' + assert dask_dataframe['etp'].dtype == 'int32' + + G = cugraph.MultiGraph(directed=True) + G.from_dask_cudf_edgelist( + dask_dataframe, source="src", destination="dst", edge_type='etp', renumber=False + ) + return G + + +def symmetrize_ddf(dask_dataframe): + source_col, dest_col = symmetrize( + dask_dataframe, + 'src', + 'dst', + multi=True, + symmetrize=True, + ) + + new_ddf = source_col.to_frame() + new_ddf['dst'] = dest_col + + return new_ddf + +def renumber_ddf(dask_df): + vertices = dask_cudf.concat([dask_df['src'], dask_df['dst']]).unique().reset_index(drop=True).persist() + vertices.name = 'v' + vertices = vertices.reset_index().set_index('v').rename(columns={'index': 'm'}).persist() + + src = dask_df.merge(vertices, left_on='src', right_on='v', how='left').m.rename('src') + dst = dask_df.merge(vertices, left_on='dst', right_on='v', how='left').m.rename('dst') + df = src.to_frame() + df['dst'] = dst + + return df.reset_index(drop=True) + +def _make_batch_ids(bdf: cudf.DataFrame, batch_size: int, num_workers: int, partition_info: Optional[Union[dict, str]] = None): + # Required by dask; need to skip dummy partitions. + if partition_info is None: + return cudf.DataFrame({ + 'batch': cudf.Series(dtype='int32'), + 'start': cudf.Series(dtype='int64') + }) + + partition = partition_info['number'] + if partition is None: + raise ValueError('division is absent') + + num_batches = int(ceil(len(bdf) / batch_size)) + + batch_ids = cupy.repeat( + cupy.arange(num_batches * partition, num_batches * (partition + 1), dtype='int32'), + batch_size + )[:len(bdf)] + + bdf = bdf.reset_index(drop=True) + bdf['batch'] = cudf.Series(batch_ids) + + return bdf + + +@get_allocation_counts_dask_persist(return_allocations=True, logging=True) +def sample_graph(G, seed=42, batch_size=500, fanout=[5, 5, 5]): + cupy.random.seed(seed) + + sampler = BulkSampler( + batch_size=batch_size, + output_path='/tmp/samples', + graph=G, + fanout_vals=fanout, + with_replacement=False, + random_state=seed, + ) + + from dask.distributed import wait, default_client + n_workers = len(default_client().scheduler_info()['workers']) + + meta = cudf.DataFrame({ + 'start': cudf.Series(dtype='int64'), + 'batch': cudf.Series(dtype='int32') + }) + + + batch_df = dask_cudf.concat([G.edgelist.edgelist_df['src'], G.edgelist.edgelist_df['dst']]).unique().rename('start').to_frame().reset_index(drop=True).persist() + batch_df = batch_df.map_partitions(_make_batch_ids, batch_size, n_workers, meta=meta).persist() + print('created batches') + + + sampler.add_batches(batch_df, start_col_name='start', batch_col_name='batch') + sampler.flush() + print('flushed all batches') + + """ + results_ddf = uniform_neighbor_sample( + G, + batch_df.start, + fanout_vals=[10,25], + batch_id_list=batch_df.batch, + with_replacement=False, + with_edge_properties=True, + random_state=seed + ) + print(results_ddf.compute()) + """ + +def benchmark_cugraph_bulk_sampling(scale, edgefactor, seed, batch_size, fanout): + """ + Entry point for the benchmark. + """ + dask_df = generate_edgelist_rmat( + scale=scale, edgefactor=edgefactor, seed=seed, unweighted=True, mg=True, + ) + dask_df = dask_df.astype("int64") + dask_df = dask_df.reset_index(drop=True) + + + dask_df = renumber_ddf(dask_df).persist() + dask_df = symmetrize_ddf(dask_df).persist() + dask_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really + + num_input_edges = len(dask_df) + print( + f"Number of input edges = {num_input_edges:,}" + ) + + G = construct_graph( + dask_df + ) + print('constructed graph') + + input_memory = G.edgelist.edgelist_df.memory_usage().sum().compute() + print(f'input memory: {input_memory}') + + _, allocation_counts = sample_graph(G, seed, batch_size, fanout) + print('allocation counts b:') + print(allocation_counts.values()) + + ( + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) = get_memory_statistics( + allocation_counts=allocation_counts, input_memory=input_memory + ) + print(f"Number of edges in final graph = {G.number_of_edges():,}") + print("-" * 80) + return ( + num_input_edges, + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) + + +def get_memory_statistics(allocation_counts, input_memory): + """ + Get memory statistics for the benchmark. + """ + output_to_peak_ratio = get_peak_output_ratio_across_workers(allocation_counts) + peak_allocation_across_workers = max( + [a["peak_bytes"] for a in allocation_counts.values()] + ) + input_memory_per_worker = input_memory / len(allocation_counts.keys()) + input_to_peak_ratio = peak_allocation_across_workers / input_memory_per_worker + print(f"Edge List Memory = {sizeof_fmt(input_memory_per_worker)}") + print(f"Peak Memory across workers = {sizeof_fmt(peak_allocation_across_workers)}") + print(f"Max Peak to output graph ratio across workers = {output_to_peak_ratio:.2f}") + print( + f"Max Peak to avg input graph ratio across workers = {input_to_peak_ratio:.2f}" + ) + return ( + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) + +# call __main__ function +if __name__ == "__main__": + client, cluster = start_dask_client(dask_worker_devices=[1], jit_unspill=False) + enable_spilling() + stats_ls = [] + client.run(enable_spilling) + #for scale in [22, 23, 24]: + for scale in [16, 22, 24]: + for fanout in [[10,25]]: + for batch_size in [500, 1000]: + print(f'scale: {scale}') + print(f'batch size: {batch_size}') + print(f'fanout: {fanout}') + + try: + stats_d = {} + ( + num_input_edges, + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) = benchmark_cugraph_bulk_sampling( + scale=scale, + edgefactor=16, + seed=123, + batch_size=batch_size, + fanout=fanout, + ) + stats_d["scale"] = scale + stats_d["num_input_edges"] = num_input_edges + stats_d["batch_size"] = batch_size + stats_d["fanout"] = fanout + stats_d["input_memory_per_worker"] = sizeof_fmt(input_memory_per_worker) + stats_d["peak_allocation_across_workers"] = sizeof_fmt( + peak_allocation_across_workers + ) + stats_d["input_to_peak_ratio"] = input_to_peak_ratio + stats_d["output_to_peak_ratio"] = output_to_peak_ratio + stats_ls.append(stats_d) + except Exception as e: + print(e) + restart_client(client) + sleep(10) + + stats_df = pd.DataFrame( + stats_ls, + columns=[ + "scale", + "num_input_edges", + "directed", + "renumber", + "input_memory_per_worker", + "peak_allocation_across_workers", + "input_to_peak_ratio", + "output_to_peak_ratio", + ], + ) + stats_df.to_csv("cugraph_graph_creation_stats.csv") + print("-" * 40 + f"scale = {scale} completed" + "-" * 40) + + # Cleanup Dask Cluster + stop_dask_client(client, cluster) diff --git a/cugraph_graph_creation_stats.csv b/cugraph_graph_creation_stats.csv new file mode 100644 index 00000000000..95634780e0b --- /dev/null +++ b/cugraph_graph_creation_stats.csv @@ -0,0 +1,5 @@ +,scale,num_input_edges,directed,renumber,input_memory_per_worker,peak_allocation_across_workers,input_to_peak_ratio,output_to_peak_ratio +0,16,2097152,,,28.0MB,2.0GB,72.04728126525879,3748.6305876658503 +1,16,2097152,,,28.0MB,2.0GB,72.0413030896868,3748.95384659188 +2,22,134217728,,,1.8GB,6.9GB,3.9640060663223267,1.6366930403696882 +3,22,134217728,,,1.8GB,6.9GB,3.959538323538644,1.3688574167519196 diff --git a/mg_utils/dask_logs-51692/scheduler_log.txt b/mg_utils/dask_logs-51692/scheduler_log.txt new file mode 100644 index 00000000000..2080214e5b5 --- /dev/null +++ b/mg_utils/dask_logs-51692/scheduler_log.txt @@ -0,0 +1,134 @@ +RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp + --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json + " +/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead + warnings.warn( +2023-05-10 13:59:27,794 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 13:59:28,532 - distributed.scheduler - INFO - State start +2023-05-10 13:59:28,545 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 13:59:28,546 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 +2023-05-10 13:59:28,547 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status +2023-05-10 13:59:37,504 - distributed.scheduler - INFO - Register worker +2023-05-10 13:59:37,506 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44009 +2023-05-10 13:59:37,506 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54160 +2023-05-10 13:59:37,551 - distributed.scheduler - INFO - Register worker +2023-05-10 13:59:37,552 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42211 +2023-05-10 13:59:37,552 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54176 +2023-05-10 13:59:39,811 - distributed.scheduler - INFO - Receive client connection: Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 +2023-05-10 13:59:39,811 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54192 +2023-05-10 13:59:40,047 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 14:00:05,364 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 14:00:05,365 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 14:00:05,370 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54176; closing. +2023-05-10 14:00:05,370 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:00:05,370 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42211 +2023-05-10 14:00:05,371 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54160; closing. +2023-05-10 14:00:05,371 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:00:05,371 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44009 +2023-05-10 14:00:05,371 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:00:09,742 - distributed.scheduler - INFO - Register worker +2023-05-10 14:00:09,742 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:43477 +2023-05-10 14:00:09,743 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52398 +2023-05-10 14:00:10,023 - distributed.scheduler - INFO - Register worker +2023-05-10 14:00:10,024 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:39457 +2023-05-10 14:00:10,024 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52406 +2023-05-10 14:00:10,196 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 14:00:18,320 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 14:00:38,277 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 14:00:38,278 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 14:00:38,284 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52406; closing. +2023-05-10 14:00:38,284 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:00:38,284 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:39457 +2023-05-10 14:00:38,284 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52398; closing. +2023-05-10 14:00:38,284 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:00:38,285 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43477 +2023-05-10 14:00:38,285 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:00:42,503 - distributed.scheduler - INFO - Register worker +2023-05-10 14:00:42,504 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:39417 +2023-05-10 14:00:42,504 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56514 +2023-05-10 14:00:42,843 - distributed.scheduler - INFO - Register worker +2023-05-10 14:00:42,843 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33977 +2023-05-10 14:00:42,844 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56518 +2023-05-10 14:00:42,862 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 14:00:51,063 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 14:01:30,572 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 14:01:30,573 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 14:01:30,581 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56518; closing. +2023-05-10 14:01:30,581 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:01:30,581 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33977 +2023-05-10 14:01:30,581 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56514; closing. +2023-05-10 14:01:30,582 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:01:30,582 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:39417 +2023-05-10 14:01:30,582 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:01:34,620 - distributed.scheduler - INFO - Register worker +2023-05-10 14:01:34,620 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35199 +2023-05-10 14:01:34,620 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58796 +2023-05-10 14:01:35,079 - distributed.scheduler - INFO - Register worker +2023-05-10 14:01:35,080 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:45061 +2023-05-10 14:01:35,080 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58804 +2023-05-10 14:01:35,193 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 14:01:43,482 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 14:02:30,888 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 14:02:30,888 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 14:02:30,895 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58796; closing. +2023-05-10 14:02:30,896 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:02:30,896 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35199 +2023-05-10 14:02:30,896 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58804; closing. +2023-05-10 14:02:30,896 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:02:30,897 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:45061 +2023-05-10 14:02:30,897 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:02:35,325 - distributed.scheduler - INFO - Register worker +2023-05-10 14:02:35,326 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35397 +2023-05-10 14:02:35,326 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35748 +2023-05-10 14:02:35,542 - distributed.scheduler - INFO - Register worker +2023-05-10 14:02:35,542 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34415 +2023-05-10 14:02:35,542 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35750 +2023-05-10 14:02:35,708 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 14:02:43,803 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 14:04:52,135 - distributed.core - INFO - Connection to tcp://10.120.104.11:35748 has been closed. +2023-05-10 14:04:52,135 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:04:52,135 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35397 +2023-05-10 14:04:53,200 - distributed.core - INFO - Connection to tcp://10.120.104.11:35750 has been closed. +2023-05-10 14:04:53,200 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:04:53,200 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34415 +2023-05-10 14:04:53,200 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:04:54,900 - distributed.scheduler - INFO - Register worker +2023-05-10 14:04:54,901 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38611 +2023-05-10 14:04:54,901 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37906 +2023-05-10 14:04:55,991 - distributed.scheduler - INFO - Register worker +2023-05-10 14:04:55,991 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44519 +2023-05-10 14:04:55,991 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37928 +2023-05-10 14:19:05,613 - distributed.scheduler - INFO - Remove client Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 +2023-05-10 14:19:05,614 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54192; closing. +2023-05-10 14:19:05,614 - distributed.scheduler - INFO - Remove client Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 +2023-05-10 14:19:05,615 - distributed.scheduler - INFO - Close client connection: Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 +2023-05-10 14:19:08,096 - distributed._signals - INFO - Received signal SIGINT (2) +2023-05-10 14:19:08,097 - distributed.scheduler - INFO - Scheduler closing... +2023-05-10 14:19:08,097 - distributed.scheduler - INFO - Scheduler closing all comms +2023-05-10 14:19:08,098 - distributed.core - INFO - Connection to tcp://10.120.104.11:37906 has been closed. +2023-05-10 14:19:08,098 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:19:08,098 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38611 +2023-05-10 14:19:08,099 - distributed.batched - INFO - Batched Comm Closed +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 317, in write + raise StreamClosedError() +tornado.iostream.StreamClosedError: Stream is closed + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send + nbytes = yield coro + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run + value = future.result() + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 328, in write + convert_stream_closed_error(self, e) + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error + raise CommClosedError(f"in {obj}: {exc}") from exc +distributed.comm.core.CommClosedError: in : Stream is closed +2023-05-10 14:19:08,100 - distributed.core - INFO - Connection to tcp://10.120.104.11:37928 has been closed. +2023-05-10 14:19:08,100 - distributed.scheduler - INFO - Remove worker +2023-05-10 14:19:08,100 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44519 +2023-05-10 14:19:08,100 - distributed.scheduler - INFO - Lost all workers +2023-05-10 14:19:08,101 - distributed.scheduler - INFO - Stopped scheduler at 'tcp://10.120.104.11:8786' +2023-05-10 14:19:08,102 - distributed.scheduler - INFO - End scheduler diff --git a/mg_utils/dask_logs-51692/worker-exp01_log.txt b/mg_utils/dask_logs-51692/worker-exp01_log.txt new file mode 100644 index 00000000000..c8f2424e425 --- /dev/null +++ b/mg_utils/dask_logs-51692/worker-exp01_log.txt @@ -0,0 +1,6152 @@ +RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G + --local-directory=/tmp/ + --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json + --memory-limit=auto + --device-memory-limit=auto + " +2023-05-10 13:59:34,809 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:46381' +2023-05-10 13:59:34,812 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:38723' +2023-05-10 13:59:36,006 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-77ixbbwy', purging +2023-05-10 13:59:36,006 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 13:59:36,006 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 13:59:36,036 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 13:59:36,036 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 13:59:36,276 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 13:59:36,313 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 13:59:37,325 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44009 +2023-05-10 13:59:37,325 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44009 +2023-05-10 13:59:37,325 - distributed.worker - INFO - dashboard at: 10.120.104.11:32867 +2023-05-10 13:59:37,325 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 13:59:37,325 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,326 - distributed.worker - INFO - Threads: 1 +2023-05-10 13:59:37,326 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 13:59:37,326 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-6fi5g73m +2023-05-10 13:59:37,327 - distributed.worker - INFO - Starting Worker plugin RMMSetup-9ff6500e-366f-49ee-aa77-3963fb1568a3 +2023-05-10 13:59:37,381 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42211 +2023-05-10 13:59:37,381 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42211 +2023-05-10 13:59:37,381 - distributed.worker - INFO - dashboard at: 10.120.104.11:40325 +2023-05-10 13:59:37,381 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 13:59:37,381 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,381 - distributed.worker - INFO - Threads: 1 +2023-05-10 13:59:37,381 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 13:59:37,381 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tderj3j8 +2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin PreImport-de6a3b63-3184-4bfc-b230-3e9c9cbdb698 +2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-79ad3a23-9ddd-40d6-923d-6323eb40d16a +2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin RMMSetup-abd6acbf-3fd0-4594-87ae-06f9d8dbab3b +2023-05-10 13:59:37,490 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-b4b518af-992a-46cc-80b5-4ee8b0ed70a5 +2023-05-10 13:59:37,491 - distributed.worker - INFO - Starting Worker plugin PreImport-fa14007d-2386-4cfe-89d3-f40597d72930 +2023-05-10 13:59:37,492 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,507 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 13:59:37,507 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,510 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 13:59:37,541 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,552 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 13:59:37,552 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 13:59:37,554 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 13:59:39,824 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 13:59:39,824 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 13:59:40,630 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 13:59:40,631 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 13:59:49,374 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 13:59:50,577 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 13:59:56,604 - distributed.core - INFO - Event loop was unresponsive in Worker for 15.31s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 13:59:56,606 - distributed.core - INFO - Event loop was unresponsive in Worker for 15.31s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 13:59:56,613 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 13:59:56,615 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:00:02,743 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:02,743 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:02,917 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:02,918 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:03,140 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:03,142 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:05,219 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:00:05,219 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:00:05,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:05,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:05,252 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:00:05,253 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:00:05,368 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:00:05,369 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:00:05,369 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42211. Reason: scheduler-restart +2023-05-10 14:00:05,370 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44009. Reason: scheduler-restart +2023-05-10 14:00:05,370 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:00:05,371 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:00:05,371 - distributed.nanny - INFO - Worker closed +sources: [ 416 416 416 ... 23848 23848 23848] +destinations: [19491 19551 14030 ... 24819 27750 8943] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 + 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 + 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93] +offset: [ 0 75021 148168 224461 298375 374491 449914 525949 601448 + 676934 748042 824458 897315 973015 1047492 1122388 1197737 1273023 + 1348527 1422582 1498579 1572661 1647960 1721121 1795818 1872171 1947146 + 2022196 2096614 2170717 2246158 2318614 2395293 2466402 2540649 2615820 + 2689630 2766076 2843490 2916943 2990792 3066636 3141777 3215261 3288880 + 3363889 3435494 3511242 3586586 3663222 3736866 3811673 3884664 3959049 + 4032054 4107057 4183996 4258162 4334353 4408581 4481098 4558003 4633789 + 4707181 4784008 4857962 4933804 5006393 5080213 5153349 5226878 5301284 + 5376629 5451909 5526524 5603952 5676375 5752358 5826333 5903439 5978230 + 6053229 6128772 6203460 6279940 6355595 6433447 6508744 6584070 6660980 + 6736229 6812545 6886950 6960563 6996771] + +2023-05-10 14:00:05,372 - distributed.nanny - INFO - Worker closed +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 14:00:07,146 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:00:07,387 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:00:08,415 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:00:08,415 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:00:08,585 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:00:08,585 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:00:08,691 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:00:08,856 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:00:09,582 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:43477 +2023-05-10 14:00:09,582 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:43477 +2023-05-10 14:00:09,582 - distributed.worker - INFO - dashboard at: 10.120.104.11:45511 +2023-05-10 14:00:09,582 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:00:09,582 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:09,582 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:00:09,582 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:00:09,582 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-e2heko57 +2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin PreImport-0f107dbd-d26a-47b0-bab3-3a1661b113d6 +2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-7ff8658c-a407-4930-89b4-fe2f1bf12ca8 +2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin RMMSetup-ef44e536-d6c1-49cf-8eb5-1083de79e2dc +2023-05-10 14:00:09,732 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:09,743 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:00:09,743 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:09,745 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:00:09,893 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:39457 +2023-05-10 14:00:09,893 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:39457 +2023-05-10 14:00:09,893 - distributed.worker - INFO - dashboard at: 10.120.104.11:35135 +2023-05-10 14:00:09,894 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:00:09,894 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:09,894 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:00:09,894 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:00:09,894 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-y3uadtnt +2023-05-10 14:00:09,894 - distributed.worker - INFO - Starting Worker plugin RMMSetup-828cab3f-83c5-43ea-84a4-38db7fab1646 +2023-05-10 14:00:10,016 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-b8170da8-c70e-4d84-9513-1920c9fd6d12 +2023-05-10 14:00:10,016 - distributed.worker - INFO - Starting Worker plugin PreImport-4bf9cade-b9e7-4217-ac1a-fd2b8f2f4f47 +2023-05-10 14:00:10,016 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:10,024 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:00:10,024 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:10,026 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:00:18,243 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:00:18,245 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.06s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:18,312 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:00:18,313 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.13s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:18,317 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:18,318 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:18,323 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:00:18,323 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:00:24,505 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.59s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:24,644 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.73s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:24,648 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:00:24,649 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:00:35,715 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:35,715 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:35,879 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:35,880 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:36,172 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:36,173 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:38,140 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:00:38,140 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:00:38,144 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:38,144 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:00:38,171 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:00:38,172 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:00:38,282 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:00:38,283 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:00:38,283 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:39457. Reason: scheduler-restart +2023-05-10 14:00:38,283 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:43477. Reason: scheduler-restart +2023-05-10 14:00:38,284 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:00:38,284 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:00:38,285 - distributed.nanny - INFO - Worker closed +2023-05-10 14:00:38,285 - distributed.nanny - INFO - Worker closed +sources: [ 149 149 152 ... 42626 42626 42626] +destinations: [28916 44586 31153 ... 6897 39391 46682] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 + 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46] +offset: [ 0 147942 297745 449049 600121 746967 896065 1046012 1195966 + 1346847 1496583 1646372 1794618 1945727 2095609 2245094 2394511 2540151 + 2688667 2842619 2989948 3140735 3287422 3433953 3584853 3734426 3882570 + 4029432 4181359 4331799 4478748 4631123 4781648 4931065 5077517 5224233 + 5373951 5523817 5673557 5823458 5975787 6125951 6277623 6431162 6581194 + 6733388 6884382 6994416] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 14:00:40,019 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:00:40,251 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:00:41,242 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:00:41,242 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:00:41,445 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:00:41,445 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:00:41,506 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:00:41,707 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:00:42,324 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:39417 +2023-05-10 14:00:42,324 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:39417 +2023-05-10 14:00:42,324 - distributed.worker - INFO - dashboard at: 10.120.104.11:44667 +2023-05-10 14:00:42,324 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:00:42,324 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,324 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:00:42,324 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:00:42,324 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-_z5vpyd7 +2023-05-10 14:00:42,325 - distributed.worker - INFO - Starting Worker plugin RMMSetup-95cd17b6-b071-443e-af40-3de905fbcd2a +2023-05-10 14:00:42,490 - distributed.worker - INFO - Starting Worker plugin PreImport-5827cbee-1e1f-4676-8f25-0404571d6e5f +2023-05-10 14:00:42,490 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-043970b5-8033-4a52-8028-f0e2b3108e4b +2023-05-10 14:00:42,492 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,504 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:00:42,504 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,506 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:00:42,707 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33977 +2023-05-10 14:00:42,708 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33977 +2023-05-10 14:00:42,708 - distributed.worker - INFO - dashboard at: 10.120.104.11:41515 +2023-05-10 14:00:42,708 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:00:42,708 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,708 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:00:42,708 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:00:42,708 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-wfl9gbzs +2023-05-10 14:00:42,709 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c168af64-50f2-4f88-b4b1-83a8f0ef32d3 +2023-05-10 14:00:42,834 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-089da187-cb28-4989-aeab-64f87911c5b9 +2023-05-10 14:00:42,834 - distributed.worker - INFO - Starting Worker plugin PreImport-65e316a9-afd3-4e52-8ef9-b5f7288431c1 +2023-05-10 14:00:42,835 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,844 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:00:42,844 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:00:42,846 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:00:50,929 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:00:50,931 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.07s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:51,055 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:00:51,056 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.19s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:51,061 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:51,061 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:00:51,066 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:00:51,066 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:00:57,254 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.59s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:57,298 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.64s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:00:57,303 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:00:57,305 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:01:12,683 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:12,683 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:13,084 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:01:13,085 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:01:13,553 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:13,555 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:16,698 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:16,698 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:17,955 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:17,955 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [1344094 1344094 1344094 ... 827747 827747 827747] +destinations: [2391962 303327 128450 ... 2375369 2207027 2370325] +batch: [200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 + 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 + 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 + 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 + 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 + 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 + 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 + 326 327 328 329 330 331 332 333 334 335 336 337 338 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distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:20,458 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:20,458 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [1708193 1708193 1708193 ... 500916 500975 500975] +destinations: [1608810 2370529 246800 ... 1359969 531220 2370583] +batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 + 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 + 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 + 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 + 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 + 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 + 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 + 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 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'set_statistics_adaptor' +2023-05-10 14:01:30,426 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:01:30,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:01:30,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:01:30,578 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:01:30,579 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:01:30,579 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33977. Reason: scheduler-restart +2023-05-10 14:01:30,580 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:39417. Reason: scheduler-restart +2023-05-10 14:01:30,581 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:01:30,581 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:01:30,582 - distributed.nanny - INFO - Worker closed +2023-05-10 14:01:30,582 - distributed.nanny - INFO - Worker closed +sources: [2000336 2000338 2000338 ... 1246236 1246236 1246236] +destinations: [ 325467 1317006 1656369 ... 1839974 2239941 800960] +batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 + 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 + 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 + 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 + 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 + 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 + 4084 4085 4086 4087 4088 4089 4090 4091 4092 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14:01:33,434 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:01:33,434 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:01:33,701 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:01:33,798 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:01:33,798 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:01:34,070 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:01:34,454 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35199 +2023-05-10 14:01:34,454 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35199 +2023-05-10 14:01:34,454 - distributed.worker - INFO - dashboard at: 10.120.104.11:43619 +2023-05-10 14:01:34,454 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:01:34,454 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:34,454 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:01:34,454 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:01:34,454 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-y1xppdqo +2023-05-10 14:01:34,455 - distributed.worker - INFO - Starting Worker plugin RMMSetup-a3ef48b6-7ad0-4076-9d46-ac9df006fdc6 +2023-05-10 14:01:34,589 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-218376e1-18a1-46fe-9c56-a57f02feb41c +2023-05-10 14:01:34,589 - distributed.worker - INFO - Starting Worker plugin PreImport-f65cdb6b-2e07-405b-b058-1ac2c638c4bb +2023-05-10 14:01:34,590 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:34,621 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:01:34,621 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:34,623 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:01:34,949 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:45061 +2023-05-10 14:01:34,950 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:45061 +2023-05-10 14:01:34,950 - distributed.worker - INFO - dashboard at: 10.120.104.11:36843 +2023-05-10 14:01:34,950 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:01:34,950 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:34,950 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:01:34,950 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:01:34,950 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-gmn5lj0i +2023-05-10 14:01:34,951 - distributed.worker - INFO - Starting Worker plugin RMMSetup-a8f1de53-5062-4fa2-9065-ab8c3f06e198 +2023-05-10 14:01:35,069 - distributed.worker - INFO - Starting Worker plugin PreImport-e6a940f4-c6da-438b-9a3a-dd289031b4da +2023-05-10 14:01:35,069 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-47df3810-4e54-40ce-b20b-74d2df1b9531 +2023-05-10 14:01:35,070 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:35,080 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:01:35,080 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:01:35,082 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:01:43,384 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:01:43,385 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.20s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:01:43,474 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:01:43,475 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.29s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:01:43,479 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:43,480 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:01:43,484 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:01:43,484 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:01:49,764 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.68s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:01:49,823 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.75s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:01:49,823 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:01:49,825 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:02:15,261 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:15,261 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:15,660 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:02:15,661 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:02:16,266 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:16,268 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:19,119 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:19,119 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:21,590 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:21,590 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:22,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:22,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:23,410 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:23,410 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:24,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:24,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 816 816 816 ... 256793 256793 256793] +destinations: [ 471433 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function '_func_ucp_listener_port' +2023-05-10 14:02:25,255 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:26,191 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:26,191 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:27,106 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:27,106 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:28,029 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:28,029 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:28,966 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:28,966 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: 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11631758 11772862 11910578 12052080 12190084 + 12330394 12468716 12607897 12750327 12890228 13027480 13167987 13307274 + 13449965 13590340 13733129 13872877 14016827] + +2023-05-10 14:02:29,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:29,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:30,744 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:02:30,744 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:02:30,748 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:02:30,748 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:02:30,780 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:02:30,782 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 14:02:30,893 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:02:30,894 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 14:02:30,894 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:35199. Reason: scheduler-restart +2023-05-10 14:02:30,895 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:45061. Reason: scheduler-restart +2023-05-10 14:02:30,895 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:02:30,896 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 14:02:30,897 - distributed.nanny - INFO - Worker closed +2023-05-10 14:02:30,897 - distributed.nanny - INFO - Worker closed +sources: [2000994 2000996 2000997 ... 1841254 1841254 1841254] +destinations: [1098820 2167314 1686724 ... 817400 2069448 701895] +batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 + 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 + 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 + 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 + 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 + 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 + 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 + 2098 2099] +offset: [ 0 138226 276099 414517 549261 694949 836327 971413 + 1113285 1247844 1388872 1526290 1669523 1810836 1950764 2095368 + 2233010 2373625 2514571 2657827 2798689 2937787 3084302 3227359 + 3367818 3507517 3647651 3785719 3920004 4063175 4202236 4342754 + 4483312 4621102 4760424 4900917 5045336 5188815 5328380 5468467 + 5609906 5747732 5887444 6031762 6173668 6316355 6453137 6589321 + 6731125 6872639 7017783 7156930 7300314 7436011 7574820 7712289 + 7847818 7985805 8128821 8268356 8405741 8541823 8684349 8826047 + 8964208 9102784 9243631 9383080 9524635 9659660 9801856 9939563 + 10078025 10214225 10355506 10493440 10631663 10771320 10915525 11058911 + 11199077 11341484 11486711 11624613 11762103 11902155 12044334 12182037 + 12321665 12461936 12598287 12739586 12882927 13025641 13169905 13307339 + 13451148 13589053 13726499 13864764 14006991] + +sources: [2200482 2200482 2200482 ... 1042095 1042095 1042095] +destinations: [ 342021 1090788 2253206 ... 2377259 1008611 2299982] +batch: [2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 + 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 + 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 + 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 + 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 + 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 + 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 + 2298 2299] +offset: [ 0 136422 285170 422026 568060 701265 840886 977956 + 1117514 1262239 1401418 1539627 1680159 1820990 1960106 2102722 + 2240682 2377721 2519699 2662050 2797847 2934638 3076449 3212855 + 3361211 3502990 3639243 3777968 3919465 4058510 4204668 4346301 + 4482897 4622950 4763702 4904772 5048649 5187711 5330480 5464527 + 5606335 5749371 5891126 6032452 6165319 6303582 6443844 6585204 + 6725168 6860348 6998120 7139578 7280519 7416266 7560633 7698618 + 7839760 7985470 8128384 8271780 8408340 8542809 8680591 8819817 + 8958894 9100032 9242771 9384649 9530211 9667530 9807053 9946129 + 10086433 10228661 10365342 10499676 10639662 10777356 10921092 11059557 + 11202704 11338313 11479348 11616659 11760191 11901153 12044518 12185287 + 12324656 12464254 12600371 12735011 12878859 13018903 13155893 13297692 + 13440427 13577946 13717291 13857137 14002657] + +sources: [1031398 1031398 1031398 ... 2029054 2029054 2029054] +destinations: [1551019 1376826 509250 ... 1441204 1080029 2070589] +batch: [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 + 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 + 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 + 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 + 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 + 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 + 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 + 2198 2199] +offset: [ 0 142358 283666 423877 563410 702278 841997 977930 + 1119732 1259762 1396747 1534510 1679616 1819801 1961837 2102704 + 2249018 2387368 2526250 2670488 2811491 2951517 3095930 3235404 + 3374214 3512905 3652922 3796208 3933485 4075379 4213097 4351547 + 4488702 4631456 4774793 4912142 5053160 5192889 5330691 5467246 + 5610326 5748227 5886900 6027209 6173183 6315077 6452249 6598491 + 6737747 6873887 7014245 7159797 7302686 7445079 7580949 7720155 + 7855661 8000213 8142186 8282068 8418939 8563268 8703334 8842894 + 8978971 9124683 9261721 9400116 9539179 9675964 9816809 9957077 + 10096541 10235373 10374366 10518954 10664425 10804726 10944105 11082745 + 11225083 11365464 11508070 11647777 11788593 11927053 12067739 12207069 + 12356137 12493961 12634489 12771322 12909865 13048745 13187594 13333844 + 13473742 13616568 13760925 13898065 14039414] + +sources: [1292351 1292351 1292351 ... 892120 892120 892120] +destinations: [2339574 757471 7294 ... 1785611 1618868 2007050] +batch: [2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 + 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 + 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 + 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 + 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 + 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 + 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396] +offset: [ 0 138514 279133 417007 552658 690675 831613 979356 + 1122163 1264131 1403376 1544806 1680257 1812387 1956932 2098955 + 2246324 2388157 2526667 2665839 2804828 2944363 3088544 3227381 + 3368778 3509505 3652293 3790711 3929320 4065157 4211006 4349717 + 4487007 4627559 4765961 4908113 5051206 5193840 5335282 5470926 + 5610234 5750502 5895391 6031628 6174062 6316621 6451550 6594532 + 6736330 6876718 7011194 7153022 7295843 7437355 7577975 7715143 + 7859667 7998310 8140836 8280726 8418057 8557620 8702492 8839230 + 8976097 9115925 9261950 9405880 9538391 9680810 9824036 9960091 + 10103353 10241108 10384447 10521848 10661256 10803683 10946101 11086221 + 11226573 11372118 11509726 11652842 11788889 11927463 12071192 12207057 + 12346734 12489050 12631973 12771718 12916943 13057770 13197621 13334416 + 13469700 13559504] + +2023-05-10 14:02:32,628 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:02:32,894 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:02:33,936 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:02:33,936 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:02:34,093 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:02:34,094 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:02:34,217 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:02:34,354 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:02:35,178 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35397 +2023-05-10 14:02:35,179 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35397 +2023-05-10 14:02:35,179 - distributed.worker - INFO - dashboard at: 10.120.104.11:37445 +2023-05-10 14:02:35,179 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:02:35,179 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,179 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:02:35,179 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:02:35,179 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-0qlx3em1 +2023-05-10 14:02:35,179 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c37209f2-33c5-41a7-aaf2-4c4a044620ed +2023-05-10 14:02:35,315 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6cf400ad-25b4-4c7a-9f2a-59dcec7896a4 +2023-05-10 14:02:35,316 - distributed.worker - INFO - Starting Worker plugin PreImport-92c8aaaa-6a06-4252-bc46-ac0a8dd417a4 +2023-05-10 14:02:35,317 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,326 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:02:35,326 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,328 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:02:35,412 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34415 +2023-05-10 14:02:35,412 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34415 +2023-05-10 14:02:35,412 - distributed.worker - INFO - dashboard at: 10.120.104.11:41157 +2023-05-10 14:02:35,412 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:02:35,412 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,412 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:02:35,412 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:02:35,412 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-msel_0xm +2023-05-10 14:02:35,413 - distributed.worker - INFO - Starting Worker plugin RMMSetup-8b35897c-93ab-4c92-85aa-e5e99b07accb +2023-05-10 14:02:35,534 - distributed.worker - INFO - Starting Worker plugin PreImport-7950f4a6-04eb-4516-ad66-cdf7bfa60ab1 +2023-05-10 14:02:35,535 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-c127e35e-075a-4309-975f-bbf71c8cc098 +2023-05-10 14:02:35,535 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,543 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:02:35,543 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:02:35,545 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:02:43,752 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:02:43,753 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.05s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:02:43,795 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 14:02:43,796 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.09s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:02:43,801 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:43,802 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:02:43,806 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:02:43,806 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 14:02:49,948 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.55s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:02:50,098 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.70s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 14:02:50,101 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:02:50,104 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 14:03:17,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:17,482 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:18,744 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:03:18,745 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:03:19,930 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:19,931 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:23,451 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:23,451 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:25,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:25,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 433 435 435 ... 7743553 7743553 7743553] +destinations: [6820643 8614304 3882419 ... 1312525 1389202 1284630] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 + 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 + 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 + 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 + 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 + 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 + 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 + 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 + 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 + 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 + 198 199] +offset: [ 0 72219 134837 201907 268178 335428 398527 464528 + 535445 604440 671096 742361 811770 879514 952791 1019326 + 1087219 1154891 1221925 1287904 1354404 1421763 1491306 1556898 + 1622049 1690602 1758834 1828785 1897670 1965590 2033993 2104741 + 2174001 2240464 2308927 2379534 2448366 2517058 2584030 2649166 + 2722723 2789699 2861353 2926117 2993223 3060304 3127724 3193380 + 3262953 3331912 3397673 3464325 3531483 3602608 3668936 3738683 + 3805412 3875432 3942611 4008004 4073160 4141024 4211282 4274751 + 4343745 4415718 4482816 4552150 4619760 4687339 4757462 4825651 + 4893471 4961109 5032831 5097358 5164838 5234124 5305240 5373521 + 5442065 5511916 5577859 5649214 5714842 5785240 5854653 5921723 + 5989095 6060517 6130882 6202759 6270522 6340644 6408550 6476971 + 6545754 6613840 6680237 6747630 6818165 6885707 6957353 7025576 + 7097293 7165794 7235811 7303224 7367745 7431577 7501118 7570492 + 7638311 7702561 7771883 7841317 7914639 7985098 8055203 8124266 + 8195627 8263082 8330684 8397467 8462715 8533826 8603743 8671943 + 8738119 8810523 8880238 8947226 9019433 9087016 9158548 9223812 + 9291639 9358441 9425439 9495171 9566687 9638603 9705436 9772285 + 9842924 9916197 9985650 10052803 10122972 10192615 10262747 10332948 + 10404160 10470965 10540130 10606499 10671725 10738912 10806738 10877384 + 10946323 11013728 11083578 11151633 11217597 11283937 11354256 11427320 + 11497116 11564428 11632373 11701257 11771186 11837841 11903811 11974028 + 12040122 12111813 12177764 12249165 12318339 12385386 12451127 12520679 + 12589485 12660529 12729368 12796345 12865351 12937123 13003570 13068558 + 13136496 13205928 13274257 13338193 13408229 13476413 13541433 13608403 + 13682920] + +sources: [ 200000 200000 200000 ... 3379430 3379430 3379430] +destinations: [2687444 6811543 4477735 ... 6605653 3902242 2296256] +batch: [400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 + 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 + 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 + 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 + 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 + 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 + 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 + 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 + 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 + 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 + 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 + 598 599] +offset: [ 0 64921 131428 200259 266955 341447 411608 480322 + 547650 613493 676573 747607 818222 886261 952352 1023252 + 1092955 1160429 1227039 1298367 1364896 1434131 1503798 1571716 + 1643339 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- INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:28,212 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 14:03:28,212 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 400337 400338 400338 ... 5463709 5463709 5463709] +destinations: [6578280 5113497 8681560 ... 7553574 2078274 5739719] +batch: [800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 + 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 + 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 + 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 + 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 + 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 + 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 + 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 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2250829 2320383 2391479 2459745 2530311 2594321 2663148 + 2730822 2800579 2869822 2938461 3006034 3075025 3143717 3210284 + 3278757 3345245 3413080 3480671 3546908 3614939 3682710 3752763 + 3820272 3891861 3959349 4031398 4098855 4166320 4233099 4303298 + 4372580 4436373 4505859 4569301 4638898 4710129 4778552 4844417 + 4910136 4979602 5046497 5114528 5181281 5248260 5315720 5387383 + 5454259 5524661 5597508 5665109 5731387 5797496 5866947 5933426 + 6002945 6065654 6130984 6202328 6271301 6338797 6411393 6483971 + 6549733 6619919 6687383 6757693 6828314 6896692 6969839 7037466 + 7107839 7178513 7247155 7316744 7385246 7451732 7521563 7590505 + 7657842 7725268 7793033 7861386 7927307 7997304 8058952 8128893 + 8196789 8267876 8331651 8400397 8470465 8542656 8608612 8678594 + 8746304 8812034 8878093 8946041 9009957 9079384 9149261 9213367 + 9284665 9353759 9424294 9486374 9552466 9620593 9687580 9757866 + 9826269 9898478 9969984 10040409 10103906 10170391 10239325 10308454 + 10372063 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13510237 13578017 + 13647139] + +sources: [8600289 8600289 8600289 ... 1473848 1473848 1473848] +destinations: [3069756 3832034 7793472 ... 2801822 4161874 4548586] +batch: [17200 17201 17202 17203 17204 17205 17206 17207 17208 17209 17210 17211 + 17212 17213 17214 17215 17216 17217 17218 17219 17220 17221 17222 17223 + 17224 17225 17226 17227 17228 17229 17230 17231 17232 17233 17234 17235 + 17236 17237 17238 17239 17240 17241 17242 17243 17244 17245 17246 17247 + 17248 17249 17250 17251 17252 17253 17254 17255 17256 17257 17258 17259 + 17260 17261 17262 17263 17264 17265 17266 17267 17268 17269 17270 17271 + 17272 17273 17274 17275 17276 17277 17278 17279 17280 17281 17282 17283 + 17284 17285 17286 17287 17288 17289 17290 17291 17292 17293 17294 17295 + 17296 17297 17298 17299 17300 17301 17302 17303 17304 17305 17306 17307 + 17308 17309 17310 17311 17312 17313 17314 17315 17316 17317 17318 17319 + 17320 17321 17322 17323 17324 17325 17326 17327 17328 17329 17330 17331 + 17332 17333 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12890710 12957208 13026398 + 13093917 13160872 13227905 13295643 13364632 13432288 13499427 13566059 + 13635450] + +2023-05-10 14:04:42,370 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:04:42,371 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 14:04:42,374 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:04:42,374 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 14:04:51,745 - distributed.utils_perf - INFO - full garbage collection released 16.01 GiB from 9750 reference cycles (threshold: 9.54 MiB) +sources: [8599823 8599936 8599936 ... 1644415 1644415 1644415] +destinations: [5022466 502030 6028000 ... 411570 1408640 8154918] +batch: [17000 17001 17002 17003 17004 17005 17006 17007 17008 17009 17010 17011 + 17012 17013 17014 17015 17016 17017 17018 17019 17020 17021 17022 17023 + 17024 17025 17026 17027 17028 17029 17030 17031 17032 17033 17034 17035 + 17036 17037 17038 17039 17040 17041 17042 17043 17044 17045 17046 17047 + 17048 17049 17050 17051 17052 17053 17054 17055 17056 17057 17058 17059 + 17060 17061 17062 17063 17064 17065 17066 17067 17068 17069 17070 17071 + 17072 17073 17074 17075 17076 17077 17078 17079 17080 17081 17082 17083 + 17084 17085 17086 17087 17088 17089 17090 17091 17092 17093 17094 17095 + 17096 17097 17098 17099 17100 17101 17102 17103 17104 17105 17106 17107 + 17108 17109 17110 17111 17112 17113 17114 17115 17116 17117 17118 17119 + 17120 17121 17122 17123 17124 17125 17126 17127 17128 17129 17130 17131 + 17132 17133 17134 17135 17136 17137 17138 17139 17140 17141 17142 17143 + 17144 17145 17146 17147 17148 17149 17150 17151 17152 17153 17154 17155 + 17156 17157 17158 17159 17160 17161 17162 17163 17164 17165 17166 17167 + 17168 17169 17170 17171 17172 17173 17174 17175 17176 17177 17178 17179 + 17180 17181 17182 17183 17184 17185 17186 17187 17188 17189 17190 17191 + 17192 17193 17194 17195 17196 17197 17198 17199] +offset: [ 0 68061 135025 206134 275770 344331 410475 478186 + 550001 620164 686210 754629 822346 889567 956731 1020886 + 1090735 1161794 1230370 1299338 1367339 1437730 1508879 1577931 + 1645580 1708340 1778033 1841586 1908305 1976212 2044771 2114324 + 2184789 2253030 2323819 2395702 2462663 2529641 2599041 2669291 + 2733070 2800779 2870351 2940324 3012999 3077955 3146873 3214740 + 3285719 3349281 3415287 3484854 3554530 3623059 3688291 3757628 + 3825530 3893717 3959426 4031167 4102071 4167940 4233668 4302170 + 4367967 4436472 4508290 4578058 4645284 4713229 4783058 4849394 + 4921058 4992820 5061946 5129623 5200285 5266226 5336783 5408092 + 5478577 5548522 5612448 5682612 5751706 5818071 5882306 5953033 + 6018224 6091202 6155952 6226560 6299157 6366610 6434596 6503154 + 6567814 6636189 6707128 6776548 6842353 6910828 6979470 7051995 + 7116412 7181473 7248511 7315309 7387493 7452992 7525478 7599794 + 7671735 7738630 7804259 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17415 17416 17417 17418 17419 17420 17421 17422 17423 + 17424 17425 17426 17427 17428 17429 17430 17431 17432 17433 17434 17435 + 17436 17437 17438 17439 17440 17441 17442 17443 17444 17445 17446 17447 + 17448 17449 17450 17451 17452 17453 17454 17455 17456 17457 17458 17459 + 17460 17461 17462 17463 17464 17465 17466 17467 17468 17469 17470 17471 + 17472 17473 17474 17475 17476 17477 17478 17479 17480 17481 17482 17483 + 17484 17485 17486 17487 17488 17489 17490 17491 17492 17493 17494 17495 + 17496 17497 17498 17499 17500 17501 17502 17503 17504 17505 17506 17507 + 17508 17509 17510 17511 17512 17513 17514 17515 17516 17517 17518 17519 + 17520 17521 17522 17523 17524 17525 17526 17527 17528 17529 17530 17531 + 17532 17533 17534 17535 17536 17537 17538 17539 17540 17541 17542 17543 + 17544 17545 17546 17547 17548 17549 17550 17551 17552 17553 17554 17555 + 17556 17557 17558 17559 17560 17561 17562 17563 17564 17565 17566 17567 + 17568 17569 17570 17571 17572 17573 17574 17575 17576 17577 17578 17579 + 17580 17581 17582 17583 17584 17585 17586 17587 17588 17589 17590 17591 + 17592 17593 17594 17595 17596 17597 17598 17599] +offset: [ 0 66560 139988 210118 280091 345785 415754 483868 + 550793 617533 688571 760676 831303 896463 963724 1028629 + 1101255 1172631 1243027 1311373 1380359 1447582 1513981 1579172 + 1646160 1714511 1783326 1851929 1921594 1988600 2056076 2123851 + 2193685 2264245 2333918 2400080 2470166 2537308 2607645 2672912 + 2739293 2806622 2876079 2945233 3013461 3081193 3149802 3218884 + 3285975 3354007 3422232 3486160 3552403 3618326 3690100 3756932 + 3825974 3891878 3961890 4031993 4099241 4168340 4239768 4307013 + 4374739 4443098 4508516 4576881 4645519 4715984 4784593 4850649 + 4918926 4988949 5054462 5123229 5194231 5261109 5331356 5397692 + 5464803 5533247 5603855 5671383 5741108 5810219 5877440 5944982 + 6014296 6082673 6153935 6221137 6284775 6355348 6424724 6494312 + 6560099 6624446 6695381 6762797 6829731 6900180 6970120 7036053 + 7105649 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out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded +2023-05-10 14:04:52,137 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35397 +2023-05-10 14:04:52,591 - distributed.nanny - INFO - Worker process 54580 was killed by signal 6 +2023-05-10 14:04:52,598 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:04:52,831 - distributed.utils_perf - INFO - full garbage collection released 18.35 GiB from 11265 reference cycles (threshold: 9.54 MiB) +terminate called after throwing an instance of 'rmm::out_of_memory' + what(): std::bad_alloc: out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded +2023-05-10 14:04:53,669 - distributed.nanny - INFO - Worker process 54587 was killed by signal 6 +2023-05-10 14:04:53,674 - distributed.nanny - WARNING - Restarting worker +2023-05-10 14:04:53,814 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:04:53,814 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:04:54,086 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:04:54,760 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38611 +2023-05-10 14:04:54,760 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38611 +2023-05-10 14:04:54,760 - distributed.worker - INFO - dashboard at: 10.120.104.11:33101 +2023-05-10 14:04:54,761 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:04:54,761 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:54,761 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:04:54,761 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:04:54,761 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-_5ed2sbz +2023-05-10 14:04:54,761 - distributed.worker - INFO - Starting Worker plugin RMMSetup-5338f63d-f995-4035-b7e7-3227f1f3ae05 +2023-05-10 14:04:54,881 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 14:04:54,881 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 14:04:54,885 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-ff7cbe29-74f3-429d-905d-5071e2d1f669 +2023-05-10 14:04:54,888 - distributed.worker - INFO - Starting Worker plugin PreImport-0faf3943-f2a6-4a9b-8bf1-173ee1483133 +2023-05-10 14:04:54,889 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:54,901 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:04:54,901 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:54,904 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:04:55,149 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 14:04:55,858 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44519 +2023-05-10 14:04:55,859 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44519 +2023-05-10 14:04:55,859 - distributed.worker - INFO - dashboard at: 10.120.104.11:42463 +2023-05-10 14:04:55,859 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 14:04:55,859 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:55,859 - distributed.worker - INFO - Threads: 1 +2023-05-10 14:04:55,859 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 14:04:55,859 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-cf3ssko5 +2023-05-10 14:04:55,860 - distributed.worker - INFO - Starting Worker plugin RMMSetup-3c19ae26-d181-4df8-a285-1b34148307dc +2023-05-10 14:04:55,979 - distributed.worker - INFO - Starting Worker plugin PreImport-60ba7f91-bbe5-447b-ac3c-641c5f5cfe7a +2023-05-10 14:04:55,980 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-f792caf5-42d3-4065-b231-2f7dbd1bc6f2 +2023-05-10 14:04:55,981 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:55,992 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 14:04:55,992 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 14:04:55,994 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 14:19:08,097 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38611. Reason: worker-close +2023-05-10 14:19:08,097 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:46381'. Reason: nanny-close +2023-05-10 14:19:08,097 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44519. Reason: worker-close +2023-05-10 14:19:08,097 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close +2023-05-10 14:19:08,098 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:38723'. Reason: nanny-close +2023-05-10 14:19:08,099 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close +2023-05-10 14:19:08,098 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:37906 remote=tcp://10.120.104.11:8786> +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run + return loop.run_until_complete(main) + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete + self.run_forever() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever + self._run_once() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once + event_list = self._selector.select(timeout) + File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select + fd_event_list = self._selector.poll(timeout, max_ev) +KeyboardInterrupt + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send + nbytes = yield coro + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run + value = future.result() + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write + raise CommClosedError() +distributed.comm.core.CommClosedError +2023-05-10 14:19:08,099 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:37928 remote=tcp://10.120.104.11:8786> +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run + return loop.run_until_complete(main) + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete + self.run_forever() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever + self._run_once() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once + event_list = self._selector.select(timeout) + File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select + fd_event_list = self._selector.poll(timeout, max_ev) +KeyboardInterrupt + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send + nbytes = yield coro + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run + value = future.result() + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write + raise CommClosedError() +distributed.comm.core.CommClosedError +2023-05-10 14:19:11,300 - distributed.nanny - WARNING - Worker process still alive after 3.1999969482421875 seconds, killing +2023-05-10 14:19:11,300 - distributed.nanny - WARNING - Worker process still alive after 3.199999389648438 seconds, killing +2023-05-10 14:19:11,438 - distributed.nanny - INFO - Worker process 56086 was killed by signal 9 +2023-05-10 14:19:11,504 - distributed.nanny - INFO - Worker process 56097 was killed by signal 9 diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 0257a56ba08..d1fcec6f21c 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -24,6 +24,7 @@ import pylibcugraph from cugraph.gnn.data_loading.bulk_sampler_io import write_samples +import logging class EXPERIMENTAL__BulkSampler: @@ -36,7 +37,8 @@ def __init__( output_path: str, graph, seeds_per_call: int = 200_000, - batches_per_partition=100, + batches_per_partition: int = 100, + log_level: int = None, **kwargs, ): """ @@ -55,15 +57,21 @@ def __init__( a single sampling call. batches_per_partition: int (optional, default=100) The number of batches outputted to a single parquet partition. + log_level: int (optional, default=None) + Whether to enable logging for this sampler. Supports 3 levels + of logging if enabled (INFO, WARNING, ERROR). If not provided, + defaults to WARNING. kwargs: kwargs Keyword arguments to be passed to the sampler (i.e. fanout). """ + self.__logger = logging.getLogger(__name__) + self.__logger.setLevel(log_level or logging.WARNING) + max_batches_per_partition = seeds_per_call // batch_size if batches_per_partition > max_batches_per_partition: - import warnings - warnings.warn( + self.__logger.warning( f"batches_per_partition ({batches_per_partition}) is >" f" seeds_per_call / batch size ({max_batches_per_partition})" "; automatically setting batches_per_partition to " @@ -163,6 +171,11 @@ def add_batches( ) if self.size >= self.seeds_per_call: + self.__logger.info( + f"Number of input seeds ({self.size})" + f" is >= seeds per call ({self.seeds_per_call})." + " Calling flush() to compute and write minibatches." + ) self.flush() def flush(self) -> None: @@ -171,7 +184,6 @@ def flush(self) -> None: """ if self.size == 0: return - self.__batches.reset_index(drop=True) min_batch_id = self.__batches[self.batch_col_name].min() if isinstance(self.__batches, dask_cudf.DataFrame): From 5ebdd51a5bd5fb11769bcb46a4fab100f03bbde1 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 10 May 2023 14:36:29 +0000 Subject: [PATCH 039/347] remove debug files --- cugraph_graph_creation_stats.csv | 5 - mg_utils/dask_logs-51692/scheduler_log.txt | 134 - mg_utils/dask_logs-51692/worker-exp01_log.txt | 6152 ----------------- 3 files changed, 6291 deletions(-) delete mode 100644 cugraph_graph_creation_stats.csv delete mode 100644 mg_utils/dask_logs-51692/scheduler_log.txt delete mode 100644 mg_utils/dask_logs-51692/worker-exp01_log.txt diff --git a/cugraph_graph_creation_stats.csv b/cugraph_graph_creation_stats.csv deleted file mode 100644 index 95634780e0b..00000000000 --- a/cugraph_graph_creation_stats.csv +++ /dev/null @@ -1,5 +0,0 @@ -,scale,num_input_edges,directed,renumber,input_memory_per_worker,peak_allocation_across_workers,input_to_peak_ratio,output_to_peak_ratio -0,16,2097152,,,28.0MB,2.0GB,72.04728126525879,3748.6305876658503 -1,16,2097152,,,28.0MB,2.0GB,72.0413030896868,3748.95384659188 -2,22,134217728,,,1.8GB,6.9GB,3.9640060663223267,1.6366930403696882 -3,22,134217728,,,1.8GB,6.9GB,3.959538323538644,1.3688574167519196 diff --git a/mg_utils/dask_logs-51692/scheduler_log.txt b/mg_utils/dask_logs-51692/scheduler_log.txt deleted file mode 100644 index 2080214e5b5..00000000000 --- a/mg_utils/dask_logs-51692/scheduler_log.txt +++ /dev/null @@ -1,134 +0,0 @@ -RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp - --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json - " -/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead - warnings.warn( -2023-05-10 13:59:27,794 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 13:59:28,532 - distributed.scheduler - INFO - State start -2023-05-10 13:59:28,545 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 13:59:28,546 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 -2023-05-10 13:59:28,547 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status -2023-05-10 13:59:37,504 - distributed.scheduler - INFO - Register worker -2023-05-10 13:59:37,506 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44009 -2023-05-10 13:59:37,506 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54160 -2023-05-10 13:59:37,551 - distributed.scheduler - INFO - Register worker -2023-05-10 13:59:37,552 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42211 -2023-05-10 13:59:37,552 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54176 -2023-05-10 13:59:39,811 - distributed.scheduler - INFO - Receive client connection: Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 -2023-05-10 13:59:39,811 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54192 -2023-05-10 13:59:40,047 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 14:00:05,364 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 14:00:05,365 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 14:00:05,370 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54176; closing. -2023-05-10 14:00:05,370 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:00:05,370 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42211 -2023-05-10 14:00:05,371 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54160; closing. -2023-05-10 14:00:05,371 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:00:05,371 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44009 -2023-05-10 14:00:05,371 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:00:09,742 - distributed.scheduler - INFO - Register worker -2023-05-10 14:00:09,742 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:43477 -2023-05-10 14:00:09,743 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52398 -2023-05-10 14:00:10,023 - distributed.scheduler - INFO - Register worker -2023-05-10 14:00:10,024 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:39457 -2023-05-10 14:00:10,024 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52406 -2023-05-10 14:00:10,196 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 14:00:18,320 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 14:00:38,277 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 14:00:38,278 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 14:00:38,284 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52406; closing. -2023-05-10 14:00:38,284 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:00:38,284 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:39457 -2023-05-10 14:00:38,284 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52398; closing. -2023-05-10 14:00:38,284 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:00:38,285 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43477 -2023-05-10 14:00:38,285 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:00:42,503 - distributed.scheduler - INFO - Register worker -2023-05-10 14:00:42,504 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:39417 -2023-05-10 14:00:42,504 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56514 -2023-05-10 14:00:42,843 - distributed.scheduler - INFO - Register worker -2023-05-10 14:00:42,843 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33977 -2023-05-10 14:00:42,844 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56518 -2023-05-10 14:00:42,862 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 14:00:51,063 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 14:01:30,572 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 14:01:30,573 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 14:01:30,581 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56518; closing. -2023-05-10 14:01:30,581 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:01:30,581 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33977 -2023-05-10 14:01:30,581 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56514; closing. -2023-05-10 14:01:30,582 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:01:30,582 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:39417 -2023-05-10 14:01:30,582 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:01:34,620 - distributed.scheduler - INFO - Register worker -2023-05-10 14:01:34,620 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35199 -2023-05-10 14:01:34,620 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58796 -2023-05-10 14:01:35,079 - distributed.scheduler - INFO - Register worker -2023-05-10 14:01:35,080 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:45061 -2023-05-10 14:01:35,080 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58804 -2023-05-10 14:01:35,193 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 14:01:43,482 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 14:02:30,888 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 14:02:30,888 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 14:02:30,895 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58796; closing. -2023-05-10 14:02:30,896 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:02:30,896 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35199 -2023-05-10 14:02:30,896 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58804; closing. -2023-05-10 14:02:30,896 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:02:30,897 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:45061 -2023-05-10 14:02:30,897 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:02:35,325 - distributed.scheduler - INFO - Register worker -2023-05-10 14:02:35,326 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35397 -2023-05-10 14:02:35,326 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35748 -2023-05-10 14:02:35,542 - distributed.scheduler - INFO - Register worker -2023-05-10 14:02:35,542 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34415 -2023-05-10 14:02:35,542 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35750 -2023-05-10 14:02:35,708 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 14:02:43,803 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 14:04:52,135 - distributed.core - INFO - Connection to tcp://10.120.104.11:35748 has been closed. -2023-05-10 14:04:52,135 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:04:52,135 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35397 -2023-05-10 14:04:53,200 - distributed.core - INFO - Connection to tcp://10.120.104.11:35750 has been closed. -2023-05-10 14:04:53,200 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:04:53,200 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34415 -2023-05-10 14:04:53,200 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:04:54,900 - distributed.scheduler - INFO - Register worker -2023-05-10 14:04:54,901 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38611 -2023-05-10 14:04:54,901 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37906 -2023-05-10 14:04:55,991 - distributed.scheduler - INFO - Register worker -2023-05-10 14:04:55,991 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44519 -2023-05-10 14:04:55,991 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37928 -2023-05-10 14:19:05,613 - distributed.scheduler - INFO - Remove client Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 -2023-05-10 14:19:05,614 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54192; closing. -2023-05-10 14:19:05,614 - distributed.scheduler - INFO - Remove client Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 -2023-05-10 14:19:05,615 - distributed.scheduler - INFO - Close client connection: Client-e8036702-ef3a-11ed-8a4f-5cff35c1a711 -2023-05-10 14:19:08,096 - distributed._signals - INFO - Received signal SIGINT (2) -2023-05-10 14:19:08,097 - distributed.scheduler - INFO - Scheduler closing... -2023-05-10 14:19:08,097 - distributed.scheduler - INFO - Scheduler closing all comms -2023-05-10 14:19:08,098 - distributed.core - INFO - Connection to tcp://10.120.104.11:37906 has been closed. -2023-05-10 14:19:08,098 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:19:08,098 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38611 -2023-05-10 14:19:08,099 - distributed.batched - INFO - Batched Comm Closed -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 317, in write - raise StreamClosedError() -tornado.iostream.StreamClosedError: Stream is closed - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send - nbytes = yield coro - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run - value = future.result() - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 328, in write - convert_stream_closed_error(self, e) - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 144, in convert_stream_closed_error - raise CommClosedError(f"in {obj}: {exc}") from exc -distributed.comm.core.CommClosedError: in : Stream is closed -2023-05-10 14:19:08,100 - distributed.core - INFO - Connection to tcp://10.120.104.11:37928 has been closed. -2023-05-10 14:19:08,100 - distributed.scheduler - INFO - Remove worker -2023-05-10 14:19:08,100 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44519 -2023-05-10 14:19:08,100 - distributed.scheduler - INFO - Lost all workers -2023-05-10 14:19:08,101 - distributed.scheduler - INFO - Stopped scheduler at 'tcp://10.120.104.11:8786' -2023-05-10 14:19:08,102 - distributed.scheduler - INFO - End scheduler diff --git a/mg_utils/dask_logs-51692/worker-exp01_log.txt b/mg_utils/dask_logs-51692/worker-exp01_log.txt deleted file mode 100644 index c8f2424e425..00000000000 --- a/mg_utils/dask_logs-51692/worker-exp01_log.txt +++ /dev/null @@ -1,6152 +0,0 @@ -RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G - --local-directory=/tmp/ - --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json - --memory-limit=auto - --device-memory-limit=auto - " -2023-05-10 13:59:34,809 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:46381' -2023-05-10 13:59:34,812 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:38723' -2023-05-10 13:59:36,006 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-77ixbbwy', purging -2023-05-10 13:59:36,006 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 13:59:36,006 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 13:59:36,036 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 13:59:36,036 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 13:59:36,276 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 13:59:36,313 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 13:59:37,325 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44009 -2023-05-10 13:59:37,325 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44009 -2023-05-10 13:59:37,325 - distributed.worker - INFO - dashboard at: 10.120.104.11:32867 -2023-05-10 13:59:37,325 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 13:59:37,325 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,326 - distributed.worker - INFO - Threads: 1 -2023-05-10 13:59:37,326 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 13:59:37,326 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-6fi5g73m -2023-05-10 13:59:37,327 - distributed.worker - INFO - Starting Worker plugin RMMSetup-9ff6500e-366f-49ee-aa77-3963fb1568a3 -2023-05-10 13:59:37,381 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42211 -2023-05-10 13:59:37,381 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42211 -2023-05-10 13:59:37,381 - distributed.worker - INFO - dashboard at: 10.120.104.11:40325 -2023-05-10 13:59:37,381 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 13:59:37,381 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,381 - distributed.worker - INFO - Threads: 1 -2023-05-10 13:59:37,381 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 13:59:37,381 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tderj3j8 -2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin PreImport-de6a3b63-3184-4bfc-b230-3e9c9cbdb698 -2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-79ad3a23-9ddd-40d6-923d-6323eb40d16a -2023-05-10 13:59:37,382 - distributed.worker - INFO - Starting Worker plugin RMMSetup-abd6acbf-3fd0-4594-87ae-06f9d8dbab3b -2023-05-10 13:59:37,490 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-b4b518af-992a-46cc-80b5-4ee8b0ed70a5 -2023-05-10 13:59:37,491 - distributed.worker - INFO - Starting Worker plugin PreImport-fa14007d-2386-4cfe-89d3-f40597d72930 -2023-05-10 13:59:37,492 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,507 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 13:59:37,507 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,510 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 13:59:37,541 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,552 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 13:59:37,552 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 13:59:37,554 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 13:59:39,824 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 13:59:39,824 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 13:59:40,630 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 13:59:40,631 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 13:59:49,374 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 13:59:50,577 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 13:59:56,604 - distributed.core - INFO - Event loop was unresponsive in Worker for 15.31s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 13:59:56,606 - distributed.core - INFO - Event loop was unresponsive in Worker for 15.31s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 13:59:56,613 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 13:59:56,615 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:00:02,743 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:02,743 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:02,917 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:02,918 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:03,140 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:03,142 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:05,219 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:00:05,219 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:00:05,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:05,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:05,252 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:00:05,253 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:00:05,368 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:00:05,369 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:00:05,369 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42211. Reason: scheduler-restart -2023-05-10 14:00:05,370 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44009. Reason: scheduler-restart -2023-05-10 14:00:05,370 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:00:05,371 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:00:05,371 - distributed.nanny - INFO - Worker closed -sources: [ 416 416 416 ... 23848 23848 23848] -destinations: [19491 19551 14030 ... 24819 27750 8943] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 - 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 - 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93] -offset: [ 0 75021 148168 224461 298375 374491 449914 525949 601448 - 676934 748042 824458 897315 973015 1047492 1122388 1197737 1273023 - 1348527 1422582 1498579 1572661 1647960 1721121 1795818 1872171 1947146 - 2022196 2096614 2170717 2246158 2318614 2395293 2466402 2540649 2615820 - 2689630 2766076 2843490 2916943 2990792 3066636 3141777 3215261 3288880 - 3363889 3435494 3511242 3586586 3663222 3736866 3811673 3884664 3959049 - 4032054 4107057 4183996 4258162 4334353 4408581 4481098 4558003 4633789 - 4707181 4784008 4857962 4933804 5006393 5080213 5153349 5226878 5301284 - 5376629 5451909 5526524 5603952 5676375 5752358 5826333 5903439 5978230 - 6053229 6128772 6203460 6279940 6355595 6433447 6508744 6584070 6660980 - 6736229 6812545 6886950 6960563 6996771] - -2023-05-10 14:00:05,372 - distributed.nanny - INFO - Worker closed -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 14:00:07,146 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:00:07,387 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:00:08,415 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:00:08,415 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:00:08,585 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:00:08,585 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:00:08,691 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:00:08,856 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:00:09,582 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:43477 -2023-05-10 14:00:09,582 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:43477 -2023-05-10 14:00:09,582 - distributed.worker - INFO - dashboard at: 10.120.104.11:45511 -2023-05-10 14:00:09,582 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:00:09,582 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:09,582 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:00:09,582 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:00:09,582 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-e2heko57 -2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin PreImport-0f107dbd-d26a-47b0-bab3-3a1661b113d6 -2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-7ff8658c-a407-4930-89b4-fe2f1bf12ca8 -2023-05-10 14:00:09,583 - distributed.worker - INFO - Starting Worker plugin RMMSetup-ef44e536-d6c1-49cf-8eb5-1083de79e2dc -2023-05-10 14:00:09,732 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:09,743 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:00:09,743 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:09,745 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:00:09,893 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:39457 -2023-05-10 14:00:09,893 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:39457 -2023-05-10 14:00:09,893 - distributed.worker - INFO - dashboard at: 10.120.104.11:35135 -2023-05-10 14:00:09,894 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:00:09,894 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:09,894 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:00:09,894 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:00:09,894 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-y3uadtnt -2023-05-10 14:00:09,894 - distributed.worker - INFO - Starting Worker plugin RMMSetup-828cab3f-83c5-43ea-84a4-38db7fab1646 -2023-05-10 14:00:10,016 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-b8170da8-c70e-4d84-9513-1920c9fd6d12 -2023-05-10 14:00:10,016 - distributed.worker - INFO - Starting Worker plugin PreImport-4bf9cade-b9e7-4217-ac1a-fd2b8f2f4f47 -2023-05-10 14:00:10,016 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:10,024 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:00:10,024 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:10,026 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:00:18,243 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:00:18,245 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.06s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:18,312 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:00:18,313 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.13s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:18,317 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:18,318 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:18,323 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:00:18,323 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:00:24,505 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.59s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:24,644 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.73s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:24,648 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:00:24,649 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:00:35,715 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:35,715 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:35,879 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:35,880 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:36,172 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:36,173 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:38,140 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:00:38,140 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:00:38,144 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:38,144 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:00:38,171 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:00:38,172 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:00:38,282 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:00:38,283 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:00:38,283 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:39457. Reason: scheduler-restart -2023-05-10 14:00:38,283 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:43477. Reason: scheduler-restart -2023-05-10 14:00:38,284 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:00:38,284 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:00:38,285 - distributed.nanny - INFO - Worker closed -2023-05-10 14:00:38,285 - distributed.nanny - INFO - Worker closed -sources: [ 149 149 152 ... 42626 42626 42626] -destinations: [28916 44586 31153 ... 6897 39391 46682] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 - 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46] -offset: [ 0 147942 297745 449049 600121 746967 896065 1046012 1195966 - 1346847 1496583 1646372 1794618 1945727 2095609 2245094 2394511 2540151 - 2688667 2842619 2989948 3140735 3287422 3433953 3584853 3734426 3882570 - 4029432 4181359 4331799 4478748 4631123 4781648 4931065 5077517 5224233 - 5373951 5523817 5673557 5823458 5975787 6125951 6277623 6431162 6581194 - 6733388 6884382 6994416] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 14:00:40,019 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:00:40,251 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:00:41,242 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:00:41,242 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:00:41,445 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:00:41,445 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:00:41,506 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:00:41,707 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:00:42,324 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:39417 -2023-05-10 14:00:42,324 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:39417 -2023-05-10 14:00:42,324 - distributed.worker - INFO - dashboard at: 10.120.104.11:44667 -2023-05-10 14:00:42,324 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:00:42,324 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,324 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:00:42,324 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:00:42,324 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-_z5vpyd7 -2023-05-10 14:00:42,325 - distributed.worker - INFO - Starting Worker plugin RMMSetup-95cd17b6-b071-443e-af40-3de905fbcd2a -2023-05-10 14:00:42,490 - distributed.worker - INFO - Starting Worker plugin PreImport-5827cbee-1e1f-4676-8f25-0404571d6e5f -2023-05-10 14:00:42,490 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-043970b5-8033-4a52-8028-f0e2b3108e4b -2023-05-10 14:00:42,492 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,504 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:00:42,504 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,506 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:00:42,707 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33977 -2023-05-10 14:00:42,708 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33977 -2023-05-10 14:00:42,708 - distributed.worker - INFO - dashboard at: 10.120.104.11:41515 -2023-05-10 14:00:42,708 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:00:42,708 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,708 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:00:42,708 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:00:42,708 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-wfl9gbzs -2023-05-10 14:00:42,709 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c168af64-50f2-4f88-b4b1-83a8f0ef32d3 -2023-05-10 14:00:42,834 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-089da187-cb28-4989-aeab-64f87911c5b9 -2023-05-10 14:00:42,834 - distributed.worker - INFO - Starting Worker plugin PreImport-65e316a9-afd3-4e52-8ef9-b5f7288431c1 -2023-05-10 14:00:42,835 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,844 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:00:42,844 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:00:42,846 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:00:50,929 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:00:50,931 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.07s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:51,055 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:00:51,056 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.19s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:51,061 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:51,061 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:00:51,066 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:00:51,066 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:00:57,254 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.59s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:57,298 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.64s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:00:57,303 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:00:57,305 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:01:12,683 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:12,683 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:13,084 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:01:13,085 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:01:13,553 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:13,555 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:16,698 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:16,698 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:17,955 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:17,955 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1344094 1344094 1344094 ... 827747 827747 827747] -destinations: [2391962 303327 128450 ... 2375369 2207027 2370325] -batch: [200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 - 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 - 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 - 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 - 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 - 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 - 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 - 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 - 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 - 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 - 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 - 398 399] -offset: [ 0 73203 137113 208729 277710 352379 420598 488107 - 556646 625680 696147 763533 835840 907528 978093 1045802 - 1116971 1190027 1258295 1330212 1395627 1463312 1533819 1602686 - 1673363 1741278 1811394 1879863 1952209 2022966 2091372 2162283 - 2231384 2301989 2372729 2444158 2509577 2576629 2646837 2714496 - 2788497 2854394 2926180 2999119 3069730 3139436 3210246 3281489 - 3354194 3422024 3492071 3563322 3629668 3699950 3770411 3841424 - 3909085 3983253 4054646 4124964 4196263 4262903 4337371 4406790 - 4476814 4546114 4614488 4681074 4749721 4818400 4887360 4959001 - 5029789 5103058 5175381 5242806 5312911 5382820 5451569 5521339 - 5590027 5663685 5733156 5802083 5873329 5948398 6017704 6090005 - 6158411 6224550 6296172 6369829 6441890 6513113 6584080 6650283 - 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162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199] -offset: [ 0 71761 137183 208387 279953 350050 420675 490839 - 560817 628013 701141 774185 843745 914524 983507 1057801 - 1127141 1197217 1266849 1333656 1406340 1475938 1544168 1610893 - 1680711 1749418 1814954 1884349 1956039 2026288 2098291 2168921 - 2239477 2311480 2383629 2450162 2522261 2594882 2665862 2735814 - 2807457 2875292 2945956 3020227 3089570 3159322 3229174 3297075 - 3365868 3435475 3505836 3577968 3651738 3722312 3789299 3858741 - 3927812 4003513 4071463 4144078 4214816 4282928 4352667 4417969 - 4484342 4554654 4628688 4697110 4763509 4834320 4906408 4975706 - 5046629 5121464 5188257 5261214 5330443 5401388 5468219 5535575 - 5606763 5676816 5749114 5818684 5891810 5964952 6036664 6107821 - 6179546 6249643 6319291 6388903 6461000 6529194 6599106 6667826 - 6742499 6808699 6882939 6952610 7022117 7093083 7159561 7229989 - 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418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 - 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 - 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 - 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 - 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 - 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 - 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 - 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 - 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 - 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 - 598 599] -offset: [ 0 68867 140686 213194 282894 356006 423057 494221 - 561948 632095 700898 772619 842262 913771 984200 1049910 - 1117344 1187443 1263203 1335333 1406334 1475928 1547110 1614105 - 1681529 1750932 1824015 1894694 1960765 2031936 2099525 2171198 - 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11213790 11283625 11349351 11413858 11484093 11550912 11622121 11692295 - 11760515 11833720 11903450 11972479 12039268 12112639 12181952 12251178 - 12318170 12388309 12465010 12534866 12606367 12672240 12742979 12814265 - 12886240 12955899 13026205 13098390 13164908 13235210 13305692 13373847 - 13445441 13516667 13588404 13655127 13729441 13793779 13868875 13937117 - 14007970] - -2023-05-10 14:01:19,205 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:19,205 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:20,458 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:20,458 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1708193 1708193 1708193 ... 500916 500975 500975] -destinations: [1608810 2370529 246800 ... 1359969 531220 2370583] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 - 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 - 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 - 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199] -offset: [ 0 68031 141564 210327 280232 350215 420525 493171 - 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6173852 6244313 6314649 6385144 6451698 6523298 6596024 6664553 - 6739474 6806987 6877333 6949961 7018197 7088301 7159643 7230255 - 7299062 7370296 7436116 7510236 7580567 7650830 7722775 7790969 - 7863849 7930789 7997683 8064178 8136012 8212377 8281320 8350962 - 8421820 8492228 8562834 8638101 8705203 8777382 8845516 8919090 - 8988262 9056460 9125740 9196895 9266821 9338888 9412385 9484960 - 9553731 9622155 9692138 9759713 9828485 9897307 9969282 10036462 - 10106145 10176485 10245488 10318563 10388947 10461657 10526439 10598859 - 10668834 10740918 10812101 10880921 10950951 11023299 11089911 11157509 - 11228394 11296350 11366656 11438380 11512669 11583965 11657796 11727330 - 11791891 11863787 11934679 12003054 12074420 12142651 12214241 12285446 - 12357346 12429059 12499110 12565535 12631989 12704291 12773503 12841481 - 12912038 12981263 13055100 13123839 13192943 13261736 13335039 13404492 - 13472761 13545397 13616627 13688579 13755711 13824503 13894221 13966900 - 14036756] - -2023-05-10 14:01:24,244 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:24,244 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:25,504 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:25,504 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1200192 1200192 1200192 ... 39436 39436 39436] -destinations: [1879569 762007 1936331 ... 235353 822930 1543956] -batch: [2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 - 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 - 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 - 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 - 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 - 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 - 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 - 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 - 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 - 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 - 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 - 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 - 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 - 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 - 2596 2597 2598 2599] -offset: [ 0 68163 142249 213134 286381 351945 420705 495787 - 565888 637579 709801 779874 847683 919553 984995 1053706 - 1124594 1196357 1268390 1334579 1402277 1475390 1546020 1621368 - 1686506 1755533 1826626 1893873 1965061 2033013 2105690 2176147 - 2248739 2320996 2394069 2465326 2530361 2600896 2670596 2736451 - 2805970 2874848 2945521 3019598 3087873 3160829 3228199 3297777 - 3367428 3438881 3514543 3580857 3654506 3724053 3794248 3865868 - 3936086 4006008 4075448 4146259 4215377 4286920 4356706 4429901 - 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12912592 12982792 13053672 13123810 13192354 13261123 13327435 13398026 - 13468375 13537240 13605049 13673571 13741856 13807989 13875663 13946707 - 14016046] - -sources: [1400387 1400391 1400391 ... 808960 808960 808960] -destinations: [1235536 1626886 1742394 ... 1499537 1018380 1186306] -batch: [2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 - 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 - 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 - 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 - 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 - 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 - 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 - 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 - 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 - 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 - 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 - 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 - 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 - 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 - 2996 2997 2998 2999] -offset: [ 0 65464 134447 204760 279074 349611 416822 488924 - 559389 632967 702316 773799 843811 912586 980515 1050366 - 1121849 1190483 1259871 1331084 1401827 1469552 1541062 1610551 - 1679897 1749742 1817652 1888896 1957642 2028104 2097033 2168898 - 2243296 2317615 2385661 2453489 2524136 2593403 2661242 2730012 - 2797261 2865271 2936749 3007902 3080009 3149922 3222964 3294003 - 3359067 3429213 3501444 3571249 3643625 3712375 3786681 3857484 - 3925942 3993670 4064628 4136944 4206321 4278463 4347333 4417034 - 4487349 4551912 4623744 4692160 4765500 4835116 4904041 4975386 - 5047381 5116690 5187520 5255347 5323965 5388089 5463834 5532605 - 5601137 5670359 5740406 5814591 5884100 5954715 6024279 6095811 - 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2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 - 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 - 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 - 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 - 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 - 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 - 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 - 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 - 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 - 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 - 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 - 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 - 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 - 2796 2797 2798 2799] -offset: [ 0 71637 141621 212853 280151 351445 421066 493633 - 565485 635507 706223 775100 846978 915926 990387 1063649 - 1134107 1201666 1269660 1339712 1410717 1480822 1555303 1624690 - 1693363 1763678 1831187 1901300 1971638 2038581 2110281 2174346 - 2246564 2313818 2385819 2458339 2526903 2598774 2667486 2738563 - 2807780 2880288 2955041 3019322 3090317 3161999 3233007 3303252 - 3371547 3439533 3508759 3580509 3650053 3723003 3790918 3859975 - 3932074 3998912 4069258 4138169 4211195 4282494 4350084 4417253 - 4487067 4559984 4627259 4695439 4765883 4835388 4907145 4979044 - 5045651 5117931 5191100 5261322 5331398 5393802 5463112 5533152 - 5602363 5673303 5739554 5812669 5885352 5953866 6021307 6090051 - 6159411 6230841 6303919 6370849 6440110 6509811 6582508 6650882 - 6718902 6789994 6865188 6934337 7008827 7079037 7147626 7213948 - 7286166 7352052 7424595 7495012 7564899 7637357 7702320 7772734 - 7842860 7910616 7978663 8048808 8119149 8187079 8256882 8329682 - 8399588 8473575 8545374 8615478 8685948 8756595 8827306 8897602 - 8964962 9036081 9103370 9172743 9243102 9309445 9378453 9452668 - 9523653 9592791 9661945 9733940 9806963 9873468 9944978 10015939 - 10088123 10156409 10227974 10295353 10364316 10439518 10510426 10577086 - 10646693 10710577 10782986 10855405 10930063 10998689 11071398 11142987 - 11214200 11281157 11350892 11423115 11493494 11563909 11631837 11699158 - 11771213 11841845 11909963 11980920 12046719 12115972 12188230 12259399 - 12332866 12403783 12470911 12542163 12616497 12684604 12754688 12828369 - 12903469 12973957 13041969 13108542 13181640 13252351 13324216 13399875 - 13470807 13538697 13609054 13676822 13748177 13817663 13884072 13955123 - 14021692] - -sources: [1279346 1279346 1279346 ... 784363 784363 784363] -destinations: [1836577 2362042 2055367 ... 2330488 1612176 1018326] -batch: [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 - 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 - 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 - 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 - 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 - 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 - 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 - 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 - 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 - 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 - 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 - 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 - 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 - 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 - 3196 3197 3198 3199] -offset: [ 0 72295 139161 208131 276375 342876 414272 484934 - 551376 621834 689607 760139 829398 900694 969602 1041790 - 1112013 1181325 1253932 1321996 1395152 1464084 1532444 1607035 - 1681687 1749681 1816080 1883998 1954467 2023973 2094908 2168037 - 2239966 2307745 2380337 2448455 2518707 2589212 2657895 2729200 - 2801795 2875459 2944386 3014788 3082027 3149696 3220296 3290279 - 3362944 3434023 3502014 3573849 3643808 3710264 3781693 3853067 - 3922269 3996004 4063315 4130985 4205752 4278004 4350618 4419043 - 4490251 4558615 4626943 4697089 4766196 4837366 4907509 4975640 - 5047798 5114774 5184840 5255732 5324569 5396494 5467088 5537353 - 5610974 5683080 5756209 5825351 5894019 5966423 6032612 6103471 - 6171129 6239232 6307014 6377576 6449499 6519598 6590725 6661991 - 6734285 6800062 6872511 6942530 7009707 7079141 7150246 7220801 - 7290303 7361837 7433670 7507661 7579162 7652485 7722695 7791865 - 7861932 7932993 7999441 8072789 8142424 8206788 8276342 8346083 - 8420033 8491431 8559463 8628573 8700392 8768523 8836900 8904620 - 8977626 9049294 9119735 9185354 9256231 9325873 9397616 9468445 - 9541182 9615945 9688143 9758820 9827093 9896881 9966341 10039461 - 10109213 10177077 10250412 10318606 10392099 10461260 10530607 10601906 - 10673856 10742348 10810103 10879908 10950481 11021850 11090311 11160722 - 11232529 11299677 11369978 11439416 11508078 11579701 11647697 11719320 - 11787927 11858031 11926620 11994136 12062653 12134806 12204864 12276587 - 12347118 12416945 12488657 12557163 12623421 12692023 12760396 12829691 - 12899361 12966445 13037051 13103702 13175702 13245329 13315854 13387633 - 13453881 13522121 13590083 13659831 13731247 13805042 13871058 13941904 - 14012054] - -2023-05-10 14:01:26,752 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:26,752 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:27,997 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:27,997 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1600080 1600080 1600080 ... 1643146 1643146 1643146] -destinations: [ 538413 1151404 2171212 ... 959167 1801391 2145791] -batch: [3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 - 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 - 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 - 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 - 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 - 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 - 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 - 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 - 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 - 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 - 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 - 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 - 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 - 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 - 3396 3397 3398 3399] -offset: [ 0 72331 138282 210801 280029 346650 415654 486417 - 555152 627145 695526 767744 839007 912005 984247 1051161 - 1121803 1194814 1268185 1338981 1406224 1476675 1548341 1613958 - 1684970 1752937 1823113 1893378 1963823 2033399 2104732 2176414 - 2242844 2311750 2382702 2456940 2526624 2596748 2666524 2729981 - 2801684 2869680 2941543 3011745 3079922 3148609 3217905 3292668 - 3360985 3431633 3503063 3572445 3641296 3710267 3778993 3851014 - 3924048 3996574 4066519 4137089 4202168 4271291 4340276 4414134 - 4487129 4555727 4624546 4696386 4767173 4838455 4903578 4974830 - 5046945 5116057 5190883 5262766 5330533 5398679 5471405 5539849 - 5608465 5680174 5746889 5818407 5893854 5966681 6038330 6106733 - 6180284 6250615 6314283 6382905 6453377 6526861 6593451 6665779 - 6731159 6801291 6873375 6943318 7012584 7084339 7151896 7225869 - 7294790 7364749 7435618 7504850 7578820 7646583 7718132 7783667 - 7855720 7925524 7992855 8066222 8136190 8208115 8275248 8345473 - 8416290 8481798 8553278 8620856 8693214 8764287 8835059 8907416 - 8976203 9044646 9110405 9184894 9254814 9327531 9394139 9464208 - 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distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:29,276 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:30,422 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:01:30,422 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:01:30,426 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:01:30,426 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:01:30,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:01:30,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:01:30,578 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:01:30,579 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:01:30,579 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33977. Reason: scheduler-restart -2023-05-10 14:01:30,580 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:39417. Reason: scheduler-restart -2023-05-10 14:01:30,581 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:01:30,581 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:01:30,582 - distributed.nanny - INFO - Worker closed -2023-05-10 14:01:30,582 - distributed.nanny - INFO - Worker closed -sources: [2000336 2000338 2000338 ... 1246236 1246236 1246236] -destinations: [ 325467 1317006 1656369 ... 1839974 2239941 800960] -batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 - 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 - 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 - 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 - 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 - 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 - 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 - 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2806276 2877716 2946198 3019299 3090366 3158929 3229442 3301843 - 3371241 3443055 3512019 3584648 3654465 3723100 3793053 3860575 - 3932015 3998569 4067571 4140670 4213498 4285492 4352349 4419169 - 4489382 4560053 4629712 4701904 4768415 4837605 4910691 4980179 - 5053643 5122447 5196228 5266097 5338173 5405956 5474076 5544045 - 5613640 5680984 5753785 5825948 5898986 5968711 6035429 6107941 - 6177657 6250927 6320773 6386201 6455631 6528060 6598782 6668867 - 6740228 6809933 6880831 6950821 7015823 7084447 7157639 7227860 - 7300117 7370094 7441739 7510798 7581985 7652150 7719297 7791383 - 7863978 7930733 8002673 8074662 8145440 8218010 8285268 8352902 - 8422271 8490258 8562250 8633382 8707116 8777700 8843623 8910140 - 8980411 9050086 9120388 9196555 9266429 9340256 9410328 9477218 - 9542722 9614858 9684935 9756860 9827745 9894094 9964133 10035618 - 10107140 10174315 10244335 10317143 10387868 10459948 10525637 10596887 - 10665358 10735744 10807522 10876643 10949339 11019687 11089816 11155290 - 11229707 11301157 11375165 11444099 11512494 11582560 11655573 11727800 - 11791585 11860992 11930212 12004242 12073818 12139186 12209925 12281411 - 12349762 12419596 12492071 12565112 12634998 12705159 12774052 12845613 - 12919167 12988792 13059827 13128551 13199945 13270302 13336839 13404358 - 13472030 13542955 13561592] - -2023-05-10 14:01:32,221 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:01:32,580 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:01:33,434 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:01:33,434 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:01:33,701 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:01:33,798 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:01:33,798 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:01:34,070 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:01:34,454 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35199 -2023-05-10 14:01:34,454 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35199 -2023-05-10 14:01:34,454 - distributed.worker - INFO - dashboard at: 10.120.104.11:43619 -2023-05-10 14:01:34,454 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:01:34,454 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:34,454 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:01:34,454 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:01:34,454 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-y1xppdqo -2023-05-10 14:01:34,455 - distributed.worker - INFO - Starting Worker plugin RMMSetup-a3ef48b6-7ad0-4076-9d46-ac9df006fdc6 -2023-05-10 14:01:34,589 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-218376e1-18a1-46fe-9c56-a57f02feb41c -2023-05-10 14:01:34,589 - distributed.worker - INFO - Starting Worker plugin PreImport-f65cdb6b-2e07-405b-b058-1ac2c638c4bb -2023-05-10 14:01:34,590 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:34,621 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:01:34,621 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:34,623 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:01:34,949 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:45061 -2023-05-10 14:01:34,950 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:45061 -2023-05-10 14:01:34,950 - distributed.worker - INFO - dashboard at: 10.120.104.11:36843 -2023-05-10 14:01:34,950 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:01:34,950 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:34,950 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:01:34,950 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:01:34,950 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-gmn5lj0i -2023-05-10 14:01:34,951 - distributed.worker - INFO - Starting Worker plugin RMMSetup-a8f1de53-5062-4fa2-9065-ab8c3f06e198 -2023-05-10 14:01:35,069 - distributed.worker - INFO - Starting Worker plugin PreImport-e6a940f4-c6da-438b-9a3a-dd289031b4da -2023-05-10 14:01:35,069 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-47df3810-4e54-40ce-b20b-74d2df1b9531 -2023-05-10 14:01:35,070 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:35,080 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:01:35,080 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:01:35,082 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:01:43,384 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:01:43,385 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.20s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:01:43,474 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:01:43,475 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.29s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:01:43,479 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:43,480 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:01:43,484 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:01:43,484 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:01:49,764 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.68s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:01:49,823 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.75s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:01:49,823 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:01:49,825 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:02:15,261 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:15,261 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:15,660 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:02:15,661 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:02:16,266 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:16,268 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:19,119 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:19,119 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:21,590 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:21,590 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:22,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:22,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:23,410 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:23,410 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:24,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:24,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 816 816 816 ... 256793 256793 256793] -destinations: [ 471433 496801 1555338 ... 207519 180681 2292768] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 - 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4492403 4633402 4777395 4917769 5057510 5195735 5333431 5477628 - 5620663 5757264 5892977 6030123 6170603 6311005 6453242 6592807 - 6729820 6871215 7011099 7151553 7287250 7421409 7563703 7701659 - 7842607 7985761 8125251 8267677 8410732 8549692 8689959 8828488 - 8971425 9109939 9244705 9384225 9525605 9669648 9817154 9955054 - 10093923 10233619 10376192 10511561 10656511 10793603 10931867 11069018 - 11207684 11343175 11483122 11624855 11764889 11904373 12046448 12186266 - 12326089 12470566 12616814 12757110 12897084 13038584 13174231 13317371 - 13459122 13600803 13737453 13879639 14022870] - -sources: [ 799503 799495 799495 ... 1019224 1019224 1019224] -destinations: [ 832935 1929235 924669 ... 577549 1277308 725159] -batch: [700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 - 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 - 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 - 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 - 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 - 790 791 792 793 794 795 796 797 798 799] -offset: [ 0 143671 288760 434483 573075 713751 854341 996075 - 1132384 1272639 1416489 1558902 1701627 1840450 1978326 2119228 - 2255517 2397496 2537604 2677906 2815390 2954591 3094932 3233591 - 3372800 3513018 3658255 3794994 3934974 4075104 4217866 4361384 - 4500930 4640108 4780552 4917035 5052847 5192175 5332478 5474939 - 5611194 5754698 5892513 6028344 6173626 6315481 6456437 6598614 - 6741738 6876825 7020833 7161158 7305184 7448910 7587603 7726037 - 7865278 8011673 8148459 8293101 8440031 8580585 8721111 8857787 - 8997280 9130364 9268758 9409723 9551656 9692228 9831580 9972105 - 10114275 10257311 10396989 10535637 10677490 10810425 10954035 11095232 - 11238393 11377676 11515807 11654407 11789671 11931958 12077414 12219134 - 12357038 12494599 12636605 12777777 12920104 13062192 13205657 13348108 - 13493758 13634894 13772472 13914583 14054053] - -sources: [ 999659 999659 999659 ... 1601451 1601451 1601451] -destinations: [2194372 1014220 2368747 ... 709880 1006027 1723782] -batch: [900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 - 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 - 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 - 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 - 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 - 990 991 992 993 994 995 996 997 998 999] -offset: [ 0 139019 274221 419277 556022 694010 837509 978919 - 1121253 1259808 1403885 1539848 1683313 1822421 1963177 2105383 - 2243554 2387846 2525560 2665299 2809203 2950355 3092293 3230682 - 3376504 3514413 3653316 3797714 3940395 4078020 4214870 4350783 - 4492755 4633333 4773850 4912778 5051660 5191608 5333352 5474494 - 5608068 5749893 5890088 6024653 6166400 6305073 6446147 6587677 - 6724641 6868048 7010330 7150234 7289018 7427446 7569847 7702832 - 7843492 7987035 8129327 8266979 8413379 8554926 8693133 8837422 - 8976443 9117355 9258803 9395695 9531666 9671907 9813504 9954529 - 10100318 10238066 10379054 10521023 10658815 10807796 10941310 11082051 - 11225568 11363321 11501686 11640673 11782128 11923553 12066618 12206337 - 12349990 12489285 12626343 12767567 12904161 13045449 13184736 13323698 - 13466398 13604552 13749221 13885596 14024962] - -2023-05-10 14:02:25,255 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:25,255 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:26,191 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:26,191 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:27,106 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:27,106 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:28,029 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:28,029 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:28,966 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:28,966 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1000291 1000291 1000291 ... 1574267 1574267 1574267] -destinations: [1324783 1782581 610794 ... 736729 1647560 1787252] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099] -offset: [ 0 141236 280134 425791 564737 710947 845973 988111 - 1129640 1265134 1405690 1547058 1678977 1820016 1965665 2102304 - 2244224 2378864 2519709 2655246 2797911 2935050 3078210 3220354 - 3356084 3499923 3634967 3775198 3918975 4055499 4199814 4337959 - 4481423 4619528 4762865 4908753 5050687 5191094 5323897 5466413 - 5601893 5743277 5885206 6025974 6169821 6307843 6448832 6585079 - 6721788 6861354 6995422 7139157 7277521 7420488 7555287 7697612 - 7837678 7978575 8122803 8263120 8403498 8546221 8686602 8826694 - 8966033 9104043 9238267 9377262 9512907 9654298 9798763 9941168 - 10081788 10220029 10357991 10494566 10636586 10778776 10920770 11062579 - 11204498 11344093 11480210 11619640 11766169 11906691 12048061 12190270 - 12330727 12469418 12605939 12738753 12882980 13027317 13166223 13304207 - 13443336 13583350 13720761 13864494 14005517] - -sources: [1200833 1200833 1200833 ... 780784 780784 780784] -destinations: [ 162405 2267318 1976535 ... 1318617 1980729 1475226] -batch: [1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 - 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 - 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 - 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 - 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 - 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 - 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 - 1298 1299] -offset: [ 0 141797 285989 420316 565377 709621 847300 984377 - 1124169 1267795 1401577 1545735 1686056 1826022 1964382 2104968 - 2248213 2393160 2530073 2670248 2805598 2944873 3087080 3226872 - 3366068 3513387 3653126 3793234 3934979 4074480 4215130 4356711 - 4501902 4644556 4784041 4922645 5062063 5202087 5341423 5481030 - 5624344 5766685 5909355 6045937 6186054 6327935 6464572 6605202 - 6741179 6877423 7023600 7159873 7301944 7440552 7578485 7722070 - 7863893 8003056 8141388 8284285 8421210 8562428 8702204 8843056 - 8985712 9130365 9273732 9409829 9551939 9692360 9832901 9973390 - 10109314 10254398 10391873 10534009 10673017 10810392 10949982 11086985 - 11231365 11366425 11511557 11652438 11795274 11932137 12067467 12204377 - 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4487283 4624294 4765940 4904454 5047736 5188039 5324136 5463605 - 5601353 5740905 5884759 6024524 6163388 6301778 6442473 6582488 - 6726067 6862910 7006649 7141384 7285398 7427106 7567161 7708507 - 7847963 7990064 8126184 8265870 8406415 8546986 8689834 8829348 - 8967842 9114864 9250808 9395155 9536008 9674837 9815225 9950722 - 10088297 10227579 10362947 10505309 10647728 10792287 10929458 11065381 - 11210009 11348287 11490162 11628788 11771802 11914276 12050408 12183710 - 12327823 12472432 12611139 12750827 12885148 13029186 13170128 13310293 - 13450740 13592459 13735715 13871821 14016680] - -sources: [1600560 1600561 1600563 ... 2351640 2351640 2351640] -destinations: [ 882729 1028167 1094620 ... 944912 1406628 1476661] -batch: [1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 - 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 - 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 - 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 - 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 - 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 - 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 - 1698 1699] -offset: [ 0 138101 279872 415485 555327 695927 839342 984858 - 1122198 1268739 1407006 1549418 1685936 1824043 1964895 2105742 - 2244050 2383692 2527599 2667665 2802806 2942384 3081088 3219291 - 3362469 3504589 3643048 3780012 3924690 4066992 4202433 4340645 - 4487183 4624748 4767019 4903212 5047025 5190767 5330947 5471860 - 5608780 5747544 5894463 6038671 6180309 6314658 6453677 6593720 - 6730769 6872499 7011843 7151132 7293700 7434183 7577366 7716546 - 7854396 7991490 8135059 8273904 8414742 8551318 8691367 8832931 - 8974046 9107926 9252591 9391831 9532528 9674991 9813796 9950441 - 10091844 10228749 10367243 10511273 10655727 10795087 10941277 11078395 - 11218511 11358587 11500328 11642218 11779974 11925692 12062160 12199156 - 12337571 12479609 12620503 12754737 12892400 13030878 13175992 13321851 - 13461122 13604517 13737951 13877170 14019561] - -sources: [1800402 1800402 1800402 ... 245484 245484 245484] -destinations: [1584924 275181 296839 ... 1187827 2367078 216567] -batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 - 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 - 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 - 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 - 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 - 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 - 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 - 1898 1899] -offset: [ 0 140010 279564 422818 560187 697979 837902 973952 - 1119480 1265425 1409494 1548155 1682789 1821960 1960503 2102882 - 2248539 2387501 2526505 2662627 2804303 2943769 3088566 3234988 - 3370131 3514414 3649699 3789306 3929384 4066904 4206375 4350706 - 4493453 4632244 4767623 4905301 5046576 5188201 5327094 5464779 - 5612543 5747378 5886478 6028511 6165005 6302210 6441093 6581068 - 6717786 6858018 7002018 7142160 7280982 7425826 7563931 7703638 - 7845021 7979495 8119999 8261202 8400544 8537116 8677708 8819935 - 8965150 9106592 9250726 9386491 9528887 9662682 9802943 9950204 - 10086390 10225769 10363036 10506242 10640584 10782018 10920710 11059529 - 11200580 11336819 11482640 11619338 11763751 11904002 12045521 12187768 - 12329987 12466618 12609283 12747238 12885276 13023667 13164698 13306906 - 13444031 13585354 13728308 13863449 14005770] - -sources: [1199802 1199802 1199802 ... 1101944 1101944 1101944] -destinations: [ 809552 964790 93960 ... 1261223 182494 1648833] -batch: [1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 - 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 - 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 - 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 - 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 - 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 - 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 - 1198 1199] -offset: [ 0 140050 276962 416199 562637 702863 842441 982885 - 1123600 1257373 1397065 1539633 1682561 1824709 1958229 2102808 - 2248870 2388726 2527343 2668925 2811896 2951778 3090012 3229981 - 3370929 3511176 3652492 3793677 3936143 4074476 4212110 4354109 - 4496823 4637091 4773162 4915890 5053096 5192819 5331222 5473654 - 5612088 5751173 5888323 6028467 6171389 6312665 6449461 6593271 - 6736703 6874287 7014880 7156350 7296052 7433115 7577453 7719994 - 7860933 7994798 8132942 8278473 8419033 8560719 8703248 8844091 - 8986607 9124003 9265212 9410889 9552210 9690262 9826885 9967653 - 10104294 10243385 10386980 10524473 10666695 10810118 10949300 11088301 - 11226992 11365170 11510939 11656511 11791009 11933554 12072648 12212734 - 12355879 12497878 12630896 12772030 12910639 13053428 13191248 13333040 - 13470710 13614389 13753781 13892018 14034245] - -sources: [807563 683810 683810 ... 114242 114242 114242] -destinations: [ 610139 1501183 1428612 ... 352866 497969 360363] -batch: [1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 - 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 - 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 - 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 - 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 - 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 - 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 - 1398 1399] -offset: [ 0 141396 279952 421578 565286 705683 846226 989492 - 1132622 1268106 1409186 1554165 1692870 1830842 1971168 2109559 - 2245764 2384736 2525400 2665826 2805724 2952768 3087630 3230515 - 3368792 3506054 3647069 3788071 3929467 4067021 4208778 4347953 - 4485470 4625523 4764035 4905459 5043910 5188871 5329062 5460748 - 5600217 5738224 5883914 6019163 6157620 6302177 6438312 6581372 - 6717599 6864202 7007732 7146336 7285120 7423096 7563294 7700472 - 7841065 7977274 8118395 8256172 8398799 8544356 8685032 8826441 - 8963830 9102119 9241757 9376858 9522589 9661367 9806935 9945099 - 10088195 10227852 10364466 10510331 10646313 10782562 10930055 11071874 - 11214574 11351239 11493867 11632334 11772150 11910647 12047608 12189849 - 12334956 12472928 12618400 12756311 12905271 13043385 13183168 13325655 - 13472552 13610708 13749819 13885775 14023145] - -sources: [844946 844946 844946 ... 472613 472613 472613] -destinations: [1619906 1481584 706863 ... 148466 284527 1370221] -batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 - 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 - 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 - 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 - 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 - 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 - 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 - 1598 1599] -offset: [ 0 138786 276015 414059 551244 690051 829803 969656 - 1112552 1254042 1395079 1532323 1681412 1815808 1954300 2094901 - 2240042 2381138 2519245 2658397 2801787 2944698 3081950 3220500 - 3363152 3502480 3643603 3781655 3922411 4062886 4204960 4349930 - 4489283 4625884 4765245 4906540 5046621 5183959 5323506 5465858 - 5609858 5754851 5892616 6031084 6169596 6305658 6448286 6590230 - 6733662 6871525 7008899 7149225 7289437 7432577 7577646 7721198 - 7860395 7997909 8141217 8275329 8418617 8558006 8698901 8834996 - 8975520 9117916 9254211 9395453 9539164 9686011 9824951 9964398 - 10107358 10248992 10390771 10529486 10672201 10807968 10948267 11087915 - 11229810 11367287 11505241 11644793 11784698 11923380 12059855 12202042 - 12344159 12485680 12620381 12757724 12896958 13034412 13172952 13312751 - 13450914 13587224 13727958 13867582 14008606] - -sources: [1291400 630754 630754 ... 1701229 1701229 1701229] -destinations: [1928980 959294 1581775 ... 223745 979303 821395] -batch: [1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 - 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 - 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 - 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 - 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 - 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 - 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 - 1798 1799] -offset: [ 0 138836 274720 411589 552500 694256 831057 975164 - 1111707 1252544 1392886 1533092 1676356 1814086 1956283 2095739 - 2235053 2377816 2524422 2662951 2801323 2942997 3081447 3219978 - 3358186 3497154 3642321 3789166 3933851 4076910 4216618 4357824 - 4495411 4640282 4780639 4920015 5056716 5199153 5339001 5476611 - 5613717 5753396 5891322 6028008 6170544 6316324 6456334 6592488 - 6739210 6877397 7016682 7155388 7295798 7437077 7578883 7718973 - 7862675 7998683 8138404 8284539 8424862 8568588 8707464 8850185 - 8989533 9130418 9271090 9413247 9556383 9695410 9832362 9972159 - 10116635 10255840 10397939 10543009 10682616 10821871 10957669 11093191 - 11229872 11367848 11508289 11645338 11787761 11923868 12063456 12201407 - 12342403 12484854 12622722 12767706 12910141 13047950 13182383 13325349 - 13462598 13602257 13743636 13883290 14020370] - -sources: [2165841 2165841 2165841 ... 760760 760760 760760] -destinations: [ 532326 1991568 354639 ... 2252653 494063 2002245] -batch: [1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 - 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 - 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 - 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 - 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 - 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 - 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 - 1998 1999] -offset: [ 0 136559 276786 416526 561552 699931 843141 979976 - 1122542 1264701 1405173 1550553 1690218 1828673 1965696 2103674 - 2241728 2384686 2522630 2665692 2805388 2944414 3089092 3233028 - 3374019 3517757 3656887 3802450 3937897 4074234 4215369 4355320 - 4499087 4637828 4778590 4916482 5059000 5197398 5336796 5480764 - 5621516 5760897 5900880 6040035 6175152 6318467 6458253 6595594 - 6734796 6877475 7022191 7158740 7299418 7435131 7572298 7712783 - 7850861 7993122 8129404 8272384 8410161 8548725 8693099 8831109 - 8970499 9111011 9258315 9394665 9530241 9672886 9812094 9953564 - 10093338 10234872 10369944 10510156 10646750 10788495 10927752 11069560 - 11211386 11354165 11492867 11631758 11772862 11910578 12052080 12190084 - 12330394 12468716 12607897 12750327 12890228 13027480 13167987 13307274 - 13449965 13590340 13733129 13872877 14016827] - -2023-05-10 14:02:29,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:29,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:30,744 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:02:30,744 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:02:30,748 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:02:30,748 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:02:30,780 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:02:30,782 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 14:02:30,893 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:02:30,894 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 14:02:30,894 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:35199. Reason: scheduler-restart -2023-05-10 14:02:30,895 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:45061. Reason: scheduler-restart -2023-05-10 14:02:30,895 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:02:30,896 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 14:02:30,897 - distributed.nanny - INFO - Worker closed -2023-05-10 14:02:30,897 - distributed.nanny - INFO - Worker closed -sources: [2000994 2000996 2000997 ... 1841254 1841254 1841254] -destinations: [1098820 2167314 1686724 ... 817400 2069448 701895] -batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 - 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 - 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 - 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 - 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 - 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 - 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 - 2098 2099] -offset: [ 0 138226 276099 414517 549261 694949 836327 971413 - 1113285 1247844 1388872 1526290 1669523 1810836 1950764 2095368 - 2233010 2373625 2514571 2657827 2798689 2937787 3084302 3227359 - 3367818 3507517 3647651 3785719 3920004 4063175 4202236 4342754 - 4483312 4621102 4760424 4900917 5045336 5188815 5328380 5468467 - 5609906 5747732 5887444 6031762 6173668 6316355 6453137 6589321 - 6731125 6872639 7017783 7156930 7300314 7436011 7574820 7712289 - 7847818 7985805 8128821 8268356 8405741 8541823 8684349 8826047 - 8964208 9102784 9243631 9383080 9524635 9659660 9801856 9939563 - 10078025 10214225 10355506 10493440 10631663 10771320 10915525 11058911 - 11199077 11341484 11486711 11624613 11762103 11902155 12044334 12182037 - 12321665 12461936 12598287 12739586 12882927 13025641 13169905 13307339 - 13451148 13589053 13726499 13864764 14006991] - -sources: [2200482 2200482 2200482 ... 1042095 1042095 1042095] -destinations: [ 342021 1090788 2253206 ... 2377259 1008611 2299982] -batch: [2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 - 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 - 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 - 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 - 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 - 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 - 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 - 2298 2299] -offset: [ 0 136422 285170 422026 568060 701265 840886 977956 - 1117514 1262239 1401418 1539627 1680159 1820990 1960106 2102722 - 2240682 2377721 2519699 2662050 2797847 2934638 3076449 3212855 - 3361211 3502990 3639243 3777968 3919465 4058510 4204668 4346301 - 4482897 4622950 4763702 4904772 5048649 5187711 5330480 5464527 - 5606335 5749371 5891126 6032452 6165319 6303582 6443844 6585204 - 6725168 6860348 6998120 7139578 7280519 7416266 7560633 7698618 - 7839760 7985470 8128384 8271780 8408340 8542809 8680591 8819817 - 8958894 9100032 9242771 9384649 9530211 9667530 9807053 9946129 - 10086433 10228661 10365342 10499676 10639662 10777356 10921092 11059557 - 11202704 11338313 11479348 11616659 11760191 11901153 12044518 12185287 - 12324656 12464254 12600371 12735011 12878859 13018903 13155893 13297692 - 13440427 13577946 13717291 13857137 14002657] - -sources: [1031398 1031398 1031398 ... 2029054 2029054 2029054] -destinations: [1551019 1376826 509250 ... 1441204 1080029 2070589] -batch: [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 - 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 - 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 - 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 - 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 - 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 - 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 - 2198 2199] -offset: [ 0 142358 283666 423877 563410 702278 841997 977930 - 1119732 1259762 1396747 1534510 1679616 1819801 1961837 2102704 - 2249018 2387368 2526250 2670488 2811491 2951517 3095930 3235404 - 3374214 3512905 3652922 3796208 3933485 4075379 4213097 4351547 - 4488702 4631456 4774793 4912142 5053160 5192889 5330691 5467246 - 5610326 5748227 5886900 6027209 6173183 6315077 6452249 6598491 - 6737747 6873887 7014245 7159797 7302686 7445079 7580949 7720155 - 7855661 8000213 8142186 8282068 8418939 8563268 8703334 8842894 - 8978971 9124683 9261721 9400116 9539179 9675964 9816809 9957077 - 10096541 10235373 10374366 10518954 10664425 10804726 10944105 11082745 - 11225083 11365464 11508070 11647777 11788593 11927053 12067739 12207069 - 12356137 12493961 12634489 12771322 12909865 13048745 13187594 13333844 - 13473742 13616568 13760925 13898065 14039414] - -sources: [1292351 1292351 1292351 ... 892120 892120 892120] -destinations: [2339574 757471 7294 ... 1785611 1618868 2007050] -batch: [2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 - 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 - 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 - 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 - 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 - 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 - 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396] -offset: [ 0 138514 279133 417007 552658 690675 831613 979356 - 1122163 1264131 1403376 1544806 1680257 1812387 1956932 2098955 - 2246324 2388157 2526667 2665839 2804828 2944363 3088544 3227381 - 3368778 3509505 3652293 3790711 3929320 4065157 4211006 4349717 - 4487007 4627559 4765961 4908113 5051206 5193840 5335282 5470926 - 5610234 5750502 5895391 6031628 6174062 6316621 6451550 6594532 - 6736330 6876718 7011194 7153022 7295843 7437355 7577975 7715143 - 7859667 7998310 8140836 8280726 8418057 8557620 8702492 8839230 - 8976097 9115925 9261950 9405880 9538391 9680810 9824036 9960091 - 10103353 10241108 10384447 10521848 10661256 10803683 10946101 11086221 - 11226573 11372118 11509726 11652842 11788889 11927463 12071192 12207057 - 12346734 12489050 12631973 12771718 12916943 13057770 13197621 13334416 - 13469700 13559504] - -2023-05-10 14:02:32,628 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:02:32,894 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:02:33,936 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:02:33,936 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:02:34,093 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:02:34,094 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:02:34,217 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:02:34,354 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:02:35,178 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35397 -2023-05-10 14:02:35,179 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35397 -2023-05-10 14:02:35,179 - distributed.worker - INFO - dashboard at: 10.120.104.11:37445 -2023-05-10 14:02:35,179 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:02:35,179 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,179 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:02:35,179 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:02:35,179 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-0qlx3em1 -2023-05-10 14:02:35,179 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c37209f2-33c5-41a7-aaf2-4c4a044620ed -2023-05-10 14:02:35,315 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6cf400ad-25b4-4c7a-9f2a-59dcec7896a4 -2023-05-10 14:02:35,316 - distributed.worker - INFO - Starting Worker plugin PreImport-92c8aaaa-6a06-4252-bc46-ac0a8dd417a4 -2023-05-10 14:02:35,317 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,326 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:02:35,326 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,328 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:02:35,412 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34415 -2023-05-10 14:02:35,412 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34415 -2023-05-10 14:02:35,412 - distributed.worker - INFO - dashboard at: 10.120.104.11:41157 -2023-05-10 14:02:35,412 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:02:35,412 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,412 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:02:35,412 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:02:35,412 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-msel_0xm -2023-05-10 14:02:35,413 - distributed.worker - INFO - Starting Worker plugin RMMSetup-8b35897c-93ab-4c92-85aa-e5e99b07accb -2023-05-10 14:02:35,534 - distributed.worker - INFO - Starting Worker plugin PreImport-7950f4a6-04eb-4516-ad66-cdf7bfa60ab1 -2023-05-10 14:02:35,535 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-c127e35e-075a-4309-975f-bbf71c8cc098 -2023-05-10 14:02:35,535 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,543 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:02:35,543 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:02:35,545 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:02:43,752 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:02:43,753 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.05s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:02:43,795 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 14:02:43,796 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.09s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:02:43,801 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:43,802 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:02:43,806 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:02:43,806 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 14:02:49,948 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.55s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:02:50,098 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.70s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 14:02:50,101 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:02:50,104 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 14:03:17,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:17,482 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:18,744 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:03:18,745 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:03:19,930 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:19,931 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:23,451 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:23,451 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:25,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:25,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 433 435 435 ... 7743553 7743553 7743553] -destinations: [6820643 8614304 3882419 ... 1312525 1389202 1284630] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199] -offset: [ 0 72219 134837 201907 268178 335428 398527 464528 - 535445 604440 671096 742361 811770 879514 952791 1019326 - 1087219 1154891 1221925 1287904 1354404 1421763 1491306 1556898 - 1622049 1690602 1758834 1828785 1897670 1965590 2033993 2104741 - 2174001 2240464 2308927 2379534 2448366 2517058 2584030 2649166 - 2722723 2789699 2861353 2926117 2993223 3060304 3127724 3193380 - 3262953 3331912 3397673 3464325 3531483 3602608 3668936 3738683 - 3805412 3875432 3942611 4008004 4073160 4141024 4211282 4274751 - 4343745 4415718 4482816 4552150 4619760 4687339 4757462 4825651 - 4893471 4961109 5032831 5097358 5164838 5234124 5305240 5373521 - 5442065 5511916 5577859 5649214 5714842 5785240 5854653 5921723 - 5989095 6060517 6130882 6202759 6270522 6340644 6408550 6476971 - 6545754 6613840 6680237 6747630 6818165 6885707 6957353 7025576 - 7097293 7165794 7235811 7303224 7367745 7431577 7501118 7570492 - 7638311 7702561 7771883 7841317 7914639 7985098 8055203 8124266 - 8195627 8263082 8330684 8397467 8462715 8533826 8603743 8671943 - 8738119 8810523 8880238 8947226 9019433 9087016 9158548 9223812 - 9291639 9358441 9425439 9495171 9566687 9638603 9705436 9772285 - 9842924 9916197 9985650 10052803 10122972 10192615 10262747 10332948 - 10404160 10470965 10540130 10606499 10671725 10738912 10806738 10877384 - 10946323 11013728 11083578 11151633 11217597 11283937 11354256 11427320 - 11497116 11564428 11632373 11701257 11771186 11837841 11903811 11974028 - 12040122 12111813 12177764 12249165 12318339 12385386 12451127 12520679 - 12589485 12660529 12729368 12796345 12865351 12937123 13003570 13068558 - 13136496 13205928 13274257 13338193 13408229 13476413 13541433 13608403 - 13682920] - -sources: [ 200000 200000 200000 ... 3379430 3379430 3379430] -destinations: [2687444 6811543 4477735 ... 6605653 3902242 2296256] -batch: [400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 - 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 - 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 - 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 - 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 - 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 - 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 - 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 - 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 - 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 - 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 - 598 599] -offset: [ 0 64921 131428 200259 266955 341447 411608 480322 - 547650 613493 676573 747607 818222 886261 952352 1023252 - 1092955 1160429 1227039 1298367 1364896 1434131 1503798 1571716 - 1643339 1714048 1783249 1854551 1921428 1995441 2065704 2129408 - 2196428 2266015 2332955 2398563 2469363 2537930 2603219 2671853 - 2736272 2807301 2878091 2943331 3009506 3078960 3145236 3216167 - 3286527 3354485 3424800 3491085 3555754 3625743 3693847 3763453 - 3836742 3903500 3973475 4040608 4110212 4179371 4245451 4314200 - 4384348 4450735 4516950 4586437 4654253 4720101 4787432 4856741 - 4921873 4992640 5062760 5131075 5203817 5268291 5337860 5401887 - 5472050 5537188 5609816 5679122 5752176 5818748 5889552 5960885 - 6028580 6101578 6165517 6231666 6298958 6366375 6435406 6504241 - 6574048 6642519 6712174 6782438 6852794 6921593 6990291 7054545 - 7126192 7194742 7266391 7335832 7403037 7471712 7536436 7604561 - 7672149 7742682 7809645 7878485 7949407 8016144 8084148 8147964 - 8212487 8279434 8348727 8418570 8485945 8554324 8625226 8693846 - 8758621 8828284 8897141 8965197 9031982 9101763 9172714 9242126 - 9310569 9376624 9446738 9513254 9578432 9648308 9720419 9783565 - 9853368 9918034 9985741 10053167 10122353 10189336 10260263 10330251 - 10396777 10464873 10530774 10598094 10667241 10730299 10799428 10869687 - 10940241 11010877 11078212 11148679 11216160 11285037 11347885 11417570 - 11484576 11553527 11616981 11686200 11753597 11818686 11885710 11954477 - 12023282 12089101 12159360 12226789 12298000 12370904 12438531 12506665 - 12577736 12646507 12716430 12779329 12847659 12914530 12985870 13053123 - 13122419 13191912 13259334 13327988 13395263 13465051 13530823 13599282 - 13664819] - -sources: [1487546 1487546 1487546 ... 1690854 1690854 1690854] -destinations: [ 909168 8670440 5066230 ... 3844431 1657179 1011540] -batch: [200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 - 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 - 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 - 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 - 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 - 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 - 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 - 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 - 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 - 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 - 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 - 398 399] -offset: [ 0 70199 140645 206953 272088 341423 407866 476143 - 542177 610441 681667 752805 818416 885615 950278 1017409 - 1086450 1158159 1223810 1290904 1359652 1428388 1496416 1564340 - 1634328 1705711 1776708 1845915 1912819 1981460 2045083 2113273 - 2182124 2248333 2317500 2384140 2450571 2521287 2586919 2651053 - 2718913 2788378 2858366 2928780 2996583 3065088 3130194 3200279 - 3268406 3336028 3403447 3474812 3548427 3621298 3686878 3755079 - 3823854 3890372 3955140 4023802 4096325 4166419 4232044 4300786 - 4372273 4439944 4505614 4573413 4641184 4710772 4777346 4846267 - 4912888 4981659 5048926 5115068 5185975 5256638 5324439 5394370 - 5460268 5530337 5597128 5666325 5737966 5806069 5873428 5941297 - 6012134 6082850 6146506 6215861 6287148 6356619 6425036 6492022 - 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618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 - 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 - 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 - 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 - 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 - 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 - 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 - 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 - 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 - 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 - 798 799] -offset: [ 0 65282 132522 197703 267139 336352 404801 476886 - 542828 608877 681775 750289 819465 886830 958140 1028544 - 1098945 1168161 1236644 1306922 1372716 1440229 1502037 1569416 - 1637372 1706590 1776504 1844232 1909063 1978949 2047221 2116483 - 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818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 - 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 - 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 - 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 - 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 - 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 - 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 - 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 - 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 - 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 - 998 999] -offset: [ 0 68539 134981 208912 279513 350494 419925 486995 - 556982 623591 692256 757649 826031 892906 955894 1021223 - 1091421 1158957 1227585 1298840 1367895 1438073 1506043 1574044 - 1643394 1713336 1782192 1848389 1916916 1981380 2047061 2118614 - 2184710 2252487 2322615 2394368 2465430 2532557 2599899 2666327 - 2732808 2802768 2869584 2939288 3005838 3074694 3141718 3211325 - 3283909 3352270 3419883 3488561 3556821 3625841 3697609 3764223 - 3831684 3900294 3963852 4029645 4095457 4163861 4232180 4301583 - 4372635 4438075 4506291 4575149 4644602 4712039 4782742 4849144 - 4916733 4984429 5055233 5127234 5201017 5270299 5340149 5408870 - 5479066 5547767 5617122 5682845 5751505 5819745 5888497 5954547 - 6023407 6091197 6157937 6225741 6295884 6366233 6434622 6504296 - 6569715 6639016 6701709 6769602 6840716 6910560 6977056 7047902 - 7118667 7188411 7255668 7327623 7393106 7461531 7527619 7598913 - 7665206 7736241 7807411 7875303 7943196 8010813 8075623 8143215 - 8208668 8275909 8344823 8413478 8483880 8549649 8619723 8689785 - 8756068 8825262 8890674 8959594 9026380 9097801 9165764 9232515 - 9300829 9370967 9434161 9503802 9571463 9636666 9706745 9773132 - 9842088 9908033 9979998 10046439 10115070 10179166 10248999 10316773 - 10387236 10454204 10525490 10596456 10661268 10731174 10796874 10867216 - 10933927 11001884 11067082 11134387 11203666 11269493 11339034 11403257 - 11472666 11541323 11610751 11675270 11743581 11813724 11883162 11948148 - 12020245 12088468 12158020 12225888 12296089 12361885 12433562 12502416 - 12573131 12637452 12707234 12774863 12845001 12911862 12981577 13052097 - 13120418 13188238 13256279 13323790 13387986 13457123 13530657 13601015 - 13670002] - -sources: [ 600245 600245 600245 ... 3950997 3950997 3950997] -destinations: [1259392 2090727 7030137 ... 4399112 2197120 6574128] -batch: [1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 - 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 - 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 - 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 - 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 - 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 - 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 - 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 - 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 - 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 - 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 - 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 - 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 - 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 - 1396 1397 1398 1399] -offset: [ 0 67773 137789 208862 275247 342314 411575 478703 - 543730 615008 682410 749197 815765 885915 949607 1013802 - 1086182 1155771 1227721 1299249 1368157 1440217 1506207 1572948 - 1642736 1710873 1780769 1847355 1911404 1981164 2050909 2118941 - 2187087 2253208 2321937 2390123 2456143 2525209 2593493 2664038 - 2730034 2799257 2869967 2941446 3006513 3071134 3136877 3204925 - 3273093 3341256 3407942 3473394 3541323 3611287 3680951 3746076 - 3814558 3878694 3947546 4011632 4081279 4147067 4217857 4289953 - 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1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199] -offset: [ 0 67059 136284 203669 268940 334215 401835 473080 - 540297 609090 678155 747899 816779 884438 952128 1024386 - 1093734 1160963 1227591 1297305 1367464 1433278 1499101 1566805 - 1636904 1705733 1772262 1840178 1906432 1974928 2043988 2113557 - 2179854 2247970 2315033 2380658 2449663 2516704 2586684 2656968 - 2727886 2796543 2862992 2931670 2999479 3070110 3135195 3200524 - 3269002 3342012 3413226 3481354 3551839 3618157 3686244 3754700 - 3822576 3892968 3964849 4030378 4099249 4170208 4240605 4309975 - 4375134 4442928 4511822 4579898 4645589 4713726 4782005 4848356 - 4917075 4980814 5049469 5113481 5181567 5249107 5316771 5384948 - 5453508 5524034 5592377 5663112 5732486 5800214 5868445 5938987 - 6005201 6073063 6142346 6210471 6281759 6350555 6419426 6487430 - 6558140 6626648 6692448 6759471 6829519 6895537 6963144 7028746 - 7099446 7166767 7236058 7306476 7371978 7442910 7511002 7576610 - 7643726 7713892 7779693 7851017 7923144 7987336 8053786 8118585 - 8187878 8255436 8323747 8392545 8462222 8533482 8602225 8666674 - 8735465 8804889 8875588 8940769 9006513 9078914 9142303 9209853 - 9277612 9346547 9412981 9481494 9547800 9618338 9691359 9759421 - 9825840 9894207 9964295 10033116 10103253 10174346 10243277 10311486 - 10381957 10445877 10513083 10582878 10651719 10718113 10786324 10859005 - 10928012 10995838 11064026 11133158 11204706 11273791 11339954 11409775 - 11479385 11547911 11615246 11684855 11757410 11823114 11890332 11962764 - 12031119 12100686 12165386 12233176 12300333 12368954 12435979 12506022 - 12578405 12648531 12716124 12785382 12851329 12918793 12989187 13062376 - 13130351 13197458 13265860 13337075 13407965 13474871 13543373 13609916 - 13679677] - -sources: [8418895 8418895 8418895 ... 2591798 2591798 2591798] -destinations: [1778187 181859 4057762 ... 5300422 3299208 4579931] -batch: [1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 - 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 - 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 - 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 - 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 - 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 - 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 - 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 - 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 - 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 - 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 - 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 - 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 - 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 - 1596 1597 1598 1599] -offset: [ 0 71437 139679 205010 273673 345607 415366 483997 - 549868 621027 685668 753028 819970 889815 959115 1030246 - 1100405 1168273 1236348 1301630 1370240 1436964 1506052 1576402 - 1646318 1713779 1782851 1848133 1914499 1982759 2051237 2118230 - 2190436 2258911 2327872 2394519 2459662 2530201 2596369 2662786 - 2730954 2800861 2872645 2942434 3011831 3082491 3148152 3215504 - 3282608 3350416 3414962 3485191 3553891 3621520 3690122 3757861 - 3827437 3896920 3963588 4032902 4099900 4168632 4240653 4305598 - 4371796 4440035 4511038 4578879 4646757 4714656 4781074 4846798 - 4914987 4984698 5053914 5121009 5192581 5259413 5328767 5395604 - 5464095 5529819 5600317 5671824 5739909 5807802 5879272 5948075 - 6014168 6082134 6151194 6222999 6288024 6356945 6429171 6499072 - 6566740 6635335 6701631 6770840 6835654 6899719 6966934 7036507 - 7104926 7175848 7244265 7312709 7380855 7450220 7517384 7588502 - 7658657 7726107 7794499 7856745 7926499 7995991 8060733 8128146 - 8197738 8263707 8336128 8407442 8475491 8543730 8616033 8687043 - 8753537 8821472 8890416 8962365 9032891 9102552 9172361 9239432 - 9305427 9373162 9441100 9509932 9574466 9643670 9712715 9779885 - 9851052 9919958 9987667 10056178 10124536 10194724 10261290 10327845 - 10397875 10467359 10536710 10603738 10671655 10740619 10808409 10880778 - 10950234 11019869 11084827 11153271 11223319 11291232 11361023 11427482 - 11495912 11564846 11634409 11702838 11772598 11835057 11903312 11970038 - 12039535 12110565 12179429 12247135 12313675 12380735 12448133 12518742 - 12584942 12652016 12718816 12786340 12855193 12924315 12992451 13062255 - 13131658 13202640 13266801 13339677 13409190 13476995 13543227 13610033 - 13674361] - -2023-05-10 14:03:29,801 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:29,801 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:31,396 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:31,396 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 800480 800480 800480 ... 3438570 3438570 3438570] -destinations: [7805304 6178381 8537229 ... 4893953 5034228 1958423] -batch: [1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 - 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 - 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 - 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 - 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 - 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 - 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 - 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 - 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 - 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 - 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 - 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 - 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 - 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 - 1796 1797 1798 1799] -offset: [ 0 68273 131083 201360 270700 337090 406789 474406 - 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2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 - 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 - 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 - 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 - 2396 2397 2398 2399] -offset: [ 0 68886 136879 202481 272641 341616 411562 477346 - 543650 611480 679277 750149 821440 891134 956104 1024769 - 1093601 1162524 1227325 1293026 1361328 1431419 1499087 1566618 - 1636563 1705099 1772124 1838073 1911626 1978386 2045470 2114561 - 2183615 2250266 2319686 2382075 2451806 2521849 2591546 2659331 - 2721738 2788690 2858748 2928240 2995639 3064293 3132767 3200248 - 3268395 3334357 3404454 3476181 3549450 3618977 3691420 3757550 - 3825047 3893285 3959902 4026366 4096211 4165277 4232813 4305743 - 4372712 4440254 4508391 4575933 4648296 4715024 4779616 4845393 - 4912837 4981810 5047825 5112527 5184003 5249504 5318282 5389032 - 5454815 5525709 5592603 5664240 5729622 5799613 5867577 5936122 - 6005458 6071638 6139830 6208555 6278691 6348298 6414154 6483018 - 6550522 6619547 6688981 6756625 6825121 6892451 6956735 7022991 - 7093759 7164289 7231007 7302269 7368652 7437472 7506813 7570911 - 7634648 7702811 7774545 7844336 7909002 7977853 8045075 8110608 - 8177744 8246631 8313995 8385323 8453472 8522257 8591249 8658223 - 8727167 8794579 8863514 8938064 9005614 9071906 9138298 9206079 - 9271916 9339274 9408244 9477402 9548342 9620138 9686423 9753645 - 9821116 9890199 9953025 10022389 10089411 10159778 10224403 10289320 - 10355612 10426118 10494039 10559336 10625732 10694853 10762020 10827494 - 10895956 10968472 11035763 11103679 11169208 11239636 11309346 11373464 - 11440473 11508907 11577148 11645006 11713850 11783039 11857632 11926426 - 11992328 12062123 12129091 12195920 12260220 12324542 12393008 12460701 - 12527302 12596946 12665877 12738451 12810107 12878220 12945070 13014262 - 13082182 13151033 13221625 13292098 13361316 13429585 13492978 13561784 - 13631518] - -2023-05-10 14:03:32,992 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:32,992 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:34,594 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:34,594 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1399762 1399762 1399762 ... 771812 6033767 6033767] -destinations: [1835781 4961437 8764262 ... 8274 2019407 6873011] -batch: [2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 - 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 - 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 - 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 - 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 - 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 - 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 - 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 - 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 - 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 - 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 - 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 - 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 - 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 - 2796 2797 2798 2799] -offset: [ 0 65378 137860 206886 276737 344093 409479 480025 - 546461 615719 684981 753518 817200 881935 948148 1014445 - 1081273 1148981 1214161 1281112 1347714 1416700 1488239 1561466 - 1627468 1690230 1760646 1827678 1894936 1963989 2030601 2098215 - 2166280 2235002 2300035 2368300 2437396 2504252 2572671 2643188 - 2717138 2786742 2855104 2926603 2990993 3058381 3128739 3200511 - 3265757 3331896 3396851 3469328 3535608 3604911 3674078 3742854 - 3811863 3879736 3949791 4019757 4091764 4159998 4229051 4297313 - 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2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 - 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 - 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 - 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 - 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 - 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 - 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 - 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 - 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 - 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 - 2996 2997 2998 2999] -offset: [ 0 67832 134749 203452 272776 342997 414513 487993 - 552346 622470 688634 758805 823723 892446 961813 1034643 - 1104301 1171145 1238741 1303748 1373398 1440956 1511677 1576944 - 1644218 1713169 1781817 1851725 1921461 1989656 2061000 2129774 - 2196759 2264685 2332379 2400668 2467965 2537691 2605897 2675755 - 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10916242 10986111 11055295 11125389 11192838 11258061 11325509 11392055 - 11459047 11525195 11591537 11661049 11727970 11794980 11863455 11932212 - 11998726 12069304 12140664 12212517 12280741 12344984 12414175 12481551 - 12550549 12622795 12688684 12758503 12829019 12898529 12970511 13038600 - 13102089 13170954 13241746 13312556 13378703 13443150 13509165 13577506 - 13646257] - -2023-05-10 14:03:36,198 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:36,198 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:37,827 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:37,831 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1600163 1600163 1600163 ... 2256963 2256963 2256963] -destinations: [6737662 4533435 7228023 ... 1817935 7085866 4834554] -batch: [3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 - 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 - 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 - 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 - 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 - 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 - 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 - 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 - 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 - 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 - 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 - 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 - 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 - 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 - 3396 3397 3398 3399] -offset: [ 0 66761 133025 203877 269019 340248 405973 470588 - 538871 606625 675540 742480 812585 882753 948273 1018549 - 1085199 1155925 1223016 1291050 1357194 1428055 1497440 1564836 - 1633915 1701069 1767431 1835219 1901696 1971689 2041851 2112741 - 2180735 2247063 2316248 2384316 2456245 2523168 2588594 2656483 - 2727971 2795362 2859410 2929983 2993300 3061488 3125732 3195813 - 3266939 3337409 3410354 3478973 3547343 3617227 3683767 3754308 - 3824831 3893413 3961703 4030525 4099322 4167859 4231684 4299907 - 4370783 4438904 4510881 4577594 4648557 4717392 4784293 4852530 - 4922570 4996981 5064981 5132095 5200503 5269109 5334122 5404249 - 5475132 5543395 5608863 5678054 5748423 5820135 5890512 5960271 - 6025317 6094188 6161163 6228125 6294195 6360816 6428303 6494990 - 6560040 6626704 6696552 6763892 6830719 6902143 6974641 7041359 - 7105486 7177011 7243850 7313756 7381464 7450007 7522418 7595444 - 7665111 7735490 7803223 7873951 7942351 8010788 8079970 8145238 - 8213282 8280587 8351328 8422441 8490499 8557350 8624216 8691559 - 8759303 8829278 8893510 8960215 9024908 9093748 9161826 9232393 - 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3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 - 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 - 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 - 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 - 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 - 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 - 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 - 3596 3597 3598 3599] -offset: [ 0 65812 137190 204628 272166 342979 413923 483171 - 553101 619258 686876 756852 825427 889281 960691 1028742 - 1093068 1160355 1231117 1299882 1370483 1435208 1502865 1573922 - 1643893 1713542 1780452 1849834 1916464 1986026 2057622 2126603 - 2193897 2265219 2329840 2398908 2469304 2538422 2605393 2673745 - 2745146 2812126 2876393 2946431 3019665 3085672 3156717 3224368 - 3291998 3362362 3430165 3498043 3568055 3636997 3705947 3770047 - 3837463 3906507 3975274 4041884 4108089 4177265 4247709 4316182 - 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12556456 12624494 12694518 12764221 12835021 12906829 12977643 13045359 - 13113341 13181653 13249501 13317384 13383878 13455281 13522810 13591122 - 13659603] - -sources: [5089516 5089516 5089516 ... 6256481 6256481 6256481] -destinations: [5605820 863789 810213 ... 665530 3818990 6870902] -batch: [3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 - 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 - 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 - 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 - 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 - 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 - 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 - 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 - 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 - 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 - 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 - 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 - 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 - 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 - 3996 3997 3998 3999] -offset: [ 0 66254 136998 202519 273895 341159 406126 476581 - 545449 616912 689050 754605 822004 890517 959347 1025411 - 1093323 1162909 1232578 1299783 1369041 1435437 1502153 1570392 - 1642924 1711373 1780048 1846839 1916229 1979055 2049007 2117207 - 2182464 2252533 2319587 2387089 2454421 2523721 2588985 2655978 - 2724545 2787534 2858082 2927299 2997028 3063064 3131185 3200731 - 3267687 3337631 3405338 3476828 3548347 3619526 3686050 3753218 - 3818023 3885882 3956483 4023983 4093083 4158412 4230299 4296145 - 4363934 4430050 4500606 4566264 4635321 4701664 4771596 4840320 - 4907158 4979801 5043991 5113157 5183169 5248672 5315332 5386572 - 5457052 5523683 5589931 5657121 5724293 5795444 5865009 5937166 - 6005198 6073182 6136640 6201023 6270375 6338176 6406389 6474962 - 6543913 6611026 6679093 6747844 6814723 6882927 6951960 7018207 - 7087772 7155860 7223859 7291846 7361926 7428243 7496786 7564765 - 7631008 7701958 7769019 7835646 7905776 7971938 8038457 8104335 - 8169326 8240167 8305583 8374438 8444543 8513632 8583676 8653979 - 8726820 8794569 8863777 8929472 8997876 9062653 9128735 9199098 - 9270086 9340697 9410158 9479613 9549982 9619156 9687981 9754480 - 9822891 9893316 9961025 10028648 10098028 10167973 10233658 10301471 - 10371233 10438314 10507457 10575986 10644569 10707238 10775298 10844842 - 10915534 10982943 11047964 11117931 11182018 11250027 11319791 11387807 - 11456266 11525161 11590207 11655102 11722576 11794261 11864478 11929868 - 11999382 12066296 12134764 12202869 12272294 12343048 12408239 12479102 - 12549580 12615750 12685281 12751759 12817482 12881773 12950483 13019186 - 13087160 13157152 13230091 13299883 13365497 13434637 13501268 13574780 - 13647430] - -2023-05-10 14:03:39,642 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:39,642 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:41,295 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:41,295 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [2000336 2000336 2000336 ... 2928373 2928373 2928373] -destinations: [7556151 7512321 6594731 ... 2896914 5613533 6151625] -batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 - 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 - 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 - 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 - 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 - 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 - 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 - 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5540 5541 5542 5543 5544 5545 5546 5547 5548 5549 5550 5551 5552 5553 - 5554 5555 5556 5557 5558 5559 5560 5561 5562 5563 5564 5565 5566 5567 - 5568 5569 5570 5571 5572 5573 5574 5575 5576 5577 5578 5579 5580 5581 - 5582 5583 5584 5585 5586 5587 5588 5589 5590 5591 5592 5593 5594 5595 - 5596 5597 5598 5599] -offset: [ 0 70986 141409 211978 279751 344825 413934 481804 - 550224 619774 690445 756898 827946 898717 966375 1038371 - 1108258 1177804 1245168 1317970 1381744 1450065 1517389 1585178 - 1651679 1720322 1786262 1854259 1922342 1992563 2057615 2127148 - 2193584 2264206 2335677 2403193 2472976 2536478 2605326 2672726 - 2741320 2811142 2877966 2944414 3012454 3079012 3148037 3213664 - 3280396 3354422 3420773 3487476 3557526 3629828 3697455 3770572 - 3837764 3905638 3970296 4037211 4104685 4172203 4240386 4304774 - 4372406 4444117 4514016 4580855 4651301 4721601 4793147 4857863 - 4928419 4996225 5061749 5130668 5202052 5269689 5337029 5405807 - 5471290 5541973 5609469 5681105 5749023 5818406 5882807 5949873 - 6017231 6087336 6154812 6224791 6295017 6362846 6436006 6502932 - 6572570 6636386 6706247 6775300 6845513 6914998 6984655 7055875 - 7127456 7195691 7265392 7331211 7395495 7464567 7535152 7600678 - 7668345 7737468 7805342 7875152 7942825 8014615 8084441 8153871 - 8218576 8286201 8355437 8423855 8487759 8554127 8621278 8688974 - 8755508 8825259 8896644 8961637 9030812 9097194 9165644 9234375 - 9304982 9370428 9442816 9512750 9582459 9652162 9719390 9788351 - 9859029 9925024 9993255 10063626 10131255 10197254 10265207 10332877 - 10405495 10474681 10543354 10607242 10675484 10742485 10810843 10880322 - 10948627 11016754 11080663 11149421 11217450 11286678 11354031 11428599 - 11498852 11567809 11633232 11701976 11772013 11843175 11912140 11978848 - 12044677 12111415 12181045 12246922 12311834 12381168 12449294 12517839 - 12586849 12657466 12727071 12788259 12857812 12924694 12997031 13063062 - 13134105 13201633 13270375 13336479 13404174 13471169 13540537 13610381 - 13677900] - -2023-05-10 14:03:46,148 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:46,148 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:47,797 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:47,797 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [2800033 2800033 2800033 ... 4267003 4267003 4267003] -destinations: [ 551465 5716790 6282267 ... 10443 5123751 3201278] -batch: [5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613 - 5614 5615 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 - 5628 5629 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641 - 5642 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655 - 5656 5657 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668 5669 - 5670 5671 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683 - 5684 5685 5686 5687 5688 5689 5690 5691 5692 5693 5694 5695 5696 5697 - 5698 5699 5700 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 - 5712 5713 5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724 5725 - 5726 5727 5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 - 5740 5741 5742 5743 5744 5745 5746 5747 5748 5749 5750 5751 5752 5753 - 5754 5755 5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 - 5768 5769 5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 - 5782 5783 5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 - 5796 5797 5798 5799] -offset: [ 0 67934 134801 204912 270486 338049 404277 473123 - 544827 613995 683135 748296 817060 888102 957414 1025170 - 1090795 1154555 1223743 1293601 1362139 1431382 1499468 1571531 - 1636356 1704287 1777422 1847233 1913308 1979255 2046910 2113909 - 2180099 2248541 2314824 2381725 2450244 2519192 2590993 2659290 - 2727001 2794682 2861189 2929924 2999094 3069449 3139955 3206475 - 3275388 3342428 3412044 3481039 3545715 3611971 3681366 3745453 - 3813067 3879247 3944887 4014261 4080337 4150450 4216157 4283007 - 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5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825 5826 5827 - 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 5840 5841 - 5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855 - 5856 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 5868 5869 - 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 5882 5883 - 5884 5885 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 5896 5897 - 5898 5899 5900 5901 5902 5903 5904 5905 5906 5907 5908 5909 5910 5911 - 5912 5913 5914 5915 5916 5917 5918 5919 5920 5921 5922 5923 5924 5925 - 5926 5927 5928 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 - 5940 5941 5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 5952 5953 - 5954 5955 5956 5957 5958 5959 5960 5961 5962 5963 5964 5965 5966 5967 - 5968 5969 5970 5971 5972 5973 5974 5975 5976 5977 5978 5979 5980 5981 - 5982 5983 5984 5985 5986 5987 5988 5989 5990 5991 5992 5993 5994 5995 - 5996 5997 5998 5999] -offset: [ 0 68359 137405 201622 270364 343279 412105 478776 - 545597 613597 680571 749563 821718 889631 960842 1030694 - 1100379 1169744 1239592 1307403 1375457 1445232 1512535 1581872 - 1649666 1718937 1792975 1860300 1928036 1992816 2058599 2130218 - 2196642 2264725 2330846 2400299 2470019 2538055 2605955 2678378 - 2744459 2813183 2879620 2947470 3016804 3086116 3154761 3224336 - 3294988 3362782 3431196 3501029 3569079 3637487 3703876 3773549 - 3843493 3911192 3979692 4049811 4119666 4187156 4252203 4320650 - 4388792 4455263 4522562 4591408 4661921 4730582 4800166 4869918 - 4939157 5007861 5073029 5140716 5208664 5281071 5352422 5416630 - 5486333 5553192 5621812 5690446 5756555 5825707 5895042 5963617 - 6030342 6096507 6167908 6233558 6303247 6369776 6439200 6507311 - 6577039 6643043 6712195 6780659 6846463 6914784 6982757 7052286 - 7121021 7191710 7257439 7325312 7396181 7465150 7536722 7608921 - 7675851 7743823 7815234 7880872 7949595 8014028 8083051 8150330 - 8220550 8285529 8353638 8421401 8492656 8560735 8625500 8691180 - 8762275 8832063 8900402 8967742 9037646 9105615 9172214 9241144 - 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6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266 6267 6268 6269 - 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279 6280 6281 6282 6283 - 6284 6285 6286 6287 6288 6289 6290 6291 6292 6293 6294 6295 6296 6297 - 6298 6299 6300 6301 6302 6303 6304 6305 6306 6307 6308 6309 6310 6311 - 6312 6313 6314 6315 6316 6317 6318 6319 6320 6321 6322 6323 6324 6325 - 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335 6336 6337 6338 6339 - 6340 6341 6342 6343 6344 6345 6346 6347 6348 6349 6350 6351 6352 6353 - 6354 6355 6356 6357 6358 6359 6360 6361 6362 6363 6364 6365 6366 6367 - 6368 6369 6370 6371 6372 6373 6374 6375 6376 6377 6378 6379 6380 6381 - 6382 6383 6384 6385 6386 6387 6388 6389 6390 6391 6392 6393 6394 6395 - 6396 6397 6398 6399] -offset: [ 0 68305 140431 209150 273313 344117 414314 487171 - 555338 624468 690859 756954 823135 892962 963652 1032480 - 1098823 1166649 1233637 1302947 1372026 1442215 1512551 1582454 - 1649041 1721179 1790508 1861422 1925862 1995053 2062206 2129705 - 2197053 2266718 2339012 2408734 2476125 2543904 2618231 2686673 - 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10950978 11017461 11089104 11156956 11226437 11294686 11364561 11429883 - 11498157 11567762 11634599 11704185 11769425 11841587 11908036 11975184 - 12044319 12112926 12181481 12247708 12312763 12383964 12448359 12518155 - 12584528 12652016 12717997 12788017 12856832 12924269 12994499 13063017 - 13128870 13197902 13267978 13333061 13400095 13468148 13539381 13605421 - 13674013] - -2023-05-10 14:03:49,448 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:49,448 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:51,086 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:51,086 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [3200240 3200240 3200240 ... 80073 80073 80073] -destinations: [7065725 6559441 2388553 ... 6483038 1657451 6719601] -batch: [6400 6401 6402 6403 6404 6405 6406 6407 6408 6409 6410 6411 6412 6413 - 6414 6415 6416 6417 6418 6419 6420 6421 6422 6423 6424 6425 6426 6427 - 6428 6429 6430 6431 6432 6433 6434 6435 6436 6437 6438 6439 6440 6441 - 6442 6443 6444 6445 6446 6447 6448 6449 6450 6451 6452 6453 6454 6455 - 6456 6457 6458 6459 6460 6461 6462 6463 6464 6465 6466 6467 6468 6469 - 6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480 6481 6482 6483 - 6484 6485 6486 6487 6488 6489 6490 6491 6492 6493 6494 6495 6496 6497 - 6498 6499 6500 6501 6502 6503 6504 6505 6506 6507 6508 6509 6510 6511 - 6512 6513 6514 6515 6516 6517 6518 6519 6520 6521 6522 6523 6524 6525 - 6526 6527 6528 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 - 6540 6541 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 - 6554 6555 6556 6557 6558 6559 6560 6561 6562 6563 6564 6565 6566 6567 - 6568 6569 6570 6571 6572 6573 6574 6575 6576 6577 6578 6579 6580 6581 - 6582 6583 6584 6585 6586 6587 6588 6589 6590 6591 6592 6593 6594 6595 - 6596 6597 6598 6599] -offset: [ 0 69824 137696 204279 276890 345554 412373 479001 - 545117 617989 685350 755223 827716 897673 965517 1034457 - 1103496 1172543 1238288 1304618 1374714 1443594 1514198 1579681 - 1648415 1716038 1782444 1850085 1917946 1988260 2054083 2126169 - 2193251 2263431 2331033 2400445 2466136 2533313 2599181 2668347 - 2738189 2809396 2874901 2938410 3008148 3075791 3141854 3208350 - 3275130 3342946 3411240 3480883 3549402 3615740 3686987 3755260 - 3826360 3895975 3964056 4030559 4104899 4175912 4248135 4319006 - 4389814 4455413 4522306 4591803 4660352 4733968 4800392 4870797 - 4939245 5009289 5075263 5141101 5205018 5273926 5340496 5411603 - 5478516 5548267 5620156 5688986 5756520 5822872 5893170 5966996 - 6032784 6100649 6171220 6238348 6309337 6376928 6446671 6514283 - 6584722 6651187 6718407 6785018 6852679 6921331 6989116 7057449 - 7126990 7200009 7264791 7334633 7401930 7472213 7544800 7610426 - 7676311 7745443 7812830 7878735 7948591 8015248 8086477 8153414 - 8222213 8293930 8365648 8429912 8498978 8566227 8640278 8708269 - 8774663 8840876 8908927 8978074 9047111 9117113 9183928 9252779 - 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distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:52,753 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:54,409 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:54,409 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [3600000 3600002 3600002 ... 5388702 5388702 5388702] -destinations: [5765003 6361538 7520961 ... 7861862 1350500 3427236] -batch: [7200 7201 7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212 7213 - 7214 7215 7216 7217 7218 7219 7220 7221 7222 7223 7224 7225 7226 7227 - 7228 7229 7230 7231 7232 7233 7234 7235 7236 7237 7238 7239 7240 7241 - 7242 7243 7244 7245 7246 7247 7248 7249 7250 7251 7252 7253 7254 7255 - 7256 7257 7258 7259 7260 7261 7262 7263 7264 7265 7266 7267 7268 7269 - 7270 7271 7272 7273 7274 7275 7276 7277 7278 7279 7280 7281 7282 7283 - 7284 7285 7286 7287 7288 7289 7290 7291 7292 7293 7294 7295 7296 7297 - 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10934576 11004934 11074188 11142507 11212353 11280728 11346306 11413965 - 11482613 11550582 11619675 11688815 11759622 11826661 11892412 11960875 - 12029263 12095734 12162158 12238537 12304368 12372536 12438299 12508936 - 12577444 12647106 12717705 12784154 12850974 12919960 12992304 13062318 - 13131381 13199207 13265930 13333505 13403943 13470057 13539412 13606416 - 13676985] - -2023-05-10 14:03:56,075 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:56,075 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:57,783 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:57,783 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [4000242 4000242 4000242 ... 3269012 3269012 3269012] -destinations: [2674513 5420738 1857962 ... 3969473 3532411 2190469] -batch: [8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013 - 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8740 8741 8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 - 8754 8755 8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 - 8768 8769 8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 - 8782 8783 8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 - 8796 8797 8798 8799] -offset: [ 0 68506 137886 205382 274360 341528 409422 475907 - 545016 615619 684481 753772 822661 891035 958858 1025867 - 1099458 1167837 1233011 1297692 1365072 1430275 1495125 1562688 - 1632438 1702010 1770796 1839967 1914114 1982987 2051918 2117640 - 2182906 2252119 2320106 2387376 2453617 2522798 2589693 2657284 - 2728864 2799372 2870377 2938915 3003196 3071984 3140201 3209452 - 3278033 3346035 3413837 3484693 3552878 3622650 3694196 3761212 - 3828409 3897834 3965363 4033149 4103789 4175257 4241480 4311007 - 4379975 4442689 4511941 4577598 4644934 4714975 4783023 4849929 - 4917865 4984931 5052985 5122874 5192263 5259135 5332230 5403005 - 5469107 5537557 5603764 5671526 5741074 5807286 5874060 5942250 - 6007484 6078164 6144074 6212267 6282988 6356857 6427588 6499928 - 6569135 6639760 6705346 6772784 6840799 6905873 6974572 7041715 - 7111484 7182209 7248446 7312075 7382261 7449849 7517237 7585902 - 7652798 7722893 7792819 7860379 7928471 7994683 8063736 8130057 - 8196801 8269754 8341006 8410523 8476818 8542464 8614324 8679925 - 8749090 8817002 8888992 8956056 9023132 9090728 9159595 9232096 - 9298699 9367001 9436510 9501944 9567191 9634198 9705281 9775570 - 9845058 9913519 9983157 10048300 10115973 10184540 10249251 10315459 - 10384367 10451188 10521432 10590161 10662328 10729810 10795973 10862404 - 10926675 10993847 11065480 11134022 11202519 11271931 11340068 11410550 - 11476663 11546785 11612308 11681231 11745268 11814243 11881478 11951657 - 12019127 12088591 12155652 12228312 12297486 12366175 12433946 12501120 - 12570312 12643510 12714757 12783601 12851724 12917829 12987650 13060330 - 13129760 13197932 13263240 13330480 13399261 13467040 13538804 13607513 - 13674694] - -2023-05-10 14:03:59,477 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:03:59,477 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:01,346 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:01,346 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [4400385 4400385 4400385 ... 4797798 4797798 4797798] -destinations: [2159574 198236 2068566 ... 5024802 6080130 4499647] -batch: [8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811 8812 8813 - 8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825 8826 8827 - 8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839 8840 8841 - 8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853 8854 8855 - 8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 8868 8869 - 8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880 8881 8882 8883 - 8884 8885 8886 8887 8888 8889 8890 8891 8892 8893 8894 8895 8896 8897 - 8898 8899 8900 8901 8902 8903 8904 8905 8906 8907 8908 8909 8910 8911 - 8912 8913 8914 8915 8916 8917 8918 8919 8920 8921 8922 8923 8924 8925 - 8926 8927 8928 8929 8930 8931 8932 8933 8934 8935 8936 8937 8938 8939 - 8940 8941 8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 8952 8953 - 8954 8955 8956 8957 8958 8959 8960 8961 8962 8963 8964 8965 8966 8967 - 8968 8969 8970 8971 8972 8973 8974 8975 8976 8977 8978 8979 8980 8981 - 8982 8983 8984 8985 8986 8987 8988 8989 8990 8991 8992 8993 8994 8995 - 8996 8997 8998 8999] -offset: [ 0 67105 135502 205663 272042 344048 410752 475946 - 544287 615627 682216 752524 821215 888592 955465 1024132 - 1092552 1160378 1229679 1296246 1364683 1434710 1505367 1574304 - 1638258 1706073 1775968 1841144 1908289 1974761 2045970 2114974 - 2188188 2257634 2323494 2393606 2459270 2527903 2597882 2666029 - 2736958 2801406 2867779 2937280 3005034 3071086 3139264 3205599 - 3272608 3341721 3408951 3481204 3550722 3613723 3682717 3751452 - 3818593 3886524 3957379 4026904 4093618 4164253 4226777 4294354 - 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10926464 10995618 11064814 11136710 11207519 11277239 11346592 11414194 - 11484734 11553353 11620896 11688274 11759175 11825053 11896800 11963093 - 12031464 12104095 12172674 12240324 12311969 12382721 12450453 12515414 - 12581464 12652756 12721241 12787620 12850900 12918938 12988307 13055857 - 13122960 13187480 13252464 13322507 13391480 13459781 13526983 13596332 - 13666214] - -2023-05-10 14:04:06,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:06,503 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:08,255 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:08,255 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [5200160 5200162 5200162 ... 6027462 6027462 6027462] -destinations: [6950202 254512 8050067 ... 1621450 5850196 3773553] -batch: [10400 10401 10402 10403 10404 10405 10406 10407 10408 10409 10410 10411 - 10412 10413 10414 10415 10416 10417 10418 10419 10420 10421 10422 10423 - 10424 10425 10426 10427 10428 10429 10430 10431 10432 10433 10434 10435 - 10436 10437 10438 10439 10440 10441 10442 10443 10444 10445 10446 10447 - 10448 10449 10450 10451 10452 10453 10454 10455 10456 10457 10458 10459 - 10460 10461 10462 10463 10464 10465 10466 10467 10468 10469 10470 10471 - 10472 10473 10474 10475 10476 10477 10478 10479 10480 10481 10482 10483 - 10484 10485 10486 10487 10488 10489 10490 10491 10492 10493 10494 10495 - 10496 10497 10498 10499 10500 10501 10502 10503 10504 10505 10506 10507 - 10508 10509 10510 10511 10512 10513 10514 10515 10516 10517 10518 10519 - 10520 10521 10522 10523 10524 10525 10526 10527 10528 10529 10530 10531 - 10532 10533 10534 10535 10536 10537 10538 10539 10540 10541 10542 10543 - 10544 10545 10546 10547 10548 10549 10550 10551 10552 10553 10554 10555 - 10556 10557 10558 10559 10560 10561 10562 10563 10564 10565 10566 10567 - 10568 10569 10570 10571 10572 10573 10574 10575 10576 10577 10578 10579 - 10580 10581 10582 10583 10584 10585 10586 10587 10588 10589 10590 10591 - 10592 10593 10594 10595 10596 10597 10598 10599] -offset: [ 0 69829 136142 200907 269256 340379 409297 480553 - 547179 612469 680710 745432 814949 884818 953967 1022899 - 1088429 1156789 1224191 1289107 1356844 1423456 1493542 1565761 - 1630038 1699272 1764214 1832813 1901348 1969426 2033813 2105543 - 2173062 2239615 2310181 2378845 2450521 2517298 2583688 2652047 - 2721070 2788255 2858920 2926970 2995937 3062737 3132252 3194892 - 3264326 3331710 3404243 3471405 3540469 3611091 3675733 3743552 - 3812297 3885189 3951068 4017229 4086971 4155824 4225272 4295600 - 4367666 4435166 4502696 4571456 4643817 4708976 4775427 4843502 - 4911750 4981545 5050397 5119072 5185619 5255610 5322133 5393006 - 5461064 5530350 5600615 5669538 5740375 5809458 5876767 5945071 - 6009723 6076446 6148125 6215976 6282783 6348794 6417356 6486486 - 6553829 6622320 6693803 6760294 6824788 6894142 6963082 7030675 - 7097471 7163917 7234475 7302728 7371906 7435942 7505716 7576788 - 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10812 10813 10814 10815 10816 10817 10818 10819 10820 10821 10822 10823 - 10824 10825 10826 10827 10828 10829 10830 10831 10832 10833 10834 10835 - 10836 10837 10838 10839 10840 10841 10842 10843 10844 10845 10846 10847 - 10848 10849 10850 10851 10852 10853 10854 10855 10856 10857 10858 10859 - 10860 10861 10862 10863 10864 10865 10866 10867 10868 10869 10870 10871 - 10872 10873 10874 10875 10876 10877 10878 10879 10880 10881 10882 10883 - 10884 10885 10886 10887 10888 10889 10890 10891 10892 10893 10894 10895 - 10896 10897 10898 10899 10900 10901 10902 10903 10904 10905 10906 10907 - 10908 10909 10910 10911 10912 10913 10914 10915 10916 10917 10918 10919 - 10920 10921 10922 10923 10924 10925 10926 10927 10928 10929 10930 10931 - 10932 10933 10934 10935 10936 10937 10938 10939 10940 10941 10942 10943 - 10944 10945 10946 10947 10948 10949 10950 10951 10952 10953 10954 10955 - 10956 10957 10958 10959 10960 10961 10962 10963 10964 10965 10966 10967 - 10968 10969 10970 10971 10972 10973 10974 10975 10976 10977 10978 10979 - 10980 10981 10982 10983 10984 10985 10986 10987 10988 10989 10990 10991 - 10992 10993 10994 10995 10996 10997 10998 10999] -offset: [ 0 67836 135361 206129 277030 342036 409946 474256 - 539649 606099 675728 742337 809455 871430 939945 1009739 - 1075539 1142395 1206935 1278535 1346966 1421262 1488658 1554670 - 1625022 1687697 1761524 1829022 1897506 1967761 2036222 2103176 - 2173368 2238147 2306561 2374681 2441856 2509395 2580154 2651885 - 2720366 2790677 2858278 2928096 2995548 3061460 3129696 3197395 - 3266124 3332757 3397625 3466790 3534171 3603671 3673108 3738211 - 3809856 3876164 3945478 4012059 4081093 4150296 4217015 4284007 - 4353431 4422120 4487452 4559342 4626777 4698575 4766657 4836008 - 4903906 4970340 5041035 5108446 5171149 5238498 5303490 5371141 - 5442901 5510031 5581417 5650887 5716113 5786171 5854223 5920763 - 5988298 6055034 6122862 6189928 6259992 6329006 6398070 6462176 - 6527946 6601218 6667532 6736144 6807538 6871122 6940797 7004059 - 7072529 7139550 7211976 7279771 7346424 7413485 7484068 7552541 - 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10612 10613 10614 10615 10616 10617 10618 10619 10620 10621 10622 10623 - 10624 10625 10626 10627 10628 10629 10630 10631 10632 10633 10634 10635 - 10636 10637 10638 10639 10640 10641 10642 10643 10644 10645 10646 10647 - 10648 10649 10650 10651 10652 10653 10654 10655 10656 10657 10658 10659 - 10660 10661 10662 10663 10664 10665 10666 10667 10668 10669 10670 10671 - 10672 10673 10674 10675 10676 10677 10678 10679 10680 10681 10682 10683 - 10684 10685 10686 10687 10688 10689 10690 10691 10692 10693 10694 10695 - 10696 10697 10698 10699 10700 10701 10702 10703 10704 10705 10706 10707 - 10708 10709 10710 10711 10712 10713 10714 10715 10716 10717 10718 10719 - 10720 10721 10722 10723 10724 10725 10726 10727 10728 10729 10730 10731 - 10732 10733 10734 10735 10736 10737 10738 10739 10740 10741 10742 10743 - 10744 10745 10746 10747 10748 10749 10750 10751 10752 10753 10754 10755 - 10756 10757 10758 10759 10760 10761 10762 10763 10764 10765 10766 10767 - 10768 10769 10770 10771 10772 10773 10774 10775 10776 10777 10778 10779 - 10780 10781 10782 10783 10784 10785 10786 10787 10788 10789 10790 10791 - 10792 10793 10794 10795 10796 10797 10798 10799] -offset: [ 0 69277 134749 198951 267668 334487 401693 467069 - 535616 605266 674573 743302 814173 879989 948944 1017448 - 1088700 1156133 1222230 1290992 1360671 1430665 1497043 1564325 - 1630103 1696128 1760767 1830527 1895066 1961664 2027817 2097387 - 2166071 2234585 2305835 2375108 2446702 2516776 2584051 2653269 - 2723712 2791486 2861849 2926543 2993448 3060129 3128615 3197511 - 3266292 3332549 3398491 3464591 3532465 3596805 3665873 3731514 - 3797916 3864679 3932259 4003265 4072022 4143142 4211886 4281324 - 4349985 4417182 4485509 4555675 4621372 4690517 4763848 4833697 - 4902452 4968525 5034767 5103890 5169553 5235802 5302480 5368429 - 5439768 5511028 5579766 5652159 5723172 5793749 5863047 5928066 - 5994340 6058830 6128649 6195748 6267137 6332103 6400183 6468440 - 6537244 6602656 6669070 6736497 6800849 6870065 6942301 7012067 - 7081393 7150896 7220802 7289503 7355288 7427996 7499913 7568361 - 7636747 7704314 7772587 7840157 7905340 7974234 8044566 8115633 - 8187937 8254028 8321562 8389322 8456306 8522264 8589006 8654182 - 8723640 8793579 8864114 8929843 8998516 9068022 9133955 9201318 - 9273910 9341153 9407751 9476413 9550112 9620017 9687714 9757998 - 9824862 9893828 9965264 10034327 10103777 10167060 10233001 10300788 - 10367785 10435015 10502885 10573509 10646481 10715733 10785940 10851761 - 10918212 10990659 11058840 11130016 11196288 11267889 11333882 11398623 - 11468228 11535225 11602885 11673329 11742581 11813624 11880745 11947831 - 12013239 12080605 12147371 12214259 12278218 12348325 12418036 12486597 - 12555569 12623494 12691541 12762128 12828896 12897677 12966354 13033686 - 13102831 13165312 13231104 13299986 13367715 13433525 13499434 13568412 - 13637596] - -sources: [2579092 2579092 2579092 ... 485269 485269 485269] -destinations: [6962501 5347196 5595644 ... 3442482 3865755 7938992] -batch: [11000 11001 11002 11003 11004 11005 11006 11007 11008 11009 11010 11011 - 11012 11013 11014 11015 11016 11017 11018 11019 11020 11021 11022 11023 - 11024 11025 11026 11027 11028 11029 11030 11031 11032 11033 11034 11035 - 11036 11037 11038 11039 11040 11041 11042 11043 11044 11045 11046 11047 - 11048 11049 11050 11051 11052 11053 11054 11055 11056 11057 11058 11059 - 11060 11061 11062 11063 11064 11065 11066 11067 11068 11069 11070 11071 - 11072 11073 11074 11075 11076 11077 11078 11079 11080 11081 11082 11083 - 11084 11085 11086 11087 11088 11089 11090 11091 11092 11093 11094 11095 - 11096 11097 11098 11099 11100 11101 11102 11103 11104 11105 11106 11107 - 11108 11109 11110 11111 11112 11113 11114 11115 11116 11117 11118 11119 - 11120 11121 11122 11123 11124 11125 11126 11127 11128 11129 11130 11131 - 11132 11133 11134 11135 11136 11137 11138 11139 11140 11141 11142 11143 - 11144 11145 11146 11147 11148 11149 11150 11151 11152 11153 11154 11155 - 11156 11157 11158 11159 11160 11161 11162 11163 11164 11165 11166 11167 - 11168 11169 11170 11171 11172 11173 11174 11175 11176 11177 11178 11179 - 11180 11181 11182 11183 11184 11185 11186 11187 11188 11189 11190 11191 - 11192 11193 11194 11195 11196 11197 11198 11199] -offset: [ 0 69231 138319 208941 277724 347080 415816 486336 - 555048 621425 690537 759408 827845 894313 965654 1032272 - 1103923 1167289 1238246 1305386 1370803 1439417 1506706 1575378 - 1641941 1710258 1780759 1850012 1918607 1985611 2056724 2127849 - 2196253 2262527 2329581 2396415 2464325 2532631 2602824 2673377 - 2738363 2808462 2872449 2942435 3011971 3082058 3148249 3220033 - 3284872 3353666 3421753 3491721 3561136 3627294 3694300 3764578 - 3832486 3899143 3967576 4035118 4102256 4174215 4244579 4312678 - 4381796 4449796 4517757 4585614 4652535 4721103 4787842 4856204 - 4925932 4993146 5060448 5127665 5199218 5268198 5339962 5405348 - 5474456 5540146 5606324 5677266 5745929 5811588 5879318 5946805 - 6016090 6084229 6150832 6221118 6294298 6361388 6424502 6490779 - 6561008 6628948 6695044 6764429 6835736 6908256 6975008 7042291 - 7109060 7178145 7247961 7317115 7384006 7456221 7520877 7590306 - 7658861 7724959 7793848 7862807 7932143 7999607 8067389 8135286 - 8203502 8270118 8341975 8411925 8478969 8547672 8619974 8690609 - 8759176 8824336 8891615 8957965 9026849 9095436 9163581 9233927 - 9304459 9371042 9436604 9504120 9572032 9637764 9705369 9773950 - 9843228 9912431 9980258 10050632 10118363 10184469 10250085 10318649 - 10390261 10461823 10532150 10600236 10668725 10734971 10804917 10872751 - 10946124 11013428 11082616 11149882 11216650 11282761 11350307 11418129 - 11489315 11554516 11623875 11691240 11761416 11830168 11902591 11975124 - 12044706 12113939 12180433 12249934 12318016 12388517 12456639 12521593 - 12586441 12658812 12724392 12790092 12860346 12930182 12999064 13066032 - 13128780 13198447 13269135 13337405 13404607 13469353 13536278 13604765 - 13672559] - -2023-05-10 14:04:09,992 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:09,992 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:11,734 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:11,734 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [5600176 5600177 5600177 ... 3808288 3808288 3808288] -destinations: [4676728 3278902 1984897 ... 7364566 4280589 5699951] -batch: [11200 11201 11202 11203 11204 11205 11206 11207 11208 11209 11210 11211 - 11212 11213 11214 11215 11216 11217 11218 11219 11220 11221 11222 11223 - 11224 11225 11226 11227 11228 11229 11230 11231 11232 11233 11234 11235 - 11236 11237 11238 11239 11240 11241 11242 11243 11244 11245 11246 11247 - 11248 11249 11250 11251 11252 11253 11254 11255 11256 11257 11258 11259 - 11260 11261 11262 11263 11264 11265 11266 11267 11268 11269 11270 11271 - 11272 11273 11274 11275 11276 11277 11278 11279 11280 11281 11282 11283 - 11284 11285 11286 11287 11288 11289 11290 11291 11292 11293 11294 11295 - 11296 11297 11298 11299 11300 11301 11302 11303 11304 11305 11306 11307 - 11308 11309 11310 11311 11312 11313 11314 11315 11316 11317 11318 11319 - 11320 11321 11322 11323 11324 11325 11326 11327 11328 11329 11330 11331 - 11332 11333 11334 11335 11336 11337 11338 11339 11340 11341 11342 11343 - 11344 11345 11346 11347 11348 11349 11350 11351 11352 11353 11354 11355 - 11356 11357 11358 11359 11360 11361 11362 11363 11364 11365 11366 11367 - 11368 11369 11370 11371 11372 11373 11374 11375 11376 11377 11378 11379 - 11380 11381 11382 11383 11384 11385 11386 11387 11388 11389 11390 11391 - 11392 11393 11394 11395 11396 11397 11398 11399] -offset: [ 0 68080 136947 210657 278915 347679 412149 481390 - 545320 612674 682619 753201 820134 884891 951198 1019839 - 1090988 1155216 1225247 1290635 1361279 1430775 1504732 1571937 - 1642488 1707113 1773665 1839161 1907288 1976868 2048547 2116195 - 2186263 2255088 2319321 2382465 2450578 2519648 2592012 2660967 - 2731031 2797557 2867005 2933732 2997393 3067720 3137891 3206086 - 3275461 3346966 3416140 3484614 3553177 3621488 3686624 3756443 - 3821503 3894458 3964405 4032697 4102320 4169014 4240525 4310026 - 4377747 4444994 4512413 4579374 4647936 4716541 4785147 4856171 - 4926527 4989572 5062114 5126415 5195763 5264653 5331931 5400538 - 5470833 5539644 5605347 5675315 5739918 5812207 5878636 5947911 - 6015538 6084821 6155839 6227035 6298214 6363078 6432553 6501370 - 6568213 6637768 6704553 6774166 6840587 6909361 6980400 7046754 - 7114381 7185125 7254820 7323361 7392192 7457183 7526845 7597027 - 7667373 7736621 7808162 7874653 7944666 8016371 8085469 8151079 - 8216793 8283019 8352345 8425756 8493515 8561963 8627959 8695602 - 8765594 8836689 8902481 8969952 9038427 9101135 9170293 9239383 - 9307117 9375827 9445598 9513979 9581575 9649535 9718775 9787505 - 9857960 9925393 9991447 10061462 10128234 10199365 10268458 10339205 - 10406859 10472112 10541620 10605216 10675363 10743583 10811051 10876905 - 10946888 11015540 11086670 11155367 11221767 11287532 11352394 11420060 - 11487733 11552446 11618848 11688194 11755181 11820170 11891794 11960736 - 12026080 12094549 12160816 12225260 12300186 12370060 12441680 12509144 - 12573360 12638753 12708162 12778516 12847057 12912772 12983901 13051828 - 13115576 13182038 13248467 13318685 13389694 13459205 13528492 13597935 - 13667081] - -sources: [5800369 5800369 5800370 ... 2838848 2838848 2838848] -destinations: [2928564 6570746 6462324 ... 4586574 1064331 1585215] -batch: [11600 11601 11602 11603 11604 11605 11606 11607 11608 11609 11610 11611 - 11612 11613 11614 11615 11616 11617 11618 11619 11620 11621 11622 11623 - 11624 11625 11626 11627 11628 11629 11630 11631 11632 11633 11634 11635 - 11636 11637 11638 11639 11640 11641 11642 11643 11644 11645 11646 11647 - 11648 11649 11650 11651 11652 11653 11654 11655 11656 11657 11658 11659 - 11660 11661 11662 11663 11664 11665 11666 11667 11668 11669 11670 11671 - 11672 11673 11674 11675 11676 11677 11678 11679 11680 11681 11682 11683 - 11684 11685 11686 11687 11688 11689 11690 11691 11692 11693 11694 11695 - 11696 11697 11698 11699 11700 11701 11702 11703 11704 11705 11706 11707 - 11708 11709 11710 11711 11712 11713 11714 11715 11716 11717 11718 11719 - 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11920 11921 11922 11923 11924 11925 11926 11927 11928 11929 11930 11931 - 11932 11933 11934 11935 11936 11937 11938 11939 11940 11941 11942 11943 - 11944 11945 11946 11947 11948 11949 11950 11951 11952 11953 11954 11955 - 11956 11957 11958 11959 11960 11961 11962 11963 11964 11965 11966 11967 - 11968 11969 11970 11971 11972 11973 11974 11975 11976 11977 11978 11979 - 11980 11981 11982 11983 11984 11985 11986 11987 11988 11989 11990 11991 - 11992 11993 11994 11995 11996 11997 11998 11999] -offset: [ 0 68669 139086 206920 274304 341033 406260 471013 - 541465 606750 679785 748137 815345 884047 952633 1025274 - 1093858 1160260 1229404 1299334 1367360 1435504 1500388 1569106 - 1639691 1709815 1778943 1850934 1918975 1988389 2057304 2123583 - 2196082 2260005 2327869 2394016 2463814 2531152 2597611 2667995 - 2737578 2806090 2876665 2946348 3017315 3090286 3157355 3225533 - 3291047 3361136 3432278 3502935 3569655 3636881 3702091 3770974 - 3836130 3906209 3977232 4043690 4109725 4177837 4244257 4312210 - 4382630 4451989 4518126 4581949 4650023 4720561 4786349 4850946 - 4919462 4986674 5057865 5123981 5190632 5260465 5330527 5401254 - 5465524 5536314 5600242 5672363 5736619 5803651 5873582 5946483 - 6011711 6081823 6148260 6214925 6287529 6355273 6424444 6490610 - 6560095 6626071 6694067 6760771 6828304 6895832 6963395 7030177 - 7099845 7168343 7236107 7303000 7372487 7443424 7507975 7575232 - 7643403 7715983 7781655 7854418 7920309 7988466 8056937 8126127 - 8190788 8260397 8332013 8399520 8466460 8531407 8600996 8669633 - 8736922 8807397 8875730 8945539 9014111 9084340 9153406 9221967 - 9293557 9356777 9422876 9493365 9559371 9626303 9699312 9768118 - 9834323 9893179 9958884 10028306 10092975 10162167 10231943 10301592 - 10371503 10439459 10506594 10575326 10640327 10708753 10776442 10844725 - 10909400 10980810 11052156 11117351 11185887 11256604 11325654 11395725 - 11462825 11530972 11599262 11671208 11742125 11810594 11877859 11945041 - 12012678 12077991 12146507 12215540 12285050 12356054 12425427 12492193 - 12559062 12630550 12696248 12766238 12833684 12900987 12968916 13040316 - 13106432 13176019 13242858 13310879 13381495 13452665 13520943 13588670 - 13655030] - -2023-05-10 14:04:13,482 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:13,482 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:15,233 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:15,233 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [6000288 6000289 6000289 ... 1876378 1876378 1876378] -destinations: [1605595 3296281 4216506 ... 7081311 4951034 1195672] -batch: [12000 12001 12002 12003 12004 12005 12006 12007 12008 12009 12010 12011 - 12012 12013 12014 12015 12016 12017 12018 12019 12020 12021 12022 12023 - 12024 12025 12026 12027 12028 12029 12030 12031 12032 12033 12034 12035 - 12036 12037 12038 12039 12040 12041 12042 12043 12044 12045 12046 12047 - 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552232 621289 690053 758136 824344 891767 959226 1027161 - 1092806 1157961 1224870 1293329 1360821 1432039 1500555 1567927 - 1637388 1707382 1772806 1841640 1906322 1973296 2041070 2109676 - 2176048 2245647 2309703 2379035 2447552 2517053 2585321 2650795 - 2721116 2785807 2851513 2919373 2987705 3055860 3124601 3194855 - 3261807 3328548 3396991 3464241 3527786 3598613 3670887 3743008 - 3812498 3876287 3946303 4018082 4086049 4151949 4219958 4287052 - 4355164 4426067 4499038 4568261 4637592 4701318 4770505 4838189 - 4905128 4972307 5041794 5114376 5182440 5254485 5327375 5391905 - 5458766 5527513 5595130 5662725 5728609 5793464 5859215 5927742 - 5996962 6064397 6138918 6211151 6281596 6351602 6424069 6489837 - 6555669 6620205 6684358 6751790 6818938 6885862 6957048 7023993 - 7091291 7158408 7225393 7295750 7365688 7432265 7495507 7564588 - 7631051 7697838 7765217 7834910 7907154 7974467 8044804 8111489 - 8180095 8246616 8313176 8382692 8449466 8517579 8588083 8656333 - 8725255 8794478 8863272 8928994 9000366 9065215 9132540 9199671 - 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12448 12449 12450 12451 12452 12453 12454 12455 12456 12457 12458 12459 - 12460 12461 12462 12463 12464 12465 12466 12467 12468 12469 12470 12471 - 12472 12473 12474 12475 12476 12477 12478 12479 12480 12481 12482 12483 - 12484 12485 12486 12487 12488 12489 12490 12491 12492 12493 12494 12495 - 12496 12497 12498 12499 12500 12501 12502 12503 12504 12505 12506 12507 - 12508 12509 12510 12511 12512 12513 12514 12515 12516 12517 12518 12519 - 12520 12521 12522 12523 12524 12525 12526 12527 12528 12529 12530 12531 - 12532 12533 12534 12535 12536 12537 12538 12539 12540 12541 12542 12543 - 12544 12545 12546 12547 12548 12549 12550 12551 12552 12553 12554 12555 - 12556 12557 12558 12559 12560 12561 12562 12563 12564 12565 12566 12567 - 12568 12569 12570 12571 12572 12573 12574 12575 12576 12577 12578 12579 - 12580 12581 12582 12583 12584 12585 12586 12587 12588 12589 12590 12591 - 12592 12593 12594 12595 12596 12597 12598 12599] -offset: [ 0 68186 131350 200141 269398 332676 403472 473201 - 543530 613756 680152 751465 815196 882882 953465 1021017 - 1088372 1154811 1220690 1289192 1357553 1427291 1496625 1562690 - 1633579 1697415 1765653 1833564 1900301 1969543 2039558 2105629 - 2173153 2244997 2317333 2384368 2452294 2522178 2588475 2656141 - 2725586 2795170 2861131 2928929 2995875 3063110 3131276 3200721 - 3269463 3340955 3413052 3481308 3545001 3610913 3679838 3748376 - 3811266 3878018 3948503 4017967 4092155 4159299 4227619 4294912 - 4364309 4434023 4501019 4569540 4635635 4707059 4771234 4841830 - 4910249 4975960 5043076 5106919 5175002 5243048 5310484 5379416 - 5446297 5515327 5587934 5655539 5722387 5787944 5855994 5925997 - 5992153 6062649 6134002 6206435 6275670 6345562 6414929 6482481 - 6554431 6619337 6688979 6752739 6823258 6891098 6959152 7024923 - 7096147 7163962 7235095 7305820 7367829 7435345 7497670 7568847 - 7638457 7706027 7775687 7843929 7913767 7983200 8053016 8119323 - 8192099 8259411 8328052 8394153 8459863 8530413 8598991 8669553 - 8740589 8808833 8873160 8943364 9011674 9081473 9147061 9216217 - 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12248 12249 12250 12251 12252 12253 12254 12255 12256 12257 12258 12259 - 12260 12261 12262 12263 12264 12265 12266 12267 12268 12269 12270 12271 - 12272 12273 12274 12275 12276 12277 12278 12279 12280 12281 12282 12283 - 12284 12285 12286 12287 12288 12289 12290 12291 12292 12293 12294 12295 - 12296 12297 12298 12299 12300 12301 12302 12303 12304 12305 12306 12307 - 12308 12309 12310 12311 12312 12313 12314 12315 12316 12317 12318 12319 - 12320 12321 12322 12323 12324 12325 12326 12327 12328 12329 12330 12331 - 12332 12333 12334 12335 12336 12337 12338 12339 12340 12341 12342 12343 - 12344 12345 12346 12347 12348 12349 12350 12351 12352 12353 12354 12355 - 12356 12357 12358 12359 12360 12361 12362 12363 12364 12365 12366 12367 - 12368 12369 12370 12371 12372 12373 12374 12375 12376 12377 12378 12379 - 12380 12381 12382 12383 12384 12385 12386 12387 12388 12389 12390 12391 - 12392 12393 12394 12395 12396 12397 12398 12399] -offset: [ 0 69839 139956 209079 276173 346238 415936 484999 - 550213 617934 684552 756187 821460 889308 957600 1025996 - 1091374 1156263 1227693 1293078 1362598 1432570 1498764 1567814 - 1639122 1704835 1772099 1842763 1913526 1985090 2053615 2124653 - 2193846 2258893 2329548 2397798 2465592 2536212 2601381 2674026 - 2744495 2815850 2881970 2951127 3017973 3084228 3153403 3221981 - 3289924 3359144 3426440 3491826 3559959 3628776 3701975 3769567 - 3838937 3909944 3979736 4048488 4115756 4184831 4253303 4323884 - 4386322 4457218 4523575 4593049 4661552 4726098 4791785 4866002 - 4936941 5005131 5071880 5142858 5210000 5277356 5343530 5414076 - 5481344 5547380 5613931 5684788 5756036 5822479 5892288 5958607 - 6027138 6094106 6161552 6228212 6294007 6363059 6432974 6498630 - 6566144 6633718 6701520 6771752 6835156 6903075 6972952 7044044 - 7111038 7179620 7246895 7317544 7382329 7452534 7524424 7592679 - 7659045 7726329 7796728 7863558 7934838 8000526 8067861 8135604 - 8206098 8271801 8342742 8413064 8482286 8551071 8618281 8687989 - 8755422 8823823 8889858 8961861 9026938 9094779 9163707 9231035 - 9302097 9371181 9441452 9508886 9575707 9642642 9715066 9783205 - 9854098 9922164 9990024 10058344 10123705 10191306 10257946 10324753 - 10394704 10465128 10531630 10601377 10668219 10735405 10805413 10875563 - 10942515 11006261 11076224 11144867 11210693 11279057 11344437 11413024 - 11478589 11549762 11617908 11686923 11755841 11821717 11888758 11957061 - 12022177 12091946 12161531 12230394 12298609 12373366 12440550 12510031 - 12573996 12642806 12709113 12777044 12844831 12913039 12976575 13046112 - 13114469 13181548 13249745 13319012 13391216 13458847 13525294 13593846 - 13667481] - -sources: [7682790 7682790 7682790 ... 2256518 2256518 2256518] -destinations: [ 604566 8525141 4875035 ... 4516008 2080018 7224383] -batch: [12600 12601 12602 12603 12604 12605 12606 12607 12608 12609 12610 12611 - 12612 12613 12614 12615 12616 12617 12618 12619 12620 12621 12622 12623 - 12624 12625 12626 12627 12628 12629 12630 12631 12632 12633 12634 12635 - 12636 12637 12638 12639 12640 12641 12642 12643 12644 12645 12646 12647 - 12648 12649 12650 12651 12652 12653 12654 12655 12656 12657 12658 12659 - 12660 12661 12662 12663 12664 12665 12666 12667 12668 12669 12670 12671 - 12672 12673 12674 12675 12676 12677 12678 12679 12680 12681 12682 12683 - 12684 12685 12686 12687 12688 12689 12690 12691 12692 12693 12694 12695 - 12696 12697 12698 12699 12700 12701 12702 12703 12704 12705 12706 12707 - 12708 12709 12710 12711 12712 12713 12714 12715 12716 12717 12718 12719 - 12720 12721 12722 12723 12724 12725 12726 12727 12728 12729 12730 12731 - 12732 12733 12734 12735 12736 12737 12738 12739 12740 12741 12742 12743 - 12744 12745 12746 12747 12748 12749 12750 12751 12752 12753 12754 12755 - 12756 12757 12758 12759 12760 12761 12762 12763 12764 12765 12766 12767 - 12768 12769 12770 12771 12772 12773 12774 12775 12776 12777 12778 12779 - 12780 12781 12782 12783 12784 12785 12786 12787 12788 12789 12790 12791 - 12792 12793 12794 12795 12796 12797 12798 12799] -offset: [ 0 70827 135741 205562 271135 337381 410047 479708 - 544719 615074 686131 753701 818849 888407 958190 1022887 - 1090577 1155293 1223888 1295362 1360474 1430703 1500033 1569540 - 1636084 1704572 1769195 1836966 1905720 1973930 2041259 2112230 - 2182381 2250545 2318836 2387842 2455869 2523463 2587419 2653254 - 2724569 2795115 2860862 2928011 2998918 3071891 3138308 3203325 - 3272683 3335206 3404415 3470900 3538486 3609825 3677540 3745915 - 3813301 3880742 3950208 4018984 4085523 4154523 4222502 4293480 - 4356833 4419646 4488219 4558857 4627848 4691046 4758675 4830014 - 4901232 4966912 5033446 5098958 5165141 5234675 5302028 5369982 - 5436803 5502833 5574641 5645765 5712879 5782529 5849769 5918120 - 5989727 6057079 6125681 6192837 6255277 6323130 6390442 6458832 - 6531841 6600067 6670782 6741449 6814879 6879718 6947763 7014273 - 7084394 7151733 7216748 7284190 7353573 7421223 7485425 7553610 - 7623783 7697490 7763847 7833433 7903059 7974128 8047450 8117429 - 8182838 8250982 8318708 8384000 8451977 8522279 8592333 8658010 - 8726249 8794355 8864021 8935717 9004363 9073481 9141795 9209364 - 9277290 9345043 9413308 9480313 9545354 9612445 9682516 9753236 - 9826287 9891507 9958645 10023571 10091572 10157448 10227890 10292957 - 10356045 10424983 10493716 10560605 10627552 10695495 10765033 10832018 - 10901324 10968836 11036883 11105858 11172668 11241955 11310641 11379540 - 11447104 11510582 11579380 11647016 11718513 11787601 11854391 11920710 - 11987639 12053653 12121244 12192117 12261527 12329034 12399519 12472805 - 12542970 12609915 12675794 12742329 12809754 12876042 12946566 13012908 - 13082816 13151623 13219895 13283371 13351890 13421337 13491970 13563364 - 13629200] - -2023-05-10 14:04:17,054 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:17,054 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:18,850 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:18,850 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [6400019 6400019 6400019 ... 171057 171057 171057] -destinations: [6347284 4354044 6348765 ... 6949585 4657838 542983] -batch: [12800 12801 12802 12803 12804 12805 12806 12807 12808 12809 12810 12811 - 12812 12813 12814 12815 12816 12817 12818 12819 12820 12821 12822 12823 - 12824 12825 12826 12827 12828 12829 12830 12831 12832 12833 12834 12835 - 12836 12837 12838 12839 12840 12841 12842 12843 12844 12845 12846 12847 - 12848 12849 12850 12851 12852 12853 12854 12855 12856 12857 12858 12859 - 12860 12861 12862 12863 12864 12865 12866 12867 12868 12869 12870 12871 - 12872 12873 12874 12875 12876 12877 12878 12879 12880 12881 12882 12883 - 12884 12885 12886 12887 12888 12889 12890 12891 12892 12893 12894 12895 - 12896 12897 12898 12899 12900 12901 12902 12903 12904 12905 12906 12907 - 12908 12909 12910 12911 12912 12913 12914 12915 12916 12917 12918 12919 - 12920 12921 12922 12923 12924 12925 12926 12927 12928 12929 12930 12931 - 12932 12933 12934 12935 12936 12937 12938 12939 12940 12941 12942 12943 - 12944 12945 12946 12947 12948 12949 12950 12951 12952 12953 12954 12955 - 12956 12957 12958 12959 12960 12961 12962 12963 12964 12965 12966 12967 - 12968 12969 12970 12971 12972 12973 12974 12975 12976 12977 12978 12979 - 12980 12981 12982 12983 12984 12985 12986 12987 12988 12989 12990 12991 - 12992 12993 12994 12995 12996 12997 12998 12999] -offset: [ 0 69457 138616 204594 268165 337495 405777 471802 - 539273 607800 678807 749040 815721 886377 954141 1020154 - 1088900 1158442 1230323 1296670 1366716 1436539 1504017 1572234 - 1643613 1712872 1780035 1844977 1916742 1986158 2054956 2120318 - 2185676 2256428 2323666 2391455 2458654 2529769 2598030 2664394 - 2729433 2797671 2865611 2934266 3003450 3072743 3143004 3212279 - 3282691 3350948 3424097 3491419 3556284 3620169 3686986 3756131 - 3826439 3892748 3959307 4029308 4099444 4168312 4236406 4300420 - 4369057 4434325 4502732 4566725 4636153 4706039 4774847 4840459 - 4908924 4978310 5046187 5116227 5182889 5251987 5318426 5388285 - 5454921 5525675 5597641 5660971 5725397 5794764 5866941 5936490 - 6004662 6070779 6138397 6209084 6279853 6347370 6413881 6481911 - 6552512 6622535 6688556 6752478 6819959 6888566 6957030 7025618 - 7086206 7156746 7224437 7290872 7358868 7427333 7497079 7565780 - 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13212 13213 13214 13215 13216 13217 13218 13219 13220 13221 13222 13223 - 13224 13225 13226 13227 13228 13229 13230 13231 13232 13233 13234 13235 - 13236 13237 13238 13239 13240 13241 13242 13243 13244 13245 13246 13247 - 13248 13249 13250 13251 13252 13253 13254 13255 13256 13257 13258 13259 - 13260 13261 13262 13263 13264 13265 13266 13267 13268 13269 13270 13271 - 13272 13273 13274 13275 13276 13277 13278 13279 13280 13281 13282 13283 - 13284 13285 13286 13287 13288 13289 13290 13291 13292 13293 13294 13295 - 13296 13297 13298 13299 13300 13301 13302 13303 13304 13305 13306 13307 - 13308 13309 13310 13311 13312 13313 13314 13315 13316 13317 13318 13319 - 13320 13321 13322 13323 13324 13325 13326 13327 13328 13329 13330 13331 - 13332 13333 13334 13335 13336 13337 13338 13339 13340 13341 13342 13343 - 13344 13345 13346 13347 13348 13349 13350 13351 13352 13353 13354 13355 - 13356 13357 13358 13359 13360 13361 13362 13363 13364 13365 13366 13367 - 13368 13369 13370 13371 13372 13373 13374 13375 13376 13377 13378 13379 - 13380 13381 13382 13383 13384 13385 13386 13387 13388 13389 13390 13391 - 13392 13393 13394 13395 13396 13397 13398 13399] -offset: [ 0 69424 136930 208044 278910 346847 414535 487020 - 553607 618701 688586 759215 827503 892829 962477 1033127 - 1102601 1168266 1236593 1303059 1368612 1436318 1505830 1578609 - 1646670 1710625 1776958 1844112 1908907 1981827 2051881 2122004 - 2189382 2260396 2326015 2395851 2463643 2527373 2596511 2665330 - 2736695 2799980 2870316 2941839 3008719 3078753 3146471 3218364 - 3288184 3355386 3428135 3493009 3561598 3627875 3692650 3764549 - 3835224 3903688 3969543 4041681 4111443 4181211 4246812 4314524 - 4384413 4450550 4523932 4589854 4654767 4725004 4793926 4857816 - 4926489 4989505 5058497 5129179 5195454 5264993 5333637 5402867 - 5470987 5537970 5607749 5676894 5747287 5814442 5883867 5951315 - 6013590 6079135 6147268 6220110 6289260 6353268 6425158 6491969 - 6560324 6631128 6698480 6767536 6838415 6909534 6976154 7040896 - 7113134 7179728 7241295 7309219 7373523 7439062 7510553 7575889 - 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13012 13013 13014 13015 13016 13017 13018 13019 13020 13021 13022 13023 - 13024 13025 13026 13027 13028 13029 13030 13031 13032 13033 13034 13035 - 13036 13037 13038 13039 13040 13041 13042 13043 13044 13045 13046 13047 - 13048 13049 13050 13051 13052 13053 13054 13055 13056 13057 13058 13059 - 13060 13061 13062 13063 13064 13065 13066 13067 13068 13069 13070 13071 - 13072 13073 13074 13075 13076 13077 13078 13079 13080 13081 13082 13083 - 13084 13085 13086 13087 13088 13089 13090 13091 13092 13093 13094 13095 - 13096 13097 13098 13099 13100 13101 13102 13103 13104 13105 13106 13107 - 13108 13109 13110 13111 13112 13113 13114 13115 13116 13117 13118 13119 - 13120 13121 13122 13123 13124 13125 13126 13127 13128 13129 13130 13131 - 13132 13133 13134 13135 13136 13137 13138 13139 13140 13141 13142 13143 - 13144 13145 13146 13147 13148 13149 13150 13151 13152 13153 13154 13155 - 13156 13157 13158 13159 13160 13161 13162 13163 13164 13165 13166 13167 - 13168 13169 13170 13171 13172 13173 13174 13175 13176 13177 13178 13179 - 13180 13181 13182 13183 13184 13185 13186 13187 13188 13189 13190 13191 - 13192 13193 13194 13195 13196 13197 13198 13199] -offset: [ 0 70375 135974 206232 276893 344253 409633 475563 - 545704 609442 673572 739779 807885 874669 946434 1014364 - 1083005 1155483 1220465 1289860 1359320 1431734 1500377 1569541 - 1636017 1702930 1767119 1835509 1900364 1968595 2034716 2100880 - 2168014 2235163 2303017 2366419 2433194 2504700 2574579 2646488 - 2713157 2780234 2847956 2915468 2983861 3053243 3122298 3194645 - 3262244 3326168 3395340 3461365 3529039 3597281 3661489 3729134 - 3796242 3865694 3930804 4001313 4068718 4139999 4208242 4274398 - 4344370 4411912 4480143 4548756 4617176 4688307 4759583 4830594 - 4898945 4969374 5039288 5104874 5173704 5242285 5311153 5376106 - 5443856 5512349 5586639 5654297 5722630 5791414 5860273 5924555 - 5990724 6059036 6128985 6196155 6261655 6334173 6401882 6472713 - 6537280 6604321 6675999 6743574 6813482 6880826 6943301 7012296 - 7079719 7148477 7215734 7285668 7357694 7428843 7498164 7565003 - 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13412 13413 13414 13415 13416 13417 13418 13419 13420 13421 13422 13423 - 13424 13425 13426 13427 13428 13429 13430 13431 13432 13433 13434 13435 - 13436 13437 13438 13439 13440 13441 13442 13443 13444 13445 13446 13447 - 13448 13449 13450 13451 13452 13453 13454 13455 13456 13457 13458 13459 - 13460 13461 13462 13463 13464 13465 13466 13467 13468 13469 13470 13471 - 13472 13473 13474 13475 13476 13477 13478 13479 13480 13481 13482 13483 - 13484 13485 13486 13487 13488 13489 13490 13491 13492 13493 13494 13495 - 13496 13497 13498 13499 13500 13501 13502 13503 13504 13505 13506 13507 - 13508 13509 13510 13511 13512 13513 13514 13515 13516 13517 13518 13519 - 13520 13521 13522 13523 13524 13525 13526 13527 13528 13529 13530 13531 - 13532 13533 13534 13535 13536 13537 13538 13539 13540 13541 13542 13543 - 13544 13545 13546 13547 13548 13549 13550 13551 13552 13553 13554 13555 - 13556 13557 13558 13559 13560 13561 13562 13563 13564 13565 13566 13567 - 13568 13569 13570 13571 13572 13573 13574 13575 13576 13577 13578 13579 - 13580 13581 13582 13583 13584 13585 13586 13587 13588 13589 13590 13591 - 13592 13593 13594 13595 13596 13597 13598 13599] -offset: [ 0 70844 137922 205869 276021 346912 416865 485843 - 550208 617170 684588 751952 820023 887730 958394 1027247 - 1094049 1161938 1229717 1294823 1358043 1425870 1491310 1563746 - 1631153 1696552 1768070 1836735 1907904 1974411 2043951 2111435 - 2178102 2247776 2312997 2383481 2452307 2520715 2588216 2657276 - 2725601 2796620 2865549 2932662 2998526 3066721 3134763 3200568 - 3268623 3332931 3403811 3474275 3541951 3612525 3681535 3748319 - 3815379 3885240 3950218 4020752 4090321 4158718 4226599 4293019 - 4363566 4432778 4502058 4570444 4635752 4707485 4773817 4842045 - 4912626 4979803 5046128 5113035 5181693 5250210 5321870 5389532 - 5457813 5526804 5597415 5666117 5732334 5803212 5871620 5941678 - 6006158 6072880 6142411 6211597 6279819 6346719 6416264 6486959 - 6554819 6625092 6694690 6760090 6828306 6893459 6962684 7033258 - 7104133 7171970 7239882 7308646 7375652 7443322 7513748 7583982 - 7652485 7719994 7788377 7855566 7928067 7995779 8063350 8134021 - 8196947 8265283 8335975 8404018 8467594 8533247 8600235 8667448 - 8738287 8807343 8878381 8948503 9014281 9083159 9151607 9223110 - 9289702 9356908 9427981 9498427 9568310 9637322 9706311 9776976 - 9844641 9915036 9982680 10049912 10116235 10186204 10256846 10329021 - 10398958 10468042 10534325 10605207 10673294 10742661 10808213 10872892 - 10940604 11008943 11077167 11142824 11209467 11279811 11349439 11419701 - 11488787 11556151 11626264 11691549 11754205 11823579 11893276 11964104 - 12029141 12094503 12165212 12233456 12299850 12366525 12431710 12501477 - 12569080 12636707 12703369 12773007 12840803 12910920 12980591 13049331 - 13117919 13183825 13248934 13318080 13388895 13459949 13528187 13596099 - 13668791] - -2023-05-10 14:04:20,697 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:20,697 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:23,444 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:23,445 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [6800288 6800288 6800288 ... 1960028 1960028 1960028] -destinations: [7504133 6144058 8055981 ... 2887388 6733339 5558246] -batch: [13600 13601 13602 13603 13604 13605 13606 13607 13608 13609 13610 13611 - 13612 13613 13614 13615 13616 13617 13618 13619 13620 13621 13622 13623 - 13624 13625 13626 13627 13628 13629 13630 13631 13632 13633 13634 13635 - 13636 13637 13638 13639 13640 13641 13642 13643 13644 13645 13646 13647 - 13648 13649 13650 13651 13652 13653 13654 13655 13656 13657 13658 13659 - 13660 13661 13662 13663 13664 13665 13666 13667 13668 13669 13670 13671 - 13672 13673 13674 13675 13676 13677 13678 13679 13680 13681 13682 13683 - 13684 13685 13686 13687 13688 13689 13690 13691 13692 13693 13694 13695 - 13696 13697 13698 13699 13700 13701 13702 13703 13704 13705 13706 13707 - 13708 13709 13710 13711 13712 13713 13714 13715 13716 13717 13718 13719 - 13720 13721 13722 13723 13724 13725 13726 13727 13728 13729 13730 13731 - 13732 13733 13734 13735 13736 13737 13738 13739 13740 13741 13742 13743 - 13744 13745 13746 13747 13748 13749 13750 13751 13752 13753 13754 13755 - 13756 13757 13758 13759 13760 13761 13762 13763 13764 13765 13766 13767 - 13768 13769 13770 13771 13772 13773 13774 13775 13776 13777 13778 13779 - 13780 13781 13782 13783 13784 13785 13786 13787 13788 13789 13790 13791 - 13792 13793 13794 13795 13796 13797 13798 13799] -offset: [ 0 65315 132521 200909 268086 337114 403202 469888 - 541965 612371 679563 748798 815982 886183 952263 1020712 - 1088169 1154707 1219483 1285375 1353116 1424858 1496875 1565154 - 1635571 1701711 1770311 1838532 1904309 1973623 2039682 2104951 - 2171890 2239019 2307002 2382717 2452403 2521051 2587349 2654886 - 2718134 2785868 2852697 2921403 2992087 3059211 3124582 3190900 - 3263487 3333522 3400414 3467302 3538608 3611063 3679971 3749734 - 3816924 3885022 3952246 4019796 4088193 4153780 4219176 4287503 - 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12528778 12598557 12666109 12733880 12805065 12875735 12945359 13020179 - 13087512 13157476 13227181 13298377 13362762 13433153 13503093 13570618 - 13642363] - -sources: [7000272 7000272 7000272 ... 2523811 2523811 2523811] -destinations: [5341899 822791 6359141 ... 8226034 389949 1080796] -batch: [14000 14001 14002 14003 14004 14005 14006 14007 14008 14009 14010 14011 - 14012 14013 14014 14015 14016 14017 14018 14019 14020 14021 14022 14023 - 14024 14025 14026 14027 14028 14029 14030 14031 14032 14033 14034 14035 - 14036 14037 14038 14039 14040 14041 14042 14043 14044 14045 14046 14047 - 14048 14049 14050 14051 14052 14053 14054 14055 14056 14057 14058 14059 - 14060 14061 14062 14063 14064 14065 14066 14067 14068 14069 14070 14071 - 14072 14073 14074 14075 14076 14077 14078 14079 14080 14081 14082 14083 - 14084 14085 14086 14087 14088 14089 14090 14091 14092 14093 14094 14095 - 14096 14097 14098 14099 14100 14101 14102 14103 14104 14105 14106 14107 - 14108 14109 14110 14111 14112 14113 14114 14115 14116 14117 14118 14119 - 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15048 15049 15050 15051 15052 15053 15054 15055 15056 15057 15058 15059 - 15060 15061 15062 15063 15064 15065 15066 15067 15068 15069 15070 15071 - 15072 15073 15074 15075 15076 15077 15078 15079 15080 15081 15082 15083 - 15084 15085 15086 15087 15088 15089 15090 15091 15092 15093 15094 15095 - 15096 15097 15098 15099 15100 15101 15102 15103 15104 15105 15106 15107 - 15108 15109 15110 15111 15112 15113 15114 15115 15116 15117 15118 15119 - 15120 15121 15122 15123 15124 15125 15126 15127 15128 15129 15130 15131 - 15132 15133 15134 15135 15136 15137 15138 15139 15140 15141 15142 15143 - 15144 15145 15146 15147 15148 15149 15150 15151 15152 15153 15154 15155 - 15156 15157 15158 15159 15160 15161 15162 15163 15164 15165 15166 15167 - 15168 15169 15170 15171 15172 15173 15174 15175 15176 15177 15178 15179 - 15180 15181 15182 15183 15184 15185 15186 15187 15188 15189 15190 15191 - 15192 15193 15194 15195 15196 15197 15198 15199] -offset: [ 0 69167 137386 205597 272298 343201 412117 482904 - 551864 617458 686355 755302 821742 889178 958011 1024905 - 1095139 1163686 1230199 1299820 1366873 1435038 1503891 1573998 - 1641985 1708203 1777602 1846452 1914242 1981255 2045720 2112775 - 2180012 2251960 2322029 2386275 2457817 2524353 2590886 2657253 - 2724391 2793596 2862406 2930672 2999706 3068887 3135512 3204798 - 3274980 3342785 3411077 3480811 3549727 3619166 3691037 3755990 - 3825055 3894555 3967603 4036650 4104733 4174117 4241069 4309149 - 4374784 4444059 4511957 4581756 4652363 4719630 4790101 4857258 - 4922839 4991314 5059241 5123672 5192952 5261728 5327342 5396577 - 5468156 5536749 5609551 5678513 5742734 5811233 5878644 5945574 - 6013199 6080073 6150247 6219221 6287316 6352510 6419201 6489176 - 6558674 6629743 6695173 6763551 6832880 6900419 6967680 7036051 - 7106807 7174309 7239238 7307393 7371786 7437704 7510350 7578840 - 7644906 7712315 7781100 7847607 7913914 7980653 8051981 8119807 - 8191525 8256074 8325535 8393222 8465117 8530481 8597753 8665062 - 8729209 8797149 8861984 8929402 9000144 9072534 9139547 9208672 - 9278122 9345911 9414738 9483005 9551824 9622439 9694161 9767488 - 9834774 9901278 9967453 10035769 10103367 10167727 10232461 10301417 - 10370917 10440187 10505606 10574791 10644472 10714158 10783178 10847089 - 10916650 10986085 11057672 11125572 11195473 11264960 11336662 11406747 - 11476021 11548050 11620352 11690816 11756286 11825136 11891638 11962605 - 12025501 12092772 12158574 12226425 12294448 12359690 12430181 12497516 - 12568113 12637388 12709052 12775808 12842442 12911615 12978474 13044003 - 13109110 13179145 13250631 13318897 13386568 13452598 13523429 13592954 - 13659457] - -2023-05-10 14:04:29,754 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:29,754 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:31,595 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:31,595 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [7600225 7600225 7600225 ... 4526405 4526405 4526405] -destinations: [ 101618 2652025 436846 ... 6249527 5671095 8468857] -batch: [15200 15201 15202 15203 15204 15205 15206 15207 15208 15209 15210 15211 - 15212 15213 15214 15215 15216 15217 15218 15219 15220 15221 15222 15223 - 15224 15225 15226 15227 15228 15229 15230 15231 15232 15233 15234 15235 - 15236 15237 15238 15239 15240 15241 15242 15243 15244 15245 15246 15247 - 15248 15249 15250 15251 15252 15253 15254 15255 15256 15257 15258 15259 - 15260 15261 15262 15263 15264 15265 15266 15267 15268 15269 15270 15271 - 15272 15273 15274 15275 15276 15277 15278 15279 15280 15281 15282 15283 - 15284 15285 15286 15287 15288 15289 15290 15291 15292 15293 15294 15295 - 15296 15297 15298 15299 15300 15301 15302 15303 15304 15305 15306 15307 - 15308 15309 15310 15311 15312 15313 15314 15315 15316 15317 15318 15319 - 15320 15321 15322 15323 15324 15325 15326 15327 15328 15329 15330 15331 - 15332 15333 15334 15335 15336 15337 15338 15339 15340 15341 15342 15343 - 15344 15345 15346 15347 15348 15349 15350 15351 15352 15353 15354 15355 - 15356 15357 15358 15359 15360 15361 15362 15363 15364 15365 15366 15367 - 15368 15369 15370 15371 15372 15373 15374 15375 15376 15377 15378 15379 - 15380 15381 15382 15383 15384 15385 15386 15387 15388 15389 15390 15391 - 15392 15393 15394 15395 15396 15397 15398 15399] -offset: [ 0 65696 134967 206966 272598 342753 409386 480561 - 550155 620021 686526 754646 825037 896679 963104 1032498 - 1105153 1172316 1239171 1308416 1379986 1448987 1518648 1584870 - 1650910 1714720 1781526 1846722 1913002 1980805 2054612 2127723 - 2195716 2260980 2329367 2391845 2459912 2526806 2591632 2659577 - 2728157 2797138 2861146 2931918 2995221 3063816 3130284 3200733 - 3269454 3338786 3406250 3471792 3539203 3604950 3674065 3740930 - 3804141 3872123 3941679 4009626 4082961 4149819 4216122 4282850 - 4350331 4417359 4486996 4557401 4627087 4698907 4767694 4834078 - 4902169 4972816 5038640 5106692 5172089 5241797 5306285 5374430 - 5444020 5509245 5580671 5648276 5717794 5786641 5853713 5920735 - 5988321 6059933 6123360 6194288 6258714 6328368 6399783 6470662 - 6538107 6607434 6679298 6744756 6812371 6883548 6955603 7023693 - 7090879 7160082 7227634 7295975 7366825 7436442 7509037 7577591 - 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15812 15813 15814 15815 15816 15817 15818 15819 15820 15821 15822 15823 - 15824 15825 15826 15827 15828 15829 15830 15831 15832 15833 15834 15835 - 15836 15837 15838 15839 15840 15841 15842 15843 15844 15845 15846 15847 - 15848 15849 15850 15851 15852 15853 15854 15855 15856 15857 15858 15859 - 15860 15861 15862 15863 15864 15865 15866 15867 15868 15869 15870 15871 - 15872 15873 15874 15875 15876 15877 15878 15879 15880 15881 15882 15883 - 15884 15885 15886 15887 15888 15889 15890 15891 15892 15893 15894 15895 - 15896 15897 15898 15899 15900 15901 15902 15903 15904 15905 15906 15907 - 15908 15909 15910 15911 15912 15913 15914 15915 15916 15917 15918 15919 - 15920 15921 15922 15923 15924 15925 15926 15927 15928 15929 15930 15931 - 15932 15933 15934 15935 15936 15937 15938 15939 15940 15941 15942 15943 - 15944 15945 15946 15947 15948 15949 15950 15951 15952 15953 15954 15955 - 15956 15957 15958 15959 15960 15961 15962 15963 15964 15965 15966 15967 - 15968 15969 15970 15971 15972 15973 15974 15975 15976 15977 15978 15979 - 15980 15981 15982 15983 15984 15985 15986 15987 15988 15989 15990 15991 - 15992 15993 15994 15995 15996 15997 15998 15999] -offset: [ 0 72079 141791 212048 285060 350015 420345 484612 - 551731 618771 684060 753389 822165 892802 962145 1030203 - 1098669 1168378 1234497 1302285 1374138 1442041 1510196 1575668 - 1646834 1717019 1785348 1853277 1922896 1993959 2062396 2128821 - 2193681 2261874 2332318 2400734 2466357 2535548 2603221 2671395 - 2737761 2809712 2878373 2947985 3013641 3082449 3151839 3221405 - 3286771 3360291 3428689 3497356 3563125 3629451 3698487 3765743 - 3835188 3902605 3971858 4038201 4104992 4171974 4238257 4304231 - 4370726 4438051 4506637 4573458 4646962 4716145 4784474 4853969 - 4920927 4989784 5057016 5125323 5194064 5263187 5329259 5396090 - 5467238 5531984 5602565 5671806 5741217 5810813 5877031 5945229 - 6012085 6081080 6148396 6214360 6285570 6355638 6422018 6484770 - 6551980 6622908 6686452 6753149 6818300 6888500 6963016 7032432 - 7101037 7171601 7240989 7306245 7380624 7451356 7520887 7587026 - 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distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:36,158 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [8000288 8000288 8000288 ... 2034435 2034435 2034435] -destinations: [3522186 3239900 2896435 ... 2756117 5272264 3771888] -batch: [16000 16001 16002 16003 16004 16005 16006 16007 16008 16009 16010 16011 - 16012 16013 16014 16015 16016 16017 16018 16019 16020 16021 16022 16023 - 16024 16025 16026 16027 16028 16029 16030 16031 16032 16033 16034 16035 - 16036 16037 16038 16039 16040 16041 16042 16043 16044 16045 16046 16047 - 16048 16049 16050 16051 16052 16053 16054 16055 16056 16057 16058 16059 - 16060 16061 16062 16063 16064 16065 16066 16067 16068 16069 16070 16071 - 16072 16073 16074 16075 16076 16077 16078 16079 16080 16081 16082 16083 - 16084 16085 16086 16087 16088 16089 16090 16091 16092 16093 16094 16095 - 16096 16097 16098 16099 16100 16101 16102 16103 16104 16105 16106 16107 - 16108 16109 16110 16111 16112 16113 16114 16115 16116 16117 16118 16119 - 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16320 16321 16322 16323 16324 16325 16326 16327 16328 16329 16330 16331 - 16332 16333 16334 16335 16336 16337 16338 16339 16340 16341 16342 16343 - 16344 16345 16346 16347 16348 16349 16350 16351 16352 16353 16354 16355 - 16356 16357 16358 16359 16360 16361 16362 16363 16364 16365 16366 16367 - 16368 16369 16370 16371 16372 16373 16374 16375 16376 16377 16378 16379 - 16380 16381 16382 16383 16384 16385 16386 16387 16388 16389 16390 16391 - 16392 16393 16394 16395 16396 16397 16398 16399] -offset: [ 0 67974 134761 205599 270339 342254 413932 484103 - 551285 613989 684988 751246 823150 890207 954884 1024249 - 1094129 1165718 1233898 1301645 1368652 1434786 1504260 1569910 - 1636531 1704584 1773117 1840048 1905479 1976631 2045154 2113774 - 2184543 2250829 2320383 2391479 2459745 2530311 2594321 2663148 - 2730822 2800579 2869822 2938461 3006034 3075025 3143717 3210284 - 3278757 3345245 3413080 3480671 3546908 3614939 3682710 3752763 - 3820272 3891861 3959349 4031398 4098855 4166320 4233099 4303298 - 4372580 4436373 4505859 4569301 4638898 4710129 4778552 4844417 - 4910136 4979602 5046497 5114528 5181281 5248260 5315720 5387383 - 5454259 5524661 5597508 5665109 5731387 5797496 5866947 5933426 - 6002945 6065654 6130984 6202328 6271301 6338797 6411393 6483971 - 6549733 6619919 6687383 6757693 6828314 6896692 6969839 7037466 - 7107839 7178513 7247155 7316744 7385246 7451732 7521563 7590505 - 7657842 7725268 7793033 7861386 7927307 7997304 8058952 8128893 - 8196789 8267876 8331651 8400397 8470465 8542656 8608612 8678594 - 8746304 8812034 8878093 8946041 9009957 9079384 9149261 9213367 - 9284665 9353759 9424294 9486374 9552466 9620593 9687580 9757866 - 9826269 9898478 9969984 10040409 10103906 10170391 10239325 10308454 - 10372063 10442335 10510101 10578987 10649310 10717569 10790018 10860731 - 10927110 10994431 11061530 11130575 11197638 11262358 11331554 11400767 - 11471538 11536800 11604910 11672500 11741217 11810871 11881988 11949673 - 12018981 12087177 12158193 12227110 12293078 12363416 12428745 12496508 - 12566053 12634454 12704202 12774567 12842531 12911545 12977406 13041739 - 13111377 13179987 13250227 13322472 13388826 13456969 13526825 13595525 - 13662676] - -sources: [8399862 8399862 8399862 ... 8300924 8300922 8300921] -destinations: [6479915 6675899 8761006 ... 5409365 1302990 5319297] -batch: [16600 16601 16602 16603 16604 16605 16606 16607 16608 16609 16610 16611 - 16612 16613 16614 16615 16616 16617 16618 16619 16620 16621 16622 16623 - 16624 16625 16626 16627 16628 16629 16630 16631 16632 16633 16634 16635 - 16636 16637 16638 16639 16640 16641 16642 16643 16644 16645 16646 16647 - 16648 16649 16650 16651 16652 16653 16654 16655 16656 16657 16658 16659 - 16660 16661 16662 16663 16664 16665 16666 16667 16668 16669 16670 16671 - 16672 16673 16674 16675 16676 16677 16678 16679 16680 16681 16682 16683 - 16684 16685 16686 16687 16688 16689 16690 16691 16692 16693 16694 16695 - 16696 16697 16698 16699 16700 16701 16702 16703 16704 16705 16706 16707 - 16708 16709 16710 16711 16712 16713 16714 16715 16716 16717 16718 16719 - 16720 16721 16722 16723 16724 16725 16726 16727 16728 16729 16730 16731 - 16732 16733 16734 16735 16736 16737 16738 16739 16740 16741 16742 16743 - 16744 16745 16746 16747 16748 16749 16750 16751 16752 16753 16754 16755 - 16756 16757 16758 16759 16760 16761 16762 16763 16764 16765 16766 16767 - 16768 16769 16770 16771 16772 16773 16774 16775 16776 16777 16778 16779 - 16780 16781 16782 16783 16784 16785 16786 16787 16788 16789 16790 16791 - 16792 16793 16794 16795 16796 16797 16798 16799] -offset: [ 0 68533 136751 199887 268446 338895 407708 477784 - 548808 612409 676843 743280 813595 880479 947964 1018365 - 1087237 1156367 1223612 1292542 1360993 1427961 1492754 1560051 - 1629278 1698582 1766119 1832492 1898042 1967014 2035617 2105577 - 2170825 2242528 2311639 2383271 2454735 2521821 2589586 2657382 - 2726894 2792632 2861943 2929592 3001320 3070009 3135976 3202000 - 3271888 3343799 3410732 3479905 3545687 3617028 3687670 3758560 - 3824698 3894511 3965413 4032162 4099722 4165707 4234227 4304992 - 4374268 4436333 4503382 4569984 4636944 4707204 4776193 4844906 - 4909691 4980188 5046251 5114104 5182165 5251016 5319948 5391693 - 5461165 5528626 5597815 5668079 5739786 5809746 5876662 5945146 - 6019549 6087996 6155358 6220372 6287650 6354281 6422570 6488998 - 6553982 6623621 6691723 6763996 6832537 6897950 6968016 7034160 - 7103313 7171256 7240513 7309085 7377558 7451152 7518382 7589542 - 7656811 7727047 7798064 7865470 7933153 8002010 8070863 8135650 - 8205034 8273737 8339340 8403909 8473677 8537181 8605010 8670606 - 8740616 8807482 8877796 8948348 9015679 9087041 9155553 9228682 - 9294246 9362235 9431647 9502994 9569491 9636918 9704685 9770772 - 9838599 9911294 9976704 10046410 10113126 10181837 10249318 10314303 - 10385880 10453797 10524329 10592119 10660415 10731294 10800737 10863634 - 10931348 10998396 11066677 11134048 11201356 11272482 11341079 11410654 - 11476715 11543004 11610811 11676597 11742984 11809607 11877501 11941508 - 12007563 12071636 12140846 12212327 12276681 12341878 12416513 12483049 - 12554331 12620559 12691714 12756772 12824322 12890892 12958090 13022277 - 13092871 13158306 13229347 13295990 13362969 13432483 13501204 13572458 - 13641811] - -2023-05-10 14:04:38,886 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:38,886 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:40,705 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 14:04:40,705 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [8400212 8400213 8400213 ... 7885458 7885458 7885458] -destinations: [7522936 3638583 6446259 ... 4410721 5815273 637210] -batch: [16800 16801 16802 16803 16804 16805 16806 16807 16808 16809 16810 16811 - 16812 16813 16814 16815 16816 16817 16818 16819 16820 16821 16822 16823 - 16824 16825 16826 16827 16828 16829 16830 16831 16832 16833 16834 16835 - 16836 16837 16838 16839 16840 16841 16842 16843 16844 16845 16846 16847 - 16848 16849 16850 16851 16852 16853 16854 16855 16856 16857 16858 16859 - 16860 16861 16862 16863 16864 16865 16866 16867 16868 16869 16870 16871 - 16872 16873 16874 16875 16876 16877 16878 16879 16880 16881 16882 16883 - 16884 16885 16886 16887 16888 16889 16890 16891 16892 16893 16894 16895 - 16896 16897 16898 16899 16900 16901 16902 16903 16904 16905 16906 16907 - 16908 16909 16910 16911 16912 16913 16914 16915 16916 16917 16918 16919 - 16920 16921 16922 16923 16924 16925 16926 16927 16928 16929 16930 16931 - 16932 16933 16934 16935 16936 16937 16938 16939 16940 16941 16942 16943 - 16944 16945 16946 16947 16948 16949 16950 16951 16952 16953 16954 16955 - 16956 16957 16958 16959 16960 16961 16962 16963 16964 16965 16966 16967 - 16968 16969 16970 16971 16972 16973 16974 16975 16976 16977 16978 16979 - 16980 16981 16982 16983 16984 16985 16986 16987 16988 16989 16990 16991 - 16992 16993 16994 16995 16996 16997 16998 16999] -offset: [ 0 68269 134968 205905 273262 339666 409051 473664 - 542407 611292 676747 746748 810282 876904 944392 1011434 - 1078643 1143535 1212486 1280777 1349343 1419312 1487962 1558031 - 1626541 1696483 1765480 1832392 1894078 1963026 2025685 2094792 - 2162245 2233627 2302282 2367140 2439301 2509953 2576326 2645930 - 2712243 2781840 2852232 2917399 2986019 3053049 3120676 3190207 - 3259290 3326642 3395760 3463529 3534220 3599780 3668010 3734754 - 3806725 3876730 3943775 4015047 4085767 4152910 4220676 4291574 - 4357004 4427677 4490289 4559635 4631938 4697458 4768276 4834480 - 4903409 4975304 5041981 5108369 5175019 5244518 5312673 5380672 - 5448427 5513218 5583681 5650619 5719031 5791920 5861071 5928779 - 5999642 6068041 6134957 6206886 6272481 6340730 6412575 6479273 - 6547535 6616898 6684887 6754405 6823901 6889518 6954654 7019921 - 7090405 7160756 7230582 7297712 7363898 7428740 7498239 7567034 - 7635055 7699942 7765147 7834557 7899767 7968565 8037430 8109705 - 8179502 8244725 8311462 8377328 8445693 8513841 8583136 8649164 - 8721094 8789155 8855120 8926870 8994342 9063808 9131062 9200536 - 9270351 9340792 9407285 9473565 9541294 9611979 9679555 9744450 - 9811479 9880143 9950532 10022185 10089937 10159794 10230983 10302330 - 10368951 10434489 10504006 10570024 10638096 10705263 10772331 10842727 - 10911815 10977960 11048121 11117717 11181171 11247698 11314749 11381471 - 11450514 11521310 11589388 11658708 11727121 11792934 11857129 11926505 - 11997046 12069676 12136975 12204906 12272253 12339461 12410233 12482951 - 12555454 12622434 12694379 12761680 12830501 12894188 12961916 13029033 - 13095696 13163382 13231652 13303083 13371020 13437947 13510237 13578017 - 13647139] - -sources: [8600289 8600289 8600289 ... 1473848 1473848 1473848] -destinations: [3069756 3832034 7793472 ... 2801822 4161874 4548586] -batch: [17200 17201 17202 17203 17204 17205 17206 17207 17208 17209 17210 17211 - 17212 17213 17214 17215 17216 17217 17218 17219 17220 17221 17222 17223 - 17224 17225 17226 17227 17228 17229 17230 17231 17232 17233 17234 17235 - 17236 17237 17238 17239 17240 17241 17242 17243 17244 17245 17246 17247 - 17248 17249 17250 17251 17252 17253 17254 17255 17256 17257 17258 17259 - 17260 17261 17262 17263 17264 17265 17266 17267 17268 17269 17270 17271 - 17272 17273 17274 17275 17276 17277 17278 17279 17280 17281 17282 17283 - 17284 17285 17286 17287 17288 17289 17290 17291 17292 17293 17294 17295 - 17296 17297 17298 17299 17300 17301 17302 17303 17304 17305 17306 17307 - 17308 17309 17310 17311 17312 17313 17314 17315 17316 17317 17318 17319 - 17320 17321 17322 17323 17324 17325 17326 17327 17328 17329 17330 17331 - 17332 17333 17334 17335 17336 17337 17338 17339 17340 17341 17342 17343 - 17344 17345 17346 17347 17348 17349 17350 17351 17352 17353 17354 17355 - 17356 17357 17358 17359 17360 17361 17362 17363 17364 17365 17366 17367 - 17368 17369 17370 17371 17372 17373 17374 17375 17376 17377 17378 17379 - 17380 17381 17382 17383 17384 17385 17386 17387 17388 17389 17390 17391 - 17392 17393 17394 17395 17396 17397 17398 17399] -offset: [ 0 66315 135011 199942 271427 345077 411483 480117 - 544484 609823 678977 748225 820455 891701 962291 1026523 - 1094556 1167064 1235215 1302209 1370513 1441799 1508321 1579451 - 1647866 1711948 1781508 1847081 1916532 1984056 2051966 2121400 - 2191446 2259745 2326582 2393691 2462360 2529085 2598203 2668941 - 2736185 2803343 2871952 2941310 3005413 3070796 3141515 3214146 - 3280789 3347904 3418377 3487378 3553583 3626194 3693234 3762425 - 3830739 3899608 3966907 4034040 4100485 4166176 4235983 4302691 - 4369751 4439762 4512882 4583389 4650736 4719499 4785626 4852633 - 4918836 4989880 5058695 5127992 5194854 5262901 5328048 5395581 - 5463685 5533570 5603241 5668907 5742328 5806225 5875672 5940056 - 6009034 6075959 6146523 6214409 6284654 6354718 6423715 6491303 - 6560003 6631600 6700089 6765841 6833971 6902484 6967055 7031122 - 7098438 7168073 7235617 7305081 7371247 7442847 7510630 7580155 - 7649763 7717246 7780227 7848802 7914498 7982541 8053554 8121222 - 8187131 8260488 8332306 8402096 8471223 8541199 8608681 8673602 - 8739942 8804813 8875413 8944664 9010959 9080923 9153479 9226227 - 9292716 9363144 9428885 9495275 9565046 9632232 9699541 9765963 - 9835419 9905508 9970034 10038744 10101250 10171158 10241802 10308276 - 10377154 10444099 10513673 10580600 10647821 10718618 10783432 10849057 - 10920445 10988131 11055933 11122495 11190335 11258920 11327642 11396255 - 11460561 11530593 11600326 11669170 11736869 11800962 11868856 11940501 - 12010602 12075870 12140529 12207021 12273865 12339780 12409220 12474527 - 12543258 12611862 12681433 12749858 12824349 12890710 12957208 13026398 - 13093917 13160872 13227905 13295643 13364632 13432288 13499427 13566059 - 13635450] - -2023-05-10 14:04:42,370 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:04:42,371 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 14:04:42,374 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:04:42,374 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 14:04:51,745 - distributed.utils_perf - INFO - full garbage collection released 16.01 GiB from 9750 reference cycles (threshold: 9.54 MiB) -sources: [8599823 8599936 8599936 ... 1644415 1644415 1644415] -destinations: [5022466 502030 6028000 ... 411570 1408640 8154918] -batch: [17000 17001 17002 17003 17004 17005 17006 17007 17008 17009 17010 17011 - 17012 17013 17014 17015 17016 17017 17018 17019 17020 17021 17022 17023 - 17024 17025 17026 17027 17028 17029 17030 17031 17032 17033 17034 17035 - 17036 17037 17038 17039 17040 17041 17042 17043 17044 17045 17046 17047 - 17048 17049 17050 17051 17052 17053 17054 17055 17056 17057 17058 17059 - 17060 17061 17062 17063 17064 17065 17066 17067 17068 17069 17070 17071 - 17072 17073 17074 17075 17076 17077 17078 17079 17080 17081 17082 17083 - 17084 17085 17086 17087 17088 17089 17090 17091 17092 17093 17094 17095 - 17096 17097 17098 17099 17100 17101 17102 17103 17104 17105 17106 17107 - 17108 17109 17110 17111 17112 17113 17114 17115 17116 17117 17118 17119 - 17120 17121 17122 17123 17124 17125 17126 17127 17128 17129 17130 17131 - 17132 17133 17134 17135 17136 17137 17138 17139 17140 17141 17142 17143 - 17144 17145 17146 17147 17148 17149 17150 17151 17152 17153 17154 17155 - 17156 17157 17158 17159 17160 17161 17162 17163 17164 17165 17166 17167 - 17168 17169 17170 17171 17172 17173 17174 17175 17176 17177 17178 17179 - 17180 17181 17182 17183 17184 17185 17186 17187 17188 17189 17190 17191 - 17192 17193 17194 17195 17196 17197 17198 17199] -offset: [ 0 68061 135025 206134 275770 344331 410475 478186 - 550001 620164 686210 754629 822346 889567 956731 1020886 - 1090735 1161794 1230370 1299338 1367339 1437730 1508879 1577931 - 1645580 1708340 1778033 1841586 1908305 1976212 2044771 2114324 - 2184789 2253030 2323819 2395702 2462663 2529641 2599041 2669291 - 2733070 2800779 2870351 2940324 3012999 3077955 3146873 3214740 - 3285719 3349281 3415287 3484854 3554530 3623059 3688291 3757628 - 3825530 3893717 3959426 4031167 4102071 4167940 4233668 4302170 - 4367967 4436472 4508290 4578058 4645284 4713229 4783058 4849394 - 4921058 4992820 5061946 5129623 5200285 5266226 5336783 5408092 - 5478577 5548522 5612448 5682612 5751706 5818071 5882306 5953033 - 6018224 6091202 6155952 6226560 6299157 6366610 6434596 6503154 - 6567814 6636189 6707128 6776548 6842353 6910828 6979470 7051995 - 7116412 7181473 7248511 7315309 7387493 7452992 7525478 7599794 - 7671735 7738630 7804259 7875730 7942607 8009928 8075104 8141426 - 8205465 8269919 8339583 8411031 8483884 8556128 8623085 8694078 - 8765386 8832297 8896177 8959648 9027258 9096011 9161756 9228946 - 9295775 9360850 9425924 9494282 9562912 9629478 9698043 9766971 - 9837225 9902320 9969858 10033133 10098477 10166069 10232700 10303835 - 10372725 10444637 10513743 10582213 10648522 10716231 10781786 10850548 - 10920639 10987942 11060521 11129685 11202306 11267472 11335236 11403225 - 11470922 11539358 11606066 11671977 11741673 11816464 11890125 11957782 - 12025326 12095299 12163510 12233368 12301033 12368202 12435159 12502733 - 12569240 12636444 12705248 12775118 12843258 12909147 12979143 13047140 - 13116747 13185924 13256308 13328157 13393116 13459021 13529065 13594925 - 13662665] - -sources: [8799883 8799883 8799883 ... 1713093 1713093 1713093] -destinations: [4342105 6336918 3631498 ... 2348422 918608 2489628] -batch: [17400 17401 17402 17403 17404 17405 17406 17407 17408 17409 17410 17411 - 17412 17413 17414 17415 17416 17417 17418 17419 17420 17421 17422 17423 - 17424 17425 17426 17427 17428 17429 17430 17431 17432 17433 17434 17435 - 17436 17437 17438 17439 17440 17441 17442 17443 17444 17445 17446 17447 - 17448 17449 17450 17451 17452 17453 17454 17455 17456 17457 17458 17459 - 17460 17461 17462 17463 17464 17465 17466 17467 17468 17469 17470 17471 - 17472 17473 17474 17475 17476 17477 17478 17479 17480 17481 17482 17483 - 17484 17485 17486 17487 17488 17489 17490 17491 17492 17493 17494 17495 - 17496 17497 17498 17499 17500 17501 17502 17503 17504 17505 17506 17507 - 17508 17509 17510 17511 17512 17513 17514 17515 17516 17517 17518 17519 - 17520 17521 17522 17523 17524 17525 17526 17527 17528 17529 17530 17531 - 17532 17533 17534 17535 17536 17537 17538 17539 17540 17541 17542 17543 - 17544 17545 17546 17547 17548 17549 17550 17551 17552 17553 17554 17555 - 17556 17557 17558 17559 17560 17561 17562 17563 17564 17565 17566 17567 - 17568 17569 17570 17571 17572 17573 17574 17575 17576 17577 17578 17579 - 17580 17581 17582 17583 17584 17585 17586 17587 17588 17589 17590 17591 - 17592 17593 17594 17595 17596 17597 17598 17599] -offset: [ 0 66560 139988 210118 280091 345785 415754 483868 - 550793 617533 688571 760676 831303 896463 963724 1028629 - 1101255 1172631 1243027 1311373 1380359 1447582 1513981 1579172 - 1646160 1714511 1783326 1851929 1921594 1988600 2056076 2123851 - 2193685 2264245 2333918 2400080 2470166 2537308 2607645 2672912 - 2739293 2806622 2876079 2945233 3013461 3081193 3149802 3218884 - 3285975 3354007 3422232 3486160 3552403 3618326 3690100 3756932 - 3825974 3891878 3961890 4031993 4099241 4168340 4239768 4307013 - 4374739 4443098 4508516 4576881 4645519 4715984 4784593 4850649 - 4918926 4988949 5054462 5123229 5194231 5261109 5331356 5397692 - 5464803 5533247 5603855 5671383 5741108 5810219 5877440 5944982 - 6014296 6082673 6153935 6221137 6284775 6355348 6424724 6494312 - 6560099 6624446 6695381 6762797 6829731 6900180 6970120 7036053 - 7105649 7173940 7239833 7307810 7374519 7440626 7510857 7580543 - 7649084 7715861 7785007 7851142 7921052 7991308 8066249 8137018 - 8207963 8274776 8343828 8408631 8480013 8547922 8611711 8677360 - 8746735 8812487 8878390 8944238 9011038 9081674 9152247 9221894 - 9293789 9360649 9428140 9497050 9568162 9632623 9700746 9767418 - 9841489 9908192 9976925 10045220 10114246 10182984 10245907 10311975 - 10383289 10453387 10521192 10591224 10659145 10728275 10795845 10866856 - 10935130 11005741 11075756 11141157 11209793 11275175 11341913 11408605 - 11479110 11548366 11618196 11687802 11757093 11821071 11888325 11959334 - 12028090 12098272 12165033 12235405 12303416 12373608 12439602 12506266 - 12571846 12637751 12706529 12777838 12848347 12914843 12983312 13049819 - 13120409 13186921 13254787 13321772 13391164 13457889 13525123 13592103 - 13661518] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -terminate called after throwing an instance of 'rmm::out_of_memory' - what(): std::bad_alloc: out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded -2023-05-10 14:04:52,137 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:35397 -2023-05-10 14:04:52,591 - distributed.nanny - INFO - Worker process 54580 was killed by signal 6 -2023-05-10 14:04:52,598 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:04:52,831 - distributed.utils_perf - INFO - full garbage collection released 18.35 GiB from 11265 reference cycles (threshold: 9.54 MiB) -terminate called after throwing an instance of 'rmm::out_of_memory' - what(): std::bad_alloc: out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded -2023-05-10 14:04:53,669 - distributed.nanny - INFO - Worker process 54587 was killed by signal 6 -2023-05-10 14:04:53,674 - distributed.nanny - WARNING - Restarting worker -2023-05-10 14:04:53,814 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:04:53,814 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:04:54,086 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:04:54,760 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38611 -2023-05-10 14:04:54,760 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38611 -2023-05-10 14:04:54,760 - distributed.worker - INFO - dashboard at: 10.120.104.11:33101 -2023-05-10 14:04:54,761 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:04:54,761 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:54,761 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:04:54,761 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:04:54,761 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-_5ed2sbz -2023-05-10 14:04:54,761 - distributed.worker - INFO - Starting Worker plugin RMMSetup-5338f63d-f995-4035-b7e7-3227f1f3ae05 -2023-05-10 14:04:54,881 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 14:04:54,881 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 14:04:54,885 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-ff7cbe29-74f3-429d-905d-5071e2d1f669 -2023-05-10 14:04:54,888 - distributed.worker - INFO - Starting Worker plugin PreImport-0faf3943-f2a6-4a9b-8bf1-173ee1483133 -2023-05-10 14:04:54,889 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:54,901 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:04:54,901 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:54,904 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:04:55,149 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 14:04:55,858 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44519 -2023-05-10 14:04:55,859 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44519 -2023-05-10 14:04:55,859 - distributed.worker - INFO - dashboard at: 10.120.104.11:42463 -2023-05-10 14:04:55,859 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 14:04:55,859 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:55,859 - distributed.worker - INFO - Threads: 1 -2023-05-10 14:04:55,859 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 14:04:55,859 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-cf3ssko5 -2023-05-10 14:04:55,860 - distributed.worker - INFO - Starting Worker plugin RMMSetup-3c19ae26-d181-4df8-a285-1b34148307dc -2023-05-10 14:04:55,979 - distributed.worker - INFO - Starting Worker plugin PreImport-60ba7f91-bbe5-447b-ac3c-641c5f5cfe7a -2023-05-10 14:04:55,980 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-f792caf5-42d3-4065-b231-2f7dbd1bc6f2 -2023-05-10 14:04:55,981 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:55,992 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 14:04:55,992 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 14:04:55,994 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 14:19:08,097 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38611. Reason: worker-close -2023-05-10 14:19:08,097 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:46381'. Reason: nanny-close -2023-05-10 14:19:08,097 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44519. Reason: worker-close -2023-05-10 14:19:08,097 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close -2023-05-10 14:19:08,098 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:38723'. Reason: nanny-close -2023-05-10 14:19:08,099 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close -2023-05-10 14:19:08,098 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:37906 remote=tcp://10.120.104.11:8786> -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run - return loop.run_until_complete(main) - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete - self.run_forever() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever - self._run_once() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once - event_list = self._selector.select(timeout) - File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select - fd_event_list = self._selector.poll(timeout, max_ev) -KeyboardInterrupt - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send - nbytes = yield coro - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run - value = future.result() - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write - raise CommClosedError() -distributed.comm.core.CommClosedError -2023-05-10 14:19:08,099 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:37928 remote=tcp://10.120.104.11:8786> -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run - return loop.run_until_complete(main) - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete - self.run_forever() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever - self._run_once() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once - event_list = self._selector.select(timeout) - File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select - fd_event_list = self._selector.poll(timeout, max_ev) -KeyboardInterrupt - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send - nbytes = yield coro - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run - value = future.result() - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write - raise CommClosedError() -distributed.comm.core.CommClosedError -2023-05-10 14:19:11,300 - distributed.nanny - WARNING - Worker process still alive after 3.1999969482421875 seconds, killing -2023-05-10 14:19:11,300 - distributed.nanny - WARNING - Worker process still alive after 3.199999389648438 seconds, killing -2023-05-10 14:19:11,438 - distributed.nanny - INFO - Worker process 56086 was killed by signal 9 -2023-05-10 14:19:11,504 - distributed.nanny - INFO - Worker process 56097 was killed by signal 9 From 200c7832b505eeac04f545c6e3aa4544d7f89dfa Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 10 May 2023 18:52:57 +0000 Subject: [PATCH 040/347] perf testing --- .../standalone/cugraph_bulk_sampling.py | 91 +- cugraph_graph_creation_stats.csv | 1 + mg_utils/dask-scheduler.json | 10 + mg_utils/dask_logs-173107/scheduler_log.txt | 51 + .../dask_logs-173107/worker-exp01_log.txt | 1122 +++ mg_utils/dask_logs-219848/scheduler_log.txt | 213 + .../dask_logs-219848/worker-exp01_log.txt | 8225 +++++++++++++++++ .../cugraph/gnn/data_loading/bulk_sampler.py | 52 +- .../gnn/data_loading/bulk_sampler_io.py | 6 +- python/cugraph/cugraph/testing/mg_utils.py | 10 + 10 files changed, 9739 insertions(+), 42 deletions(-) create mode 100644 cugraph_graph_creation_stats.csv create mode 100644 mg_utils/dask-scheduler.json create mode 100644 mg_utils/dask_logs-173107/scheduler_log.txt create mode 100644 mg_utils/dask_logs-173107/worker-exp01_log.txt create mode 100644 mg_utils/dask_logs-219848/scheduler_log.txt create mode 100644 mg_utils/dask_logs-219848/worker-exp01_log.txt diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index 189642a4e52..f87d6e1bbd0 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -11,9 +11,12 @@ # See the License for the specific language governing permissions and # limitations under the License. +import logging + from cugraph.testing.mg_utils import ( generate_edgelist_rmat, get_allocation_counts_dask_persist, + get_allocation_counts_dask_lazy, sizeof_fmt, get_peak_output_ratio_across_workers, restart_client, @@ -118,8 +121,8 @@ def _make_batch_ids(bdf: cudf.DataFrame, batch_size: int, num_workers: int, part return bdf -@get_allocation_counts_dask_persist(return_allocations=True, logging=True) -def sample_graph(G, seed=42, batch_size=500, fanout=[5, 5, 5]): +@get_allocation_counts_dask_lazy(return_allocations=True, logging=True) +def sample_graph(G, seed=42, batch_size=500, seeds_per_call=200000, fanout=[5, 5, 5]): cupy.random.seed(seed) sampler = BulkSampler( @@ -129,6 +132,9 @@ def sample_graph(G, seed=42, batch_size=500, fanout=[5, 5, 5]): fanout_vals=fanout, with_replacement=False, random_state=seed, + seeds_per_call=seeds_per_call, + batches_per_partition=200_000 // batch_size, + log_level = 'INFO' ) from dask.distributed import wait, default_client @@ -162,7 +168,7 @@ def sample_graph(G, seed=42, batch_size=500, fanout=[5, 5, 5]): print(results_ddf.compute()) """ -def benchmark_cugraph_bulk_sampling(scale, edgefactor, seed, batch_size, fanout): +def benchmark_cugraph_bulk_sampling(scale, edgefactor, seed, batch_size, seeds_per_call, fanout): """ Entry point for the benchmark. """ @@ -190,7 +196,7 @@ def benchmark_cugraph_bulk_sampling(scale, edgefactor, seed, batch_size, fanout) input_memory = G.edgelist.edgelist_df.memory_usage().sum().compute() print(f'input memory: {input_memory}') - _, allocation_counts = sample_graph(G, seed, batch_size, fanout) + _, allocation_counts = sample_graph(G, seed, batch_size, seeds_per_call, fanout) print('allocation counts b:') print(allocation_counts.values()) @@ -238,48 +244,53 @@ def get_memory_statistics(allocation_counts, input_memory): # call __main__ function if __name__ == "__main__": + logging.basicConfig() + client, cluster = start_dask_client(dask_worker_devices=[1], jit_unspill=False) enable_spilling() stats_ls = [] client.run(enable_spilling) - #for scale in [22, 23, 24]: - for scale in [16, 22, 24]: + for scale in [22, 23, 24]: for fanout in [[10,25]]: for batch_size in [500, 1000]: - print(f'scale: {scale}') - print(f'batch size: {batch_size}') - print(f'fanout: {fanout}') - - try: - stats_d = {} - ( - num_input_edges, - input_to_peak_ratio, - output_to_peak_ratio, - input_memory_per_worker, - peak_allocation_across_workers, - ) = benchmark_cugraph_bulk_sampling( - scale=scale, - edgefactor=16, - seed=123, - batch_size=batch_size, - fanout=fanout, - ) - stats_d["scale"] = scale - stats_d["num_input_edges"] = num_input_edges - stats_d["batch_size"] = batch_size - stats_d["fanout"] = fanout - stats_d["input_memory_per_worker"] = sizeof_fmt(input_memory_per_worker) - stats_d["peak_allocation_across_workers"] = sizeof_fmt( - peak_allocation_across_workers - ) - stats_d["input_to_peak_ratio"] = input_to_peak_ratio - stats_d["output_to_peak_ratio"] = output_to_peak_ratio - stats_ls.append(stats_d) - except Exception as e: - print(e) - restart_client(client) - sleep(10) + for seeds_per_call in [500_000, 1_000_000, 2_000_000]: + print(f'scale: {scale}') + print(f'batch size: {batch_size}') + print(f'fanout: {fanout}') + print(f'seeds_per_call: {seeds_per_call}') + + try: + stats_d = {} + ( + num_input_edges, + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) = benchmark_cugraph_bulk_sampling( + scale=scale, + edgefactor=16, + seed=123, + batch_size=batch_size, + seeds_per_call=seeds_per_call, + fanout=fanout, + ) + stats_d["scale"] = scale + stats_d["num_input_edges"] = num_input_edges + stats_d["batch_size"] = batch_size + stats_d["fanout"] = fanout + stats_d["seeds_per_call"] = seeds_per_call + stats_d["input_memory_per_worker"] = sizeof_fmt(input_memory_per_worker) + stats_d["peak_allocation_across_workers"] = sizeof_fmt( + peak_allocation_across_workers + ) + stats_d["input_to_peak_ratio"] = input_to_peak_ratio + stats_d["output_to_peak_ratio"] = output_to_peak_ratio + stats_ls.append(stats_d) + except Exception as e: + print(e) + restart_client(client) + sleep(10) stats_df = pd.DataFrame( stats_ls, diff --git a/cugraph_graph_creation_stats.csv b/cugraph_graph_creation_stats.csv new file mode 100644 index 00000000000..7a9d37aca09 --- /dev/null +++ b/cugraph_graph_creation_stats.csv @@ -0,0 +1 @@ +,scale,num_input_edges,directed,renumber,input_memory_per_worker,peak_allocation_across_workers,input_to_peak_ratio,output_to_peak_ratio diff --git a/mg_utils/dask-scheduler.json b/mg_utils/dask-scheduler.json new file mode 100644 index 00000000000..1cf92274c9d --- /dev/null +++ b/mg_utils/dask-scheduler.json @@ -0,0 +1,10 @@ +{ + "type": "Scheduler", + "id": "Scheduler-b1dc610e-4f16-46b6-bd2e-6fdf923ce419", + "address": "tcp://10.120.104.11:8786", + "services": { + "dashboard": 8787 + }, + "started": 1683742414.140298, + "workers": {} +} \ No newline at end of file diff --git a/mg_utils/dask_logs-173107/scheduler_log.txt b/mg_utils/dask_logs-173107/scheduler_log.txt new file mode 100644 index 00000000000..2f52d694fa3 --- /dev/null +++ b/mg_utils/dask_logs-173107/scheduler_log.txt @@ -0,0 +1,51 @@ +RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp + --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json + " +/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead + warnings.warn( +2023-05-10 16:44:47,821 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 16:44:48,604 - distributed.scheduler - INFO - State start +2023-05-10 16:44:48,618 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 16:44:48,619 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 +2023-05-10 16:44:48,619 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status +2023-05-10 16:44:57,572 - distributed.scheduler - INFO - Register worker +2023-05-10 16:44:57,574 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33799 +2023-05-10 16:44:57,574 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:44942 +2023-05-10 16:44:57,575 - distributed.scheduler - INFO - Register worker +2023-05-10 16:44:57,575 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46351 +2023-05-10 16:44:57,575 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:44956 +2023-05-10 16:45:01,931 - distributed.scheduler - INFO - Receive client connection: Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 +2023-05-10 16:45:01,931 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37608 +2023-05-10 16:45:02,173 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 16:45:51,267 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 16:45:51,268 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 16:45:51,292 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:44942; closing. +2023-05-10 16:45:51,292 - distributed.scheduler - INFO - Remove worker +2023-05-10 16:45:51,292 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33799 +2023-05-10 16:45:51,293 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:44956; closing. +2023-05-10 16:45:51,293 - distributed.scheduler - INFO - Remove worker +2023-05-10 16:45:51,293 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46351 +2023-05-10 16:45:51,293 - distributed.scheduler - INFO - Lost all workers +2023-05-10 16:45:55,725 - distributed.scheduler - INFO - Register worker +2023-05-10 16:45:55,726 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36221 +2023-05-10 16:45:55,726 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33566 +2023-05-10 16:45:55,942 - distributed.scheduler - INFO - Register worker +2023-05-10 16:45:55,942 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40001 +2023-05-10 16:45:55,942 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33574 +2023-05-10 16:45:56,084 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Remove client Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 +2023-05-10 16:46:03,444 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:37608; closing. +2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Remove client Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 +2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Close client connection: Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 +2023-05-10 16:48:28,617 - distributed._signals - INFO - Received signal SIGINT (2) +2023-05-10 16:48:28,617 - distributed.core - INFO - Connection to tcp://10.120.104.11:33574 has been closed. +2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Remove worker +2023-05-10 16:48:28,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40001 +2023-05-10 16:48:28,618 - distributed.core - INFO - Connection to tcp://10.120.104.11:33566 has been closed. +2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Remove worker +2023-05-10 16:48:28,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36221 +2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Lost all workers +2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Scheduler closing... +2023-05-10 16:48:28,619 - distributed.scheduler - INFO - Scheduler closing all comms +2023-05-10 16:48:28,620 - distributed.scheduler - INFO - Stopped scheduler at 'tcp://10.120.104.11:8786' +2023-05-10 16:48:28,620 - distributed.scheduler - INFO - End scheduler diff --git a/mg_utils/dask_logs-173107/worker-exp01_log.txt b/mg_utils/dask_logs-173107/worker-exp01_log.txt new file mode 100644 index 00000000000..9d1f05f9974 --- /dev/null +++ b/mg_utils/dask_logs-173107/worker-exp01_log.txt @@ -0,0 +1,1122 @@ +RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G + --local-directory=/tmp/ + --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json + --memory-limit=auto + --device-memory-limit=auto + " +2023-05-10 16:44:54,827 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:37403' +2023-05-10 16:44:54,830 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:45501' +2023-05-10 16:44:56,019 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 16:44:56,019 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 16:44:56,047 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 16:44:56,048 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 16:44:56,282 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 16:44:56,312 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 16:44:57,425 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33799 +2023-05-10 16:44:57,425 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33799 +2023-05-10 16:44:57,425 - distributed.worker - INFO - dashboard at: 10.120.104.11:33945 +2023-05-10 16:44:57,425 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 16:44:57,426 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,426 - distributed.worker - INFO - Threads: 1 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 16:44:57,426 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-o5pzcq52 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46351 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46351 +2023-05-10 16:44:57,426 - distributed.worker - INFO - dashboard at: 10.120.104.11:34261 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 16:44:57,426 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,426 - distributed.worker - INFO - Threads: 1 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 16:44:57,426 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-l5q_xedg +2023-05-10 16:44:57,426 - distributed.worker - INFO - Starting Worker plugin RMMSetup-9c56cc40-5e1d-49d8-ab54-23b0b91cfc46 +2023-05-10 16:44:57,426 - distributed.worker - INFO - Starting Worker plugin RMMSetup-243e825e-5562-4e11-b7c2-ef53e729b5b6 +2023-05-10 16:44:57,563 - distributed.worker - INFO - Starting Worker plugin PreImport-54b6b654-dcd5-4381-ad84-1d89093915f4 +2023-05-10 16:44:57,563 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-37ebf0f3-ae1e-423e-ae1b-f541c9428fae +2023-05-10 16:44:57,564 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-2d355367-a471-49ed-b299-8386546715bd +2023-05-10 16:44:57,564 - distributed.worker - INFO - Starting Worker plugin PreImport-aad90f77-0639-4887-8530-ebd1b0bd7ea9 +2023-05-10 16:44:57,564 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,564 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,575 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 16:44:57,575 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,576 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 16:44:57,576 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:44:57,577 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 16:44:57,578 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 16:45:01,940 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:01,941 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:02,762 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 16:45:02,763 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 16:45:11,728 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 16:45:11,964 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 16:45:17,976 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.58s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 16:45:17,976 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.58s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 16:45:17,985 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 16:45:17,985 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 16:45:37,980 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:37,980 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:38,390 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 16:45:38,391 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 16:45:39,123 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:39,124 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [1416929 1416929 1416929 ... 300812 300812 300812] +destinations: [ 860899 908466 1972881 ... 995640 586261 676432] 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41982960 + 42051620] +2023-05-10 16:45:50,225 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 16:45:50,225 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' + +sources: [1800064 1800065 1800065 ... 591821 591821 591821] +destinations: [ 627165 861193 1143053 ... 1675552 513820 1022869] +batch: [3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 + 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 + 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 + 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 + 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 + 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 + 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 + 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 + 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 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38421991 38493884 38559727 38630994 + 38699279 38769814 38841436 38910545 38983494 39053498 39123695 39189223 + 39263756 39335127 39409373 39478273 39546770 39617135 39689945 39762270 + 39826078 39895330 39964850 40038895 40108334 40173837 40244651 40316006 + 40384216 40454217 40526623 40599877 40669671 40739924 40809082 40880981 + 40954386 41023970 41095311 41163805 41235018 41305586 41372366 41440055 + 41507554 41578706 41597336] +2023-05-10 16:45:51,154 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 16:45:51,154 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 16:45:51,158 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 16:45:51,158 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 16:45:51,161 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 16:45:51,161 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 16:45:51,274 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 16:45:51,277 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 16:45:51,291 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33799. Reason: scheduler-restart +2023-05-10 16:45:51,291 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46351. Reason: scheduler-restart +2023-05-10 16:45:51,292 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 16:45:51,292 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 16:45:51,294 - distributed.nanny - INFO - Worker closed +2023-05-10 16:45:51,294 - distributed.nanny - INFO - Worker closed + + +2023-05-10 16:45:52,979 - distributed.nanny - WARNING - Restarting worker +2023-05-10 16:45:53,275 - distributed.nanny - WARNING - Restarting worker +2023-05-10 16:45:54,275 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 16:45:54,275 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 16:45:54,411 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 16:45:54,412 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 16:45:54,577 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 16:45:54,670 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 16:45:55,561 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36221 +2023-05-10 16:45:55,561 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36221 +2023-05-10 16:45:55,561 - distributed.worker - INFO - dashboard at: 10.120.104.11:39713 +2023-05-10 16:45:55,561 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 16:45:55,561 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,561 - distributed.worker - INFO - Threads: 1 +2023-05-10 16:45:55,561 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 16:45:55,561 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-8o8aa785 +2023-05-10 16:45:55,562 - distributed.worker - INFO - Starting Worker plugin RMMSetup-270aa02c-d320-46cb-a4e1-12447808dcb7 +2023-05-10 16:45:55,715 - distributed.worker - INFO - Starting Worker plugin PreImport-73c7d9f2-9917-4631-96ee-581efba10be7 +2023-05-10 16:45:55,715 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-a69e529b-fed1-4a58-bea7-d5dc887c5d78 +2023-05-10 16:45:55,716 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,726 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 16:45:55,726 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,728 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 16:45:55,813 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40001 +2023-05-10 16:45:55,813 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40001 +2023-05-10 16:45:55,814 - distributed.worker - INFO - dashboard at: 10.120.104.11:34779 +2023-05-10 16:45:55,814 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 16:45:55,814 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,814 - distributed.worker - INFO - Threads: 1 +2023-05-10 16:45:55,814 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 16:45:55,814 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-dug6rnk1 +2023-05-10 16:45:55,814 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c45dc54c-b170-4ed6-9a3f-dd543c1de3f4 +2023-05-10 16:45:55,934 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-fcc80c05-811a-4c69-ba6b-b1c9be594151 +2023-05-10 16:45:55,934 - distributed.worker - INFO - Starting Worker plugin PreImport-15918582-2844-4b03-a98a-f96b77b6ae34 +2023-05-10 16:45:55,934 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,942 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 16:45:55,943 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 16:45:55,944 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 16:46:04,116 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 16:46:04,117 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.03s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 16:46:04,244 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 16:46:04,245 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.16s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 16:48:28,617 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36221. Reason: worker-close +2023-05-10 16:48:28,617 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40001. Reason: worker-handle-scheduler-connection-broken +2023-05-10 16:48:28,617 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:37403'. Reason: nanny-close +2023-05-10 16:48:28,618 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close +2023-05-10 16:48:28,618 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:45501'. Reason: nanny-close +2023-05-10 16:48:28,619 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close +2023-05-10 16:48:28,618 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:33566 remote=tcp://10.120.104.11:8786> +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run + return loop.run_until_complete(main) + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete + self.run_forever() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever + self._run_once() + File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once + event_list = self._selector.select(timeout) + File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select + fd_event_list = self._selector.poll(timeout, max_ev) +KeyboardInterrupt + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send + nbytes = yield coro + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run + value = future.result() + File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write + raise CommClosedError() +distributed.comm.core.CommClosedError +2023-05-10 16:48:31,820 - distributed.nanny - WARNING - Worker process still alive after 3.1999958801269535 seconds, killing +2023-05-10 16:48:31,820 - distributed.nanny - WARNING - Worker process still alive after 3.199999389648438 seconds, killing +2023-05-10 16:48:31,980 - distributed.nanny - INFO - Worker process 174097 was killed by signal 9 +2023-05-10 16:48:32,040 - distributed.nanny - INFO - Worker process 174086 was killed by signal 9 diff --git a/mg_utils/dask_logs-219848/scheduler_log.txt b/mg_utils/dask_logs-219848/scheduler_log.txt new file mode 100644 index 00000000000..ba0cf0ea018 --- /dev/null +++ b/mg_utils/dask_logs-219848/scheduler_log.txt @@ -0,0 +1,213 @@ +RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp + --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json + " +/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead + warnings.warn( +2023-05-10 18:13:34,139 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 18:13:34,863 - distributed.scheduler - INFO - State start +2023-05-10 18:13:34,876 - distributed.scheduler - INFO - ----------------------------------------------- +2023-05-10 18:13:34,877 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 +2023-05-10 18:13:34,877 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status +2023-05-10 18:13:43,826 - distributed.scheduler - INFO - Register worker +2023-05-10 18:13:43,829 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38389 +2023-05-10 18:13:43,829 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48740 +2023-05-10 18:13:43,859 - distributed.scheduler - INFO - Register worker +2023-05-10 18:13:43,859 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34233 +2023-05-10 18:13:43,859 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48746 +2023-05-10 18:13:59,347 - distributed.scheduler - INFO - Receive client connection: Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 +2023-05-10 18:13:59,348 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41942 +2023-05-10 18:13:59,581 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:14:48,078 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:14:48,079 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:14:48,086 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48746; closing. +2023-05-10 18:14:48,086 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:14:48,086 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34233 +2023-05-10 18:14:48,087 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48740; closing. +2023-05-10 18:14:48,087 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:14:48,087 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38389 +2023-05-10 18:14:48,087 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:14:52,831 - distributed.scheduler - INFO - Register worker +2023-05-10 18:14:52,832 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44757 +2023-05-10 18:14:52,832 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35598 +2023-05-10 18:14:52,872 - distributed.scheduler - INFO - Register worker +2023-05-10 18:14:52,872 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:43467 +2023-05-10 18:14:52,872 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35600 +2023-05-10 18:14:53,066 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:15:01,047 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:15:40,594 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:15:40,596 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:15:40,618 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:35600; closing. +2023-05-10 18:15:40,618 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:15:40,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43467 +2023-05-10 18:15:40,619 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:35598; closing. +2023-05-10 18:15:40,619 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:15:40,620 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44757 +2023-05-10 18:15:40,620 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:15:44,605 - distributed.scheduler - INFO - Register worker +2023-05-10 18:15:44,606 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42023 +2023-05-10 18:15:44,606 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58238 +2023-05-10 18:15:45,061 - distributed.scheduler - INFO - Register worker +2023-05-10 18:15:45,062 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37619 +2023-05-10 18:15:45,062 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58254 +2023-05-10 18:15:45,165 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:15:53,544 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:16:29,176 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:16:29,176 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:16:29,184 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58254; closing. +2023-05-10 18:16:29,184 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:16:29,184 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37619 +2023-05-10 18:16:29,185 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58238; closing. +2023-05-10 18:16:29,185 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:16:29,185 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42023 +2023-05-10 18:16:29,185 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:16:33,548 - distributed.scheduler - INFO - Register worker +2023-05-10 18:16:33,549 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:41227 +2023-05-10 18:16:33,549 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:40448 +2023-05-10 18:16:33,839 - distributed.scheduler - INFO - Register worker +2023-05-10 18:16:33,839 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42727 +2023-05-10 18:16:33,839 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:40450 +2023-05-10 18:16:33,862 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:16:41,858 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:17:20,731 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:17:20,732 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:17:20,739 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:40448; closing. +2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:17:20,740 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:41227 +2023-05-10 18:17:20,740 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:40450; closing. +2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:17:20,740 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42727 +2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:17:25,159 - distributed.scheduler - INFO - Register worker +2023-05-10 18:17:25,159 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38521 +2023-05-10 18:17:25,159 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41184 +2023-05-10 18:17:25,451 - distributed.scheduler - INFO - Register worker +2023-05-10 18:17:25,451 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40329 +2023-05-10 18:17:25,451 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41196 +2023-05-10 18:17:25,623 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:17:33,739 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:18:13,220 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:18:13,221 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:18:13,229 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41184; closing. +2023-05-10 18:18:13,229 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:18:13,229 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38521 +2023-05-10 18:18:13,230 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41196; closing. +2023-05-10 18:18:13,230 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:18:13,230 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40329 +2023-05-10 18:18:13,230 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:18:17,596 - distributed.scheduler - INFO - Register worker +2023-05-10 18:18:17,597 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46207 +2023-05-10 18:18:17,597 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52398 +2023-05-10 18:18:17,928 - distributed.scheduler - INFO - Register worker +2023-05-10 18:18:17,929 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36349 +2023-05-10 18:18:17,929 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52414 +2023-05-10 18:18:17,965 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:18:25,967 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:19:11,422 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:19:11,423 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:19:11,430 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52414; closing. +2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:19:11,431 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36349 +2023-05-10 18:19:11,431 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52398; closing. +2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:19:11,431 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46207 +2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:19:15,895 - distributed.scheduler - INFO - Register worker +2023-05-10 18:19:15,896 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38111 +2023-05-10 18:19:15,896 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48034 +2023-05-10 18:19:16,170 - distributed.scheduler - INFO - Register worker +2023-05-10 18:19:16,170 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42615 +2023-05-10 18:19:16,170 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48042 +2023-05-10 18:19:16,189 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:19:24,276 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:20:08,085 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:20:08,086 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:20:08,093 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48034; closing. +2023-05-10 18:20:08,093 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:20:08,093 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38111 +2023-05-10 18:20:08,094 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48042; closing. +2023-05-10 18:20:08,094 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:20:08,094 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42615 +2023-05-10 18:20:08,095 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:20:12,283 - distributed.scheduler - INFO - Register worker +2023-05-10 18:20:12,283 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37861 +2023-05-10 18:20:12,283 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54504 +2023-05-10 18:20:12,731 - distributed.scheduler - INFO - Register worker +2023-05-10 18:20:12,732 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34921 +2023-05-10 18:20:12,732 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54506 +2023-05-10 18:20:12,754 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:20:20,635 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:21:18,259 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:21:18,260 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:21:18,270 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54506; closing. +2023-05-10 18:21:18,270 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:21:18,270 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34921 +2023-05-10 18:21:18,301 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54504; closing. +2023-05-10 18:21:18,301 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:21:18,301 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37861 +2023-05-10 18:21:18,301 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:21:22,796 - distributed.scheduler - INFO - Register worker +2023-05-10 18:21:22,796 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37045 +2023-05-10 18:21:22,796 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52620 +2023-05-10 18:21:23,084 - distributed.scheduler - INFO - Register worker +2023-05-10 18:21:23,084 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40247 +2023-05-10 18:21:23,084 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52624 +2023-05-10 18:21:23,087 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:21:31,210 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:22:46,731 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:22:46,732 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:22:46,778 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52620; closing. +2023-05-10 18:22:46,778 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:22:46,778 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37045 +2023-05-10 18:22:46,780 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52624; closing. +2023-05-10 18:22:46,780 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:22:46,780 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40247 +2023-05-10 18:22:46,780 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:22:51,018 - distributed.scheduler - INFO - Register worker +2023-05-10 18:22:51,019 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33561 +2023-05-10 18:22:51,019 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56756 +2023-05-10 18:22:51,394 - distributed.scheduler - INFO - Register worker +2023-05-10 18:22:51,394 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:45751 +2023-05-10 18:22:51,394 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:42188 +2023-05-10 18:22:51,444 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:22:59,419 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:23:42,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:23:42,463 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:23:42,471 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56756; closing. +2023-05-10 18:23:42,471 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:23:42,471 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33561 +2023-05-10 18:23:42,472 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:42188; closing. +2023-05-10 18:23:42,472 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:23:42,472 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:45751 +2023-05-10 18:23:42,472 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:23:47,392 - distributed.scheduler - INFO - Register worker +2023-05-10 18:23:47,393 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36171 +2023-05-10 18:23:47,393 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48216 +2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Register worker +2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46001 +2023-05-10 18:23:47,438 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48220 +2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:23:55,423 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:24:41,919 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' +2023-05-10 18:24:41,920 - distributed.scheduler - INFO - Restarting workers and releasing all keys. +2023-05-10 18:24:41,928 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48216; closing. +2023-05-10 18:24:41,928 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:24:41,928 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36171 +2023-05-10 18:24:41,929 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48220; closing. +2023-05-10 18:24:41,929 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:24:41,929 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46001 +2023-05-10 18:24:41,929 - distributed.scheduler - INFO - Lost all workers +2023-05-10 18:24:46,152 - distributed.scheduler - INFO - Register worker +2023-05-10 18:24:46,153 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35339 +2023-05-10 18:24:46,153 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33682 +2023-05-10 18:24:46,512 - distributed.scheduler - INFO - Register worker +2023-05-10 18:24:46,513 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36565 +2023-05-10 18:24:46,513 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33688 +2023-05-10 18:24:46,519 - distributed.scheduler - INFO - Restarting finished. +2023-05-10 18:24:54,912 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' +2023-05-10 18:33:34,880 - distributed.scheduler - WARNING - Worker failed to heartbeat within 300 seconds. Closing: +2023-05-10 18:33:34,881 - distributed.scheduler - INFO - Remove worker +2023-05-10 18:33:34,881 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36565 +2023-05-10 18:52:21,783 - distributed.scheduler - INFO - Remove client Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 +2023-05-10 18:52:21,786 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41942; closing. +2023-05-10 18:52:21,786 - distributed.scheduler - INFO - Remove client Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 +2023-05-10 18:52:21,787 - distributed.scheduler - INFO - Close client connection: Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 diff --git a/mg_utils/dask_logs-219848/worker-exp01_log.txt b/mg_utils/dask_logs-219848/worker-exp01_log.txt new file mode 100644 index 00000000000..ac7a245cbd3 --- /dev/null +++ b/mg_utils/dask_logs-219848/worker-exp01_log.txt @@ -0,0 +1,8225 @@ +RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G + --local-directory=/tmp/ + --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json + --memory-limit=auto + --device-memory-limit=auto + " +2023-05-10 18:13:41,185 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:38551' +2023-05-10 18:13:41,188 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:34413' +2023-05-10 18:13:42,385 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:13:42,385 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:13:42,393 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:13:42,393 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:13:42,657 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:13:42,670 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:13:43,689 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38389 +2023-05-10 18:13:43,689 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38389 +2023-05-10 18:13:43,689 - distributed.worker - INFO - dashboard at: 10.120.104.11:46597 +2023-05-10 18:13:43,689 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:13:43,689 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,689 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:13:43,689 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:13:43,689 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-0w581hpr +2023-05-10 18:13:43,690 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-83c4b061-5681-4748-8491-ab9b3db71c97 +2023-05-10 18:13:43,692 - distributed.worker - INFO - Starting Worker plugin RMMSetup-31ac136c-a620-4242-b5f2-f5c42d68b4f6 +2023-05-10 18:13:43,725 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34233 +2023-05-10 18:13:43,725 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34233 +2023-05-10 18:13:43,725 - distributed.worker - INFO - dashboard at: 10.120.104.11:39159 +2023-05-10 18:13:43,725 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:13:43,725 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,725 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:13:43,725 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:13:43,726 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rumtx2h8 +2023-05-10 18:13:43,726 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6a189c25-7bb4-4be6-923c-9a00bc1605ac +2023-05-10 18:13:43,815 - distributed.worker - INFO - Starting Worker plugin PreImport-29435f28-45f3-440e-9413-9c7a9115f9eb +2023-05-10 18:13:43,816 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,829 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:13:43,829 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,832 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:13:43,851 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-1064e1c7-5d90-42f6-a702-dcae9f623c93 +2023-05-10 18:13:43,851 - distributed.worker - INFO - Starting Worker plugin PreImport-8d1d4fe9-841e-4498-a97d-a06ccba23a40 +2023-05-10 18:13:43,852 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,859 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:13:43,859 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:13:43,861 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:13:59,357 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:13:59,359 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:14:00,162 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:14:00,162 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:14:09,034 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:14:09,039 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:14:15,422 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:14:15,424 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:14:15,431 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:14:15,433 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:14:35,596 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:14:35,598 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:14:36,003 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:14:36,004 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:14:36,603 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:14:36,604 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [599983 599983 599983 ... 984504 984504 984504] +destinations: [ 649861 2039126 2365095 ... 1153947 1056145 2353159] +batch: [ 600 601 602 603 604 605 606 607 608 609 610 611 612 613 + 614 615 616 617 618 619 620 621 622 623 624 625 626 627 + 628 629 630 631 632 633 634 635 636 637 638 639 640 641 + 642 643 644 645 646 647 648 649 650 651 652 653 654 655 + 656 657 658 659 660 661 662 663 664 665 666 667 668 669 + 670 671 672 673 674 675 676 677 678 679 680 681 682 683 + 684 685 686 687 688 689 690 691 692 693 694 695 696 697 + 698 699 700 701 702 703 704 705 706 707 708 709 710 711 + 712 713 714 715 716 717 718 719 720 721 722 723 724 725 + 726 727 728 729 730 731 732 733 734 735 736 737 738 739 + 740 741 742 743 744 745 746 747 748 749 750 751 752 753 + 754 755 756 757 758 759 760 761 762 763 764 765 766 767 + 768 769 770 771 772 773 774 775 776 777 778 779 780 781 + 782 783 784 785 786 787 788 789 790 791 792 793 794 795 + 796 797 798 799 800 801 802 803 804 805 806 807 808 809 + 810 811 812 813 814 815 816 817 818 819 820 821 822 823 + 824 825 826 827 828 829 830 831 832 833 834 835 836 837 + 838 839 840 841 842 843 844 845 846 847 848 849 850 851 + 852 853 854 855 856 857 858 859 860 861 862 863 864 865 + 866 867 868 869 870 871 872 873 874 875 876 877 878 879 + 880 881 882 883 884 885 886 887 888 889 890 891 892 893 + 894 895 896 897 898 899 900 901 902 903 904 905 906 907 + 908 909 910 911 912 913 914 915 916 917 918 919 920 921 + 922 923 924 925 926 927 928 929 930 931 932 933 934 935 + 936 937 938 939 940 941 942 943 944 945 946 947 948 949 + 950 951 952 953 954 955 956 957 958 959 960 961 962 963 + 964 965 966 967 968 969 970 971 972 973 974 975 976 977 + 978 979 980 981 982 983 984 985 986 987 988 989 990 991 + 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 + 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 + 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 + 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 + 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 + 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 + 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 + 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 + 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 + 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 + 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 + 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 + 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 + 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 + 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199] +offset: [ 0 71361 142032 209074 283201 354841 425859 496453 + 561576 635342 702171 773507 843333 911285 981692 1047392 + 1120737 1187878 1263391 1335636 1404762 1471793 1543019 1614491 + 1683136 1758005 1824898 1894990 1966481 2037938 2105280 2174891 + 2249168 2321899 2395527 2464296 2533635 2602029 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+2023-05-10 18:14:47,043 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:14:47,043 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' + +sources: [1031861 1031861 1031861 ... 996745 996745 996745] +destinations: [ 375434 1199689 1794122 ... 1126821 2359381 353257] +batch: [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 + 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 + 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 + 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 + 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 + 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 + 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 + 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 + 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 + 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23815227 23886620 23957068 24028177 + 24096580 24166690 24238017 24307193 24373308 24442413 24513557 24583547 + 24650236 24722545 24792112 24862227 24931267 25001350 25073426 25145379 + 25215424 25288732 25358266 25425769 25496892 25568288 25636276 25705845 + 25777632 25846955 25915308 25985375 26056456 26126191 26194481 26265994 + 26335118 26404186 26473495 26540963 26612713 26680576 26755021 26821214 + 26894772 26964899 27031993 27100992 27172600 27240635 27311860 27384952 + 27454052 27523003 27594122 27667418 27736727 27807166 27876301 27949041 + 28020569 28091590 28158392 28227976 28296519 28364993 28434888 28500113 + 28569813 28643105 28715670 28785401 28857230 28925033 28992130 29065420 + 29134496 29203229 29269346 29342357 29410752 29480039 29547884 29623071 + 29691541 29764274 29832918 29900746 29972858 30044095 30117140 30188405 + 30255140 30324787 30395658 30466287 30536670 30608953 30680378 30747906 + 30821420 30889666 30960431 31034727 31107256 31178538 31250177 31319737 + 31390515 31461907 31530621 31600955 31670807 31742787 31808793 31877443 + 31942872 32011936 32085747 32155442 32224808 32293507 32365555 32433995 + 32506581 32574088 32643986 32712541 32783605 32853446 32924449 32997034 + 33069098 33140681 33212774 33283626 33352600 33421285 33492410 33562292 + 33633941 33704391 33771236 33839512 33911393 33982189 34055266 34130202 + 34197583 34269409 34340078 34406266 34476702 34546227 34612536 34682642 + 34754064 34826783 34895830 34968793 35041157 35110602 35180226 35254139 + 35324016 35391269 35459593 35527010 35598446 35664957 35735810 35806203 + 35871355 35940204 36009387 36082230 36152492 36224562 36292125 36360872 + 36429390 36496781 36565568 36637710 36708017 36780042 36849616 36918021 + 36987423 37058079 37126048 37197772 37266568 37336298 37405957 37478516 + 37547121 37616683 37682522 37753028 37824845 37894570 37962547 38033744 + 38100832 38170251 38237234 38309435 38378250 38445828 38516359 38582562 + 38655256 38722661 38794664 38866610 38938504 39010596 39081616 39152169 + 39221840 39292155 39363807 39436090 39508933 39578170 39646768 39715915 + 39784084 39855764 39923866 39995288 40066088 40132693 40203825 40271517 + 40343655 40412251 40483534 40551833 40620128 40690532 40761328 40833481 + 40904520 40977317 41046927 41118770 41191056 41260002 41328735 41400764 + 41472616 41541660 41610320 41678409 41747888 41815419 41886368 41960233 + 42029037] +2023-05-10 18:14:47,965 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:14:47,965 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:14:47,968 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:14:47,968 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:14:47,972 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:14:47,972 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:14:48,083 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:14:48,085 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:14:48,085 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:34233. Reason: scheduler-restart +2023-05-10 18:14:48,085 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38389. Reason: scheduler-restart +2023-05-10 18:14:48,086 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:14:48,087 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:14:48,088 - distributed.nanny - INFO - Worker closed +2023-05-10 18:14:48,088 - distributed.nanny - INFO - Worker closed + + +2023-05-10 18:14:49,729 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:14:50,055 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:14:51,150 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:14:51,150 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:14:51,242 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:14:51,242 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:14:51,502 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:14:51,506 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:14:52,651 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44757 +2023-05-10 18:14:52,651 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44757 +2023-05-10 18:14:52,651 - distributed.worker - INFO - dashboard at: 10.120.104.11:36835 +2023-05-10 18:14:52,651 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:14:52,651 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,651 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:14:52,651 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:14:52,651 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-e_y1rlpi +2023-05-10 18:14:52,652 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-4f2560f0-e8c4-429d-ba1a-6525126ca38d +2023-05-10 18:14:52,652 - distributed.worker - INFO - Starting Worker plugin RMMSetup-ccd1dd8e-67b6-45a0-b9e7-ef9a054df822 +2023-05-10 18:14:52,694 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:43467 +2023-05-10 18:14:52,695 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:43467 +2023-05-10 18:14:52,695 - distributed.worker - INFO - dashboard at: 10.120.104.11:45197 +2023-05-10 18:14:52,695 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:14:52,695 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,695 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:14:52,695 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:14:52,695 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-g39sbkyj +2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6d188862-221c-4daf-a8e2-eef96fb9e501 +2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin PreImport-cd3e0c92-cbc9-44a7-83b1-666cd689d1b8 +2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin RMMSetup-1a486981-ea33-4215-99d1-ded8645e74f7 +2023-05-10 18:14:52,817 - distributed.worker - INFO - Starting Worker plugin PreImport-1f660af5-ca3a-4f57-a9ef-004cab47c62d +2023-05-10 18:14:52,819 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,832 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:14:52,833 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,835 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:14:52,865 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,872 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:14:52,872 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:14:52,874 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:15:00,995 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:15:00,996 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.94s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:01,039 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:15:01,041 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.99s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:01,044 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:01,045 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:01,049 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:15:01,050 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:15:07,255 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.56s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:07,366 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.67s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:07,369 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:15:07,372 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:15:32,813 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:32,813 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:33,221 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:15:33,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:15:33,983 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:33,984 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:37,773 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:37,773 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 80 80 80 ... 1726817 1726817 1726817] +destinations: [1272647 659642 739610 ... 2305638 1875895 836345] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 + 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 + 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 + 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 + 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 + 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 + 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 + 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 + 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 + 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 + 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 + 216 217 218 219 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43518364 43588812 43656652 + 43724486 43788675 43856735 43928269 44000724 44071152 44142952 44219057 + 44290560 44363221 44432363 44499796 44570747 44640965 44710118 44780826 + 44848891 44920148 44988245 45061430 45132510 45200916 45271434 45343291 + 45412804 45484917 45553834 45626626 45696535 45765097 45835041 45902480 + 45973760 46040592 46109389 46182267 46255155 46327083 46393802 46460672 + 46531174 46602376 46671754 46743729 46809977 46878925 46952437 47022061 + 47095501 47164451 47238373 47308281 47380209 47447920 47515687 47585361 + 47654643 47721966 47794718 47866912 47939838 48009736 48076261 48148712 + 48218775 48291886 48361844 48427290 48496530 48568736 48639199 48709552 + 48781118 48850800 48921710 48991411 49056278 49125162 49198302 49268707 + 49341163 49411395 49482867 49552047 49623537 49693584 49760794 49833077 + 49905345 49972086 50044133 50116088 50186829 50259367 50326739 50394455 + 50463678 50531376 50603194 50674119 50747666 50818272 50884363 50950893 + 51021084 51090569 51160644 51237201 51306701 51380577 51450993 51518222 + 51583664 51655953 51726063 51798222 51869314 51935616 52005518 52077060 + 52148783 52216127 52286427 52359206 52429803 52501661 52567346 52638660 + 52706891 52777321 52849071 52918280 52991228 53061238 53131499 53197248 + 53271604 53343107 53417430 53486244 53554588 53624889 53697949 53770022 + 53833856 53903020 53972774 54046670 54116060 54181428 54252218 54323704 + 54392048 54461824 54534073 54607022 54676893 54747092 54816309 54888020 + 54961353 55030737 55101732 55170287 55241724 55312096 55378897 55446640 + 55514447 55585653 55604236] +2023-05-10 18:15:40,473 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:15:40,474 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:15:40,479 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:15:40,479 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:15:40,482 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:15:40,482 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:15:40,601 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:15:40,602 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:15:40,616 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:43467. Reason: scheduler-restart +2023-05-10 18:15:40,617 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:15:40,618 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44757. Reason: scheduler-restart +2023-05-10 18:15:40,619 - distributed.nanny - INFO - Worker closed +2023-05-10 18:15:40,619 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43467 + +2023-05-10 18:15:40,620 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:15:40,621 - distributed.nanny - INFO - Worker closed +sources: [ 494512 494512 494512 ... 1500995 1500995 1500995] +destinations: [ 673396 1041959 1011819 ... 1886203 899690 674715] +batch: [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 + 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 + 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 + 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 + 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 + 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 + 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 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3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 + 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 + 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 + 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 + 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 + 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 + 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 + 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 + 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 + 3994 3995 3996 3997 3998 3999] +offset: [ 0 72286 139228 ... 69927048 69999562 70071001] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 18:15:42,201 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:15:42,554 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:15:43,393 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:15:43,393 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:15:43,646 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:15:43,750 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:15:43,750 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:15:44,016 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:15:44,434 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42023 +2023-05-10 18:15:44,435 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42023 +2023-05-10 18:15:44,435 - distributed.worker - INFO - dashboard at: 10.120.104.11:34109 +2023-05-10 18:15:44,435 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:15:44,435 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:44,435 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:15:44,436 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:15:44,436 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-vjwan3v8 +2023-05-10 18:15:44,437 - distributed.worker - INFO - Starting Worker plugin RMMSetup-d91b6e51-3a58-4eaa-93dc-caeaa15a6c1a +2023-05-10 18:15:44,596 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-3bbdc39f-1199-4b17-8513-97eddf7694b6 +2023-05-10 18:15:44,596 - distributed.worker - INFO - Starting Worker plugin PreImport-e0ec5e41-1b51-49af-ba1d-e051eadaca65 +2023-05-10 18:15:44,597 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:44,606 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:15:44,606 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:44,608 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:15:44,926 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37619 +2023-05-10 18:15:44,926 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37619 +2023-05-10 18:15:44,926 - distributed.worker - INFO - dashboard at: 10.120.104.11:39813 +2023-05-10 18:15:44,927 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:15:44,927 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:44,927 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:15:44,927 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:15:44,927 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-by6ymd4e +2023-05-10 18:15:44,927 - distributed.worker - INFO - Starting Worker plugin RMMSetup-63f8c9d4-7ef8-455a-a6b2-81ddc7de7d07 +2023-05-10 18:15:45,051 - distributed.worker - INFO - Starting Worker plugin PreImport-8d23a30e-717f-470f-a833-56b141880f5d +2023-05-10 18:15:45,051 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-4c055384-37f2-4fee-9548-ecd43f581779 +2023-05-10 18:15:45,052 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:45,062 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:15:45,063 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:15:45,065 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:15:53,347 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:15:53,349 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.18s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:53,536 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:15:53,537 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.37s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:53,541 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:53,542 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:15:53,546 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:15:53,546 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:15:59,779 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.63s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:59,909 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.76s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:15:59,912 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:15:59,914 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:16:15,093 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:15,094 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:15,504 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:16:15,505 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:16:16,314 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:16,316 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:21,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:21,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 352 352 352 ... 1381549 1381549 1381549] +destinations: [ 22367 22092 590483 ... 714835 1250990 929093] +batch: [ 0 1 2 ... 1997 1998 1999] +offset: [ 0 71569 136683 ... 140135585 140205396 140275051] + +sources: [2000144 2000144 2000144 ... 1306294 1306294 1306294] +destinations: [ 351641 1074226 1365172 ... 1590856 2018837 2180008] +batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 + 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 + 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 + 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 + 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 + 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 + 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 + 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 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42597445 42668778 42735774 42809075 42877296 42950773 43025019 43095113 + 43167558 43241813 43309829 43381174 43449156 43519887 43590542 43658299 + 43726249 43790232 43858282 43929859 44002468 44072867 44144323 44220442 + 44291825 44364267 44433500 44501165 44572083 44642499 44711623 44782379 + 44850511 44921809 44990150 45063242 45134385 45202942 45273546 45345438 + 45414847 45486639 45555482 45628290 45698253 45767104 45836939 45904463 + 45976081 46042885 46111854 46184927 46257699 46329674 46396519 46463642 + 46534127 46605141 46674888 46746994 46813495 46882669 46955692 47025562 + 47099225 47168204 47241885 47311730 47383803 47451567 47519336 47589159 + 47658694 47726225 47799328 47871436 47944265 48014015 48080751 48153291 + 48223190 48296167 48366481 48431993 48501203 48573644 48644229 48714635 + 48785874 48855432 48926412 48996085 49061000 49129742 49203045 49273258 + 49345302 49415229 49486969 49556032 49627572 49697611 49764828 49837140 + 49909568 49976306 50048312 50120029 50190558 50263355 50330387 50398342 + 50467476 50535352 50607053 50678172 50751829 50822461 50888486 50955001 + 51025335 51095187 51165620 51241870 51311469 51385419 51455545 51522466 + 51587835 51660544 51730813 51802990 51873792 51940015 52009964 52081739 + 52153496 52221003 52291445 52364173 52434752 52506644 52572252 52643513 + 52711849 52782467 52854186 52923204 52996125 53066273 53136607 53202203 + 53276857 53348359 53422483 53491378 53559416 53629728 53702832 53774830 + 53838528 53907859 53977315 54051518 54121074 54186225 54257207 54328702 + 54396990 54466863 54539404 54612518 54682398 54752798 54821860 54893491 + 54966848 55036362 55107325 55175884 55247321 55317883 55384379 55451994 + 55519593 55590607 55609280] +2023-05-10 18:16:29,056 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:16:29,056 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:16:29,060 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:16:29,060 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:16:29,064 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:16:29,068 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:16:29,181 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:16:29,183 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:16:29,183 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37619. Reason: scheduler-restart +2023-05-10 18:16:29,184 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42023. Reason: scheduler-restart +2023-05-10 18:16:29,184 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:16:29,185 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37619 +2023-05-10 18:16:29,185 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:16:29,186 - distributed.nanny - INFO - Worker closed + +2023-05-10 18:16:29,186 - distributed.nanny - INFO - Worker closed +sources: [1000432 1000432 1000432 ... 22125 22125 22125] +destinations: [1518285 367680 1396431 ... 1194909 251848 1741944] +batch: [2000 2001 2002 ... 3997 3998 3999] +offset: [ 0 71098 140874 ... 140029578 140102124 140173556] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 18:16:30,981 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:16:31,252 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:16:32,226 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:16:32,226 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:16:32,456 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:16:32,456 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:16:32,512 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:16:32,722 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:16:33,379 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:41227 +2023-05-10 18:16:33,379 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:41227 +2023-05-10 18:16:33,379 - distributed.worker - INFO - dashboard at: 10.120.104.11:45597 +2023-05-10 18:16:33,379 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:16:33,379 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,379 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:16:33,380 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:16:33,380 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tmgtys_v +2023-05-10 18:16:33,380 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-1d8084e5-e8ae-4bbd-9f79-e017971076a7 +2023-05-10 18:16:33,381 - distributed.worker - INFO - Starting Worker plugin PreImport-90b03a88-0279-4c16-9ad0-f5bbcc0b445e +2023-05-10 18:16:33,381 - distributed.worker - INFO - Starting Worker plugin RMMSetup-617d7ca7-7c03-4526-a01e-38a3da7e5e71 +2023-05-10 18:16:33,538 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,549 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:16:33,549 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,552 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:16:33,706 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42727 +2023-05-10 18:16:33,707 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42727 +2023-05-10 18:16:33,707 - distributed.worker - INFO - dashboard at: 10.120.104.11:43471 +2023-05-10 18:16:33,707 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:16:33,707 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,707 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:16:33,707 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:16:33,707 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rgvu4k9j +2023-05-10 18:16:33,707 - distributed.worker - INFO - Starting Worker plugin RMMSetup-2cb667fb-f09d-42dd-8573-f0d5a280f9c0 +2023-05-10 18:16:33,827 - distributed.worker - INFO - Starting Worker plugin PreImport-222b94d4-e644-4537-8c40-30f98c08ea28 +2023-05-10 18:16:33,827 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-5b28f5dd-8f02-40a8-aa97-bd640a34cfba +2023-05-10 18:16:33,828 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,840 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:16:33,840 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:16:33,842 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:16:41,708 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:16:41,709 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.86s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:16:41,849 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:16:41,851 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.99s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:16:41,854 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:41,856 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:16:41,860 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:16:41,860 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:16:47,962 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.49s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:16:47,975 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.51s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:16:47,978 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:16:47,980 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:17:13,248 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:13,248 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:13,649 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:17:13,650 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:17:14,324 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:14,325 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:17,364 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:17,364 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 599486 599486 599486 ... 2198540 2198540 2198540] +destinations: [ 262545 528383 2173920 ... 1759461 108204 2338644] +batch: [300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 + 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 + 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 + 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 + 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 + 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 + 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 + 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 + 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 + 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 + 480 481 482 483 484 485 486 487 488 489 490 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1494 1495 1496 1497 1498 1499] +offset: [ 0 142111 286256 420782 565497 709511 847543 984721 + 1124783 1268603 1402855 1546997 1687822 1827691 1966122 2106761 + 2250021 2395142 2532109 2672123 2807308 2946596 3088789 3228643 + 3367884 3515106 3655054 3795126 3937021 4075952 4216147 4357482 + 4502618 4644960 4784481 4923278 5063019 5202739 5342101 5481823 + 5625378 5767695 5909923 6046930 6187135 6328836 6465643 6606589 + 6742499 6878483 7024973 7161405 7303231 7441849 7580285 7723673 + 7865565 8004589 8142796 8285729 8422889 8564164 8704215 8845146 + 8987480 9131868 9274807 9410911 9553178 9693930 9834089 9974545 + 10110541 10256024 10393691 10535903 10675069 10812444 10951789 11088977 + 11233607 11368239 11513435 11654253 11797110 11934250 12069584 12206477 + 12345019 12489652 12627984 12767360 12912298 13052817 13191691 13326876 + 13467597 13604157 13740821 13874708 14015336 14156871 14295158 14436896 + 14580808 14721352 14862271 15005506 15149149 15284803 15425503 15570260 + 15708813 15846663 15986883 16125317 16261829 16400651 16541311 16681970 + 16822212 16969810 17104858 17247244 17385870 17522695 17663966 17805297 + 17946531 18084184 18225747 18364388 18501531 18641550 18780038 18920968 + 19059273 19204632 19344688 19476238 19616092 19753810 19899413 20034884 + 20173020 20317495 20453738 20596840 20732692 20879079 21023030 21161677 + 21300375 21438467 21579297 21716537 21857100 21993283 22133758 22271450 + 22414023 22559652 22700320 22841865 22979208 23117763 23257450 23392886 + 23538443 23677178 23822233 23960553 24103401 24243527 24380059 24525612 + 24662102 24798581 24945548 25087243 25230146 25367007 25509575 25648338 + 25787895 25926889 26063635 26205489 26350252 26488424 26633844 26772176 + 26921292 27059440 27198667 27341233 27487878 27626157 27765427 27901505 + 28038844 28173529 28318409 28455914 28598384 28741178 28882268 29018955 + 29160262 29298412 29440498 29579494 29718090 29856172 29996158 30135240 + 30281720 30424072 30562706 30700143 30836280 30975648 31118988 31262077 + 31397743 31539834 31681773 31824793 31963877 32102578 32244132 32385047 + 32525236 32662117 32803822 32942347 33085493 33225429 33361526 33501173 + 33638711 33778425 33921989 34062183 34200887 34339067 34480117 34619898 + 34763442 34899963 35043422 35178157 35321809 35463342 35603442 35744772 + 35884155 36026739 36163278 36302869 36443719 36584421 36726997 36866503 + 37004683 37151630 37287320 37431317 37571946 37710583 37851260 37987025 + 38124225 38262930 38398175 38540375 38682650 38826916 38964043 39099941 + 39244307 39382461 39524016 39662679 39805551 39947888 40084235 40217623 + 40361143 40505804 40644737 40784627 40918898 41062797 41203962 41343887 + 41484887 41626940 41770325 41906459 42051401] + +2023-05-10 18:17:20,608 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:17:20,608 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:17:20,611 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:17:20,611 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:17:20,614 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:17:20,614 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:17:20,736 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:17:20,738 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:17:20,738 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:41227. Reason: scheduler-restart +2023-05-10 18:17:20,739 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42727. Reason: scheduler-restart +2023-05-10 18:17:20,739 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:17:20,740 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:17:20,741 - distributed.nanny - INFO - Worker closed +sources: [1800064 1800065 1800065 ... 1877776 1877776 1877776] +destinations: [ 627165 401781 157256 ... 2099278 2124881 958074] +batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 + 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 + 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 + 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 + 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 + 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 + 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 + 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 + 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 + 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 + 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 + 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 + 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 + 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 + 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 + 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 + 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 + 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 + 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 + 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 + 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 + 2094 2095 2096 2097 2098 2099] +offset: [ 0 139977 279646 422533 559912 697685 837604 973667 + 1119445 1265141 1409754 1548316 1682645 1821784 1960134 2102224 + 2247812 2387202 2525836 2661661 2802877 2942436 3087131 3233383 + 3368777 3513170 3648112 3787881 3927770 4065592 4205188 4349897 + 4492629 4631779 4767026 4904577 5045821 5187110 5325848 5463376 + 5610962 5745823 5884652 6026280 6162325 6300259 6439513 6579640 + 6716025 6856332 7000683 7140976 7279168 7424156 7562283 7702179 + 7843386 7977781 8117979 8259507 8398911 8535643 8676495 8818209 + 8963846 9105132 9248948 9384850 9527581 9661713 9801589 9949582 + 10085822 10225111 10362242 10505862 10639988 10781607 10920975 11059739 + 11200746 11337115 11483496 11620271 11764589 11904604 12045697 12187947 + 12330254 12467129 12609738 12747981 12886008 13024070 13164984 13307167 + 13444481 13585832 13728537 13863555 14005922 14142484 14282422 14422270 + 14567348 14706181 14849562 14986610 15129295 15271569 15411593 15557132 + 15696598 15835001 15972171 16110307 16248385 16391567 16529540 16673015 + 16812967 16952227 17096626 17240761 17381531 17525108 17663981 17809390 + 17944846 18081208 18222269 18362333 18505823 18644248 18784986 18922897 + 19065267 19203710 19342844 19486618 19626809 19766350 19905762 20044989 + 20180301 20323898 20462992 20600233 20739446 20882167 21026626 21163454 + 21304369 21440172 21577267 21718523 21856469 21998698 22134852 22277494 + 22415036 22553556 22697671 22835516 22975324 23115598 23262664 23399042 + 23534722 23677293 23816603 23958487 24098223 24239804 24375249 24516118 + 24652798 24794509 24933678 25075676 25217673 25360699 25499350 25638702 + 25779916 25917317 26058709 26196871 26337487 26475656 26614539 26756869 + 26896603 27033966 27174876 27314288 27456960 27597033 27739889 27879229 + 28023065 28161348 28299652 28437999 28572401 28718304 28859501 28994674 + 29136992 29271812 29413218 29550442 29694054 29835477 29975654 30120072 + 30258232 30398808 30539827 30683239 30824186 30963238 31110360 31253714 + 31394260 31533945 31674451 31812138 31945736 32088661 32227803 32368985 + 32509402 32646994 32786256 32927098 33071139 33214404 33354483 33494340 + 33635502 33772954 33913071 34057105 34198719 34341261 34477713 34613841 + 34755655 34897163 35042583 35181688 35325324 35460853 35599506 35736699 + 35871864 36010022 36153311 36293131 36430576 36566889 36709681 36851650 + 36989576 37128112 37268437 37408030 37549473 37684828 37826789 37964855 + 38102974 38239099 38379978 38518009 38656578 38795844 38939639 39082932 + 39222802 39365104 39510200 39647925 39785409 39925610 40067835 40205142 + 40344986 40484881 40621658 40763132 40906221 41048584 41192999 41330316 + 41474233 41611956 41748978 41887116 42029310] + +2023-05-10 18:17:20,742 - distributed.nanny - INFO - Worker closed +sources: [ 214646 214646 214646 ... 1813254 1813254 1813254] +destinations: [ 943903 1507770 530165 ... 1129690 1996099 1724999] +batch: [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 + 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 + 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 + 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 + 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 + 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 + 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 + 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 + 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 + 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 + 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 + 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 + 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 + 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 + 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 + 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 + 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 + 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 + 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 + 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 + 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 + 2394 2395 2396] +offset: [ 0 142494 283821 424216 564214 702709 842557 978501 + 1120381 1260693 1397887 1535909 1680941 1821588 1963835 2104654 + 2250562 2388874 2527628 2671679 2812733 2952511 3096625 3236089 + 3374470 3513183 3653136 3796743 3934363 4076223 4214416 4353100 + 4490130 4632652 4775833 4912815 5053610 5193254 5331201 5467962 + 5611232 5749088 5887758 6027919 6173978 6315919 6452913 6599165 + 6738583 6874735 7014990 7160713 7303514 7445801 7582384 7721117 + 7857035 8001612 8143837 8283456 8420399 8564434 8704521 8844036 + 8979914 9125558 9262478 9400581 9539620 9676813 9817316 9957521 + 10096654 10235279 10374500 10519455 10665363 10805277 10944371 11083372 + 11225955 11366178 11508587 11647891 11788414 11926950 12067080 12206378 + 12355528 12493250 12633748 12770265 12908984 13048019 13187014 13333134 + 13472870 13615503 13760183 13896961 14038155 14175072 14324110 14461107 + 14607366 14740469 14880328 15017551 15156742 15301129 15440330 15578293 + 15718903 15859533 15998793 16141652 16279306 16415954 16558029 16700046 + 16836008 16973169 17114787 17251333 17399699 17541293 17677986 17816638 + 17958289 18097303 18243238 18384986 18521707 18661761 18802763 18943595 + 19087312 19226376 19369516 19503578 19645533 19788595 19930288 20071715 + 20204469 20342804 20483116 20624445 20764375 20899514 21036929 21178149 + 21319191 21455021 21599382 21737437 21878850 22024697 22167933 22311452 + 22447807 22582282 22720625 22859788 22998479 23139477 23281876 23423838 + 23569425 23706632 23846414 23985501 24125921 24268131 24404710 24538641 + 24678185 24816600 24960063 25098621 25241719 25377054 25518018 25655214 + 25798993 25940033 26082943 26223441 26362683 26502425 26638363 26772792 + 26916696 27056956 27194021 27335150 27478165 27615196 27754717 27894546 + 28040483 28179165 28319688 28457466 28593769 28731995 28872855 29020357 + 29162904 29304849 29444271 29585770 29721304 29853904 29997973 30139841 + 30287108 30429051 30567639 30707246 30846270 30986074 31130381 31269370 + 31411123 31551922 31694291 31832165 31970988 32106830 32252672 32391391 + 32528282 32668660 32806821 32949022 33091909 33234595 33376090 33511937 + 33650760 33791196 33936075 34072272 34214487 34357305 34492183 34635151 + 34776967 34917647 35052226 35194141 35336956 35478623 35619269 35756055 + 35900779 36039590 36182087 36321970 36459066 36598904 36743642 36880455 + 37017313 37157203 37303567 37447588 37579515 37721900 37864949 38000931 + 38144137 38281771 38425214 38563036 38702694 38844834 38986886 39127314 + 39267314 39412841 39550303 39693514 39829603 39968380 40111942 40248123 + 40388058 40530428 40673474 40812914 40957742 41098404 41238421 41375128 + 41510546 41600361] + +2023-05-10 18:17:22,553 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:17:22,814 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:17:23,755 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:17:23,755 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:17:24,027 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:17:24,067 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:17:24,067 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:17:24,340 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:17:24,948 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38521 +2023-05-10 18:17:24,948 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38521 +2023-05-10 18:17:24,949 - distributed.worker - INFO - dashboard at: 10.120.104.11:38261 +2023-05-10 18:17:24,949 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:17:24,949 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:24,949 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:17:24,949 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:17:24,949 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-8644o16d +2023-05-10 18:17:24,949 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6e3a0512-8f7c-475c-8763-9309df3ccd27 +2023-05-10 18:17:24,950 - distributed.worker - INFO - Starting Worker plugin RMMSetup-4d3a65ce-4df5-4641-92e2-3b7eca1fdf5b +2023-05-10 18:17:25,150 - distributed.worker - INFO - Starting Worker plugin PreImport-fba04452-d1fb-4e2e-ab0d-e5c2bf1aca58 +2023-05-10 18:17:25,151 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:25,159 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:17:25,159 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:25,162 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:17:25,321 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40329 +2023-05-10 18:17:25,321 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40329 +2023-05-10 18:17:25,321 - distributed.worker - INFO - dashboard at: 10.120.104.11:36925 +2023-05-10 18:17:25,321 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:17:25,321 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:25,321 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:17:25,321 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:17:25,321 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-51an_5tn +2023-05-10 18:17:25,321 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-7001931d-69cb-4c27-a8bc-232acb581394 +2023-05-10 18:17:25,322 - distributed.worker - INFO - Starting Worker plugin PreImport-e4d711a7-fa8c-4785-b9de-2348895315dc +2023-05-10 18:17:25,322 - distributed.worker - INFO - Starting Worker plugin RMMSetup-3f526ce4-02de-404d-8c4d-9854d24b2c40 +2023-05-10 18:17:25,442 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:25,451 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:17:25,452 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:17:25,454 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:17:33,497 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:17:33,498 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.88s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:17:33,729 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:17:33,731 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.12s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:17:33,735 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:33,737 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:17:33,741 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:17:33,741 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:17:40,000 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:17:40,064 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.68s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:17:40,069 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:17:40,069 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:18:05,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:05,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:05,824 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:05,825 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:06,581 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:06,583 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:10,374 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:10,374 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [1712950 1712950 1712950 ... 226264 226264 226264] +destinations: [ 819856 622935 1634974 ... 1258299 2330085 1714337] +batch: [500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 + 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 + 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 + 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 + 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 + 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 + 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 + 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 + 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 + 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 + 680 681 682 683 684 685 686 687 688 689 690 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distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:18:13,102 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:18:13,105 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:13,105 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:13,108 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:18:13,108 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:18:13,226 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:18:13,228 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:18:13,228 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38521. Reason: scheduler-restart +2023-05-10 18:18:13,229 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40329. Reason: scheduler-restart +2023-05-10 18:18:13,229 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:18:13,230 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:18:13,230 - distributed.nanny - INFO - Worker closed +sources: [2000658 2000658 2000658 ... 2333960 2333960 2333960] +destinations: [1615682 1124131 604924 ... 129982 864431 54650] +batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 + 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 + 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 + 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 + 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 + 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 + 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 + 2098 2099 2100 2101 2102 2103 2104 2105 2106 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54119905 54256196 54396194 54538535 54681511 54821194 + 54966500 55107512 55247571 55384665 55520100 55609840] + +2023-05-10 18:18:13,231 - distributed.nanny - INFO - Worker closed +sources: [1999769 1999771 1999772 ... 1257064 1257064 1257064] +destinations: [ 199153 1927813 554875 ... 769924 511648 1394495] +batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 + 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 + 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 + 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 + 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 + 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 + 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 + 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 + 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 + 1626 1627 1628 1629 1630 1631 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63216010 63356650 63491963 63629294 63770208 + 63908827 64051192 64187712 64330597 64467941 64606738 64750991 64888749 + 65028590 65169128 65316461 65452994 65588834 65731484 65870807 66012528 + 66152110 66293568 66429133 66569464 66706057 66848073 66987292 67129502 + 67271715 67414616 67553232 67692446 67833311 67970969 68112129 68250081 + 68390818 68529100 68668256 68811008 68950293 69087573 69227987 69367299 + 69509705 69650382 69792780 69932111 70076126] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +sys:1: RuntimeWarning: coroutine 'BlockingMode._arm_worker' was never awaited +Task was destroyed but it is pending! +task: > +2023-05-10 18:18:15,065 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:18:15,355 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:18:16,271 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:18:16,271 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:18:16,534 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:18:16,534 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:18:16,535 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:18:16,825 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:18:17,431 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46207 +2023-05-10 18:18:17,431 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46207 +2023-05-10 18:18:17,431 - distributed.worker - INFO - dashboard at: 10.120.104.11:38377 +2023-05-10 18:18:17,431 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:18:17,431 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,431 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:18:17,431 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:18:17,431 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-u1xdhp42 +2023-05-10 18:18:17,432 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c16c7aed-35a6-426d-b105-f53e865cb07c +2023-05-10 18:18:17,584 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-bf9ee86a-ed61-46e2-83e1-71e720c11db6 +2023-05-10 18:18:17,585 - distributed.worker - INFO - Starting Worker plugin PreImport-9956544d-d30b-4074-9fa5-b85bad533c80 +2023-05-10 18:18:17,586 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,597 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:18:17,597 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,600 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:18:17,801 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36349 +2023-05-10 18:18:17,802 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36349 +2023-05-10 18:18:17,802 - distributed.worker - INFO - dashboard at: 10.120.104.11:43345 +2023-05-10 18:18:17,802 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:18:17,802 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,802 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:18:17,802 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:18:17,802 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-bsjmqb9m +2023-05-10 18:18:17,802 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-cb689d63-917b-4559-badb-e40378f3d2c4 +2023-05-10 18:18:17,802 - distributed.worker - INFO - Starting Worker plugin RMMSetup-429c05c1-d990-4871-a357-a462004329c9 +2023-05-10 18:18:17,921 - distributed.worker - INFO - Starting Worker plugin PreImport-ff040de1-0fea-471b-a543-2ea6fee20392 +2023-05-10 18:18:17,921 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,929 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:18:17,929 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:18:17,931 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:18:25,722 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:18:25,723 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.77s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:18:25,959 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:18:25,960 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.00s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:18:25,964 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:25,965 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:25,970 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:18:25,970 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:18:32,176 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:18:32,229 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.67s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:18:32,232 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:18:32,235 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:18:57,561 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:57,561 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:57,970 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:57,971 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:18:58,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:18:58,934 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:03,829 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:03,829 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 288 288 288 ... 734451 734451 734451] +destinations: [1226797 2084768 1532974 ... 926960 1135535 2291206] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 + 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 + 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 + 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 + 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 + 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 + 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 + 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 + 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 + 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 + 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 + 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 + 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 + 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 + 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 + 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 + 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 + 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 + 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 + 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 + 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 + 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 + 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 + 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 + 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 + 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 + 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 + 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 + 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 + 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 + 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 + 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 + 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 + 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 + 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 + 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 + 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 + 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 + 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 + 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 + 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 + 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 + 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 + 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 + 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 + 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 + 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 + 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 + 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 + 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 + 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 + 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 + 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 + 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 + 990 991 992 993 994 995 996 997 998 999] +offset: [ 0 136743 279698 ... 140016388 140152646 140291973] + +2023-05-10 18:19:11,300 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:19:11,300 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:19:11,304 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:19:11,304 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:19:11,307 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:19:11,307 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:19:11,427 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:19:11,429 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:19:11,429 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36349. Reason: scheduler-restart +2023-05-10 18:19:11,430 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46207. Reason: scheduler-restart +2023-05-10 18:19:11,430 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:19:11,431 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:19:11,432 - distributed.nanny - INFO - Worker closed +2023-05-10 18:19:11,432 - distributed.nanny - INFO - Worker closed +sources: [2000144 2000144 2000144 ... 2333960 2333960 2333960] +destinations: [ 351641 1365172 1074226 ... 1084884 338426 453403] +batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 + 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 + 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 + 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 + 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 + 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 + 2084 2085 2086 2087 2088 2089 2090 2091 2092 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1909 + 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 + 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 + 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 + 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 + 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 + 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 + 1994 1995 1996 1997 1998 1999] +offset: [ 0 141036 279393 ... 139882294 140021874 140166162] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 18:19:13,325 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:19:13,575 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:19:14,572 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:19:14,572 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:19:14,767 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:19:14,767 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:19:14,838 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:19:15,031 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:19:15,713 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38111 +2023-05-10 18:19:15,713 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38111 +2023-05-10 18:19:15,713 - distributed.worker - INFO - dashboard at: 10.120.104.11:34215 +2023-05-10 18:19:15,713 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:19:15,713 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:15,713 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:19:15,714 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:19:15,714 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-vrdjdvub +2023-05-10 18:19:15,714 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-17840f5f-9d29-4627-ab20-80a9d182329c +2023-05-10 18:19:15,715 - distributed.worker - INFO - Starting Worker plugin PreImport-b53430c8-ee70-47ad-a4c9-4c47e6c29810 +2023-05-10 18:19:15,715 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6e5dd437-dab3-4461-8855-772f359f2367 +2023-05-10 18:19:15,885 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:15,896 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:19:15,896 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:15,898 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:19:16,042 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42615 +2023-05-10 18:19:16,042 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42615 +2023-05-10 18:19:16,042 - distributed.worker - INFO - dashboard at: 10.120.104.11:41991 +2023-05-10 18:19:16,042 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:19:16,042 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:16,042 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:19:16,042 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:19:16,042 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tf_3am1a +2023-05-10 18:19:16,043 - distributed.worker - INFO - Starting Worker plugin RMMSetup-b70dea3f-c97b-44f2-b05f-1fb2dcb0be10 +2023-05-10 18:19:16,162 - distributed.worker - INFO - Starting Worker plugin PreImport-c3345aa4-25d1-484e-bfb7-d6e4c3862de5 +2023-05-10 18:19:16,162 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-cf055071-425d-4c8d-a704-76f161a0042b +2023-05-10 18:19:16,163 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:16,171 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:19:16,171 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:19:16,173 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:19:23,986 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:19:23,987 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.80s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:19:24,269 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:19:24,270 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.09s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:19:24,274 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:24,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:24,279 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:19:24,279 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:19:30,515 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.65s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:19:30,552 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.69s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:19:30,555 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:19:30,557 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:19:50,039 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:50,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:50,729 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:19:50,731 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:19:51,549 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:51,549 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [3862678 3862678 3862678 ... 1159388 1159388 1159388] +destinations: [2983596 4560301 1908136 ... 98130 1023500 1517166] 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40517182 40584278 40652840 + 40722179 40791479 40862029 40929427 40999153 41067149 41137507 41204620 + 41270329] +2023-05-10 18:19:54,607 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:19:54,607 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' + +sources: [1199596 1199596 1199596 ... 978623 978623 978623] +destinations: [3880104 1333946 2402572 ... 1651702 532716 94047] +batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 + 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 + 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 + 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 + 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 + 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 + 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 + 1898 1899 1900 1901 1902 1903 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41307843] +2023-05-10 18:20:02,474 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:20:02,474 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' + +sources: [4193918 4193918 4193918 ... 4390489 4390489 4390489] +destinations: [3199105 296079 2532015 ... 2813384 1602833 382634] +batch: [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 + 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 + 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 + 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 + 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 + 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 + 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 + 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 + 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 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27432134 + 27498989 27561824 27629522 27697229 27765900 27837014 27904026 27973718 + 28047956 28120125 28195146 28261068 28334839 28401951 28472451 28543231 + 28612017 28679609 28750586 28818831 28890141 28960581 29030750 29099136 + 29168395 29234537 29302738 29373280 29440041 29511662 29579127 29647966 + 29719550 29786641 29858809 29930103 30000489 30068672 30136498 30205191 + 30272561 30342150 30413245 30482159 30548377 30615996 30684625 30753074 + 30820658 30892060 30959101 31027617 31093845 31162010 31230957 31301506 + 31372645 31439806 31509461 31575668 31644817 31716831 31789217 31859127 + 31930056 32003820 32071894 32139597 32207022 32278275 32349012 32416445 + 32486246 32557967 32626202 32693743 32760510 32829834 32898880 32971138 + 33037022 33104867 33174512 33241403 33311261 33380904 33452524 33518816 + 33584245 33654052 33722687 33790231 33860760 33931607 34002082 34070162 + 34138520 34209463 34279747 34347648 34418953 34485651 34556081 34622485 + 34691655 34761966 34829172 34900037 34969242 35036622 35105785 35177226 + 35243094 35310902 35380058 35449301 35520868 35585707 35653490 35722194 + 35791488 35853863 35922189 35992973 36062990 36129012 36198116 36268725 + 36338530 36405483 36473101 36538756 36608542 36676597 36745473 36813025 + 36881717 36953878 37025297 37097037 37162592 37231649 37302160 37370629 + 37438584 37504635 37573482 37641545 37710121 37778633 37845099 37914986 + 37986169 38053061 38122814 38190558 38259370 38324529 38394698 38466597 + 38539758 38606951 38673937 38741844 38812090 38878742 38947956 39014961 + 39083755 39152661 39224266 39290993 39360456 39427092 39499543 39563613 + 39632289 39700360 39769905 39837498 39904897 39974854 40044676 40111825 + 40181709 40252380 40324457 40392278 40459011 40525172 40596938 40669512 + 40740349 40807066 40875446 40946967 41014388 41081463 41152543 41226215 + 41295303] +2023-05-10 18:20:07,943 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:20:07,943 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:20:07,946 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:20:07,946 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:20:07,949 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:20:07,949 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:20:08,090 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:20:08,091 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:20:08,092 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38111. Reason: scheduler-restart +2023-05-10 18:20:08,092 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42615. Reason: scheduler-restart +2023-05-10 18:20:08,093 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:20:08,094 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:20:08,095 - distributed.nanny - INFO - Worker closed +2023-05-10 18:20:08,095 - distributed.nanny - INFO - Worker closed + + +sources: [ 174152 174152 174152 ... 4500066 4500066 4500066] +destinations: [3186417 3513003 4290052 ... 3353253 1179243 553011] +batch: [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013 + 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027 + 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041 + 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055 + 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069 + 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083 + 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097 + 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 + 9112 9113 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 + 9126 9127 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 + 9140 9141 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 + 9154 9155 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 + 9168 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 + 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 + 9196 9197 9198 9199 9200 9201 9202 9203 9204 9205 9206 9207 9208 9209 + 9210 9211 9212 9213 9214 9215 9216 9217 9218 9219 9220 9221 9222] +offset: [ 0 66042 132678 200393 265732 334616 404384 473958 + 539925 611288 679247 747692 815616 880934 952700 1020974 + 1091404 1161284 1227354 1296683 1365414 1433973 1502683 1570447 + 1641673 1715341 1783254 1853601 1922862 1991927 2063683 2130517 + 2196256 2267054 2334758 2405416 2472510 2543797 2609880 2680846 + 2748703 2822659 2891807 2963095 3033829 3100188 3168586 3234340 + 3298722 3372950 3440616 3507325 3579607 3646212 3713261 3781324 + 3851195 3921056 3988009 4056493 4127013 4199669 4271954 4342608 + 4409230 4478158 4545271 4615261 4683988 4756964 4825489 4891730 + 4958969 5030945 5097234 5167708 5238094 5302648 5375011 5441230 + 5511402 5579598 5649827 5718531 5782856 5849107 5920351 5983951 + 6050068 6118448 6189914 6260779 6330171 6400463 6469841 6535906 + 6607087 6674835 6740045 6806565 6875707 6942881 7014872 7084531 + 7154119 7224445 7292205 7362330 7428305 7496230 7560408 7631069 + 7699000 7767614 7838852 7907570 7976232 8042693 8114582 8179870 + 8250737 8321284 8389466 8455778 8523005 8593530 8663873 8731811 + 8803143 8872849 8942423 9012284 9080640 9147824 9221135 9294245 + 9365877 9431122 9501957 9569524 9642763 9714400 9785502 9854593 + 9919374 9988436 10053970 10123530 10190675 10260020 10326000 10398794 + 10467781 10539270 10607862 10678689 10749159 10814755 10885408 10953030 + 11024458 11092604 11164883 11235573 11304392 11373808 11440350 11511468 + 11578023 11647229 11714553 11782503 11850384 11920922 11991852 12058430 + 12124180 12193990 12263048 12331000 12400184 12469339 12542136 12616018 + 12684433 12752628 12819705 12889437 12956993 13027749 13097935 13166800 + 13233530 13299902 13367048 13435368 13504249 13567803 13637909 13704580 + 13772610 13841840 13911317 13980156 14048930 14118844 14184108 14255280 + 14321391 14392791 14459800 14527247 14596997 14663565 14735867 14802512 + 14871315 14938856 15007728 15079555 15145847 15214882 15285290 15286953] + +2023-05-10 18:20:09,794 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:20:10,142 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:20:11,031 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:20:11,031 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:20:11,304 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:20:11,375 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:20:11,375 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:20:11,653 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:20:12,132 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37861 +2023-05-10 18:20:12,132 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37861 +2023-05-10 18:20:12,132 - distributed.worker - INFO - dashboard at: 10.120.104.11:40621 +2023-05-10 18:20:12,133 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:20:12,133 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,133 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:20:12,133 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:20:12,133 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-brjvc3pt +2023-05-10 18:20:12,134 - distributed.worker - INFO - Starting Worker plugin RMMSetup-13b7ea2c-2794-4bc5-8de0-63bb897b99cf +2023-05-10 18:20:12,272 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-afb3e3a9-91cf-43ff-b440-6c5f3ecd3c0f +2023-05-10 18:20:12,272 - distributed.worker - INFO - Starting Worker plugin PreImport-1edeff9e-0e60-47ad-b0d5-57757417b635 +2023-05-10 18:20:12,273 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,284 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:20:12,284 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,286 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:20:12,593 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34921 +2023-05-10 18:20:12,593 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34921 +2023-05-10 18:20:12,593 - distributed.worker - INFO - dashboard at: 10.120.104.11:38961 +2023-05-10 18:20:12,593 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:20:12,593 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,593 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:20:12,593 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:20:12,593 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rl90_bj6 +2023-05-10 18:20:12,593 - distributed.worker - INFO - Starting Worker plugin RMMSetup-d7758436-04a2-441e-b55b-1f524fb1e32b +2023-05-10 18:20:12,720 - distributed.worker - INFO - Starting Worker plugin PreImport-aa261f41-f283-4f5c-a5a5-d3067947deeb +2023-05-10 18:20:12,720 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-e21bd1eb-17da-442e-9874-1ea0bcf8b46b +2023-05-10 18:20:12,721 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,732 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:20:12,732 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:20:12,734 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:20:20,577 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:20:20,578 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.83s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:20:20,627 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:20:20,628 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.88s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:20:20,632 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:20:20,634 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:20:20,638 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:20:20,638 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:20:26,888 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.65s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:20:27,009 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.77s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:20:27,013 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:20:27,014 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:21:06,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:06,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:06,832 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:21:06,833 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:21:07,845 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:07,847 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:11,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:11,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 272 272 272 ... 1478069 1478069 1478069] +destinations: [1547254 296390 175481 ... 3094924 438284 1710104] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 + 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 + 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 + 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 + 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 + 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 + 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 + 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 + 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 + 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 + 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 + 216 217 218 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18:21:18,266 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:21:18,268 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:21:18,268 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:34921. Reason: scheduler-restart +2023-05-10 18:21:18,270 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:21:18,271 - distributed.nanny - INFO - Worker closed +sources: [4000096 4000098 4000098 ... 1553298 1553298 1553298] +destinations: [4124038 160075 1866771 ... 1876656 4235938 2792120] +batch: [8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013 + 8014 8015 8016 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027 + 8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041 + 8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052 8053 8054 8055 + 8056 8057 8058 8059 8060 8061 8062 8063 8064 8065 8066 8067 8068 8069 + 8070 8071 8072 8073 8074 8075 8076 8077 8078 8079 8080 8081 8082 8083 + 8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097 + 8098 8099 8100 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111 + 8112 8113 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124 8125 + 8126 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8710 8711 8712 8713 + 8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724 8725 8726 8727 + 8728 8729 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739 8740 8741 + 8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755 + 8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769 + 8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783 + 8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796 8797 + 8798 8799 8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811 + 8812 8813 8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825 + 8826 8827 8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839 + 8840 8841 8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853 + 8854 8855 8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 + 8868 8869 8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880 8881 + 8882 8883 8884 8885 8886 8887 8888 8889 8890 8891 8892 8893 8894 8895 + 8896 8897 8898 8899 8900 8901 8902 8903 8904 8905 8906 8907 8908 8909 + 8910 8911 8912 8913 8914 8915 8916 8917 8918 8919 8920 8921 8922 8923 + 8924 8925 8926 8927 8928 8929 8930 8931 8932 8933 8934 8935 8936 8937 + 8938 8939 8940 8941 8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 + 8952 8953 8954 8955 8956 8957 8958 8959 8960 8961 8962 8963 8964 8965 + 8966 8967 8968 8969 8970 8971 8972 8973 8974 8975 8976 8977 8978 8979 + 8980 8981 8982 8983 8984 8985 8986 8987 8988 8989 8990 8991 8992 8993 + 8994 8995 8996 8997 8998 8999] +offset: [ 0 67687 133835 ... 68683498 68757293 68826457] + +2023-05-10 18:21:18,299 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34921 +2023-05-10 18:21:18,299 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37861. Reason: scheduler-restart +2023-05-10 18:21:18,301 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:21:18,302 - distributed.nanny - INFO - Worker closed +sources: [ 353244 353244 353244 ... 4500234 4500234 4500234] +destinations: [3411338 3407598 4106750 ... 3134371 2892048 833324] +batch: [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013 + 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027 + 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041 + 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055 + 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069 + 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083 + 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097 + 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 + 9112 9113 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 + 9126 9127 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 + 9140 9141 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 + 9154 9155 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 + 9168 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 + 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 + 9196 9197 9198 9199 9200 9201 9202 9203 9204 9205 9206 9207 9208 9209 + 9210 9211 9212 9213 9214 9215 9216 9217 9218 9219 9220 9221 9222] +offset: [ 0 66248 132977 200660 266073 334996 404752 474494 + 540358 611772 679897 748503 816547 881878 953491 1021527 + 1091869 1161535 1227410 1296856 1365248 1433642 1502416 1570250 + 1641803 1715228 1783071 1853620 1922381 1991152 2062909 2129937 + 2195854 2266440 2334284 2404708 2471783 2542872 2609076 2679937 + 2747579 2821278 2890582 2961965 3032593 3099371 3167408 3233080 + 3297586 3371671 3439292 3506136 3578277 3644955 3712045 3779969 + 3849900 3919907 3986829 4055591 4126227 4199224 4271867 4342639 + 4409516 4478431 4545512 4615438 4683915 4757013 4825573 4891739 + 4958875 5030503 5097090 5167417 5237782 5302198 5374634 5440807 + 5510885 5578873 5648998 5717646 5781815 5848094 5919437 5983291 + 6049210 6117458 6189059 6259761 6329314 6399001 6468245 6534717 + 6605590 6673240 6738404 6805106 6874095 6941093 7013070 7082614 + 7152057 7222533 7290484 7360512 7426702 7494713 7558860 7629649 + 7697727 7766308 7837473 7905948 7974878 8041665 8113383 8178400 + 8249555 8320093 8388187 8454475 8521790 8591865 8662210 8729796 + 8801210 8870815 8940387 9009935 9078263 9145301 9218947 9292235 + 9364312 9429575 9500543 9568209 9641456 9713013 9783996 9852730 + 9917607 9986270 10051795 10121178 10188353 10257887 10324048 10396663 + 10465609 10537305 10605756 10676672 10747354 10813036 10883384 10950818 + 11022238 11090554 11163020 11233659 11302325 11371921 11438276 11509432 + 11576001 11645200 11712464 11780221 11848147 11918770 11989722 12055864 + 12121835 12191535 12260565 12328238 12397383 12466484 12539062 12612880 + 12680897 12749216 12816156 12885991 12953726 13024408 13094641 13163236 + 13229531 13295976 13363230 13431418 13500314 13564030 13634194 13700883 + 13768963 13837907 13907474 13976363 14045058 14114365 14179580 14250688 + 14316994 14388444 14455673 14522872 14592855 14659462 14731840 14798318 + 14867011 14934514 15003358 15075139 15141735 15210896 15280872 15282499] + +2023-05-10 18:21:20,211 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:21:20,478 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:21:21,469 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:21:21,470 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:21:21,654 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:21:21,655 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:21:21,740 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:21:21,912 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:21:22,593 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37045 +2023-05-10 18:21:22,594 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37045 +2023-05-10 18:21:22,594 - distributed.worker - INFO - dashboard at: 10.120.104.11:38993 +2023-05-10 18:21:22,594 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:21:22,594 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:22,594 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:21:22,594 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:21:22,594 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-ru07ko5g +2023-05-10 18:21:22,594 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-e87a5689-86fb-496f-9926-1010208c04d4 +2023-05-10 18:21:22,596 - distributed.worker - INFO - Starting Worker plugin PreImport-9dddbba2-a8c4-401a-b3c6-edc849bb7417 +2023-05-10 18:21:22,596 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6ccdc2ef-a38b-4bf9-bbbb-6b1cd203d9c4 +2023-05-10 18:21:22,786 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:22,797 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:21:22,797 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:22,799 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:21:22,955 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40247 +2023-05-10 18:21:22,955 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40247 +2023-05-10 18:21:22,955 - distributed.worker - INFO - dashboard at: 10.120.104.11:39891 +2023-05-10 18:21:22,955 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:21:22,955 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:22,955 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:21:22,955 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:21:22,956 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-n2gur77n +2023-05-10 18:21:22,956 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-c3cee309-df29-4528-936d-6f64a21d480f +2023-05-10 18:21:22,956 - distributed.worker - INFO - Starting Worker plugin RMMSetup-25eee593-d2df-4e66-8686-1b04ad78e878 +2023-05-10 18:21:23,076 - distributed.worker - INFO - Starting Worker plugin PreImport-6440e4d9-f937-466d-b4c9-b47602454e6e +2023-05-10 18:21:23,077 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:23,085 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:21:23,085 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:21:23,087 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:21:31,132 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:21:31,133 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.05s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:21:31,202 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:21:31,203 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.13s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:21:31,207 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:31,208 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:21:31,212 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:21:31,212 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:21:37,383 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.60s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:21:37,527 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.74s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:21:37,530 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:21:37,532 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:22:17,052 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:17,053 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:17,715 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:22:17,716 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:22:18,998 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:18,999 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:24,209 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:24,209 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:45,046 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:45,046 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:46,616 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:22:46,616 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:22:46,619 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:22:46,619 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:22:46,622 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:22:46,622 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:22:46,736 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:22:46,738 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:22:46,776 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37045. Reason: scheduler-restart +2023-05-10 18:22:46,778 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:22:46,778 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40247. Reason: scheduler-restart +2023-05-10 18:22:46,779 - distributed.nanny - INFO - Worker closed +2023-05-10 18:22:46,779 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37045 +sources: [ 498 498 498 ... 3499043 3499043 3499043] +destinations: [2635286 40553 4459323 ... 2179131 2147358 3967418] +batch: [ 0 1 2 ... 1997 1998 1999] +offset: [ 0 70419 136927 ... 137455044 137520691 137589183] + +sources: [2000320 2000321 2000321 ... 2528936 2528936 2528936] +destinations: [4605167 1908584 2577623 ... 964686 111297 1911773] +batch: [4000 4001 4002 ... 5997 5998 5999] +offset: [ 0 65558 134876 ... 137425737 137494878 137563830] + +sources: [4000096 4000098 4000098 ... 1322689 1322689 1322689] +destinations: [4124038 2907526 3584833 ... 3341187 904853 2966264] +batch: [8000 8001 8002 ... 9220 9221 9222] +offset: [ 0 67697 133541 ... 84041893 84111662 84113298] + +2023-05-10 18:22:46,780 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:22:46,782 - distributed.nanny - INFO - Worker closed +sources: [1000097 1000097 1000097 ... 261476 261476 261476] +destinations: [1158198 2457900 2147393 ... 3911980 3206366 4263871] +batch: [2000 2001 2002 ... 3997 3998 3999] +offset: [ 0 69817 136164 ... 137431018 137500095 137567719] + +sources: [3000482 3000483 3000483 ... 1752925 1752925 1752925] +destinations: [4274300 1498441 3942694 ... 2305443 584555 538665] +batch: [6000 6001 6002 ... 7997 7998 7999] +offset: [ 0 64866 129344 ... 137541133 137612982 137678909] + +sources: [] +destinations: [] +batch: [] +offset: [0] + +2023-05-10 18:22:48,550 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:22:48,835 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:22:49,760 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:22:49,761 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:22:50,019 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:22:50,079 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:22:50,079 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:22:50,367 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:22:50,813 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33561 +2023-05-10 18:22:50,813 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33561 +2023-05-10 18:22:50,813 - distributed.worker - INFO - dashboard at: 10.120.104.11:39275 +2023-05-10 18:22:50,813 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:22:50,813 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:50,814 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:22:50,814 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:22:50,814 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-64pqmdos +2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-221c8562-790c-4b5b-99f3-fe95bcd5f481 +2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin PreImport-ccb2f45e-7f8e-4a11-beb5-c6c5ec3656f2 +2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin RMMSetup-fd7fbc32-2177-4671-bd43-fd76ad7ba619 +2023-05-10 18:22:50,988 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:51,019 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:22:51,019 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:51,021 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:22:51,262 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:45751 +2023-05-10 18:22:51,262 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:45751 +2023-05-10 18:22:51,262 - distributed.worker - INFO - dashboard at: 10.120.104.11:42099 +2023-05-10 18:22:51,262 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:22:51,262 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:51,262 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:22:51,262 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:22:51,262 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-whwl07vs +2023-05-10 18:22:51,262 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c9ffcc58-86c1-4182-81be-b374abd36260 +2023-05-10 18:22:51,384 - distributed.worker - INFO - Starting Worker plugin PreImport-4668b8be-505a-44b7-bc90-a39a3b6b6b89 +2023-05-10 18:22:51,384 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-f298efeb-ea1e-4fb5-ab23-73cc367e7ed4 +2023-05-10 18:22:51,385 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:51,395 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:22:51,395 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:22:51,397 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:22:59,284 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:22:59,285 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.84s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:22:59,411 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:22:59,412 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.98s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:22:59,416 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:59,417 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:22:59,421 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:22:59,421 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:23:05,615 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:23:05,688 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.69s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:23:05,688 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:23:05,689 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:23:24,839 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:24,840 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:25,529 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:23:25,530 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:23:26,318 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:26,319 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:29,326 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:29,326 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [ 880 880 880 ... 2379593 2379593 2379593] +destinations: [2854843 1915172 274740 ... 3951768 1566329 2807515] +batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 + 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 + 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 + 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 + 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 + 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 + 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 + 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 + 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 + 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 + 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 + 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 + 216 217 218 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35542404 35675574 35812004 35948166 36085075 36224914 + 36360351 36503155 36638489 36774098 36916545 37048146 37186968 37326511 + 37465086 37605611 37742783 37881305 38018723 38160273 38299723 38441635 + 38579014 38720084 38854601 38988041 39126112 39261706 39402006 39538799 + 39682295 39823000 39959496 40097846 40231536 40368531 40506057 40649255 + 40783669 40921304 41058056 41194324 41335995] + +2023-05-10 18:23:42,340 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:23:42,340 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:23:42,344 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:23:42,344 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:23:42,346 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:23:42,346 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:23:42,468 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:23:42,469 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:23:42,470 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33561. Reason: scheduler-restart +2023-05-10 18:23:42,470 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:45751. Reason: scheduler-restart +2023-05-10 18:23:42,471 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:23:42,471 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:23:42,473 - distributed.nanny - INFO - Worker closed +2023-05-10 18:23:42,473 - distributed.nanny - INFO - Worker closed +sources: [4200915 4200915 4200915 ... 4285919 4285919 4285919] +destinations: [2594935 4296605 1792376 ... 2546237 3693783 3652788] +batch: [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 + 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 + 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 + 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 + 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 + 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 + 4284 4285 4286 4287 4288 4289 4290 4291 4292 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3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 + 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 + 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 + 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 + 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 + 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 + 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 + 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 + 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 + 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 + 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 + 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 + 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 + 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 + 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38843188 38978211 39111242 39249299 39381484 39522560 + 39657111 39796679 39936424 40076692 40215697 40350479 40486583 40622221 + 40762872 40899568 41033824 41170810 41313249] + +sources: [4610697 4610697 4610700 ... 4500573 4500573 4500572] +destinations: [1187165 3211298 4223301 ... 3463338 3671315 3056813] +batch: [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 + 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 + 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 + 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 + 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 + 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 + 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 + 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611] +offset: [ 0 133228 266058 404964 540173 679579 815883 952718 + 1091201 1227376 1365395 1502296 1641551 1783067 1922692 2063630 + 2196102 2334694 2472020 2609138 2747632 2890415 3032613 3167366 + 3297351 3439018 3578149 3712104 3849825 3986889 4126227 4271834 + 4409445 4545320 4683775 4825260 4958699 5096930 5237972 5374761 + 5511122 5649026 5782492 5919921 6049562 6189707 6330137 6469509 + 6606739 6739541 6875077 7013852 7153449 7291685 7428165 7560613 + 7699173 7838323 7976089 8114582 8250673 8389246 8522851 8663164 + 8801985 8941046 9079152 9219795 9365178 9501141 9642065 9784642 + 9918792 10053138 10190043 10326011 10467614 10607785 10749313 10885558 + 11024282 11164836 11304297 11440088 11577603 11714081 11849799 11991208 + 12123706 12262218 12399492 12541161 12683347 12818643 12956039 13097231 + 13232651 13366210 13503568 13637399 13772377 13910967 14048643 14183189 + 14320469 14458719 14595814 14734913 14870317 15006893 15145398 15284450 + 15286055] + +2023-05-10 18:23:44,624 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:23:44,627 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:23:45,848 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:23:45,848 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:23:45,848 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:23:45,848 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:23:46,131 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:23:46,140 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:23:47,257 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36171 +2023-05-10 18:23:47,257 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36171 +2023-05-10 18:23:47,257 - distributed.worker - INFO - dashboard at: 10.120.104.11:42317 +2023-05-10 18:23:47,257 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:23:47,257 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,257 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:23:47,257 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:23:47,257 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-1quet7lg +2023-05-10 18:23:47,258 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-5123bb3b-7637-45a5-a564-d26cd0c05d74 +2023-05-10 18:23:47,258 - distributed.worker - INFO - Starting Worker plugin RMMSetup-690f26d5-e63a-4d34-b09e-c32d2703ba02 +2023-05-10 18:23:47,307 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46001 +2023-05-10 18:23:47,308 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46001 +2023-05-10 18:23:47,308 - distributed.worker - INFO - dashboard at: 10.120.104.11:37321 +2023-05-10 18:23:47,308 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:23:47,308 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,308 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:23:47,308 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:23:47,308 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-p_e7q_5k +2023-05-10 18:23:47,309 - distributed.worker - INFO - Starting Worker plugin RMMSetup-81c6c7a9-3861-46e8-a602-9192d937ca02 +2023-05-10 18:23:47,382 - distributed.worker - INFO - Starting Worker plugin PreImport-7abfc3b1-d474-49a0-87a0-6b7f1d4616eb +2023-05-10 18:23:47,383 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,393 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:23:47,393 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,395 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:23:47,429 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-22296338-f086-4de6-b2e6-56411accd318 +2023-05-10 18:23:47,430 - distributed.worker - INFO - Starting Worker plugin PreImport-d7c0db34-8ba0-467b-a85d-eb4a8cffcc1c +2023-05-10 18:23:47,430 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,438 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:23:47,438 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:23:47,440 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:23:55,307 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:23:55,308 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.87s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:23:55,416 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:23:55,417 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.98s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:23:55,420 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:55,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:23:55,425 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:23:55,425 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:24:01,536 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.52s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:24:01,663 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.64s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:24:01,666 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:24:01,668 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:24:21,117 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:21,118 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:21,808 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:24:21,809 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:24:22,669 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:22,670 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:26,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:26,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +sources: [1775325 1775325 1775325 ... 1269982 1269982 1269982] +destinations: [1431345 158227 3349855 ... 4275548 2044741 633003] +batch: [500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 + 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 + 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 + 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 + 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 + 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 + 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 + 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 + 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 + 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 + 680 681 682 683 684 685 686 687 688 689 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56836093 56967957 57107036 + 57242319 57382686 57522490 57654953 57791632 57928800 58070172 58209470 + 58346998 58493161 58632305 58769837 58907712 59047279 59184316 59322134 + 59462311 59596221 59731014 59866826 60003550 60138300 60285891 60427899 + 60559736 60699068 60835273 60973318 61106281 61248464 61387152 61523920 + 61662920 61796462 61932762 62070954 62211707 62345417 62481849 62616967 + 62754713 62889498 63027469 63164062 63299130 63436489 63571251 63706162 + 63841058 63975837 64114270 64248956 64389559 64524710 64662682 64798103 + 64942202 65078365 65215650 65352091 65494004 65634408 65768759 65907074 + 66047577 66190019 66323762 66460635 66598184 66737566 66881260 67018306 + 67155245 67290345 67431180 67566356 67703948 67841792 67979091 68123396 + 68261650 68396150 68536456 68672272 68811722] + +2023-05-10 18:24:41,804 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:24:41,804 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' +2023-05-10 18:24:41,807 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:24:41,807 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:24:41,810 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:24:41,810 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' +2023-05-10 18:24:41,925 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:24:41,926 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart +2023-05-10 18:24:41,927 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36171. Reason: scheduler-restart +2023-05-10 18:24:41,927 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46001. Reason: scheduler-restart +2023-05-10 18:24:41,928 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:24:41,929 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. +2023-05-10 18:24:41,929 - distributed.nanny - INFO - Worker closed +sources: [4000512 4000513 4000513 ... 4187130 4187130 4187130] +destinations: [3575264 27291 2931938 ... 2728522 441286 1700138] +batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 + 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 + 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 + 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 + 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 + 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 + 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 + 4098 4099 4100 4101 4102 4103 4104 4105 4106 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4495 4496 4497 4498 4499] +offset: [ 0 134054 271990 406432 540508 679745 813486 953735 + 1092368 1238723 1381814 1518832 1657885 1796209 1931404 2069463 + 2204530 2343026 2478772 2617112 2757302 2891689 3029210 3170314 + 3307364 3449231 3590364 3724250 3861002 3995224 4132730 4274491 + 4408209 4544009 4683132 4819140 4952295 5087876 5224788 5359692 + 5499055 5633546 5770630 5907805 6047376 6181710 6320432 6456540 + 6597526 6737972 6875351 7020061 7158285 7302641 7435877 7572363 + 7708098 7849090 7981358 8117886 8253481 8392357 8531197 8669063 + 8809765 8944241 9079486 9217662 9351413 9495295 9631568 9766938 + 9905486 10038549 10178457 10318471 10451303 10593019 10726514 10862823 + 11002269 11141779 11281369 11417134 11555724 11694504 11835015 11968776 + 12108341 12245835 12383602 12522091 12658296 12795735 12937478 13074192 + 13210545 13349539 13484585 13626082 13762546 13901749 14040580 14176568 + 14312178 14450203 14593230 14726300 14860982 15000449 15136819 15272851 + 15410212 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45280191 45415270 45549670 45684993 45818570 45957455 46096815 + 46231578 46371632 46504706 46642603 46782579 46915794 47051814 47188646 + 47326249 47463011 47604745 47740191 47877648 48013358 48149674 48285281 + 48420705 48557846 48693706 48831027 48969010 49107847 49247283 49385170 + 49522163 49661641 49794621 49938566 50076370 50210631 50348359 50485035 + 50625826 50760102 50903282 51041760 51180952 51313786 51455079 51592197 + 51732044 51872479 52011827 52148434 52284105 52415258 52554120 52697359 + 52832079 52970998 53107130 53245155 53388903 53525654 53666121 53798454 + 53937028 54074687 54210967 54344254 54481718 54623063 54759839 54901757 + 55033796 55164365 55300838 55439330 55583174 55730193 55869691 56007811 + 56147822 56286309 56426121 56566412 56703739 56837997 56975301 57114493 + 57254931 57394170 57535930 57671512 57807093 57947431 58082630 58218816 + 58354872 58493001 58627903 58765193 58907141 59043703 59178903 59324155 + 59465152 59606482 59741726 59883821 60021284 60161293 60295462 60434046 + 60572502 60710197 60846607 60987543 61119459 61257661 61395962 61537061 + 61673117 61814662 61953585 62090983 62226606 62363939 62503955 62640256 + 62778015 62914836 63055978 63188497 63325961 63457256 63598201 63733212 + 63873391 64007645 64143415 64280323 64416756 64560350 64697909 64837213 + 64973530 65108091 65245043 65380037 65520891 65657425 65793987 65929192 + 66074345 66208441 66346588 66482274 66618171 66758737 66895318 67033908 + 67166504 67304066 67439079 67578875 67715786 67858234 67992215 68129439 + 68272724 68407534 68546408 68684557 68827158] + +2023-05-10 18:24:41,930 - distributed.nanny - INFO - Worker closed +sources: [4235513 4235513 4235513 ... 4500005 4500005 4500005] +destinations: [2130107 400795 2585337 ... 4276265 4029929 3520657] +batch: [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 + 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 + 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 + 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 + 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 + 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 + 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 + 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611] +offset: [ 0 133076 266465 405182 540249 679830 816321 953373 + 1092170 1228094 1365979 1503036 1642248 1783936 1923416 2064354 + 2197256 2335851 2473204 2610687 2749143 2891946 3034118 3168885 + 3299138 3441094 3579971 3713710 3851519 3988532 4127275 4272700 + 4410401 4546394 4685189 4827113 4960458 5098567 5239231 5376443 + 5512797 5651377 5784872 5922440 6052195 6191750 6331904 6471034 + 6608400 6741299 6876946 7016230 7155240 7293499 7429660 7561798 + 7700493 7840323 7977808 8116464 8252757 8391190 8524600 8664872 + 8804047 8943262 9081315 9222073 9367340 9503657 9644442 9787243 + 9921092 10055644 10192038 10327680 10469322 10609326 10750908 10886944 + 11025891 11166459 11305833 11441913 11579762 11716107 11851887 11993688 + 12126090 12264783 12401745 12542948 12684978 12820521 12958219 13099273 + 13234776 13368194 13505201 13639141 13773592 13911799 14049225 14184025 + 14321320 14459971 14597333 14735935 14871159 15007764 15145973 15284737 + 15286371] + +2023-05-10 18:24:43,600 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:24:43,909 - distributed.nanny - WARNING - Restarting worker +2023-05-10 18:24:44,837 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:24:44,837 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:24:45,103 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:24:45,111 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize +2023-05-10 18:24:45,111 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize +2023-05-10 18:24:45,389 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize +2023-05-10 18:24:45,963 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35339 +2023-05-10 18:24:45,963 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35339 +2023-05-10 18:24:45,963 - distributed.worker - INFO - dashboard at: 10.120.104.11:37449 +2023-05-10 18:24:45,963 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:24:45,963 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:45,963 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:24:45,963 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:24:45,963 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-czt_ysng +2023-05-10 18:24:45,964 - distributed.worker - INFO - Starting Worker plugin RMMSetup-bf2d6ef0-3255-4d93-8d14-16a0595067cd +2023-05-10 18:24:46,140 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-235f11ac-77a4-4d1f-9037-f3b8b74babff +2023-05-10 18:24:46,140 - distributed.worker - INFO - Starting Worker plugin PreImport-4a8d27e2-7651-41b3-9bc3-46f6fb81a287 +2023-05-10 18:24:46,141 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:46,153 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:24:46,153 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:46,156 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:24:46,382 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36565 +2023-05-10 18:24:46,383 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36565 +2023-05-10 18:24:46,383 - distributed.worker - INFO - dashboard at: 10.120.104.11:42337 +2023-05-10 18:24:46,383 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 +2023-05-10 18:24:46,383 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:46,383 - distributed.worker - INFO - Threads: 1 +2023-05-10 18:24:46,383 - distributed.worker - INFO - Memory: 755.28 GiB +2023-05-10 18:24:46,383 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-wkkkpxxz +2023-05-10 18:24:46,384 - distributed.worker - INFO - Starting Worker plugin RMMSetup-907f7e00-244b-47ac-9e67-b83c849788ee +2023-05-10 18:24:46,504 - distributed.worker - INFO - Starting Worker plugin PreImport-02ec5a93-71b1-424e-a16f-8c3e2614cc5c +2023-05-10 18:24:46,504 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-07b82603-46d5-4e89-b008-0965403ccc5b +2023-05-10 18:24:46,505 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:46,513 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 +2023-05-10 18:24:46,513 - distributed.worker - INFO - ------------------------------------------------- +2023-05-10 18:24:46,516 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 +2023-05-10 18:24:54,581 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:24:54,582 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.07s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:24:54,903 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' +2023-05-10 18:24:54,905 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.39s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:24:54,909 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:54,910 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:24:54,914 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:24:54,914 - distributed.worker - INFO - Run out-of-band function '_func_init_all' +2023-05-10 18:25:01,014 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.51s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:25:01,155 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.66s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. +2023-05-10 18:25:01,157 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:25:01,158 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' +2023-05-10 18:25:20,341 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:20,341 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:21,044 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:25:21,045 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' +2023-05-10 18:25:22,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:22,328 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:27,493 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:27,493 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' +2023-05-10 18:25:28,068 - distributed.worker - WARNING - Compute Failed +Key: _call_plc_uniform_neighbor_sample-311a2cc0-dd67-4f2c-8686-db64d7a6c7f1 +Function: _call_plc_uniform_neighbor_sample +args: (b'\xc1\x93Al]\xf3A\xe7\x99\x99\x01gr\xd2&\xcd', , _START_ _BATCH_ +2007008 2007008 2007 +2007009 2007009 2007 +2007010 2007010 2007 +2007011 2007011 2007 +2007012 2007012 2007 +... ... ... +2996187 2996187 2996 +2996188 2996188 2996 +2996189 2996189 2996 +2996190 2996190 2996 +2996191 2996191 2996 + +[1000000 rows x 2 columns], array([2000, 2001, 2002, ..., 3997, 3998, 3999], dtype=int32), 0 0 +1 0 +2 0 +3 0 +4 0 + .. +1995 1 +1996 1 +1997 1 +1998 1 +1999 1 +Length: 2000, dtype: int32, array([10, 25], dtype=int32), False) +kwargs: {'weight_t': 'float32', 'with_edge_properties': True, 'random_state': 3720334982212921840, 'return_offsets': True} +Exception: "RuntimeError('non-success value returned from cugraph_uniform_neighbor_sample_with_edge_properties: CUGRAPH_UNKNOWN_ERROR std::bad_alloc: out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded')" + +2023-05-10 18:33:34,883 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36565 diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index d1fcec6f21c..4a083e90a12 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -24,7 +24,10 @@ import pylibcugraph from cugraph.gnn.data_loading.bulk_sampler_io import write_samples + +import warnings import logging +import time class EXPERIMENTAL__BulkSampler: @@ -71,7 +74,7 @@ def __init__( max_batches_per_partition = seeds_per_call // batch_size if batches_per_partition > max_batches_per_partition: - self.__logger.warning( + warnings.warn( f"batches_per_partition ({batches_per_partition}) is >" f" seeds_per_call / batch size ({max_batches_per_partition})" "; automatically setting batches_per_partition to " @@ -185,6 +188,8 @@ def flush(self) -> None: if self.size == 0: return + start_time_calc_batches = time.perf_counter() + min_batch_id = self.__batches[self.batch_col_name].min() if isinstance(self.__batches, dask_cudf.DataFrame): min_batch_id = min_batch_id.compute() @@ -199,6 +204,13 @@ def flush(self) -> None: max_batch_id = min_batch_id + npartitions * self.batches_per_partition - 1 batch_id_filter = self.__batches[self.batch_col_name] <= max_batch_id + end_time_calc_batches = time.perf_counter() + self.__logger.info( + f'Calculated batches to sample; min = {min_batch_id}' + f' and max = {max_batch_id};' + f' took {end_time_calc_batches - start_time_calc_batches:.4f} s' + ) + if isinstance(self.__graph._plc_graph, pylibcugraph.graphs.SGGraph): sample_fn = cugraph.uniform_neighbor_sample else: @@ -215,6 +227,9 @@ def flush(self) -> None: } ) + start_time_sample_call = time.perf_counter() + + # Call uniform neighbor sample samples, offsets = sample_fn( self.__graph, **self.__sample_call_args, @@ -224,10 +239,45 @@ def flush(self) -> None: return_offsets=True, ) + end_time_sample_call = time.perf_counter() + sample_runtime = end_time_sample_call - start_time_sample_call + self.__logger.info( + f'Called uniform neighbor sample, took {sample_runtime:.4f} s' + f' ({(sample_runtime) / (max_batch_id - min_batch_id):.4f} s' + ' per batch)' + ) + + start_time_filter_batches = time.perf_counter() + + # Filter batches to remove those already processed self.__batches = self.__batches[~batch_id_filter] + if hasattr(self.__batches, 'compute'): + self.__batches = self.__batches.persist() + + end_time_filter_batches = time.perf_counter() + self.__logger.info( + 'Filtered batches, took ' + f'{end_time_filter_batches - start_time_filter_batches} s' + ) + + start_time_write = time.perf_counter() + + # Write batches to parquet self.__write(samples, offsets) + end_time_write = time.perf_counter() + write_runtime = end_time_write - start_time_write + self.__logger.info( + f'Wrote samples to parquet, took {write_runtime} seconds' + f' ({(write_runtime) / (max_batch_id - min_batch_id):.4f} s' + ' per batch)' + ) + if self.size > 0: + self.__logger.info( + f'There are still {self.size} samples remaining, ' + 'calling flush() again...' + ) self.flush() def __write( diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py index fd7366cbe40..34683b46a9c 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -69,7 +69,11 @@ def _write_samples_to_parquet( results_p["batch_id"] = offsets_p.batch_id.repeat( cupy.diff(offsets_p.offsets.values, append=end_ix) ).values - results_p.to_parquet(full_output_path) + results_p.to_parquet( + full_output_path, + compression=None, + index=False + ) def write_samples( diff --git a/python/cugraph/cugraph/testing/mg_utils.py b/python/cugraph/cugraph/testing/mg_utils.py index ef650ae8b68..6cf3a88f05a 100644 --- a/python/cugraph/cugraph/testing/mg_utils.py +++ b/python/cugraph/cugraph/testing/mg_utils.py @@ -192,6 +192,15 @@ def persist_dask_object(arg): # Function to convert bytes into human readable format def sizeof_fmt(num, suffix="B"): + if isinstance(num, str): + if num[-2:] == 'GB': + return num[:-2] + 'G' + elif num[-2:] == 'MB': + return num[:-2] + 'M' + elif num[-2:] == 'KB': + return num[:-2] + 'K' + else: + raise ValueError('unknown unit') for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: if abs(num) < 1024.0: return "%3.1f%s%s" % (num, unit, suffix) @@ -222,6 +231,7 @@ def wrapper(*args, **kwargs): worker_id: _parse_allocation_counts(worker_allocations) for worker_id, worker_allocations in allocation_counts.items() } + if logging: _print_allocation_statistics( func, args, kwargs, et - st, allocation_counts From 298d0f0c30420527f222b70da4f9a55e5f10a428 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 10 May 2023 18:53:34 +0000 Subject: [PATCH 041/347] remove files --- cugraph_graph_creation_stats.csv | 1 - mg_utils/dask-scheduler.json | 10 - mg_utils/dask_logs-173107/scheduler_log.txt | 51 - .../dask_logs-173107/worker-exp01_log.txt | 1122 --- mg_utils/dask_logs-219848/scheduler_log.txt | 213 - .../dask_logs-219848/worker-exp01_log.txt | 8225 ----------------- 6 files changed, 9622 deletions(-) delete mode 100644 cugraph_graph_creation_stats.csv delete mode 100644 mg_utils/dask-scheduler.json delete mode 100644 mg_utils/dask_logs-173107/scheduler_log.txt delete mode 100644 mg_utils/dask_logs-173107/worker-exp01_log.txt delete mode 100644 mg_utils/dask_logs-219848/scheduler_log.txt delete mode 100644 mg_utils/dask_logs-219848/worker-exp01_log.txt diff --git a/cugraph_graph_creation_stats.csv b/cugraph_graph_creation_stats.csv deleted file mode 100644 index 7a9d37aca09..00000000000 --- a/cugraph_graph_creation_stats.csv +++ /dev/null @@ -1 +0,0 @@ -,scale,num_input_edges,directed,renumber,input_memory_per_worker,peak_allocation_across_workers,input_to_peak_ratio,output_to_peak_ratio diff --git a/mg_utils/dask-scheduler.json b/mg_utils/dask-scheduler.json deleted file mode 100644 index 1cf92274c9d..00000000000 --- a/mg_utils/dask-scheduler.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "type": "Scheduler", - "id": "Scheduler-b1dc610e-4f16-46b6-bd2e-6fdf923ce419", - "address": "tcp://10.120.104.11:8786", - "services": { - "dashboard": 8787 - }, - "started": 1683742414.140298, - "workers": {} -} \ No newline at end of file diff --git a/mg_utils/dask_logs-173107/scheduler_log.txt b/mg_utils/dask_logs-173107/scheduler_log.txt deleted file mode 100644 index 2f52d694fa3..00000000000 --- a/mg_utils/dask_logs-173107/scheduler_log.txt +++ /dev/null @@ -1,51 +0,0 @@ -RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp - --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json - " -/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead - warnings.warn( -2023-05-10 16:44:47,821 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 16:44:48,604 - distributed.scheduler - INFO - State start -2023-05-10 16:44:48,618 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 16:44:48,619 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 -2023-05-10 16:44:48,619 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status -2023-05-10 16:44:57,572 - distributed.scheduler - INFO - Register worker -2023-05-10 16:44:57,574 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33799 -2023-05-10 16:44:57,574 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:44942 -2023-05-10 16:44:57,575 - distributed.scheduler - INFO - Register worker -2023-05-10 16:44:57,575 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46351 -2023-05-10 16:44:57,575 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:44956 -2023-05-10 16:45:01,931 - distributed.scheduler - INFO - Receive client connection: Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 -2023-05-10 16:45:01,931 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:37608 -2023-05-10 16:45:02,173 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 16:45:51,267 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 16:45:51,268 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 16:45:51,292 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:44942; closing. -2023-05-10 16:45:51,292 - distributed.scheduler - INFO - Remove worker -2023-05-10 16:45:51,292 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33799 -2023-05-10 16:45:51,293 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:44956; closing. -2023-05-10 16:45:51,293 - distributed.scheduler - INFO - Remove worker -2023-05-10 16:45:51,293 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46351 -2023-05-10 16:45:51,293 - distributed.scheduler - INFO - Lost all workers -2023-05-10 16:45:55,725 - distributed.scheduler - INFO - Register worker -2023-05-10 16:45:55,726 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36221 -2023-05-10 16:45:55,726 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33566 -2023-05-10 16:45:55,942 - distributed.scheduler - INFO - Register worker -2023-05-10 16:45:55,942 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40001 -2023-05-10 16:45:55,942 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33574 -2023-05-10 16:45:56,084 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Remove client Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 -2023-05-10 16:46:03,444 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:37608; closing. -2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Remove client Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 -2023-05-10 16:46:03,444 - distributed.scheduler - INFO - Close client connection: Client-020ec905-ef52-11ed-a4ac-5cff35c1a711 -2023-05-10 16:48:28,617 - distributed._signals - INFO - Received signal SIGINT (2) -2023-05-10 16:48:28,617 - distributed.core - INFO - Connection to tcp://10.120.104.11:33574 has been closed. -2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Remove worker -2023-05-10 16:48:28,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40001 -2023-05-10 16:48:28,618 - distributed.core - INFO - Connection to tcp://10.120.104.11:33566 has been closed. -2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Remove worker -2023-05-10 16:48:28,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36221 -2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Lost all workers -2023-05-10 16:48:28,618 - distributed.scheduler - INFO - Scheduler closing... -2023-05-10 16:48:28,619 - distributed.scheduler - INFO - Scheduler closing all comms -2023-05-10 16:48:28,620 - distributed.scheduler - INFO - Stopped scheduler at 'tcp://10.120.104.11:8786' -2023-05-10 16:48:28,620 - distributed.scheduler - INFO - End scheduler diff --git a/mg_utils/dask_logs-173107/worker-exp01_log.txt b/mg_utils/dask_logs-173107/worker-exp01_log.txt deleted file mode 100644 index 9d1f05f9974..00000000000 --- a/mg_utils/dask_logs-173107/worker-exp01_log.txt +++ /dev/null @@ -1,1122 +0,0 @@ -RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G - --local-directory=/tmp/ - --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json - --memory-limit=auto - --device-memory-limit=auto - " -2023-05-10 16:44:54,827 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:37403' -2023-05-10 16:44:54,830 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:45501' -2023-05-10 16:44:56,019 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 16:44:56,019 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 16:44:56,047 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 16:44:56,048 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 16:44:56,282 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 16:44:56,312 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 16:44:57,425 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33799 -2023-05-10 16:44:57,425 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33799 -2023-05-10 16:44:57,425 - distributed.worker - INFO - dashboard at: 10.120.104.11:33945 -2023-05-10 16:44:57,425 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 16:44:57,426 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,426 - distributed.worker - INFO - Threads: 1 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 16:44:57,426 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-o5pzcq52 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46351 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46351 -2023-05-10 16:44:57,426 - distributed.worker - INFO - dashboard at: 10.120.104.11:34261 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 16:44:57,426 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,426 - distributed.worker - INFO - Threads: 1 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 16:44:57,426 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-l5q_xedg -2023-05-10 16:44:57,426 - distributed.worker - INFO - Starting Worker plugin RMMSetup-9c56cc40-5e1d-49d8-ab54-23b0b91cfc46 -2023-05-10 16:44:57,426 - distributed.worker - INFO - Starting Worker plugin RMMSetup-243e825e-5562-4e11-b7c2-ef53e729b5b6 -2023-05-10 16:44:57,563 - distributed.worker - INFO - Starting Worker plugin PreImport-54b6b654-dcd5-4381-ad84-1d89093915f4 -2023-05-10 16:44:57,563 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-37ebf0f3-ae1e-423e-ae1b-f541c9428fae -2023-05-10 16:44:57,564 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-2d355367-a471-49ed-b299-8386546715bd -2023-05-10 16:44:57,564 - distributed.worker - INFO - Starting Worker plugin PreImport-aad90f77-0639-4887-8530-ebd1b0bd7ea9 -2023-05-10 16:44:57,564 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,564 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,575 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 16:44:57,575 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,576 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 16:44:57,576 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:44:57,577 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 16:44:57,578 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 16:45:01,940 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:01,941 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:02,762 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 16:45:02,763 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 16:45:11,728 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 16:45:11,964 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 16:45:17,976 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.58s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 16:45:17,976 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.58s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 16:45:17,985 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 16:45:17,985 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 16:45:37,980 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:37,980 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:38,390 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 16:45:38,391 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 16:45:39,123 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:39,124 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1416929 1416929 1416929 ... 300812 300812 300812] -destinations: [ 860899 908466 1972881 ... 995640 586261 676432] -batch: [ 600 601 602 603 604 605 606 607 608 609 610 611 612 613 - 614 615 616 617 618 619 620 621 622 623 624 625 626 627 - 628 629 630 631 632 633 634 635 636 637 638 639 640 641 - 642 643 644 645 646 647 648 649 650 651 652 653 654 655 - 656 657 658 659 660 661 662 663 664 665 666 667 668 669 - 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 - 698 699 700 701 702 703 704 705 706 707 708 709 710 711 - 712 713 714 715 716 717 718 719 720 721 722 723 724 725 - 726 727 728 729 730 731 732 733 734 735 736 737 738 739 - 740 741 742 743 744 745 746 747 748 749 750 751 752 753 - 754 755 756 757 758 759 760 761 762 763 764 765 766 767 - 768 769 770 771 772 773 774 775 776 777 778 779 780 781 - 782 783 784 785 786 787 788 789 790 791 792 793 794 795 - 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 - 824 825 826 827 828 829 830 831 832 833 834 835 836 837 - 838 839 840 841 842 843 844 845 846 847 848 849 850 851 - 852 853 854 855 856 857 858 859 860 861 862 863 864 865 - 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28058213 28125969 28199358 28268053 28337982 28408265 28478566 28550775 - 28618495 28692665 28758328 28829350 28897111 28966278 29035579 29109026 - 29178648 29244793 29317279 29387334 29461899 29529479 29601489 29671816 - 29742030 29809927 29878316 29950650 30020695 30095500 30164589 30234533 - 30307794 30373520 30444149 30511786 30584071 30653458 30725758 30794441 - 30865563 30937357 31008010 31080954 31149862 31219295 31290205 31359445 - 31430310 31495134 31567066 31635578 31706919 31775780 31846533 31919181 - 31988095 32060156 32127774 32197219 32263728 32335587 32408151 32480566 - 32550767 32621525 32691448 32762858 32835478 32904850 32975728 33046627 - 33115376 33184581 33254063 33326478 33391976 33461867 33536183 33607707 - 33678862 33747267 33815613 33884821 33950485 34019667 34087896 34157046 - 34228463 34299920 34369213 34441899 34511581 34582939 34651327 34719998 - 34788374 34860093 34930175 34999245 35070228 35139245 35210399 35278627 - 35346372 35414423 35480154 35552494 35622720 35691088 35760882 35830754 - 35901680 35971976 36044920 36116491 36183764 36256473 36326271 36397476 - 36469629 36535848 36608607 36677323 36748902 36819343 36887157 36956085 - 37030183 37098144 37168658 37235317 37303361 37372087 37442935 37515928 - 37584378 37658939 37728151 37802172 37875702 37944734 38013900 38086390 - 38152599 38220821 38292551 38364160 38435233 38504608 38570738 38641062 - 38715515 38785574 38852834 38920257 38990994 39060894 39128023 39195962 - 39266867 39334462 39402222 39475360 39542427 39611919 39684371 39753046 - 39824732 39894514 39964310 40036687 40106002 40177485 40245815 40317054 - 40385070 40457890 40529630 40600587 40676115 40745285 40816829 40886330 - 40956730 41029974 41098209 41166492 41233692 41302849 41376582 41450636 - 41518235 41589737 41660140 41727345 41796506 41864254 41938894 42011372 - 42082131] -2023-05-10 16:45:42,247 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 16:45:42,247 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 0 0 0 ... 2169086 2169086 2169086] -destinations: [ 137513 220422 2201664 ... 1142022 2391407 1773642] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 - 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 - 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 - 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 - 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 - 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 - 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 - 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 - 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 - 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 - 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 - 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 - 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 - 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 - 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 - 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 - 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 - 594 595 596 597 598 599] -offset: [ 0 71889 137078 208466 279999 350328 421155 491125 - 560783 627776 700956 774088 843684 914323 983403 1057725 - 1127304 1197194 1266930 1333976 1406972 1476562 1545085 1612004 - 1681817 1750664 1816383 1885797 1957654 2027842 2099845 2170589 - 2241203 2313375 2385705 2452300 2524511 2596745 2667747 2737911 - 2809482 2877501 2948236 3022240 3091513 3161423 3231308 3299338 - 3368258 3437891 3508370 3580243 3654134 3724671 3791767 3861104 - 3930270 4006047 4074144 4146578 4217336 4285676 4355591 4420912 - 4487289 4557895 4631844 4700103 4766772 4837565 4909798 4979262 - 5050034 5124678 5191303 5264206 5333387 5404849 5471364 5538721 - 5609840 5680131 5752654 5821898 5895006 5967748 6039337 6110173 - 6181826 6251801 6321655 6391387 6463441 6531601 6601372 6670140 - 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33088607 33157500 33231023 33300826 33372526 33440468 33508143 33577822 - 33647229 33714695 33787717 33859855 33932744 34002539 34069191 34141712 - 34211409 34284549 34354664 34419940 34489291 34561628 34632244 34702533 - 34774003 34843545 34914334 34983875 35048710 35117429 35190615 35260948 - 35333607 35403717 35475332 35544333 35616016 35686033 35753007 35825227 - 35897873 35964860 36036759 36108865 36179417 36251997 36319148 36386876 - 36456125 36523962 36595610 36666603 36740288 36810789 36876855 36943191 - 37013351 37082861 37153167 37229676 37299387 37373215 37443381 37510579 - 37576015 37648240 37718411 37790652 37861565 37927740 37997651 38069356 - 38141017 38208373 38278812 38351480 38421991 38493884 38559727 38630994 - 38699279 38769814 38841436 38910545 38983494 39053498 39123695 39189223 - 39263756 39335127 39409373 39478273 39546770 39617135 39689945 39762270 - 39826078 39895330 39964850 40038895 40108334 40173837 40244651 40316006 - 40384216 40454217 40526623 40599877 40669671 40739924 40809082 40880981 - 40954386 41023970 41095311 41163805 41235018 41305586 41372366 41440055 - 41507554 41578706 41597336] -2023-05-10 16:45:51,154 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 16:45:51,154 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 16:45:51,158 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 16:45:51,158 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 16:45:51,161 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 16:45:51,161 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 16:45:51,274 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 16:45:51,277 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 16:45:51,291 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33799. Reason: scheduler-restart -2023-05-10 16:45:51,291 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46351. Reason: scheduler-restart -2023-05-10 16:45:51,292 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 16:45:51,292 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 16:45:51,294 - distributed.nanny - INFO - Worker closed -2023-05-10 16:45:51,294 - distributed.nanny - INFO - Worker closed - - -2023-05-10 16:45:52,979 - distributed.nanny - WARNING - Restarting worker -2023-05-10 16:45:53,275 - distributed.nanny - WARNING - Restarting worker -2023-05-10 16:45:54,275 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 16:45:54,275 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 16:45:54,411 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 16:45:54,412 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 16:45:54,577 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 16:45:54,670 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 16:45:55,561 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36221 -2023-05-10 16:45:55,561 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36221 -2023-05-10 16:45:55,561 - distributed.worker - INFO - dashboard at: 10.120.104.11:39713 -2023-05-10 16:45:55,561 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 16:45:55,561 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,561 - distributed.worker - INFO - Threads: 1 -2023-05-10 16:45:55,561 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 16:45:55,561 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-8o8aa785 -2023-05-10 16:45:55,562 - distributed.worker - INFO - Starting Worker plugin RMMSetup-270aa02c-d320-46cb-a4e1-12447808dcb7 -2023-05-10 16:45:55,715 - distributed.worker - INFO - Starting Worker plugin PreImport-73c7d9f2-9917-4631-96ee-581efba10be7 -2023-05-10 16:45:55,715 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-a69e529b-fed1-4a58-bea7-d5dc887c5d78 -2023-05-10 16:45:55,716 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,726 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 16:45:55,726 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,728 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 16:45:55,813 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40001 -2023-05-10 16:45:55,813 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40001 -2023-05-10 16:45:55,814 - distributed.worker - INFO - dashboard at: 10.120.104.11:34779 -2023-05-10 16:45:55,814 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 16:45:55,814 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,814 - distributed.worker - INFO - Threads: 1 -2023-05-10 16:45:55,814 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 16:45:55,814 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-dug6rnk1 -2023-05-10 16:45:55,814 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c45dc54c-b170-4ed6-9a3f-dd543c1de3f4 -2023-05-10 16:45:55,934 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-fcc80c05-811a-4c69-ba6b-b1c9be594151 -2023-05-10 16:45:55,934 - distributed.worker - INFO - Starting Worker plugin PreImport-15918582-2844-4b03-a98a-f96b77b6ae34 -2023-05-10 16:45:55,934 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,942 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 16:45:55,943 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 16:45:55,944 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 16:46:04,116 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 16:46:04,117 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.03s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 16:46:04,244 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 16:46:04,245 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.16s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 16:48:28,617 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36221. Reason: worker-close -2023-05-10 16:48:28,617 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40001. Reason: worker-handle-scheduler-connection-broken -2023-05-10 16:48:28,617 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:37403'. Reason: nanny-close -2023-05-10 16:48:28,618 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close -2023-05-10 16:48:28,618 - distributed.nanny - INFO - Closing Nanny at 'tcp://10.120.104.11:45501'. Reason: nanny-close -2023-05-10 16:48:28,619 - distributed.nanny - INFO - Nanny asking worker to close. Reason: nanny-close -2023-05-10 16:48:28,618 - distributed.batched - INFO - Batched Comm Closed Scheduler local=tcp://10.120.104.11:33566 remote=tcp://10.120.104.11:8786> -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/runners.py", line 44, in run - return loop.run_until_complete(main) - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete - self.run_forever() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 603, in run_forever - self._run_once() - File "/opt/conda/envs/rapids/lib/python3.10/asyncio/base_events.py", line 1868, in _run_once - event_list = self._selector.select(timeout) - File "/opt/conda/envs/rapids/lib/python3.10/selectors.py", line 469, in select - fd_event_list = self._selector.poll(timeout, max_ev) -KeyboardInterrupt - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/batched.py", line 115, in _background_send - nbytes = yield coro - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/tornado/gen.py", line 769, in run - value = future.result() - File "/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/comm/tcp.py", line 269, in write - raise CommClosedError() -distributed.comm.core.CommClosedError -2023-05-10 16:48:31,820 - distributed.nanny - WARNING - Worker process still alive after 3.1999958801269535 seconds, killing -2023-05-10 16:48:31,820 - distributed.nanny - WARNING - Worker process still alive after 3.199999389648438 seconds, killing -2023-05-10 16:48:31,980 - distributed.nanny - INFO - Worker process 174097 was killed by signal 9 -2023-05-10 16:48:32,040 - distributed.nanny - INFO - Worker process 174086 was killed by signal 9 diff --git a/mg_utils/dask_logs-219848/scheduler_log.txt b/mg_utils/dask_logs-219848/scheduler_log.txt deleted file mode 100644 index ba0cf0ea018..00000000000 --- a/mg_utils/dask_logs-219848/scheduler_log.txt +++ /dev/null @@ -1,213 +0,0 @@ -RUNNING: "python -m distributed.cli.dask_scheduler --protocol=tcp - --scheduler-file /root/cugraph/mg_utils/dask-scheduler.json - " -/opt/conda/envs/rapids/lib/python3.10/site-packages/distributed/cli/dask_scheduler.py:140: FutureWarning: dask-scheduler is deprecated and will be removed in a future release; use `dask scheduler` instead - warnings.warn( -2023-05-10 18:13:34,139 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 18:13:34,863 - distributed.scheduler - INFO - State start -2023-05-10 18:13:34,876 - distributed.scheduler - INFO - ----------------------------------------------- -2023-05-10 18:13:34,877 - distributed.scheduler - INFO - Scheduler at: tcp://10.120.104.11:8786 -2023-05-10 18:13:34,877 - distributed.scheduler - INFO - dashboard at: http://10.120.104.11:8787/status -2023-05-10 18:13:43,826 - distributed.scheduler - INFO - Register worker -2023-05-10 18:13:43,829 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38389 -2023-05-10 18:13:43,829 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48740 -2023-05-10 18:13:43,859 - distributed.scheduler - INFO - Register worker -2023-05-10 18:13:43,859 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34233 -2023-05-10 18:13:43,859 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48746 -2023-05-10 18:13:59,347 - distributed.scheduler - INFO - Receive client connection: Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 -2023-05-10 18:13:59,348 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41942 -2023-05-10 18:13:59,581 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:14:48,078 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:14:48,079 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:14:48,086 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48746; closing. -2023-05-10 18:14:48,086 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:14:48,086 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34233 -2023-05-10 18:14:48,087 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48740; closing. -2023-05-10 18:14:48,087 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:14:48,087 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38389 -2023-05-10 18:14:48,087 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:14:52,831 - distributed.scheduler - INFO - Register worker -2023-05-10 18:14:52,832 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:44757 -2023-05-10 18:14:52,832 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35598 -2023-05-10 18:14:52,872 - distributed.scheduler - INFO - Register worker -2023-05-10 18:14:52,872 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:43467 -2023-05-10 18:14:52,872 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:35600 -2023-05-10 18:14:53,066 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:15:01,047 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:15:40,594 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:15:40,596 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:15:40,618 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:35600; closing. -2023-05-10 18:15:40,618 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:15:40,618 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43467 -2023-05-10 18:15:40,619 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:35598; closing. -2023-05-10 18:15:40,619 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:15:40,620 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:44757 -2023-05-10 18:15:40,620 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:15:44,605 - distributed.scheduler - INFO - Register worker -2023-05-10 18:15:44,606 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42023 -2023-05-10 18:15:44,606 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58238 -2023-05-10 18:15:45,061 - distributed.scheduler - INFO - Register worker -2023-05-10 18:15:45,062 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37619 -2023-05-10 18:15:45,062 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:58254 -2023-05-10 18:15:45,165 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:15:53,544 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:16:29,176 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:16:29,176 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:16:29,184 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58254; closing. -2023-05-10 18:16:29,184 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:16:29,184 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37619 -2023-05-10 18:16:29,185 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:58238; closing. -2023-05-10 18:16:29,185 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:16:29,185 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42023 -2023-05-10 18:16:29,185 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:16:33,548 - distributed.scheduler - INFO - Register worker -2023-05-10 18:16:33,549 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:41227 -2023-05-10 18:16:33,549 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:40448 -2023-05-10 18:16:33,839 - distributed.scheduler - INFO - Register worker -2023-05-10 18:16:33,839 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42727 -2023-05-10 18:16:33,839 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:40450 -2023-05-10 18:16:33,862 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:16:41,858 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:17:20,731 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:17:20,732 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:17:20,739 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:40448; closing. -2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:17:20,740 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:41227 -2023-05-10 18:17:20,740 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:40450; closing. -2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:17:20,740 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42727 -2023-05-10 18:17:20,740 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:17:25,159 - distributed.scheduler - INFO - Register worker -2023-05-10 18:17:25,159 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38521 -2023-05-10 18:17:25,159 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41184 -2023-05-10 18:17:25,451 - distributed.scheduler - INFO - Register worker -2023-05-10 18:17:25,451 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40329 -2023-05-10 18:17:25,451 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:41196 -2023-05-10 18:17:25,623 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:17:33,739 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:18:13,220 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:18:13,221 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:18:13,229 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41184; closing. -2023-05-10 18:18:13,229 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:18:13,229 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38521 -2023-05-10 18:18:13,230 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41196; closing. -2023-05-10 18:18:13,230 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:18:13,230 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40329 -2023-05-10 18:18:13,230 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:18:17,596 - distributed.scheduler - INFO - Register worker -2023-05-10 18:18:17,597 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46207 -2023-05-10 18:18:17,597 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52398 -2023-05-10 18:18:17,928 - distributed.scheduler - INFO - Register worker -2023-05-10 18:18:17,929 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36349 -2023-05-10 18:18:17,929 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52414 -2023-05-10 18:18:17,965 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:18:25,967 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:19:11,422 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:19:11,423 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:19:11,430 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52414; closing. -2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:19:11,431 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36349 -2023-05-10 18:19:11,431 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52398; closing. -2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:19:11,431 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46207 -2023-05-10 18:19:11,431 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:19:15,895 - distributed.scheduler - INFO - Register worker -2023-05-10 18:19:15,896 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:38111 -2023-05-10 18:19:15,896 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48034 -2023-05-10 18:19:16,170 - distributed.scheduler - INFO - Register worker -2023-05-10 18:19:16,170 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:42615 -2023-05-10 18:19:16,170 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48042 -2023-05-10 18:19:16,189 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:19:24,276 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:20:08,085 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:20:08,086 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:20:08,093 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48034; closing. -2023-05-10 18:20:08,093 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:20:08,093 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:38111 -2023-05-10 18:20:08,094 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48042; closing. -2023-05-10 18:20:08,094 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:20:08,094 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:42615 -2023-05-10 18:20:08,095 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:20:12,283 - distributed.scheduler - INFO - Register worker -2023-05-10 18:20:12,283 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37861 -2023-05-10 18:20:12,283 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54504 -2023-05-10 18:20:12,731 - distributed.scheduler - INFO - Register worker -2023-05-10 18:20:12,732 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:34921 -2023-05-10 18:20:12,732 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:54506 -2023-05-10 18:20:12,754 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:20:20,635 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:21:18,259 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:21:18,260 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:21:18,270 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54506; closing. -2023-05-10 18:21:18,270 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:21:18,270 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34921 -2023-05-10 18:21:18,301 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:54504; closing. -2023-05-10 18:21:18,301 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:21:18,301 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37861 -2023-05-10 18:21:18,301 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:21:22,796 - distributed.scheduler - INFO - Register worker -2023-05-10 18:21:22,796 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:37045 -2023-05-10 18:21:22,796 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52620 -2023-05-10 18:21:23,084 - distributed.scheduler - INFO - Register worker -2023-05-10 18:21:23,084 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:40247 -2023-05-10 18:21:23,084 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:52624 -2023-05-10 18:21:23,087 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:21:31,210 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:22:46,731 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:22:46,732 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:22:46,778 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52620; closing. -2023-05-10 18:22:46,778 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:22:46,778 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37045 -2023-05-10 18:22:46,780 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:52624; closing. -2023-05-10 18:22:46,780 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:22:46,780 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:40247 -2023-05-10 18:22:46,780 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:22:51,018 - distributed.scheduler - INFO - Register worker -2023-05-10 18:22:51,019 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:33561 -2023-05-10 18:22:51,019 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:56756 -2023-05-10 18:22:51,394 - distributed.scheduler - INFO - Register worker -2023-05-10 18:22:51,394 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:45751 -2023-05-10 18:22:51,394 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:42188 -2023-05-10 18:22:51,444 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:22:59,419 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:23:42,462 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:23:42,463 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:23:42,471 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:56756; closing. -2023-05-10 18:23:42,471 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:23:42,471 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:33561 -2023-05-10 18:23:42,472 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:42188; closing. -2023-05-10 18:23:42,472 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:23:42,472 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:45751 -2023-05-10 18:23:42,472 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:23:47,392 - distributed.scheduler - INFO - Register worker -2023-05-10 18:23:47,393 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36171 -2023-05-10 18:23:47,393 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48216 -2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Register worker -2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:46001 -2023-05-10 18:23:47,438 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:48220 -2023-05-10 18:23:47,438 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:23:55,423 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:24:41,919 - distributed.worker - INFO - Run out-of-band function '_func_destroy_scheduler_session' -2023-05-10 18:24:41,920 - distributed.scheduler - INFO - Restarting workers and releasing all keys. -2023-05-10 18:24:41,928 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48216; closing. -2023-05-10 18:24:41,928 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:24:41,928 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36171 -2023-05-10 18:24:41,929 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:48220; closing. -2023-05-10 18:24:41,929 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:24:41,929 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:46001 -2023-05-10 18:24:41,929 - distributed.scheduler - INFO - Lost all workers -2023-05-10 18:24:46,152 - distributed.scheduler - INFO - Register worker -2023-05-10 18:24:46,153 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:35339 -2023-05-10 18:24:46,153 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33682 -2023-05-10 18:24:46,512 - distributed.scheduler - INFO - Register worker -2023-05-10 18:24:46,513 - distributed.scheduler - INFO - Starting worker compute stream, tcp://10.120.104.11:36565 -2023-05-10 18:24:46,513 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:33688 -2023-05-10 18:24:46,519 - distributed.scheduler - INFO - Restarting finished. -2023-05-10 18:24:54,912 - distributed.worker - INFO - Run out-of-band function '_func_set_scheduler_as_nccl_root' -2023-05-10 18:33:34,880 - distributed.scheduler - WARNING - Worker failed to heartbeat within 300 seconds. Closing: -2023-05-10 18:33:34,881 - distributed.scheduler - INFO - Remove worker -2023-05-10 18:33:34,881 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36565 -2023-05-10 18:52:21,783 - distributed.scheduler - INFO - Remove client Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 -2023-05-10 18:52:21,786 - distributed.core - INFO - Received 'close-stream' from tcp://10.120.104.11:41942; closing. -2023-05-10 18:52:21,786 - distributed.scheduler - INFO - Remove client Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 -2023-05-10 18:52:21,787 - distributed.scheduler - INFO - Close client connection: Client-6f67cee7-ef5e-11ed-9beb-5cff35c1a711 diff --git a/mg_utils/dask_logs-219848/worker-exp01_log.txt b/mg_utils/dask_logs-219848/worker-exp01_log.txt deleted file mode 100644 index ac7a245cbd3..00000000000 --- a/mg_utils/dask_logs-219848/worker-exp01_log.txt +++ /dev/null @@ -1,8225 +0,0 @@ -RUNNING: "python -m dask_cuda.cli.dask_cuda_worker --rmm-pool-size=12G - --local-directory=/tmp/ - --scheduler-file=/root/cugraph/mg_utils/dask-scheduler.json - --memory-limit=auto - --device-memory-limit=auto - " -2023-05-10 18:13:41,185 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:38551' -2023-05-10 18:13:41,188 - distributed.nanny - INFO - Start Nanny at: 'tcp://10.120.104.11:34413' -2023-05-10 18:13:42,385 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:13:42,385 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:13:42,393 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:13:42,393 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:13:42,657 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:13:42,670 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:13:43,689 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38389 -2023-05-10 18:13:43,689 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38389 -2023-05-10 18:13:43,689 - distributed.worker - INFO - dashboard at: 10.120.104.11:46597 -2023-05-10 18:13:43,689 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:13:43,689 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,689 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:13:43,689 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:13:43,689 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-0w581hpr -2023-05-10 18:13:43,690 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-83c4b061-5681-4748-8491-ab9b3db71c97 -2023-05-10 18:13:43,692 - distributed.worker - INFO - Starting Worker plugin RMMSetup-31ac136c-a620-4242-b5f2-f5c42d68b4f6 -2023-05-10 18:13:43,725 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34233 -2023-05-10 18:13:43,725 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34233 -2023-05-10 18:13:43,725 - distributed.worker - INFO - dashboard at: 10.120.104.11:39159 -2023-05-10 18:13:43,725 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:13:43,725 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,725 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:13:43,725 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:13:43,726 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rumtx2h8 -2023-05-10 18:13:43,726 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6a189c25-7bb4-4be6-923c-9a00bc1605ac -2023-05-10 18:13:43,815 - distributed.worker - INFO - Starting Worker plugin PreImport-29435f28-45f3-440e-9413-9c7a9115f9eb -2023-05-10 18:13:43,816 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,829 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:13:43,829 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,832 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:13:43,851 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-1064e1c7-5d90-42f6-a702-dcae9f623c93 -2023-05-10 18:13:43,851 - distributed.worker - INFO - Starting Worker plugin PreImport-8d1d4fe9-841e-4498-a97d-a06ccba23a40 -2023-05-10 18:13:43,852 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,859 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:13:43,859 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:13:43,861 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:13:59,357 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:13:59,359 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:14:00,162 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:14:00,162 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:14:09,034 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:14:09,039 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:14:15,422 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:14:15,424 - distributed.core - INFO - Event loop was unresponsive in Worker for 14.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:14:15,431 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:14:15,433 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:14:35,596 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:14:35,598 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:14:36,003 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:14:36,004 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:14:36,603 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:14:36,604 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [599983 599983 599983 ... 984504 984504 984504] -destinations: [ 649861 2039126 2365095 ... 1153947 1056145 2353159] -batch: [ 600 601 602 603 604 605 606 607 608 609 610 611 612 613 - 614 615 616 617 618 619 620 621 622 623 624 625 626 627 - 628 629 630 631 632 633 634 635 636 637 638 639 640 641 - 642 643 644 645 646 647 648 649 650 651 652 653 654 655 - 656 657 658 659 660 661 662 663 664 665 666 667 668 669 - 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 - 698 699 700 701 702 703 704 705 706 707 708 709 710 711 - 712 713 714 715 716 717 718 719 720 721 722 723 724 725 - 726 727 728 729 730 731 732 733 734 735 736 737 738 739 - 740 741 742 743 744 745 746 747 748 749 750 751 752 753 - 754 755 756 757 758 759 760 761 762 763 764 765 766 767 - 768 769 770 771 772 773 774 775 776 777 778 779 780 781 - 782 783 784 785 786 787 788 789 790 791 792 793 794 795 - 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 - 824 825 826 827 828 829 830 831 832 833 834 835 836 837 - 838 839 840 841 842 843 844 845 846 847 848 849 850 851 - 852 853 854 855 856 857 858 859 860 861 862 863 864 865 - 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1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 - 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 - 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 - 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 - 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 - 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 - 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 - 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199] -offset: [ 0 71361 142032 209074 283201 354841 425859 496453 - 561576 635342 702171 773507 843333 911285 981692 1047392 - 1120737 1187878 1263391 1335636 1404762 1471793 1543019 1614491 - 1683136 1758005 1824898 1894990 1966481 2037938 2105280 2174891 - 2249168 2321899 2395527 2464296 2533635 2602029 2676640 2748651 - 2816026 2891586 2960960 3031533 3101307 3171567 3241087 3312660 - 3382736 3450293 3520920 3590867 3659916 3732580 3803070 3875945 - 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28058758 28126686 28200389 28269263 28339378 28409315 28479379 28551799 - 28619502 28693323 28759088 28830195 28898090 28967251 29036443 29109651 - 29179358 29245482 29317987 29388061 29462729 29530430 29602536 29672768 - 29742804 29810677 29878742 29951130 30021249 30096196 30165372 30235445 - 30308967 30374642 30445329 30513329 30585381 30655096 30727477 30795956 - 30867117 30939198 31009477 31082114 31151268 31220777 31291468 31360763 - 31431552 31496447 31568470 31636683 31708283 31777318 31848200 31921085 - 31990136 32062452 32129802 32199146 32265669 32337644 32410093 32482796 - 32553002 32623539 32693667 32765161 32837791 32906981 32977808 33048811 - 33117394 33186753 33255997 33328627 33394018 33464036 33538441 33610321 - 33681492 33750194 33818572 33887838 33953623 34022566 34090734 34159721 - 34231301 34302857 34372319 34444748 34514507 34585902 34654465 34722992 - 34791116 34862834 34933095 35002372 35073360 35142360 35213509 35281697 - 35349179 35417367 35483327 35555724 35625818 35694262 35763982 35833820 - 35905138 35975939 36048609 36120628 36187841 36260385 36330053 36400894 - 36473268 36539475 36612114 36680839 36752541 36823061 36891105 36960218 - 37034274 37102424 37172945 37239964 37307656 37376187 37447010 37519576 - 37587913 37662665 37731602 37805482 37878816 37947604 38016588 38089203 - 38155454 38223647 38295311 38367047 38437965 38507067 38573242 38643736 - 38718350 38788168 38855657 38922996 38993512 39063603 39130264 39198157 - 39269230 39336806 39404401 39477346 39544368 39613848 39686258 39754871 - 39826634 39896581 39966433 40038982 40108211 40179546 40247619 40319245 - 40387151 40460151 40531900 40602848 40678210 40747341 40818557 40887988 - 40958512 41031918 41099851 41168107 41235362 41304506 41378338 41452223 - 41519685 41591029 41661313 41728445 41797607 41865438 41940053 42012651 - 42083239] -2023-05-10 18:14:39,642 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:14:39,642 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' - -sources: [194121 194121 194121 ... 900586 900586 900583] -destinations: [ 777758 1283001 1515574 ... 2141265 962470 1111344] -batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 - 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 - 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 - 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 - 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 - 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 - 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 - 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 - 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 - 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 - 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 - 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 - 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 - 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42029037] -2023-05-10 18:14:47,965 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:14:47,965 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:14:47,968 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:14:47,968 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:14:47,972 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:14:47,972 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:14:48,083 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:14:48,085 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:14:48,085 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:34233. Reason: scheduler-restart -2023-05-10 18:14:48,085 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38389. Reason: scheduler-restart -2023-05-10 18:14:48,086 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:14:48,087 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:14:48,088 - distributed.nanny - INFO - Worker closed -2023-05-10 18:14:48,088 - distributed.nanny - INFO - Worker closed - - -2023-05-10 18:14:49,729 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:14:50,055 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:14:51,150 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:14:51,150 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:14:51,242 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:14:51,242 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:14:51,502 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:14:51,506 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:14:52,651 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:44757 -2023-05-10 18:14:52,651 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:44757 -2023-05-10 18:14:52,651 - distributed.worker - INFO - dashboard at: 10.120.104.11:36835 -2023-05-10 18:14:52,651 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:14:52,651 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,651 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:14:52,651 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:14:52,651 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-e_y1rlpi -2023-05-10 18:14:52,652 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-4f2560f0-e8c4-429d-ba1a-6525126ca38d -2023-05-10 18:14:52,652 - distributed.worker - INFO - Starting Worker plugin RMMSetup-ccd1dd8e-67b6-45a0-b9e7-ef9a054df822 -2023-05-10 18:14:52,694 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:43467 -2023-05-10 18:14:52,695 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:43467 -2023-05-10 18:14:52,695 - distributed.worker - INFO - dashboard at: 10.120.104.11:45197 -2023-05-10 18:14:52,695 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:14:52,695 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,695 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:14:52,695 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:14:52,695 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-g39sbkyj -2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6d188862-221c-4daf-a8e2-eef96fb9e501 -2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin PreImport-cd3e0c92-cbc9-44a7-83b1-666cd689d1b8 -2023-05-10 18:14:52,696 - distributed.worker - INFO - Starting Worker plugin RMMSetup-1a486981-ea33-4215-99d1-ded8645e74f7 -2023-05-10 18:14:52,817 - distributed.worker - INFO - Starting Worker plugin PreImport-1f660af5-ca3a-4f57-a9ef-004cab47c62d -2023-05-10 18:14:52,819 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,832 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:14:52,833 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,835 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:14:52,865 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,872 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:14:52,872 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:14:52,874 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:15:00,995 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:15:00,996 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.94s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:01,039 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:15:01,041 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.99s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:01,044 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:01,045 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:01,049 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:15:01,050 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:15:07,255 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.56s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:07,366 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.67s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:07,369 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:15:07,372 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:15:32,813 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:32,813 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:33,221 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:15:33,223 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:15:33,983 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:33,984 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:37,773 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:37,773 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 80 80 80 ... 1726817 1726817 1726817] -destinations: [1272647 659642 739610 ... 2305638 1875895 836345] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 - 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 - 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 - 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 - 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 - 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 - 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 - 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 - 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 - 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 - 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 - 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 - 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 - 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 - 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 - 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 - 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 - 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 - 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 - 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 - 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 - 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 - 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 - 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 - 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 - 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 - 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 - 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 - 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 - 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 - 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 - 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 - 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 - 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 - 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 - 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 - 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 - 990 991 992 993 994 995 996 997 998 999] -offset: [ 0 71638 136888 ... 69958114 70029243 70097654] - -sources: [479612 479612 479612 ... 500789 500789 500789] -destinations: [ 782871 1125214 1374125 ... 442977 1008704 96752] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 - 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 - 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 - 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 - 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 - 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 - 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 - 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 - 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 - 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 - 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 - 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 - 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 - 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 - 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 - 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 - 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 - 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 - 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 - 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 - 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 - 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 - 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 - 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 - 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 - 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 - 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 - 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 - 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 - 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 - 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 - 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 - 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 - 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 - 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 - 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 - 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 - 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 - 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 - 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 - 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 - 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 - 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 - 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 - 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 - 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 - 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 - 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 - 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 - 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 - 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 - 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 - 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 - 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 - 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 - 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 - 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 - 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 - 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 - 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 - 1994 1995 1996 1997 1998 1999] -offset: [ 0 67850 141452 ... 70046489 70116073 70185662] - -2023-05-10 18:15:39,430 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:39,430 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1000147 1000147 1000147 ... 628215 628215 628215] -destinations: [2361720 1626774 1593829 ... 503009 473052 225725] -batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 - 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 - 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 - 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 - 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 - 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 - 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 - 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 - 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 - 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 - 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 - 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 - 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 - 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 - 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 - 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 - 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 - 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 - 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 - 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 - 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 - 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 - 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 - 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 - 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 - 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 - 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 - 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 - 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 - 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 - 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 - 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 - 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48781118 48850800 48921710 48991411 49056278 49125162 49198302 49268707 - 49341163 49411395 49482867 49552047 49623537 49693584 49760794 49833077 - 49905345 49972086 50044133 50116088 50186829 50259367 50326739 50394455 - 50463678 50531376 50603194 50674119 50747666 50818272 50884363 50950893 - 51021084 51090569 51160644 51237201 51306701 51380577 51450993 51518222 - 51583664 51655953 51726063 51798222 51869314 51935616 52005518 52077060 - 52148783 52216127 52286427 52359206 52429803 52501661 52567346 52638660 - 52706891 52777321 52849071 52918280 52991228 53061238 53131499 53197248 - 53271604 53343107 53417430 53486244 53554588 53624889 53697949 53770022 - 53833856 53903020 53972774 54046670 54116060 54181428 54252218 54323704 - 54392048 54461824 54534073 54607022 54676893 54747092 54816309 54888020 - 54961353 55030737 55101732 55170287 55241724 55312096 55378897 55446640 - 55514447 55585653 55604236] -2023-05-10 18:15:40,473 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:15:40,474 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:15:40,479 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:15:40,479 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:15:40,482 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:15:40,482 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:15:40,601 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:15:40,602 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:15:40,616 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:43467. Reason: scheduler-restart -2023-05-10 18:15:40,617 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:15:40,618 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:44757. Reason: scheduler-restart -2023-05-10 18:15:40,619 - distributed.nanny - INFO - Worker closed -2023-05-10 18:15:40,619 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:43467 - -2023-05-10 18:15:40,620 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:15:40,621 - distributed.nanny - INFO - Worker closed -sources: [ 494512 494512 494512 ... 1500995 1500995 1500995] -destinations: [ 673396 1041959 1011819 ... 1886203 899690 674715] -batch: [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 - 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 - 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 - 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 - 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 - 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 - 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 - 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3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 - 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 - 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 - 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 - 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 - 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 - 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 - 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 - 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 - 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 - 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 - 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 - 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 - 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 - 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 - 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 - 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 - 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 - 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 - 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 - 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 - 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 - 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 - 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 - 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 - 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 - 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 - 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 - 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 - 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 - 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 - 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 - 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 - 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 - 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 - 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 - 3994 3995 3996 3997 3998 3999] -offset: [ 0 72286 139228 ... 69927048 69999562 70071001] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 18:15:42,201 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:15:42,554 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:15:43,393 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:15:43,393 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:15:43,646 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:15:43,750 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:15:43,750 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:15:44,016 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:15:44,434 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42023 -2023-05-10 18:15:44,435 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42023 -2023-05-10 18:15:44,435 - distributed.worker - INFO - dashboard at: 10.120.104.11:34109 -2023-05-10 18:15:44,435 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:15:44,435 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:44,435 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:15:44,436 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:15:44,436 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-vjwan3v8 -2023-05-10 18:15:44,437 - distributed.worker - INFO - Starting Worker plugin RMMSetup-d91b6e51-3a58-4eaa-93dc-caeaa15a6c1a -2023-05-10 18:15:44,596 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-3bbdc39f-1199-4b17-8513-97eddf7694b6 -2023-05-10 18:15:44,596 - distributed.worker - INFO - Starting Worker plugin PreImport-e0ec5e41-1b51-49af-ba1d-e051eadaca65 -2023-05-10 18:15:44,597 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:44,606 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:15:44,606 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:44,608 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:15:44,926 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37619 -2023-05-10 18:15:44,926 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37619 -2023-05-10 18:15:44,926 - distributed.worker - INFO - dashboard at: 10.120.104.11:39813 -2023-05-10 18:15:44,927 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:15:44,927 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:44,927 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:15:44,927 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:15:44,927 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-by6ymd4e -2023-05-10 18:15:44,927 - distributed.worker - INFO - Starting Worker plugin RMMSetup-63f8c9d4-7ef8-455a-a6b2-81ddc7de7d07 -2023-05-10 18:15:45,051 - distributed.worker - INFO - Starting Worker plugin PreImport-8d23a30e-717f-470f-a833-56b141880f5d -2023-05-10 18:15:45,051 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-4c055384-37f2-4fee-9548-ecd43f581779 -2023-05-10 18:15:45,052 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:45,062 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:15:45,063 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:15:45,065 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:15:53,347 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:15:53,349 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.18s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:53,536 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:15:53,537 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.37s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:53,541 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:53,542 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:15:53,546 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:15:53,546 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:15:59,779 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.63s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:59,909 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.76s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:15:59,912 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:15:59,914 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:16:15,093 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:15,094 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:15,504 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:16:15,505 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:16:16,314 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:16,316 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:21,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:21,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 352 352 352 ... 1381549 1381549 1381549] -destinations: [ 22367 22092 590483 ... 714835 1250990 929093] -batch: [ 0 1 2 ... 1997 1998 1999] -offset: [ 0 71569 136683 ... 140135585 140205396 140275051] - -sources: [2000144 2000144 2000144 ... 1306294 1306294 1306294] -destinations: [ 351641 1074226 1365172 ... 1590856 2018837 2180008] -batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 - 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 - 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 - 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 - 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 - 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 - 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 - 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 - 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44291825 44364267 44433500 44501165 44572083 44642499 44711623 44782379 - 44850511 44921809 44990150 45063242 45134385 45202942 45273546 45345438 - 45414847 45486639 45555482 45628290 45698253 45767104 45836939 45904463 - 45976081 46042885 46111854 46184927 46257699 46329674 46396519 46463642 - 46534127 46605141 46674888 46746994 46813495 46882669 46955692 47025562 - 47099225 47168204 47241885 47311730 47383803 47451567 47519336 47589159 - 47658694 47726225 47799328 47871436 47944265 48014015 48080751 48153291 - 48223190 48296167 48366481 48431993 48501203 48573644 48644229 48714635 - 48785874 48855432 48926412 48996085 49061000 49129742 49203045 49273258 - 49345302 49415229 49486969 49556032 49627572 49697611 49764828 49837140 - 49909568 49976306 50048312 50120029 50190558 50263355 50330387 50398342 - 50467476 50535352 50607053 50678172 50751829 50822461 50888486 50955001 - 51025335 51095187 51165620 51241870 51311469 51385419 51455545 51522466 - 51587835 51660544 51730813 51802990 51873792 51940015 52009964 52081739 - 52153496 52221003 52291445 52364173 52434752 52506644 52572252 52643513 - 52711849 52782467 52854186 52923204 52996125 53066273 53136607 53202203 - 53276857 53348359 53422483 53491378 53559416 53629728 53702832 53774830 - 53838528 53907859 53977315 54051518 54121074 54186225 54257207 54328702 - 54396990 54466863 54539404 54612518 54682398 54752798 54821860 54893491 - 54966848 55036362 55107325 55175884 55247321 55317883 55384379 55451994 - 55519593 55590607 55609280] -2023-05-10 18:16:29,056 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:16:29,056 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:16:29,060 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:16:29,060 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:16:29,064 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:16:29,068 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:16:29,181 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:16:29,183 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:16:29,183 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37619. Reason: scheduler-restart -2023-05-10 18:16:29,184 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42023. Reason: scheduler-restart -2023-05-10 18:16:29,184 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:16:29,185 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37619 -2023-05-10 18:16:29,185 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:16:29,186 - distributed.nanny - INFO - Worker closed - -2023-05-10 18:16:29,186 - distributed.nanny - INFO - Worker closed -sources: [1000432 1000432 1000432 ... 22125 22125 22125] -destinations: [1518285 367680 1396431 ... 1194909 251848 1741944] -batch: [2000 2001 2002 ... 3997 3998 3999] -offset: [ 0 71098 140874 ... 140029578 140102124 140173556] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 18:16:30,981 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:16:31,252 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:16:32,226 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:16:32,226 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:16:32,456 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:16:32,456 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:16:32,512 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:16:32,722 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:16:33,379 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:41227 -2023-05-10 18:16:33,379 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:41227 -2023-05-10 18:16:33,379 - distributed.worker - INFO - dashboard at: 10.120.104.11:45597 -2023-05-10 18:16:33,379 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:16:33,379 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,379 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:16:33,380 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:16:33,380 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tmgtys_v -2023-05-10 18:16:33,380 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-1d8084e5-e8ae-4bbd-9f79-e017971076a7 -2023-05-10 18:16:33,381 - distributed.worker - INFO - Starting Worker plugin PreImport-90b03a88-0279-4c16-9ad0-f5bbcc0b445e -2023-05-10 18:16:33,381 - distributed.worker - INFO - Starting Worker plugin RMMSetup-617d7ca7-7c03-4526-a01e-38a3da7e5e71 -2023-05-10 18:16:33,538 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,549 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:16:33,549 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,552 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:16:33,706 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42727 -2023-05-10 18:16:33,707 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42727 -2023-05-10 18:16:33,707 - distributed.worker - INFO - dashboard at: 10.120.104.11:43471 -2023-05-10 18:16:33,707 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:16:33,707 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,707 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:16:33,707 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:16:33,707 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rgvu4k9j -2023-05-10 18:16:33,707 - distributed.worker - INFO - Starting Worker plugin RMMSetup-2cb667fb-f09d-42dd-8573-f0d5a280f9c0 -2023-05-10 18:16:33,827 - distributed.worker - INFO - Starting Worker plugin PreImport-222b94d4-e644-4537-8c40-30f98c08ea28 -2023-05-10 18:16:33,827 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-5b28f5dd-8f02-40a8-aa97-bd640a34cfba -2023-05-10 18:16:33,828 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,840 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:16:33,840 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:16:33,842 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:16:41,708 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:16:41,709 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.86s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:16:41,849 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:16:41,851 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.99s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:16:41,854 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:41,856 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:16:41,860 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:16:41,860 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:16:47,962 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.49s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:16:47,975 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.51s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:16:47,978 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:16:47,980 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:17:13,248 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:13,248 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:13,649 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:17:13,650 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:17:14,324 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:14,325 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:17,364 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:17,364 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 599486 599486 599486 ... 2198540 2198540 2198540] -destinations: [ 262545 528383 2173920 ... 1759461 108204 2338644] -batch: [300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 - 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 - 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 - 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 - 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 - 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 - 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 - 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 - 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 - 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 - 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 - 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 - 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 - 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 - 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 - 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 - 588 589 590 591 592 593 594 595 596 597 598 599] -offset: [ 0 142212 283161 425758 561511 702046 843614 981748 - 1120618 1263191 1404531 1542605 1682762 1825160 1967623 2106352 - 2250116 2396331 2534454 2677393 2817336 2962348 3103197 3243410 - 3384961 3523503 3662705 3806026 3948613 4090328 4230129 4368334 - 4507079 4647149 4784854 4923182 5064144 5206429 5345588 5489116 - 5633645 5774728 5913169 6054672 6192140 6334460 6477726 6620212 - 6763207 6902446 7041982 7183744 7324827 7461541 7603792 7744250 - 7884391 8019823 8164647 8305096 8446230 8585405 8724917 8861453 - 9000689 9137883 9276754 9421699 9558395 9703107 9840198 9975054 - 10112099 10258776 10401888 10545553 10687857 10827620 10972790 11106283 - 11246750 11390658 11527787 11663252 11805023 11942202 12086277 12229420 - 12368051 12504797 12643981 12786335 12930592 13066206 13208846 13349007 - 13490901 13626154 13763301 13900473 14037652 14176006 14322146 14468137 - 14601691 14741014 14884697 15026404 15164017 15302874 15445066 15585038 - 15724553 15863341 16007609 16147168 16288137 16426710 16568340 16708908 - 16844609 16990619 17132244 17273972 17412620 17549960 17690120 17830444 - 17975184 18112246 18257779 18394766 18527892 18670278 18807899 18951573 - 19093983 19233830 19370286 19505870 19642830 19782638 19928728 20071840 - 20214362 20354321 20492212 20630607 20770595 20908523 21047902 21188935 - 21327502 21468378 21605356 21746621 21885103 22022961 22162443 22304787 - 22446926 22588268 22728917 22870213 23007638 23143218 23284331 23431160 - 23575883 23717805 23857399 23996942 24133554 24275938 24420951 24563435 - 24706403 24845415 24981762 25123681 25261311 25397588 25537941 25676628 - 25818316 25954178 26091021 26240441 26382639 26524404 26661009 26797659 - 26943405 27083964 27223879 27362465 27502169 27641480 27780493 27919924 - 28059945 28201309 28339971 28480266 28620507 28760068 28898781 29037217 - 29180126 29318591 29463362 29603369 29743602 29879363 30021965 30166048 - 30309620 30445687 30586083 30728009 30867572 31009666 31151869 31292013 - 31432187 31568851 31708390 31848287 31989894 32129545 32265437 32409616 - 32552687 32693447 32837243 32977044 33116634 33255095 33393311 33537109 - 33680534 33816885 33951646 34088942 34229847 34370565 34512936 34652899 - 34789739 34931378 35071362 35211710 35347957 35482180 35624481 35762607 - 35903378 36046242 36185630 36327909 36471274 36610141 36750608 36889250 - 37032010 37170591 37304884 37444526 37585887 37729530 37877183 38015262 - 38153940 38293948 38436438 38571883 38716506 38853419 38991789 39128683 - 39267550 39402881 39543029 39684978 39825493 39965193 40107002 40246595 - 40386227 40530821 40677435 40818020 40957454 41098613 41234210 41377015 - 41518719 41660286 41796334 41938730 42081925] - -sources: [ 880 880 880 ... 2092223 2092223 2092223] -destinations: [1685287 424192 1960446 ... 362571 1363720 2124881] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299] -offset: [ 0 136563 279750 420414 560639 700859 843418 983327 - 1127112 1267125 1406612 1544596 1680965 1815438 1956878 2098864 - 2239932 2384344 2522901 2665915 2807882 2946279 3089704 3229120 - 3365897 3506165 3651914 3789801 3928369 4072060 4215498 4353689 - 4485322 4630091 4764832 4907576 5047590 5189148 5331621 5469513 - 5607858 5750374 5892745 6037505 6180220 6319879 6461503 6599302 - 6742814 6883369 7022042 7159421 7299817 7441298 7584018 7724238 - 7867907 8009999 8149887 8282786 8425487 8562899 8706591 8850818 - 8993258 9138624 9275499 9413900 9554898 9693793 9834710 9975164 - 10120329 10255710 10392462 10533184 10679266 10817637 10957300 11098638 - 11235147 11371761 11507106 11645306 11785289 11920004 12065556 12203558 - 12345379 12481237 12619531 12761942 12900632 13043851 13180577 13324343 - 13465776 13605541 13745137 13882653 14028875 14165824 14306370 14449121 - 14585587 14725276 14864861 15006964 15145517 15286625 15423869 15562223 - 15701760 15840145 15980886 16120687 16260680 16401465 16538104 16675872 - 16817394 16954527 17098442 17238975 17383107 17520954 17658068 17799107 - 17937955 18083615 18225426 18366748 18506388 18644233 18779589 18916987 - 19059084 19204480 19341816 19480135 19618442 19761757 19901876 20046080 - 20186929 20324751 20470043 20612320 20753341 20889798 21027589 21167727 - 21308348 21447288 21587320 21728680 21869145 22008564 22150831 22290646 - 22427057 22571184 22713870 22854312 22995878 23139041 23280122 23421833 - 23563532 23702021 23839899 23977832 24122409 24262229 24397152 24541839 - 24680162 24819498 24956555 25097430 25236766 25377359 25521569 25660633 - 25800841 25938844 26081049 26221531 26361655 26505119 26644765 26780531 - 26918600 27058712 27197165 27339657 27476523 27616250 27756622 27897417 - 28038277 28179189 28321193 28461510 28600304 28739803 28880867 29022939 - 29156459 29302510 29445820 29586722 29721077 29863332 29999619 30138682 - 30277587 30418683 30558626 30698842 30844101 30984321 31118668 31262120 - 31402008 31542369 31675313 31816699 31952326 32088704 32233892 32372383 - 32514393 32657798 32798501 32939985 33081860 33221197 33361866 33502164 - 33644154 33787065 33924794 34066498 34208422 34345285 34481941 34623941 - 34767330 34909372 35048633 35187471 35324469 35463704 35601236 35741221 - 35878904 36020299 36162056 36300226 36436627 36577967 36720234 36859101 - 37001192 37141590 37279957 37420728 37559602 37701726 37842277 37979657 - 38121389 38261259 38399693 38542640 38688069 38829362 38972504 39109425 - 39249088 39384889 39519360 39657733 39796306 39939311 40075278 40217552 - 40353719 40500590 40641863 40778164 40921441 41061124 41200022 41340638 - 41480322 41623667 41764794 41904422 42043475] - -2023-05-10 18:17:18,526 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:18,526 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1199157 1199160 1199160 ... 901154 901154 901154] -destinations: [2135425 409528 1153387 ... 1621302 1023465 317544] -batch: [ 900 901 902 903 904 905 906 907 908 909 910 911 912 913 - 914 915 916 917 918 919 920 921 922 923 924 925 926 927 - 928 929 930 931 932 933 934 935 936 937 938 939 940 941 - 942 943 944 945 946 947 948 949 950 951 952 953 954 955 - 956 957 958 959 960 961 962 963 964 965 966 967 968 969 - 970 971 972 973 974 975 976 977 978 979 980 981 982 983 - 984 985 986 987 988 989 990 991 992 993 994 995 996 997 - 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 - 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 - 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 - 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 - 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 - 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 - 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 - 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 - 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 - 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 - 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 - 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 - 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 - 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 - 1194 1195 1196 1197 1198 1199] -offset: [ 0 138977 274179 419411 556357 694401 837960 978529 - 1120882 1259576 1403614 1539587 1683177 1822530 1963281 2106068 - 2244407 2388504 2526448 2666326 2810152 2951276 3093225 3231564 - 3377262 3515554 3654345 3799104 3941334 4078973 4215822 4351588 - 4493548 4633882 4774830 4913619 5052937 5193121 5334861 5475523 - 5609161 5751191 5891516 6026410 6168122 6306963 6447712 6589582 - 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39329277 39468080 39610702 39751298 39890833 40033197 40172907 40315020 - 40453096 40586572 40729072 40867552 41013428 41153421 41293096 41432992 - 41574299 41711701 41851039 41995605 42137449] - -2023-05-10 18:17:19,689 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:19,689 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 671210 671210 1775383 ... 1501203 1501203 1501203] -destinations: [ 731105 67381 2027993 ... 881711 1695106 1423252] -batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 - 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 - 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 - 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 - 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 - 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 - 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 - 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 - 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 - 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 - 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 - 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 - 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 - 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 - 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 - 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 - 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 - 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 - 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 - 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 - 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 - 1794 1795 1796 1797 1798 1799] -offset: [ 0 138995 275929 414302 551497 690280 829968 970323 - 1113659 1255070 1396235 1533625 1682718 1817272 1955665 2096240 - 2241025 2381498 2519707 2659099 2802464 2945601 3082813 3221107 - 3363579 3502249 3643546 3781521 3922106 4062741 4204906 4349916 - 4489231 4626233 4765552 4906562 5046675 5184038 5324050 5466316 - 5610387 5755641 5892951 6031100 6169822 6305925 6448727 6590454 - 6733990 6871907 7009383 7149995 7290329 7433204 7578287 7721939 - 7861345 7999113 8142082 8276137 8419504 8558676 8699371 8835420 - 8975953 9117966 9254206 9395507 9538917 9685738 9824354 9963713 - 10106734 10247652 10389533 10528184 10671236 10807150 10947195 11086918 - 11228938 11366497 11504332 11643558 11783628 11922187 12058828 12201315 - 12343566 12484606 12619404 12756599 12895475 13032891 13171247 13311542 - 13450062 13586407 13727641 13867276 14008123 14146241 14288010 14423827 - 14563553 14704066 14847114 14993084 15130683 15277348 15415386 15557705 - 15693881 15832205 15972892 16114112 16252328 16391727 16535627 16675396 - 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1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 - 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 - 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 - 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 - 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 - 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 - 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 - 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 - 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 - 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 - 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 - 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 - 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 - 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 - 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 - 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 - 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 - 1494 1495 1496 1497 1498 1499] -offset: [ 0 142111 286256 420782 565497 709511 847543 984721 - 1124783 1268603 1402855 1546997 1687822 1827691 1966122 2106761 - 2250021 2395142 2532109 2672123 2807308 2946596 3088789 3228643 - 3367884 3515106 3655054 3795126 3937021 4075952 4216147 4357482 - 4502618 4644960 4784481 4923278 5063019 5202739 5342101 5481823 - 5625378 5767695 5909923 6046930 6187135 6328836 6465643 6606589 - 6742499 6878483 7024973 7161405 7303231 7441849 7580285 7723673 - 7865565 8004589 8142796 8285729 8422889 8564164 8704215 8845146 - 8987480 9131868 9274807 9410911 9553178 9693930 9834089 9974545 - 10110541 10256024 10393691 10535903 10675069 10812444 10951789 11088977 - 11233607 11368239 11513435 11654253 11797110 11934250 12069584 12206477 - 12345019 12489652 12627984 12767360 12912298 13052817 13191691 13326876 - 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29160262 29298412 29440498 29579494 29718090 29856172 29996158 30135240 - 30281720 30424072 30562706 30700143 30836280 30975648 31118988 31262077 - 31397743 31539834 31681773 31824793 31963877 32102578 32244132 32385047 - 32525236 32662117 32803822 32942347 33085493 33225429 33361526 33501173 - 33638711 33778425 33921989 34062183 34200887 34339067 34480117 34619898 - 34763442 34899963 35043422 35178157 35321809 35463342 35603442 35744772 - 35884155 36026739 36163278 36302869 36443719 36584421 36726997 36866503 - 37004683 37151630 37287320 37431317 37571946 37710583 37851260 37987025 - 38124225 38262930 38398175 38540375 38682650 38826916 38964043 39099941 - 39244307 39382461 39524016 39662679 39805551 39947888 40084235 40217623 - 40361143 40505804 40644737 40784627 40918898 41062797 41203962 41343887 - 41484887 41626940 41770325 41906459 42051401] - -2023-05-10 18:17:20,608 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:17:20,608 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:17:20,611 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:17:20,611 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:17:20,614 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:17:20,614 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:17:20,736 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:17:20,738 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:17:20,738 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:41227. Reason: scheduler-restart -2023-05-10 18:17:20,739 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42727. Reason: scheduler-restart -2023-05-10 18:17:20,739 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:17:20,740 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:17:20,741 - distributed.nanny - INFO - Worker closed -sources: [1800064 1800065 1800065 ... 1877776 1877776 1877776] -destinations: [ 627165 401781 157256 ... 2099278 2124881 958074] -batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 - 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 - 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 - 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 - 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 - 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 - 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 - 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 - 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 - 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 - 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 - 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 - 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 - 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 - 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 - 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 - 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 - 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 - 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 - 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 - 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 - 2094 2095 2096 2097 2098 2099] -offset: [ 0 139977 279646 422533 559912 697685 837604 973667 - 1119445 1265141 1409754 1548316 1682645 1821784 1960134 2102224 - 2247812 2387202 2525836 2661661 2802877 2942436 3087131 3233383 - 3368777 3513170 3648112 3787881 3927770 4065592 4205188 4349897 - 4492629 4631779 4767026 4904577 5045821 5187110 5325848 5463376 - 5610962 5745823 5884652 6026280 6162325 6300259 6439513 6579640 - 6716025 6856332 7000683 7140976 7279168 7424156 7562283 7702179 - 7843386 7977781 8117979 8259507 8398911 8535643 8676495 8818209 - 8963846 9105132 9248948 9384850 9527581 9661713 9801589 9949582 - 10085822 10225111 10362242 10505862 10639988 10781607 10920975 11059739 - 11200746 11337115 11483496 11620271 11764589 11904604 12045697 12187947 - 12330254 12467129 12609738 12747981 12886008 13024070 13164984 13307167 - 13444481 13585832 13728537 13863555 14005922 14142484 14282422 14422270 - 14567348 14706181 14849562 14986610 15129295 15271569 15411593 15557132 - 15696598 15835001 15972171 16110307 16248385 16391567 16529540 16673015 - 16812967 16952227 17096626 17240761 17381531 17525108 17663981 17809390 - 17944846 18081208 18222269 18362333 18505823 18644248 18784986 18922897 - 19065267 19203710 19342844 19486618 19626809 19766350 19905762 20044989 - 20180301 20323898 20462992 20600233 20739446 20882167 21026626 21163454 - 21304369 21440172 21577267 21718523 21856469 21998698 22134852 22277494 - 22415036 22553556 22697671 22835516 22975324 23115598 23262664 23399042 - 23534722 23677293 23816603 23958487 24098223 24239804 24375249 24516118 - 24652798 24794509 24933678 25075676 25217673 25360699 25499350 25638702 - 25779916 25917317 26058709 26196871 26337487 26475656 26614539 26756869 - 26896603 27033966 27174876 27314288 27456960 27597033 27739889 27879229 - 28023065 28161348 28299652 28437999 28572401 28718304 28859501 28994674 - 29136992 29271812 29413218 29550442 29694054 29835477 29975654 30120072 - 30258232 30398808 30539827 30683239 30824186 30963238 31110360 31253714 - 31394260 31533945 31674451 31812138 31945736 32088661 32227803 32368985 - 32509402 32646994 32786256 32927098 33071139 33214404 33354483 33494340 - 33635502 33772954 33913071 34057105 34198719 34341261 34477713 34613841 - 34755655 34897163 35042583 35181688 35325324 35460853 35599506 35736699 - 35871864 36010022 36153311 36293131 36430576 36566889 36709681 36851650 - 36989576 37128112 37268437 37408030 37549473 37684828 37826789 37964855 - 38102974 38239099 38379978 38518009 38656578 38795844 38939639 39082932 - 39222802 39365104 39510200 39647925 39785409 39925610 40067835 40205142 - 40344986 40484881 40621658 40763132 40906221 41048584 41192999 41330316 - 41474233 41611956 41748978 41887116 42029310] - -2023-05-10 18:17:20,742 - distributed.nanny - INFO - Worker closed -sources: [ 214646 214646 214646 ... 1813254 1813254 1813254] -destinations: [ 943903 1507770 530165 ... 1129690 1996099 1724999] -batch: [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 - 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 - 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 - 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 - 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 - 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 - 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 - 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 - 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 - 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 - 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 - 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 - 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 - 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 - 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 - 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 - 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 - 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 - 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 - 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 - 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 - 2394 2395 2396] -offset: [ 0 142494 283821 424216 564214 702709 842557 978501 - 1120381 1260693 1397887 1535909 1680941 1821588 1963835 2104654 - 2250562 2388874 2527628 2671679 2812733 2952511 3096625 3236089 - 3374470 3513183 3653136 3796743 3934363 4076223 4214416 4353100 - 4490130 4632652 4775833 4912815 5053610 5193254 5331201 5467962 - 5611232 5749088 5887758 6027919 6173978 6315919 6452913 6599165 - 6738583 6874735 7014990 7160713 7303514 7445801 7582384 7721117 - 7857035 8001612 8143837 8283456 8420399 8564434 8704521 8844036 - 8979914 9125558 9262478 9400581 9539620 9676813 9817316 9957521 - 10096654 10235279 10374500 10519455 10665363 10805277 10944371 11083372 - 11225955 11366178 11508587 11647891 11788414 11926950 12067080 12206378 - 12355528 12493250 12633748 12770265 12908984 13048019 13187014 13333134 - 13472870 13615503 13760183 13896961 14038155 14175072 14324110 14461107 - 14607366 14740469 14880328 15017551 15156742 15301129 15440330 15578293 - 15718903 15859533 15998793 16141652 16279306 16415954 16558029 16700046 - 16836008 16973169 17114787 17251333 17399699 17541293 17677986 17816638 - 17958289 18097303 18243238 18384986 18521707 18661761 18802763 18943595 - 19087312 19226376 19369516 19503578 19645533 19788595 19930288 20071715 - 20204469 20342804 20483116 20624445 20764375 20899514 21036929 21178149 - 21319191 21455021 21599382 21737437 21878850 22024697 22167933 22311452 - 22447807 22582282 22720625 22859788 22998479 23139477 23281876 23423838 - 23569425 23706632 23846414 23985501 24125921 24268131 24404710 24538641 - 24678185 24816600 24960063 25098621 25241719 25377054 25518018 25655214 - 25798993 25940033 26082943 26223441 26362683 26502425 26638363 26772792 - 26916696 27056956 27194021 27335150 27478165 27615196 27754717 27894546 - 28040483 28179165 28319688 28457466 28593769 28731995 28872855 29020357 - 29162904 29304849 29444271 29585770 29721304 29853904 29997973 30139841 - 30287108 30429051 30567639 30707246 30846270 30986074 31130381 31269370 - 31411123 31551922 31694291 31832165 31970988 32106830 32252672 32391391 - 32528282 32668660 32806821 32949022 33091909 33234595 33376090 33511937 - 33650760 33791196 33936075 34072272 34214487 34357305 34492183 34635151 - 34776967 34917647 35052226 35194141 35336956 35478623 35619269 35756055 - 35900779 36039590 36182087 36321970 36459066 36598904 36743642 36880455 - 37017313 37157203 37303567 37447588 37579515 37721900 37864949 38000931 - 38144137 38281771 38425214 38563036 38702694 38844834 38986886 39127314 - 39267314 39412841 39550303 39693514 39829603 39968380 40111942 40248123 - 40388058 40530428 40673474 40812914 40957742 41098404 41238421 41375128 - 41510546 41600361] - -2023-05-10 18:17:22,553 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:17:22,814 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:17:23,755 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:17:23,755 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:17:24,027 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:17:24,067 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:17:24,067 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:17:24,340 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:17:24,948 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38521 -2023-05-10 18:17:24,948 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38521 -2023-05-10 18:17:24,949 - distributed.worker - INFO - dashboard at: 10.120.104.11:38261 -2023-05-10 18:17:24,949 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:17:24,949 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:24,949 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:17:24,949 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:17:24,949 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-8644o16d -2023-05-10 18:17:24,949 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-6e3a0512-8f7c-475c-8763-9309df3ccd27 -2023-05-10 18:17:24,950 - distributed.worker - INFO - Starting Worker plugin RMMSetup-4d3a65ce-4df5-4641-92e2-3b7eca1fdf5b -2023-05-10 18:17:25,150 - distributed.worker - INFO - Starting Worker plugin PreImport-fba04452-d1fb-4e2e-ab0d-e5c2bf1aca58 -2023-05-10 18:17:25,151 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:25,159 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:17:25,159 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:25,162 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:17:25,321 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40329 -2023-05-10 18:17:25,321 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40329 -2023-05-10 18:17:25,321 - distributed.worker - INFO - dashboard at: 10.120.104.11:36925 -2023-05-10 18:17:25,321 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:17:25,321 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:25,321 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:17:25,321 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:17:25,321 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-51an_5tn -2023-05-10 18:17:25,321 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-7001931d-69cb-4c27-a8bc-232acb581394 -2023-05-10 18:17:25,322 - distributed.worker - INFO - Starting Worker plugin PreImport-e4d711a7-fa8c-4785-b9de-2348895315dc -2023-05-10 18:17:25,322 - distributed.worker - INFO - Starting Worker plugin RMMSetup-3f526ce4-02de-404d-8c4d-9854d24b2c40 -2023-05-10 18:17:25,442 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:25,451 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:17:25,452 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:17:25,454 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:17:33,497 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:17:33,498 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.88s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:17:33,729 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:17:33,731 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.12s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:17:33,735 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:33,737 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:17:33,741 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:17:33,741 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:17:40,000 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:17:40,064 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.68s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:17:40,069 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:17:40,069 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:18:05,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:05,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:05,824 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:05,825 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:06,581 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:06,583 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:10,374 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:10,374 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1712950 1712950 1712950 ... 226264 226264 226264] -destinations: [ 819856 622935 1634974 ... 1258299 2330085 1714337] -batch: [500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 - 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 - 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 - 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 - 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 - 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 - 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 - 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 - 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 - 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 - 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 - 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 - 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 - 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 - 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 - 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 - 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 - 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 - 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 - 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 - 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 - 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 - 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 - 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 - 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 - 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 - 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 - 986 987 988 989 990 991 992 993 994 995 996 997 998 999] -offset: [ 0 141783 280273 420564 560722 700056 839031 977564 - 1120678 1259543 1403895 1543615 1684153 1820200 1962873 2107130 - 2250533 2386807 2527024 2669071 2808636 2950959 3093057 3232900 - 3373102 3509290 3649120 3789205 3930696 4070479 4206499 4350484 - 4493085 4633880 4777780 4917789 5057426 5196071 5333471 5477509 - 5620689 5757733 5892716 6029849 6170487 6311062 6453314 6593304 - 6730102 6871836 7011595 7151870 7287797 7421829 7564522 7702551 - 7843364 7986525 8125639 8268309 8411632 8550460 8690831 8829591 - 8972474 9110751 9245532 9384856 9525793 9669177 9816398 9954084 - 10093083 10232881 10375670 10511039 10655440 10792438 10930755 11067716 - 11206394 11342043 11482094 11624005 11764676 11904323 12046193 12185793 - 12325849 12470214 12616714 12756825 12896841 13038544 13174286 13317561 - 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44926254 45064613 45209053 45347649 45490763 45628679 45760721 45904831 - 46047866 46186483 46327070 46466479 46605030 46747260 46889513 47029709 - 47167203 47307562 47446913 47585688 47728603 47872509 48015178 48160073 - 48298790 48440163 48578704 48718793 48859468 49002867 49139258 49279702 - 49418879 49556226 49694934 49840372 49977747 50111222 50248651 50386036 - 50531530 50666646 50810136 50945904 51082177 51222952 51366558 51505886 - 51651955 51795922 51937946 52077791 52219889 52360020 52501103 52643620 - 52783101 52921274 53061458 53205005 53347722 53486739 53629493 53770166 - 53909474 54052182 54191943 54334022 54472089 54605662 54748474 54887241 - 55033368 55173509 55313420 55453290 55594923 55732143 55871320 56015694 - 56158056 56297313 56432245 56577696 56714671 56852667 56996454 57137177 - 57279523 57417579 57561790 57697552 57841113 57980230 58121091 58263554 - 58401646 58545725 58683460 58823312 58967626 59108619 59250602 59388796 - 59534465 59672775 59811859 59956616 60099001 60236508 60373451 60509069 - 60651004 60791347 60931939 61070964 61210022 61350309 61492063 61633176 - 61766885 61908928 62048704 62183583 62324983 62464243 62605426 62746861 - 62883708 63027266 63169371 63309178 63447810 63586688 63728846 63861700 - 64002479 64146115 64288028 64425808 64572297 64713796 64851753 64995812 - 65134772 65275283 65416962 65553993 65689990 65830409 65971858 66113083 - 66258667 66396426 66537154 66679260 66817352 66966211 67099503 67239999 - 67383388 67521059 67659182 67798034 67939518 68080657 68223411 68363386 - 68506676 68646495 68783651 68924916 69061566 69203083 69342622 69482043 - 69624959 69763057 69907908 70043912 70183052] - -sources: [ 80 80 80 ... 708104 708104 708104] -destinations: [ 159344 232024 739610 ... 1820287 1503615 781805] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 - 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 - 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 - 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 - 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 - 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 - 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 - 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 - 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 - 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 - 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 - 486 487 488 489 490 491 492 493 494 495 496 497 498 499] -offset: [ 0 137198 280412 421007 560845 700661 843273 983227 - 1127175 1266746 1406372 1544102 1680698 1814959 1956144 2098293 - 2239381 2384005 2522628 2665585 2807123 2945196 3088836 3228575 - 3365327 3505113 3650868 3788864 3927470 4071220 4214704 4353158 - 4484780 4629601 4764580 4907865 5048152 5189318 5331390 5469428 - 5607826 5750346 5892677 6037344 6179780 6319332 6461043 6598860 - 6742077 6882540 7021478 7159350 7299570 7441151 7583866 7724257 - 7867492 8009217 8149073 8281911 8424569 8562295 8705946 8849958 - 8992370 9137748 9274027 9412235 9553405 9692641 9833647 9973922 - 10119053 10254254 10391048 10532171 10678269 10816969 10956247 11097906 - 11234424 11370947 11506371 11644281 11784165 11919432 12065312 12203183 - 12344906 12480796 12618892 12761425 12900291 13043202 13180049 13324047 - 13465738 13605669 13745286 13883234 14029874 14166726 14307320 14450094 - 14586665 14726145 14865401 15007576 15145839 15286936 15424533 15562947 - 15702381 15840678 15981284 16120624 16260370 16401067 16537889 16675438 - 16816885 16954306 17097999 17238636 17382998 17520519 17657875 17799186 - 17937907 18083438 18225630 18366886 18506384 18644001 18779493 18917195 - 19059459 19204894 19342104 19480753 19619713 19762865 19903076 20047054 - 20187625 20325358 20470251 20612424 20753368 20889730 21027689 21167735 - 21308255 21447250 21587110 21728620 21869050 22008448 22151047 22290692 - 22426743 22570567 22713375 22853858 22995229 23138180 23279670 23421523 - 23563067 23701188 23839088 23977463 24121671 24261695 24396744 24541436 - 24679806 24819287 24956686 25097413 25236709 25377386 25521900 25660609 - 25801190 25939275 26081023 26220912 26361195 26504648 26644058 26779946 - 26917631 27057649 27196243 27338060 27475056 27614498 27754803 27895501 - 28036197 28177024 28319220 28459363 28597973 28737003 28878271 29020851 - 29154026 29299944 29442571 29583286 29717861 29860236 29996753 30136070 - 30274988 30415551 30555328 30695364 30840442 30980407 31114591 31257917 - 31397362 31537354 31670740 31812341 31948030 32084499 32230290 32368779 - 32511034 32654548 32794945 32936366 33078459 33217937 33358895 33498869 - 33640693 33783602 33920990 34063036 34205140 34342021 34479043 34621368 - 34764815 34906549 35045885 35185157 35322431 35461729 35599255 35739467 - 35877200 36018270 36160118 36298106 36434547 36576006 36717934 36856774 - 36998901 37139261 37277600 37418648 37557838 37700170 37840423 37977901 - 38119620 38259464 38398075 38540704 38685999 38827904 38971380 39108062 - 39247991 39383648 39517884 39656191 39794736 39937392 40073139 40215888 - 40352167 40498988 40640286 40776776 40920199 41060037 41198803 41339702 - 41479534 41622965 41764312 41904044 42042504 42184465 42325822 42468560 - 42604391 42744669 42885952 43023982 43162847 43305434 43446605 43584676 - 43725020 43866832 44008467 44147433 44291655 44438063 44576277 44719177 - 44858657 45003677 45144484 45284390 45426107 45564652 45703622 45846913 - 45989285 46130815 46270916 46409019 46548079 46688166 46825808 46963720 - 47104953 47247443 47386619 47530049 47674404 47814907 47953317 48094605 - 48231821 48373963 48517449 48659704 48802607 48941514 49081249 49223099 - 49364322 49501155 49643622 49784400 49924532 50059979 50205005 50345904 - 50486930 50626548 50765521 50901879 51041108 51178548 51317617 51462374 - 51599436 51744021 51881026 52015887 52152870 52299177 52441768 52585151 - 52727265 52867285 53012213 53145551 53285951 53430084 53567205 53702940 - 53844624 53981683 54125612 54268549 54406948 54544009 54683564 54826294 - 54970837 55106449 55248472 55389275 55531490 55666536 55803521 55940838 - 56078128 56216682 56362205 56508188 56641575 56780573 56924853 57066508 - 57204214 57342751 57484922 57624830 57764215 57903118 58047471 58187307 - 58328119 58466758 58608656 58749408 58884615 59030878 59172552 59314566 - 59452877 59590074 59730515 59870984 60016152 60153287 60298813 60435414 - 60568231 60710584 60848324 60991862 61133699 61273848 61410429 61545962 - 61682769 61822776 61968965 62111892 62254731 62394345 62532450 62670754 - 62810867 62948770 63087967 63228647 63367355 63508258 63645722 63787007 - 63925617 64063621 64203497 64345686 64488034 64629130 64770072 64911563 - 65048812 65184518 65325303 65472202 65617138 65759114 65899169 66039115 - 66175396 66318185 66463028 66605179 66747784 66886850 67023449 67165587 - 67303265 67439723 67580224 67719219 67860562 67996514 68132951 68282343 - 68424613 68565983 68702663 68839082 68984746 69125148 69265609 69404039 - 69543885 69683200 69822233 69961496 70101408] - -2023-05-10 18:18:12,067 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:12,067 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1000322 1000322 1000322 ... 465267 465267 465267] -destinations: [1715486 1702510 2369975 ... 1780713 2219107 2155689] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 - 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 - 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 - 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 - 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 - 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 - 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 - 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 - 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 - 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 - 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 - 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 - 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 - 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 - 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 - 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 - 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 - 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 - 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 - 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 - 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 - 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 - 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 - 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 - 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499] -offset: [ 0 141240 280066 425834 564661 710556 845550 987811 - 1129418 1265069 1405419 1547385 1679084 1820226 1966963 2103067 - 2245377 2379833 2520318 2656014 2798314 2935565 3078898 3221078 - 3356419 3500151 3635125 3775435 3918664 4055489 4199249 4337888 - 4481365 4618996 4762085 4907994 5049772 5190311 5323145 5465530 - 5600950 5742298 5884104 6024307 6167947 6305920 6447078 6583051 - 6719953 6859181 6993086 7136327 7275043 7418169 7553149 7695631 - 7835586 7976473 8120377 8260448 8400900 8543716 8684335 8824062 - 8963151 9101712 9236575 9375520 9511323 9653157 9797744 9940389 - 10080523 10219163 10357800 10494696 10636119 10778481 10920675 11062294 - 11203960 11343699 11479590 11619383 11766203 11906998 12048865 12191090 - 12331236 12469807 12605861 12739196 12883257 13027258 13165814 13303759 - 13442690 13583052 13720884 13864339 14005136 14145309 14282433 14421776 - 14567819 14708145 14847699 14988298 15129207 15262992 15402770 15544991 - 15688099 15829900 15963764 16108842 16254881 16394786 16533509 16675398 - 16818232 16958000 17096650 17236766 17377718 17518066 17659147 17800742 - 17943141 18081750 18219297 18361412 18504211 18644566 18780554 18923411 - 19060541 19200243 19338512 19481329 19619956 19758951 19895765 20036732 - 20179471 20320514 20457567 20601665 20745183 20882911 21023644 21165246 - 21304681 21441748 21585871 21728382 21869453 22003712 22141908 22286902 - 22427530 22569045 22711580 22852073 22994580 23132393 23273634 23419183 - 23560162 23698145 23834353 23974943 24111535 24251138 24394866 24532120 - 24674462 24817537 24956546 25095508 25234016 25372203 25517946 25663291 - 25797415 25940061 26079347 26219520 26362678 26504671 26637732 26778897 - 26917470 27060245 27197984 27339599 27477175 27620776 27760147 27898359 - 28040980 28182936 28326721 28460963 28606216 28750854 28888993 29026264 - 29165748 29309325 29443508 29587303 29727686 29867422 30005778 30146414 - 30289536 30434318 30571282 30711245 30846244 30985795 31128243 31268150 - 31407409 31554730 31694500 31834597 31976196 32115225 32255315 32396432 - 32541429 32683909 32823234 32961952 33101553 33241662 33381215 33520929 - 33664367 33806326 33949075 34085621 34225710 34367601 34504296 34645327 - 34781374 34917505 35064107 35200540 35342270 35480728 35618918 35762567 - 35904464 36043673 36182011 36324951 36462126 36603623 36743449 36884085 - 37026511 37171219 37314289 37450309 37592618 37733822 37874443 38014599 - 38150710 38296084 38433453 38575588 38714425 38851802 38990795 39128065 - 39272553 39407237 39552405 39693533 39836411 39973731 40109030 40246157 - 40385173 40529692 40668090 40807727 40952677 41093294 41231761 41366850 - 41507847 41644460 41781402 41915166 42055778 42196915 42335615 42476589 - 42620807 42761887 42902780 43045991 43188958 43324324 43465580 43609762 - 43748126 43886421 44026936 44165393 44301862 44440676 44581790 44722324 - 44862461 45009774 45145223 45288167 45426513 45563893 45705041 45846653 - 45987625 46125260 46266782 46405310 46542611 46682680 46821190 46962530 - 47100720 47246085 47386272 47517436 47657069 47794517 47940179 48075709 - 48213909 48357863 48494011 48636823 48772775 48919067 49062837 49201692 - 49340186 49477941 49618317 49755572 49896349 50032424 50172951 50310815 - 50453679 50599248 50739920 50880991 51018566 51156848 51296464 51431908 - 51577331 51715837 51861009 51999138 52141593 52281249 52417841 52563569 - 52699704 52835925 52982685 53124280 53266995 53403483 53545908 53684680 - 53824546 53963309 54099912 54241882 54386633 54525026 54670348 54808646 - 54957490 55095538 55234948 55377651 55524251 55662543 55801786 55937743 - 56075346 56209876 56354585 56492404 56634928 56777710 56919063 57055597 - 57197095 57335031 57477026 57616211 57754457 57892335 58032072 58171063 - 58317376 58459788 58598456 58735815 58871857 59011150 59154672 59297899 - 59433939 59576335 59718775 59862241 60001535 60139914 60281174 60422230 - 60562533 60699128 60840633 60979406 61122592 61262793 61398995 61538642 - 61676199 61815973 61959706 62099371 62238032 62376160 62517115 62657104 - 62800589 62936768 63080647 63215077 63358799 63500223 63640326 63781690 - 63921269 64063842 64200010 64340022 64480411 64620927 64763835 64903346 - 65042083 65188651 65324833 65469265 65609980 65748711 65889399 66025267 - 66162441 66301627 66436704 66579256 66721645 66866278 67003586 67139669 - 67284067 67422586 67564637 67703343 67846265 67988899 68125369 68258471 - 68402367 68547336 68686367 68826237 68960503 69104708 69245869 69385914 - 69526580 69668490 69811889 69947954 70092673] - -2023-05-10 18:18:13,102 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:18:13,102 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:18:13,105 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:13,105 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:13,108 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:18:13,108 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:18:13,226 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:18:13,228 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:18:13,228 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38521. Reason: scheduler-restart -2023-05-10 18:18:13,229 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40329. Reason: scheduler-restart -2023-05-10 18:18:13,229 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:18:13,230 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:18:13,230 - distributed.nanny - INFO - Worker closed -sources: [2000658 2000658 2000658 ... 2333960 2333960 2333960] -destinations: [1615682 1124131 604924 ... 129982 864431 54650] -batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 - 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 - 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 - 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 - 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 - 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 - 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 - 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 - 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 - 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 - 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 - 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 - 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 - 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 - 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 - 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 - 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 - 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 - 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 - 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 - 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 - 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 - 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 - 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 - 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 - 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 - 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 - 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 - 2392 2393 2394 2395 2396] -offset: [ 0 137951 276057 414450 549079 694883 835770 970829 - 1113214 1247787 1389319 1526743 1670372 1811652 1951677 2096278 - 2234137 2374764 2515881 2659884 2800826 2939931 3086747 3230292 - 3370745 3510777 3650957 3788863 3922818 4065509 4204301 4344965 - 4485440 4623300 4762642 4903231 5046940 5190431 5330156 5470009 - 5611369 5748891 5888707 6033159 6174938 6317917 6454488 6590235 - 6732001 6873979 7019586 7158503 7302097 7437768 7576525 7713868 - 7849537 7987583 8130883 8270414 8408216 8544118 8686593 8828028 - 8966233 9104746 9244813 9384266 9525702 9660869 9802956 9940696 - 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41484171 41621472 41760669 41900390 42046326 42185095 42325985 42463625 - 42599586 42738367 42879853 43027415 43170006 43311923 43450934 43592509 - 43728193 43860788 44005018 44147153 44294417 44436296 44574736 44714043 - 44853083 44992898 45137162 45276133 45417841 45558496 45701012 45839604 - 45978295 46114261 46259772 46398566 46535870 46676564 46814798 46957308 - 47100429 47243126 47384569 47520234 47659681 47800095 47945036 48081400 - 48223982 48367154 48501660 48644311 48785703 48926137 49060686 49202426 - 49344643 49486246 49626461 49763467 49907798 50046523 50189057 50328888 - 50465840 50605646 50750043 50886942 51023715 51163578 51309268 51453728 - 51586805 51729483 51872829 52008840 52152272 52290156 52433384 52570963 - 52710653 52853082 52995078 53135295 53275491 53421064 53558767 53702074 - 53838006 53976608 54119905 54256196 54396194 54538535 54681511 54821194 - 54966500 55107512 55247571 55384665 55520100 55609840] - -2023-05-10 18:18:13,231 - distributed.nanny - INFO - Worker closed -sources: [1999769 1999771 1999772 ... 1257064 1257064 1257064] -destinations: [ 199153 1927813 554875 ... 769924 511648 1394495] -batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 - 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 - 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 - 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 - 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 - 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 - 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 - 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 - 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 - 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 - 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 - 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 - 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 - 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 - 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 - 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1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 - 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 - 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 - 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 - 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 - 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 - 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999] -offset: [ 0 139069 276100 413898 551088 689842 829182 969349 - 1112319 1253502 1394913 1532561 1681409 1816207 1954504 2095030 - 2239781 2380261 2518704 2657853 2801086 2943942 3081457 3219929 - 3362451 3501273 3642465 3780545 3921132 4061989 4204001 4348815 - 4488138 4624898 4764364 4905708 5046137 5183381 5323191 5465337 - 5609313 5754556 5891810 6030066 6168994 6305006 6447238 6588525 - 6731904 6870226 7007584 7147808 7288038 7431087 7576540 7719927 - 7859089 7996814 8140088 8273950 8417330 8556600 8697645 8833768 - 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24667106 24806146 24952458 25089559 25229749 25369963 25511056 25653556 - 25791211 25936623 26073410 26210544 26349009 26491322 26632133 26765875 - 26903635 27041898 27186756 27332613 27471697 27615301 27748601 27887348 - 28029921 28169339 28305273 28442230 28583224 28725046 28861404 29006071 - 29142490 29283477 29423645 29564070 29707287 29845253 29988225 30128143 - 30267891 30410536 30557003 30695842 30833747 30975464 31113861 31252739 - 31390982 31529728 31675242 31822184 31966658 32109943 32249473 32390573 - 32528068 32672711 32812782 32952073 33088578 33230440 33370545 33508075 - 33645503 33785257 33923445 34060102 34202546 34348540 34488565 34624611 - 34771174 34909675 35048621 35187050 35327023 35468464 35610154 35750605 - 35894150 36029998 36169637 36315934 36456349 36599685 36739044 36881775 - 37020852 37161819 37302195 37444622 37588032 37727534 37864709 38004159 - 38148109 38287195 38429041 38573637 38713371 38852205 38987759 39123467 - 39260374 39398678 39539047 39675997 39818919 39954780 40094845 40232604 - 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56056464 56193169 56333254 56473375 56618507 56756931 56900277 57037176 - 57180070 57322319 57462508 57607770 57747457 57885909 58023067 58160797 - 58299154 58441841 58579638 58722680 58862302 59001465 59146363 59290722 - 59431345 59575329 59714590 59859815 59995310 60131753 60272988 60413042 - 60556955 60695834 60836650 60974878 61116948 61255617 61395072 61539122 - 61679399 61818774 61958685 62097668 62232875 62376490 62516026 62653073 - 62792237 62934969 63079568 63216010 63356650 63491963 63629294 63770208 - 63908827 64051192 64187712 64330597 64467941 64606738 64750991 64888749 - 65028590 65169128 65316461 65452994 65588834 65731484 65870807 66012528 - 66152110 66293568 66429133 66569464 66706057 66848073 66987292 67129502 - 67271715 67414616 67553232 67692446 67833311 67970969 68112129 68250081 - 68390818 68529100 68668256 68811008 68950293 69087573 69227987 69367299 - 69509705 69650382 69792780 69932111 70076126] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -sys:1: RuntimeWarning: coroutine 'BlockingMode._arm_worker' was never awaited -Task was destroyed but it is pending! -task: > -2023-05-10 18:18:15,065 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:18:15,355 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:18:16,271 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:18:16,271 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:18:16,534 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:18:16,534 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:18:16,535 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:18:16,825 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:18:17,431 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46207 -2023-05-10 18:18:17,431 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46207 -2023-05-10 18:18:17,431 - distributed.worker - INFO - dashboard at: 10.120.104.11:38377 -2023-05-10 18:18:17,431 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:18:17,431 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,431 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:18:17,431 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:18:17,431 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-u1xdhp42 -2023-05-10 18:18:17,432 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c16c7aed-35a6-426d-b105-f53e865cb07c -2023-05-10 18:18:17,584 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-bf9ee86a-ed61-46e2-83e1-71e720c11db6 -2023-05-10 18:18:17,585 - distributed.worker - INFO - Starting Worker plugin PreImport-9956544d-d30b-4074-9fa5-b85bad533c80 -2023-05-10 18:18:17,586 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,597 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:18:17,597 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,600 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:18:17,801 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36349 -2023-05-10 18:18:17,802 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36349 -2023-05-10 18:18:17,802 - distributed.worker - INFO - dashboard at: 10.120.104.11:43345 -2023-05-10 18:18:17,802 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:18:17,802 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,802 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:18:17,802 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:18:17,802 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-bsjmqb9m -2023-05-10 18:18:17,802 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-cb689d63-917b-4559-badb-e40378f3d2c4 -2023-05-10 18:18:17,802 - distributed.worker - INFO - Starting Worker plugin RMMSetup-429c05c1-d990-4871-a357-a462004329c9 -2023-05-10 18:18:17,921 - distributed.worker - INFO - Starting Worker plugin PreImport-ff040de1-0fea-471b-a543-2ea6fee20392 -2023-05-10 18:18:17,921 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,929 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:18:17,929 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:18:17,931 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:18:25,722 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:18:25,723 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.77s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:18:25,959 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:18:25,960 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.00s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:18:25,964 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:25,965 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:25,970 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:18:25,970 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:18:32,176 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:18:32,229 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.67s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:18:32,232 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:18:32,235 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:18:57,561 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:57,561 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:57,970 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:57,971 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:18:58,929 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:18:58,934 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:03,829 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:03,829 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 288 288 288 ... 734451 734451 734451] -destinations: [1226797 2084768 1532974 ... 926960 1135535 2291206] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 - 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 - 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 - 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 - 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 - 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 - 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 - 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 - 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 - 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 - 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 - 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 - 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 - 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 - 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 - 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 - 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 - 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 - 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 - 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 - 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 - 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 - 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 - 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 - 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 - 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 - 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 - 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 - 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 - 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 - 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 - 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 - 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 - 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 - 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 - 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 - 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 - 990 991 992 993 994 995 996 997 998 999] -offset: [ 0 136743 279698 ... 140016388 140152646 140291973] - -2023-05-10 18:19:11,300 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:19:11,300 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:19:11,304 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:19:11,304 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:19:11,307 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:19:11,307 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:19:11,427 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:19:11,429 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:19:11,429 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36349. Reason: scheduler-restart -2023-05-10 18:19:11,430 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46207. Reason: scheduler-restart -2023-05-10 18:19:11,430 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:19:11,431 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:19:11,432 - distributed.nanny - INFO - Worker closed -2023-05-10 18:19:11,432 - distributed.nanny - INFO - Worker closed -sources: [2000144 2000144 2000144 ... 2333960 2333960 2333960] -destinations: [ 351641 1365172 1074226 ... 1084884 338426 453403] -batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 - 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 - 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 - 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 - 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 - 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 - 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 - 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 - 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 - 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 - 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 - 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 - 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 - 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 - 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 - 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 - 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 - 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 - 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 - 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 - 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 - 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 - 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 - 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 - 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 - 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 - 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 - 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 - 2392 2393 2394 2395 2396] -offset: [ 0 138034 276184 414261 549112 694893 836084 971193 - 1113237 1247789 1388748 1526133 1669592 1811163 1951128 2095554 - 2233310 2373873 2515296 2659122 2800214 2938882 3086068 3229482 - 3369975 3509788 3649864 3787835 3921737 4064803 4203909 4344521 - 4484954 4622947 4762523 4903485 5047785 5190740 5330536 5470579 - 5612184 5749733 5889681 6033934 6175696 6318526 6454572 6590574 - 6732187 6873565 7019105 7158316 7302044 7437799 7576358 7713643 - 7849193 7987365 8130893 8270887 8408564 8544124 8686397 8827969 - 8966281 9105154 9245607 9385338 9526766 9662251 9804790 9942408 - 10080398 10216553 10357644 10495778 10634642 10774593 10918605 11062162 - 11202491 11344961 11490311 11628215 11765723 11905708 12048017 12185904 - 12325867 12465926 12602382 12743656 12886456 13028994 13173305 13310596 - 13454676 13592299 13729766 13868034 14010309 14152742 14294176 14434311 - 14574520 14713253 14853139 14988522 15130460 15270733 15408261 15546476 - 15691679 15831929 15973969 16114846 16260914 16399540 16538658 16682582 - 16823732 16963581 17107539 17247119 17385457 17524405 17664612 17807937 - 17945583 18087714 18225319 18363698 18501027 18643718 18787190 18924395 - 19065313 19204866 19342505 19478730 19621617 19759529 19898215 20038388 - 20184392 20325909 20462899 20609177 20748289 20884547 21025136 21170945 - 21313618 21456204 21592217 21731221 21866784 22011468 22153841 22293357 - 22430380 22574530 22714447 22853801 22989754 23135284 23272433 23410247 - 23549216 23686071 23826262 23966658 24105844 24244752 24383938 24528616 - 24674090 24813973 24952992 25091660 25233832 25374088 25516972 25656178 - 25796941 25935060 26075741 26215541 26364824 26502547 26642765 26779211 - 26917988 27056841 27195780 27341874 27481196 27623787 27767977 27905030 - 28046078 28182633 28331556 28468792 28615445 28748581 28888400 29025320 - 29164759 29309357 29448333 29586414 29726689 29867455 30006788 30149592 - 30287232 30424143 30566110 30708529 30843891 30980684 31122283 31258928 - 31407243 31549219 31685853 31824844 31966521 32105454 32251603 32393178 - 32529874 32669970 32810514 32951333 33095218 33234565 33377391 33511196 - 33653475 33796070 33937939 34078836 34211486 34349825 34489975 34631346 - 34771539 34906712 35044590 35185904 35326934 35462960 35607001 35744744 - 35885534 36031524 36174562 36318125 36454927 36589596 36727840 36867129 - 37005681 37146968 37289616 37431407 37577069 37714388 37853755 37992705 - 38132782 38274804 38411525 38545459 38684969 38822753 38966439 39104939 - 39247660 39383157 39523886 39661075 39804789 39945850 40088684 40229279 - 40368200 40507813 40644112 40778478 40922833 41063262 41199993 41341402 - 41483824 41621389 41760823 41900398 42045763 42184605 42325428 42462928 - 42598946 42737281 42878294 43025809 43168202 43310357 43449397 43590937 - 43726385 43858791 44002970 44144896 44292481 44434861 44573780 44713219 - 44852407 44992407 45136783 45275936 45417440 45558105 45700762 45839258 - 45978395 46114224 46259789 46398495 46535755 46676390 46815112 46957365 - 47100305 47242972 47384770 47520268 47659200 47799837 47944830 48080996 - 48223399 48366399 48501296 48643903 48785885 48926180 49060842 49202808 - 49345081 49486734 49627344 49764110 49908843 50047507 50189927 50329795 - 50467183 50607115 50751877 50888690 51025388 51165130 51311363 51455327 - 51587970 51730458 51873475 52009617 52153045 52290840 52433821 52571483 - 52711001 52852933 52994998 53135479 53275700 53421324 53558619 53701609 - 53837628 53976243 54119753 54256002 54395905 54538225 54681305 54820859 - 54966002 55106792 55246450 55383299 55518428 55608093] - -sources: [1000304 1000309 1000309 ... 2107098 2107098 2107098] -destinations: [ 106242 888573 1489801 ... 1639549 1366582 1627325] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 - 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 - 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 - 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 - 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 - 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 - 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 - 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 - 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 - 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 - 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 - 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 - 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 - 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 - 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 - 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 - 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 - 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 - 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 - 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 - 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 - 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 - 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 - 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 - 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 - 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 - 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 - 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 - 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 - 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 - 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 - 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 - 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 - 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 - 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 - 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 - 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 - 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 - 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 - 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 - 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 - 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 - 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 - 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 - 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 - 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 - 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 - 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 - 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 - 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 - 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 - 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 - 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 - 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 - 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 - 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 - 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 - 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 - 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 - 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 - 1994 1995 1996 1997 1998 1999] -offset: [ 0 141036 279393 ... 139882294 140021874 140166162] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 18:19:13,325 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:19:13,575 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:19:14,572 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:19:14,572 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:19:14,767 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:19:14,767 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:19:14,838 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:19:15,031 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:19:15,713 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:38111 -2023-05-10 18:19:15,713 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:38111 -2023-05-10 18:19:15,713 - distributed.worker - INFO - dashboard at: 10.120.104.11:34215 -2023-05-10 18:19:15,713 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:19:15,713 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:15,713 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:19:15,714 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:19:15,714 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-vrdjdvub -2023-05-10 18:19:15,714 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-17840f5f-9d29-4627-ab20-80a9d182329c -2023-05-10 18:19:15,715 - distributed.worker - INFO - Starting Worker plugin PreImport-b53430c8-ee70-47ad-a4c9-4c47e6c29810 -2023-05-10 18:19:15,715 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6e5dd437-dab3-4461-8855-772f359f2367 -2023-05-10 18:19:15,885 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:15,896 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:19:15,896 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:15,898 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:19:16,042 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:42615 -2023-05-10 18:19:16,042 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:42615 -2023-05-10 18:19:16,042 - distributed.worker - INFO - dashboard at: 10.120.104.11:41991 -2023-05-10 18:19:16,042 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:19:16,042 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:16,042 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:19:16,042 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:19:16,042 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-tf_3am1a -2023-05-10 18:19:16,043 - distributed.worker - INFO - Starting Worker plugin RMMSetup-b70dea3f-c97b-44f2-b05f-1fb2dcb0be10 -2023-05-10 18:19:16,162 - distributed.worker - INFO - Starting Worker plugin PreImport-c3345aa4-25d1-484e-bfb7-d6e4c3862de5 -2023-05-10 18:19:16,162 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-cf055071-425d-4c8d-a704-76f161a0042b -2023-05-10 18:19:16,163 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:16,171 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:19:16,171 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:19:16,173 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:19:23,986 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:19:23,987 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.80s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:19:24,269 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:19:24,270 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.09s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:19:24,274 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:24,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:24,279 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:19:24,279 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:19:30,515 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.65s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:19:30,552 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.69s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:19:30,555 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:19:30,557 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:19:50,039 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:50,041 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:50,729 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:19:50,731 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:19:51,549 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:19:51,549 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [3862678 3862678 3862678 ... 1159388 1159388 1159388] -destinations: [2983596 4560301 1908136 ... 98130 1023500 1517166] -batch: [ 600 601 602 603 604 605 606 607 608 609 610 611 612 613 - 614 615 616 617 618 619 620 621 622 623 624 625 626 627 - 628 629 630 631 632 633 634 635 636 637 638 639 640 641 - 642 643 644 645 646 647 648 649 650 651 652 653 654 655 - 656 657 658 659 660 661 662 663 664 665 666 667 668 669 - 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 - 698 699 700 701 702 703 704 705 706 707 708 709 710 711 - 712 713 714 715 716 717 718 719 720 721 722 723 724 725 - 726 727 728 729 730 731 732 733 734 735 736 737 738 739 - 740 741 742 743 744 745 746 747 748 749 750 751 752 753 - 754 755 756 757 758 759 760 761 762 763 764 765 766 767 - 768 769 770 771 772 773 774 775 776 777 778 779 780 781 - 782 783 784 785 786 787 788 789 790 791 792 793 794 795 - 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 - 824 825 826 827 828 829 830 831 832 833 834 835 836 837 - 838 839 840 841 842 843 844 845 846 847 848 849 850 851 - 852 853 854 855 856 857 858 859 860 861 862 863 864 865 - 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-sources: [1199596 1199596 1199596 ... 978623 978623 978623] -destinations: [3880104 1333946 2402572 ... 1651702 532716 94047] -batch: [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 - 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 - 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 - 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 - 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 - 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 - 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 - 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 - 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 - 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 - 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 - 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 - 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 - 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 - 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 - 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 - 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 - 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 - 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 - 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 - 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 - 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 - 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 - 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 - 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 - 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 - 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7410 7411 7412 7413 7414 7415 7416 7417 7418 7419 7420 7421 7422 7423 - 7424 7425 7426 7427 7428 7429 7430 7431 7432 7433 7434 7435 7436 7437 - 7438 7439 7440 7441 7442 7443 7444 7445 7446 7447 7448 7449 7450 7451 - 7452 7453 7454 7455 7456 7457 7458 7459 7460 7461 7462 7463 7464 7465 - 7466 7467 7468 7469 7470 7471 7472 7473 7474 7475 7476 7477 7478 7479 - 7480 7481 7482 7483 7484 7485 7486 7487 7488 7489 7490 7491 7492 7493 - 7494 7495 7496 7497 7498 7499 7500 7501 7502 7503 7504 7505 7506 7507 - 7508 7509 7510 7511 7512 7513 7514 7515 7516 7517 7518 7519 7520 7521 - 7522 7523 7524 7525 7526 7527 7528 7529 7530 7531 7532 7533 7534 7535 - 7536 7537 7538 7539 7540 7541 7542 7543 7544 7545 7546 7547 7548 7549 - 7550 7551 7552 7553 7554 7555 7556 7557 7558 7559 7560 7561 7562 7563 - 7564 7565 7566 7567 7568 7569 7570 7571 7572 7573 7574 7575 7576 7577 - 7578 7579 7580 7581 7582 7583 7584 7585 7586 7587 7588 7589 7590 7591 - 7592 7593 7594 7595 7596 7597 7598 7599 7600 7601 7602 7603 7604 7605 - 7606 7607 7608 7609 7610 7611 7612 7613 7614 7615 7616 7617 7618 7619 - 7620 7621 7622 7623 7624 7625 7626 7627 7628 7629 7630 7631 7632 7633 - 7634 7635 7636 7637 7638 7639 7640 7641 7642 7643 7644 7645 7646 7647 - 7648 7649 7650 7651 7652 7653 7654 7655 7656 7657 7658 7659 7660 7661 - 7662 7663 7664 7665 7666 7667 7668 7669 7670 7671 7672 7673 7674 7675 - 7676 7677 7678 7679 7680 7681 7682 7683 7684 7685 7686 7687 7688 7689 - 7690 7691 7692 7693 7694 7695 7696 7697 7698 7699 7700 7701 7702 7703 - 7704 7705 7706 7707 7708 7709 7710 7711 7712 7713 7714 7715 7716 7717 - 7718 7719 7720 7721 7722 7723 7724 7725 7726 7727 7728 7729 7730 7731 - 7732 7733 7734 7735 7736 7737 7738 7739 7740 7741 7742 7743 7744 7745 - 7746 7747 7748 7749 7750 7751 7752 7753 7754 7755 7756 7757 7758 7759 - 7760 7761 7762 7763 7764 7765 7766 7767 7768 7769 7770 7771 7772 7773 - 7774 7775 7776 7777 7778 7779 7780 7781 7782 7783 7784 7785 7786 7787 - 7788 7789 7790 7791 7792 7793 7794 7795 7796 7797 7798 7799] -offset: [ 0 68864 136424 208707 278228 344923 412692 483121 - 555138 620832 688244 759901 831911 903648 970357 1037612 - 1109610 1179758 1251289 1316484 1386053 1456347 1522150 1590646 - 1662166 1731716 1800236 1872171 1941324 2008882 2075725 2142878 - 2210590 2278743 2346334 2412959 2482540 2549565 2621231 2692523 - 2763352 2830113 2897641 2967111 3035969 3103005 3173979 3242574 - 3311377 3380127 3442448 3511640 3576957 3647147 3716907 3782965 - 3849845 3918471 3989651 4060683 4125371 4193911 4264977 4333924 - 4399105 4470048 4539720 4610931 4677265 4746226 4814446 4885619 - 4955593 5024020 5091487 5163091 5231413 5297782 5368379 5433552 - 5502882 5569444 5636525 5706972 5774570 5841149 5911979 5980664 - 6046497 6120879 6187759 6257744 6324398 6394609 6463153 6531874 - 6602448 6672045 6740987 6807435 6878037 6946100 7014253 7082948 - 7148618 7218589 7287878 7355269 7426244 7493343 7565855 7631430 - 7697933 7769014 7841851 7912573 7977398 8049246 8115059 8186631 - 8255305 8324252 8393604 8464225 8535225 8604695 8673807 8742525 - 8812212 8881028 8947540 9017487 9087071 9156940 9224685 9293654 - 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24795995 24862425 24930592 25000007 25066025 25138333 25208782 25272789 - 25342597 25409691 25479333 25546752 25617395 25687412 25754902 25823318 - 25894308 25966713 26039622 26105749 26174458 26241414 26308804 26376495 - 26448573 26514906 26588040 26654817 26725589 26795886 26865325 26932951 - 27001847 27075720 27143631 27213938 27280786 27349776 27422250 27491288 - 27561836 27628484 27698974 27771248 27838270 27906683 27974846 28043458 - 28114812 28183135 28251941 28323907 28391112 28458660 28526591 28594604 - 28663814 28731969 28801599 28869415 28940215 29004744 29075146 29141454 - 29210464 29280734 29344950 29412289 29482703 29555241 29627155 29694029 - 29761254 29831740 29897493 29967310 30033615 30103146 30175294 30243513 - 30307749 30376851 30448784 30518611 30586826 30655545 30724619 30794237 - 30863395 30929410 31001205 31070389 31138653 31205370 31272259 31345104 - 31415886 31483381 31549465 31618569 31689523 31752429 31820116 31887639 - 31956636 32024162 32094211 32162258 32233060 32305370 32373475 32443869 - 32514360 32581590 32653188 32719551 32785967 32853871 32924551 32992299 - 33060168 33131873 33201864 33264543 33333420 33403642 33475111 33542038 - 33609680 33678656 33745587 33814479 33882112 33949499 34014709 34083305 - 34154069 34224161 34294764 34360667 34434795 34503437 34571951 34642002 - 34712819 34781540 34848996 34920060 34988347 35057967 35125574 35198944 - 35268255 35337633 35404997 35474404 35543652 35611047 35676413 35740444 - 35812854 35880377 35948945 36015332 36085898 36155949 36225489 36291071 - 36360758 36433737 36503656 36573004 36638834 36709090 36774174 36849134 - 36916537 36985143 37048333 37116375 37187147 37255006 37326542 37392065 - 37465124 37536168 37605465 37674244 37742591 37809343 37881179 37948652 - 38018486 38087890 38159818 38228464 38299305 38371136 38441230 38511451 - 38578783 38647373 38719462 38787135 38853676 38919569 38987141 39055952 - 39125240 39193837 39260358 39330157 39400668 39468143 39537477 39609175 - 39681248 39754003 39821959 39889194 39958172 40025773 40096376 40161177 - 40230252 40295388 40367395 40437403 40504822 40575290 40647842 40716034 - 40782566 40848831 40920439 40987763 41057252 41128922 41193643 41267752 - 41335149] -2023-05-10 18:20:07,064 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:20:07,064 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' - -sources: [4200336 4200336 4200336 ... 506251 506251 506251] -destinations: [1625279 4235784 3683032 ... 694241 4430306 2468024] -batch: [8400 8401 8402 8403 8404 8405 8406 8407 8408 8409 8410 8411 8412 8413 - 8414 8415 8416 8417 8418 8419 8420 8421 8422 8423 8424 8425 8426 8427 - 8428 8429 8430 8431 8432 8433 8434 8435 8436 8437 8438 8439 8440 8441 - 8442 8443 8444 8445 8446 8447 8448 8449 8450 8451 8452 8453 8454 8455 - 8456 8457 8458 8459 8460 8461 8462 8463 8464 8465 8466 8467 8468 8469 - 8470 8471 8472 8473 8474 8475 8476 8477 8478 8479 8480 8481 8482 8483 - 8484 8485 8486 8487 8488 8489 8490 8491 8492 8493 8494 8495 8496 8497 - 8498 8499 8500 8501 8502 8503 8504 8505 8506 8507 8508 8509 8510 8511 - 8512 8513 8514 8515 8516 8517 8518 8519 8520 8521 8522 8523 8524 8525 - 8526 8527 8528 8529 8530 8531 8532 8533 8534 8535 8536 8537 8538 8539 - 8540 8541 8542 8543 8544 8545 8546 8547 8548 8549 8550 8551 8552 8553 - 8554 8555 8556 8557 8558 8559 8560 8561 8562 8563 8564 8565 8566 8567 - 8568 8569 8570 8571 8572 8573 8574 8575 8576 8577 8578 8579 8580 8581 - 8582 8583 8584 8585 8586 8587 8588 8589 8590 8591 8592 8593 8594 8595 - 8596 8597 8598 8599 8600 8601 8602 8603 8604 8605 8606 8607 8608 8609 - 8610 8611 8612 8613 8614 8615 8616 8617 8618 8619 8620 8621 8622 8623 - 8624 8625 8626 8627 8628 8629 8630 8631 8632 8633 8634 8635 8636 8637 - 8638 8639 8640 8641 8642 8643 8644 8645 8646 8647 8648 8649 8650 8651 - 8652 8653 8654 8655 8656 8657 8658 8659 8660 8661 8662 8663 8664 8665 - 8666 8667 8668 8669 8670 8671 8672 8673 8674 8675 8676 8677 8678 8679 - 8680 8681 8682 8683 8684 8685 8686 8687 8688 8689 8690 8691 8692 8693 - 8694 8695 8696 8697 8698 8699 8700 8701 8702 8703 8704 8705 8706 8707 - 8708 8709 8710 8711 8712 8713 8714 8715 8716 8717 8718 8719 8720 8721 - 8722 8723 8724 8725 8726 8727 8728 8729 8730 8731 8732 8733 8734 8735 - 8736 8737 8738 8739 8740 8741 8742 8743 8744 8745 8746 8747 8748 8749 - 8750 8751 8752 8753 8754 8755 8756 8757 8758 8759 8760 8761 8762 8763 - 8764 8765 8766 8767 8768 8769 8770 8771 8772 8773 8774 8775 8776 8777 - 8778 8779 8780 8781 8782 8783 8784 8785 8786 8787 8788 8789 8790 8791 - 8792 8793 8794 8795 8796 8797 8798 8799 8800 8801 8802 8803 8804 8805 - 8806 8807 8808 8809 8810 8811 8812 8813 8814 8815 8816 8817 8818 8819 - 8820 8821 8822 8823 8824 8825 8826 8827 8828 8829 8830 8831 8832 8833 - 8834 8835 8836 8837 8838 8839 8840 8841 8842 8843 8844 8845 8846 8847 - 8848 8849 8850 8851 8852 8853 8854 8855 8856 8857 8858 8859 8860 8861 - 8862 8863 8864 8865 8866 8867 8868 8869 8870 8871 8872 8873 8874 8875 - 8876 8877 8878 8879 8880 8881 8882 8883 8884 8885 8886 8887 8888 8889 - 8890 8891 8892 8893 8894 8895 8896 8897 8898 8899 8900 8901 8902 8903 - 8904 8905 8906 8907 8908 8909 8910 8911 8912 8913 8914 8915 8916 8917 - 8918 8919 8920 8921 8922 8923 8924 8925 8926 8927 8928 8929 8930 8931 - 8932 8933 8934 8935 8936 8937 8938 8939 8940 8941 8942 8943 8944 8945 - 8946 8947 8948 8949 8950 8951 8952 8953 8954 8955 8956 8957 8958 8959 - 8960 8961 8962 8963 8964 8965 8966 8967 8968 8969 8970 8971 8972 8973 - 8974 8975 8976 8977 8978 8979 8980 8981 8982 8983 8984 8985 8986 8987 - 8988 8989 8990 8991 8992 8993 8994 8995 8996 8997 8998 8999] -offset: [ 0 64195 135398 204181 271353 340387 411160 480712 - 548536 621331 692756 760777 828636 899850 970947 1037631 - 1111353 1181854 1250139 1320948 1383685 1450277 1518357 1591981 - 1660521 1729549 1798159 1867261 1938422 2003433 2072294 2136547 - 2203886 2272523 2341115 2411486 2476737 2544960 2614797 2686338 - 2755505 2820112 2886879 2954856 3026026 3093976 3159001 3231963 - 3301345 3367314 3437982 3507466 3572543 3642151 3709688 3774222 - 3844136 3914312 3984308 4049210 4120765 4188413 4259928 4324673 - 4397368 4467129 4536401 4607413 4677328 4751493 4819036 4887516 - 4955438 5024654 5089386 5164551 5232895 5298018 5368560 5436436 - 5505377 5571255 5640189 5713232 5783379 5848782 5918829 5987649 - 6057689 6129338 6196877 6265452 6337454 6409078 6477134 6542274 - 6611994 6680943 6748023 6816122 6883790 6952207 7025841 7098076 - 7167307 7235444 7303646 7368318 7436502 7503186 7570984 7639204 - 7707127 7776068 7848975 7919778 7991767 8064216 8127994 8195143 - 8265219 8335345 8399892 8470861 8537354 8609058 8675213 8746468 - 8813365 8884547 8954131 9024936 9091814 9162054 9229850 9297444 - 9363513 9430228 9499787 9569044 9635083 9707407 9777801 9847262 - 9916579 9985066 10056322 10124195 10193651 10260834 10327391 10395265 - 10465379 10535623 10605295 10673813 10743393 10813734 10884846 10954193 - 11025980 11093271 11164064 11234695 11302229 11372581 11446297 11515953 - 11583858 11652907 11721658 11788234 11856738 11924802 11992135 12060126 - 12132252 12205024 12271403 12339983 12411259 12482178 12547751 12615626 - 12684746 12754713 12824281 12893163 12962408 13030973 13102461 13168214 - 13234161 13299998 13369341 13436943 13506558 13577711 13646871 13717865 - 13785046 13852631 13916328 13985817 14054372 14120527 14189115 14258341 - 14328460 14397621 14467758 14539777 14608674 14679998 14748094 14813413 - 14884246 14951440 15020157 15088427 15157151 15225154 15292600 15359409 - 15427672 15497033 15564599 15633563 15701170 15769803 15838173 15904605 - 15971289 16037034 16108425 16175300 16243487 16311398 16383418 16454614 - 16526125 16594160 16656739 16727177 16795778 16862916 16931174 16999949 - 17067880 17138632 17202187 17272026 17337093 17404300 17470391 17538123 - 17607528 17674236 17744888 17810437 17880098 17950495 18014500 18081416 - 18149490 18216830 18283128 18349497 18422407 18491823 18561657 18629714 - 18696311 18767146 18836384 18901772 18969454 19037756 19107002 19179605 - 19247281 19315017 19380927 19452536 19517226 19585935 19653855 19722340 - 19791178 19859037 19927953 19996804 20069291 20136649 20204615 20275998 - 20341840 20409956 20477686 20543997 20614053 20681030 20750016 20814333 - 20885322 20952294 21022538 21087369 21158452 21226019 21296063 21362489 - 21433996 21503520 21573180 21645533 21712976 21781174 21850436 21917797 - 21987325 22056979 22127027 22194510 22259759 22331381 22403623 22470831 - 22541605 22609006 22675704 22744767 22813388 22880587 22949870 23017298 - 23090814 23158334 23225511 23298812 23368644 23438301 23506882 23574257 - 23645952 23712150 23778338 23847944 23919285 23988776 24056595 24124875 - 24196893 24267073 24337318 24405939 24477075 24542946 24613390 24681840 - 24749100 24817783 24879761 24952704 25018785 25089681 25162215 25232035 - 25296826 25365501 25436010 25504609 25572430 25639507 25710211 25783698 - 25853974 25923778 25990946 26061276 26131673 26196705 26264114 26333953 - 26402741 26474282 26540617 26611537 26676789 26744762 26810183 26877651 - 26947553 27019497 27089003 27154328 27225784 27295750 27367281 27432134 - 27498989 27561824 27629522 27697229 27765900 27837014 27904026 27973718 - 28047956 28120125 28195146 28261068 28334839 28401951 28472451 28543231 - 28612017 28679609 28750586 28818831 28890141 28960581 29030750 29099136 - 29168395 29234537 29302738 29373280 29440041 29511662 29579127 29647966 - 29719550 29786641 29858809 29930103 30000489 30068672 30136498 30205191 - 30272561 30342150 30413245 30482159 30548377 30615996 30684625 30753074 - 30820658 30892060 30959101 31027617 31093845 31162010 31230957 31301506 - 31372645 31439806 31509461 31575668 31644817 31716831 31789217 31859127 - 31930056 32003820 32071894 32139597 32207022 32278275 32349012 32416445 - 32486246 32557967 32626202 32693743 32760510 32829834 32898880 32971138 - 33037022 33104867 33174512 33241403 33311261 33380904 33452524 33518816 - 33584245 33654052 33722687 33790231 33860760 33931607 34002082 34070162 - 34138520 34209463 34279747 34347648 34418953 34485651 34556081 34622485 - 34691655 34761966 34829172 34900037 34969242 35036622 35105785 35177226 - 35243094 35310902 35380058 35449301 35520868 35585707 35653490 35722194 - 35791488 35853863 35922189 35992973 36062990 36129012 36198116 36268725 - 36338530 36405483 36473101 36538756 36608542 36676597 36745473 36813025 - 36881717 36953878 37025297 37097037 37162592 37231649 37302160 37370629 - 37438584 37504635 37573482 37641545 37710121 37778633 37845099 37914986 - 37986169 38053061 38122814 38190558 38259370 38324529 38394698 38466597 - 38539758 38606951 38673937 38741844 38812090 38878742 38947956 39014961 - 39083755 39152661 39224266 39290993 39360456 39427092 39499543 39563613 - 39632289 39700360 39769905 39837498 39904897 39974854 40044676 40111825 - 40181709 40252380 40324457 40392278 40459011 40525172 40596938 40669512 - 40740349 40807066 40875446 40946967 41014388 41081463 41152543 41226215 - 41295303] -2023-05-10 18:20:07,943 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:20:07,943 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:20:07,946 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:20:07,946 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:20:07,949 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:20:07,949 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:20:08,090 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:20:08,091 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:20:08,092 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:38111. Reason: scheduler-restart -2023-05-10 18:20:08,092 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:42615. Reason: scheduler-restart -2023-05-10 18:20:08,093 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:20:08,094 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:20:08,095 - distributed.nanny - INFO - Worker closed -2023-05-10 18:20:08,095 - distributed.nanny - INFO - Worker closed - - -sources: [ 174152 174152 174152 ... 4500066 4500066 4500066] -destinations: [3186417 3513003 4290052 ... 3353253 1179243 553011] -batch: [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013 - 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027 - 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041 - 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055 - 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069 - 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083 - 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097 - 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 - 9112 9113 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 - 9126 9127 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 - 9140 9141 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 - 9154 9155 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 - 9168 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 - 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 - 9196 9197 9198 9199 9200 9201 9202 9203 9204 9205 9206 9207 9208 9209 - 9210 9211 9212 9213 9214 9215 9216 9217 9218 9219 9220 9221 9222] -offset: [ 0 66042 132678 200393 265732 334616 404384 473958 - 539925 611288 679247 747692 815616 880934 952700 1020974 - 1091404 1161284 1227354 1296683 1365414 1433973 1502683 1570447 - 1641673 1715341 1783254 1853601 1922862 1991927 2063683 2130517 - 2196256 2267054 2334758 2405416 2472510 2543797 2609880 2680846 - 2748703 2822659 2891807 2963095 3033829 3100188 3168586 3234340 - 3298722 3372950 3440616 3507325 3579607 3646212 3713261 3781324 - 3851195 3921056 3988009 4056493 4127013 4199669 4271954 4342608 - 4409230 4478158 4545271 4615261 4683988 4756964 4825489 4891730 - 4958969 5030945 5097234 5167708 5238094 5302648 5375011 5441230 - 5511402 5579598 5649827 5718531 5782856 5849107 5920351 5983951 - 6050068 6118448 6189914 6260779 6330171 6400463 6469841 6535906 - 6607087 6674835 6740045 6806565 6875707 6942881 7014872 7084531 - 7154119 7224445 7292205 7362330 7428305 7496230 7560408 7631069 - 7699000 7767614 7838852 7907570 7976232 8042693 8114582 8179870 - 8250737 8321284 8389466 8455778 8523005 8593530 8663873 8731811 - 8803143 8872849 8942423 9012284 9080640 9147824 9221135 9294245 - 9365877 9431122 9501957 9569524 9642763 9714400 9785502 9854593 - 9919374 9988436 10053970 10123530 10190675 10260020 10326000 10398794 - 10467781 10539270 10607862 10678689 10749159 10814755 10885408 10953030 - 11024458 11092604 11164883 11235573 11304392 11373808 11440350 11511468 - 11578023 11647229 11714553 11782503 11850384 11920922 11991852 12058430 - 12124180 12193990 12263048 12331000 12400184 12469339 12542136 12616018 - 12684433 12752628 12819705 12889437 12956993 13027749 13097935 13166800 - 13233530 13299902 13367048 13435368 13504249 13567803 13637909 13704580 - 13772610 13841840 13911317 13980156 14048930 14118844 14184108 14255280 - 14321391 14392791 14459800 14527247 14596997 14663565 14735867 14802512 - 14871315 14938856 15007728 15079555 15145847 15214882 15285290 15286953] - -2023-05-10 18:20:09,794 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:20:10,142 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:20:11,031 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:20:11,031 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:20:11,304 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:20:11,375 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:20:11,375 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:20:11,653 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:20:12,132 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37861 -2023-05-10 18:20:12,132 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37861 -2023-05-10 18:20:12,132 - distributed.worker - INFO - dashboard at: 10.120.104.11:40621 -2023-05-10 18:20:12,133 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:20:12,133 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,133 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:20:12,133 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:20:12,133 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-brjvc3pt -2023-05-10 18:20:12,134 - distributed.worker - INFO - Starting Worker plugin RMMSetup-13b7ea2c-2794-4bc5-8de0-63bb897b99cf -2023-05-10 18:20:12,272 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-afb3e3a9-91cf-43ff-b440-6c5f3ecd3c0f -2023-05-10 18:20:12,272 - distributed.worker - INFO - Starting Worker plugin PreImport-1edeff9e-0e60-47ad-b0d5-57757417b635 -2023-05-10 18:20:12,273 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,284 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:20:12,284 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,286 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:20:12,593 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:34921 -2023-05-10 18:20:12,593 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:34921 -2023-05-10 18:20:12,593 - distributed.worker - INFO - dashboard at: 10.120.104.11:38961 -2023-05-10 18:20:12,593 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:20:12,593 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,593 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:20:12,593 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:20:12,593 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-rl90_bj6 -2023-05-10 18:20:12,593 - distributed.worker - INFO - Starting Worker plugin RMMSetup-d7758436-04a2-441e-b55b-1f524fb1e32b -2023-05-10 18:20:12,720 - distributed.worker - INFO - Starting Worker plugin PreImport-aa261f41-f283-4f5c-a5a5-d3067947deeb -2023-05-10 18:20:12,720 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-e21bd1eb-17da-442e-9874-1ea0bcf8b46b -2023-05-10 18:20:12,721 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,732 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:20:12,732 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:20:12,734 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:20:20,577 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:20:20,578 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.83s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:20:20,627 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:20:20,628 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.88s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:20:20,632 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:20:20,634 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:20:20,638 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:20:20,638 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:20:26,888 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.65s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:20:27,009 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.77s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:20:27,013 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:20:27,014 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:21:06,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:06,166 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:06,832 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:21:06,833 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:21:07,845 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:07,847 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:11,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:11,481 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 272 272 272 ... 1478069 1478069 1478069] -destinations: [1547254 296390 175481 ... 3094924 438284 1710104] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 - 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 - 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 - 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 - 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 - 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 - 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 - 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 - 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 - 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 - 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 - 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 - 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 - 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 - 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 - 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 - 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 - 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 - 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 - 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 - 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 - 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 - 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 - 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 - 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 - 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 - 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 - 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 - 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 - 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 - 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 - 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 - 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 - 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 - 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 - 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 - 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 - 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 - 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 - 990 991 992 993 994 995 996 997 998 999] -offset: [ 0 70575 136755 ... 68611907 68680178 68752317] - -sources: [3684949 3684949 3684949 ... 2700994 2700994 2700994] -destinations: [1374757 2500832 1660338 ... 1846807 1707593 2353575] -batch: [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 - 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 - 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 - 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 - 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 - 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 - 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 - 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 - 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 - 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 - 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 - 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 - 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 - 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 - 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 - 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 - 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 - 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 - 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 - 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 - 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 - 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 - 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 - 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 - 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 - 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 - 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 - 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 - 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 - 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 - 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 - 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 - 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 - 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 - 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 - 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 - 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 - 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 - 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 - 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 - 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 - 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 - 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 - 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 - 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 - 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 - 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 - 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 - 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 - 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 - 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 - 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 - 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 - 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 - 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 - 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 - 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 - 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 - 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 - 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 - 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 - 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 - 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 - 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 - 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 - 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 - 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 - 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 - 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 - 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 - 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 - 1994 1995 1996 1997 1998 1999] -offset: [ 0 70255 139827 ... 68700585 68766214 68834775] - -2023-05-10 18:21:13,270 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:13,270 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1000147 1000147 1000147 ... 1308859 1308859 1308859] -destinations: [ 597977 67303 1525719 ... 156424 2205798 3480521] -batch: [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 - 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 - 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 - 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 - 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 - 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 - 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 - 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 - 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 - 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 - 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 - 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 - 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 - 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 - 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 - 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 - 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 - 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 - 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 - 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 - 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 - 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 - 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 - 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 - 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 - 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 - 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 - 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 - 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 - 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 - 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 - 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 - 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 - 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 - 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 - 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 - 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 - 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 - 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 - 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 - 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 - 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 - 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 - 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 - 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 - 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 - 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 - 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 - 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 - 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 - 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 - 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 - 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 - 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 - 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 - 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 - 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 - 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 - 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 - 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 - 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 - 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 - 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 - 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 - 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 - 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 - 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 - 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 - 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 - 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 - 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 - 2994 2995 2996 2997 2998 2999] -offset: [ 0 70065 136075 ... 68649644 68718692 68785107] - -sources: [ 574912 574912 574912 ... 1500517 1500517 1500517] -destinations: [3028348 2372343 41001 ... 4479147 253308 275036] -batch: [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 - 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 - 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 - 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 - 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 - 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 - 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 - 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 - 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 - 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 - 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 - 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 - 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 - 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 - 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 - 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 - 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 - 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 - 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 - 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 - 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 - 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 - 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 - 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 - 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 - 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 - 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 - 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 - 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 - 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 - 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 - 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 - 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 - 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 - 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 - 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 - 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 - 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 - 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 - 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 - 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 - 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 - 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 - 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 - 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 - 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 - 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 - 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 - 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 - 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 - 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 - 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 - 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 - 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 - 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 - 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 - 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 - 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 - 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 - 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 - 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 - 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 - 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 - 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 - 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 - 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 - 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 - 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 - 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 - 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 - 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 - 3994 3995 3996 3997 3998 3999] -offset: [ 0 68123 137316 ... 68648492 68717540 68785202] - -2023-05-10 18:21:15,059 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:15,060 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [2000417 2000417 2000417 ... 4294842 4294842 4294842] -destinations: [2670565 2547625 2462050 ... 400721 2833571 1590778] -batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 - 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 - 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 - 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 - 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 - 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 - 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 - 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 - 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 - 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 - 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 - 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 - 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 - 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 - 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 - 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 - 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 - 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 - 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 - 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 - 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 - 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 - 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 - 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 - 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 - 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 - 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 - 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 - 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 - 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 - 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 - 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 - 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 - 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 - 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 - 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 - 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517 - 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 - 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545 - 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 - 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 - 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 - 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 - 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615 - 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629 - 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 - 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657 - 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 - 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685 - 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699 - 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 - 4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727 - 4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741 - 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755 - 4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769 - 4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 - 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797 - 4798 4799 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 - 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 - 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 - 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 - 4854 4855 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 - 4868 4869 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 - 4882 4883 4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 - 4896 4897 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 - 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 - 4924 4925 4926 4927 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 - 4938 4939 4940 4941 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 - 4952 4953 4954 4955 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 - 4966 4967 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 - 4980 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 - 4994 4995 4996 4997 4998 4999] -offset: [ 0 65375 134661 ... 68622867 68690269 68761150] - -sources: [3923251 3923251 3923251 ... 846540 846540 846540] -destinations: [1810486 3463882 3113360 ... 1894801 394069 2780473] -batch: [5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013 - 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 - 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041 - 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055 - 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 - 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083 - 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097 - 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 - 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 - 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 - 5140 5141 5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153 - 5154 5155 5156 5157 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167 - 5168 5169 5170 5171 5172 5173 5174 5175 5176 5177 5178 5179 5180 5181 - 5182 5183 5184 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195 - 5196 5197 5198 5199 5200 5201 5202 5203 5204 5205 5206 5207 5208 5209 - 5210 5211 5212 5213 5214 5215 5216 5217 5218 5219 5220 5221 5222 5223 - 5224 5225 5226 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 - 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247 5248 5249 5250 5251 - 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265 - 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275 5276 5277 5278 5279 - 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291 5292 5293 - 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304 5305 5306 5307 - 5308 5309 5310 5311 5312 5313 5314 5315 5316 5317 5318 5319 5320 5321 - 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 5332 5333 5334 5335 - 5336 5337 5338 5339 5340 5341 5342 5343 5344 5345 5346 5347 5348 5349 - 5350 5351 5352 5353 5354 5355 5356 5357 5358 5359 5360 5361 5362 5363 - 5364 5365 5366 5367 5368 5369 5370 5371 5372 5373 5374 5375 5376 5377 - 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387 5388 5389 5390 5391 - 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405 - 5406 5407 5408 5409 5410 5411 5412 5413 5414 5415 5416 5417 5418 5419 - 5420 5421 5422 5423 5424 5425 5426 5427 5428 5429 5430 5431 5432 5433 - 5434 5435 5436 5437 5438 5439 5440 5441 5442 5443 5444 5445 5446 5447 - 5448 5449 5450 5451 5452 5453 5454 5455 5456 5457 5458 5459 5460 5461 - 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472 5473 5474 5475 - 5476 5477 5478 5479 5480 5481 5482 5483 5484 5485 5486 5487 5488 5489 - 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499 5500 5501 5502 5503 - 5504 5505 5506 5507 5508 5509 5510 5511 5512 5513 5514 5515 5516 5517 - 5518 5519 5520 5521 5522 5523 5524 5525 5526 5527 5528 5529 5530 5531 - 5532 5533 5534 5535 5536 5537 5538 5539 5540 5541 5542 5543 5544 5545 - 5546 5547 5548 5549 5550 5551 5552 5553 5554 5555 5556 5557 5558 5559 - 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569 5570 5571 5572 5573 - 5574 5575 5576 5577 5578 5579 5580 5581 5582 5583 5584 5585 5586 5587 - 5588 5589 5590 5591 5592 5593 5594 5595 5596 5597 5598 5599 5600 5601 - 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615 - 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 5628 5629 - 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641 5642 5643 - 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655 5656 5657 - 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668 5669 5670 5671 - 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683 5684 5685 - 5686 5687 5688 5689 5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 - 5700 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713 - 5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724 5725 5726 5727 - 5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741 - 5742 5743 5744 5745 5746 5747 5748 5749 5750 5751 5752 5753 5754 5755 - 5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769 - 5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783 - 5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797 - 5798 5799 5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811 - 5812 5813 5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825 - 5826 5827 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 - 5840 5841 5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 - 5854 5855 5856 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 - 5868 5869 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 - 5882 5883 5884 5885 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 - 5896 5897 5898 5899 5900 5901 5902 5903 5904 5905 5906 5907 5908 5909 - 5910 5911 5912 5913 5914 5915 5916 5917 5918 5919 5920 5921 5922 5923 - 5924 5925 5926 5927 5928 5929 5930 5931 5932 5933 5934 5935 5936 5937 - 5938 5939 5940 5941 5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 - 5952 5953 5954 5955 5956 5957 5958 5959 5960 5961 5962 5963 5964 5965 - 5966 5967 5968 5969 5970 5971 5972 5973 5974 5975 5976 5977 5978 5979 - 5980 5981 5982 5983 5984 5985 5986 5987 5988 5989 5990 5991 5992 5993 - 5994 5995 5996 5997 5998 5999] -offset: [ 0 72081 143596 ... 68659671 68728720 68797618] - -2023-05-10 18:21:16,786 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:16,788 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1024146 1024146 1024146 ... 2753591 2753591 2753591] -destinations: [2018026 290669 754778 ... 124860 584617 2849764] -batch: [7000 7001 7002 7003 7004 7005 7006 7007 7008 7009 7010 7011 7012 7013 - 7014 7015 7016 7017 7018 7019 7020 7021 7022 7023 7024 7025 7026 7027 - 7028 7029 7030 7031 7032 7033 7034 7035 7036 7037 7038 7039 7040 7041 - 7042 7043 7044 7045 7046 7047 7048 7049 7050 7051 7052 7053 7054 7055 - 7056 7057 7058 7059 7060 7061 7062 7063 7064 7065 7066 7067 7068 7069 - 7070 7071 7072 7073 7074 7075 7076 7077 7078 7079 7080 7081 7082 7083 - 7084 7085 7086 7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097 - 7098 7099 7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 - 7112 7113 7114 7115 7116 7117 7118 7119 7120 7121 7122 7123 7124 7125 - 7126 7127 7128 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139 - 7140 7141 7142 7143 7144 7145 7146 7147 7148 7149 7150 7151 7152 7153 - 7154 7155 7156 7157 7158 7159 7160 7161 7162 7163 7164 7165 7166 7167 - 7168 7169 7170 7171 7172 7173 7174 7175 7176 7177 7178 7179 7180 7181 - 7182 7183 7184 7185 7186 7187 7188 7189 7190 7191 7192 7193 7194 7195 - 7196 7197 7198 7199 7200 7201 7202 7203 7204 7205 7206 7207 7208 7209 - 7210 7211 7212 7213 7214 7215 7216 7217 7218 7219 7220 7221 7222 7223 - 7224 7225 7226 7227 7228 7229 7230 7231 7232 7233 7234 7235 7236 7237 - 7238 7239 7240 7241 7242 7243 7244 7245 7246 7247 7248 7249 7250 7251 - 7252 7253 7254 7255 7256 7257 7258 7259 7260 7261 7262 7263 7264 7265 - 7266 7267 7268 7269 7270 7271 7272 7273 7274 7275 7276 7277 7278 7279 - 7280 7281 7282 7283 7284 7285 7286 7287 7288 7289 7290 7291 7292 7293 - 7294 7295 7296 7297 7298 7299 7300 7301 7302 7303 7304 7305 7306 7307 - 7308 7309 7310 7311 7312 7313 7314 7315 7316 7317 7318 7319 7320 7321 - 7322 7323 7324 7325 7326 7327 7328 7329 7330 7331 7332 7333 7334 7335 - 7336 7337 7338 7339 7340 7341 7342 7343 7344 7345 7346 7347 7348 7349 - 7350 7351 7352 7353 7354 7355 7356 7357 7358 7359 7360 7361 7362 7363 - 7364 7365 7366 7367 7368 7369 7370 7371 7372 7373 7374 7375 7376 7377 - 7378 7379 7380 7381 7382 7383 7384 7385 7386 7387 7388 7389 7390 7391 - 7392 7393 7394 7395 7396 7397 7398 7399 7400 7401 7402 7403 7404 7405 - 7406 7407 7408 7409 7410 7411 7412 7413 7414 7415 7416 7417 7418 7419 - 7420 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431 7432 7433 - 7434 7435 7436 7437 7438 7439 7440 7441 7442 7443 7444 7445 7446 7447 - 7448 7449 7450 7451 7452 7453 7454 7455 7456 7457 7458 7459 7460 7461 - 7462 7463 7464 7465 7466 7467 7468 7469 7470 7471 7472 7473 7474 7475 - 7476 7477 7478 7479 7480 7481 7482 7483 7484 7485 7486 7487 7488 7489 - 7490 7491 7492 7493 7494 7495 7496 7497 7498 7499 7500 7501 7502 7503 - 7504 7505 7506 7507 7508 7509 7510 7511 7512 7513 7514 7515 7516 7517 - 7518 7519 7520 7521 7522 7523 7524 7525 7526 7527 7528 7529 7530 7531 - 7532 7533 7534 7535 7536 7537 7538 7539 7540 7541 7542 7543 7544 7545 - 7546 7547 7548 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558 7559 - 7560 7561 7562 7563 7564 7565 7566 7567 7568 7569 7570 7571 7572 7573 - 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583 7584 7585 7586 7587 - 7588 7589 7590 7591 7592 7593 7594 7595 7596 7597 7598 7599 7600 7601 - 7602 7603 7604 7605 7606 7607 7608 7609 7610 7611 7612 7613 7614 7615 - 7616 7617 7618 7619 7620 7621 7622 7623 7624 7625 7626 7627 7628 7629 - 7630 7631 7632 7633 7634 7635 7636 7637 7638 7639 7640 7641 7642 7643 - 7644 7645 7646 7647 7648 7649 7650 7651 7652 7653 7654 7655 7656 7657 - 7658 7659 7660 7661 7662 7663 7664 7665 7666 7667 7668 7669 7670 7671 - 7672 7673 7674 7675 7676 7677 7678 7679 7680 7681 7682 7683 7684 7685 - 7686 7687 7688 7689 7690 7691 7692 7693 7694 7695 7696 7697 7698 7699 - 7700 7701 7702 7703 7704 7705 7706 7707 7708 7709 7710 7711 7712 7713 - 7714 7715 7716 7717 7718 7719 7720 7721 7722 7723 7724 7725 7726 7727 - 7728 7729 7730 7731 7732 7733 7734 7735 7736 7737 7738 7739 7740 7741 - 7742 7743 7744 7745 7746 7747 7748 7749 7750 7751 7752 7753 7754 7755 - 7756 7757 7758 7759 7760 7761 7762 7763 7764 7765 7766 7767 7768 7769 - 7770 7771 7772 7773 7774 7775 7776 7777 7778 7779 7780 7781 7782 7783 - 7784 7785 7786 7787 7788 7789 7790 7791 7792 7793 7794 7795 7796 7797 - 7798 7799 7800 7801 7802 7803 7804 7805 7806 7807 7808 7809 7810 7811 - 7812 7813 7814 7815 7816 7817 7818 7819 7820 7821 7822 7823 7824 7825 - 7826 7827 7828 7829 7830 7831 7832 7833 7834 7835 7836 7837 7838 7839 - 7840 7841 7842 7843 7844 7845 7846 7847 7848 7849 7850 7851 7852 7853 - 7854 7855 7856 7857 7858 7859 7860 7861 7862 7863 7864 7865 7866 7867 - 7868 7869 7870 7871 7872 7873 7874 7875 7876 7877 7878 7879 7880 7881 - 7882 7883 7884 7885 7886 7887 7888 7889 7890 7891 7892 7893 7894 7895 - 7896 7897 7898 7899 7900 7901 7902 7903 7904 7905 7906 7907 7908 7909 - 7910 7911 7912 7913 7914 7915 7916 7917 7918 7919 7920 7921 7922 7923 - 7924 7925 7926 7927 7928 7929 7930 7931 7932 7933 7934 7935 7936 7937 - 7938 7939 7940 7941 7942 7943 7944 7945 7946 7947 7948 7949 7950 7951 - 7952 7953 7954 7955 7956 7957 7958 7959 7960 7961 7962 7963 7964 7965 - 7966 7967 7968 7969 7970 7971 7972 7973 7974 7975 7976 7977 7978 7979 - 7980 7981 7982 7983 7984 7985 7986 7987 7988 7989 7990 7991 7992 7993 - 7994 7995 7996 7997 7998 7999] -offset: [ 0 70689 135360 ... 68722141 68794006 68859596] - -sources: [3000418 3000418 3000418 ... 655293 655293 655293] -destinations: [1358807 3617336 2980130 ... 2269838 2948411 1579655] -batch: [6000 6001 6002 6003 6004 6005 6006 6007 6008 6009 6010 6011 6012 6013 - 6014 6015 6016 6017 6018 6019 6020 6021 6022 6023 6024 6025 6026 6027 - 6028 6029 6030 6031 6032 6033 6034 6035 6036 6037 6038 6039 6040 6041 - 6042 6043 6044 6045 6046 6047 6048 6049 6050 6051 6052 6053 6054 6055 - 6056 6057 6058 6059 6060 6061 6062 6063 6064 6065 6066 6067 6068 6069 - 6070 6071 6072 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083 - 6084 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097 - 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111 - 6112 6113 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125 - 6126 6127 6128 6129 6130 6131 6132 6133 6134 6135 6136 6137 6138 6139 - 6140 6141 6142 6143 6144 6145 6146 6147 6148 6149 6150 6151 6152 6153 - 6154 6155 6156 6157 6158 6159 6160 6161 6162 6163 6164 6165 6166 6167 - 6168 6169 6170 6171 6172 6173 6174 6175 6176 6177 6178 6179 6180 6181 - 6182 6183 6184 6185 6186 6187 6188 6189 6190 6191 6192 6193 6194 6195 - 6196 6197 6198 6199 6200 6201 6202 6203 6204 6205 6206 6207 6208 6209 - 6210 6211 6212 6213 6214 6215 6216 6217 6218 6219 6220 6221 6222 6223 - 6224 6225 6226 6227 6228 6229 6230 6231 6232 6233 6234 6235 6236 6237 - 6238 6239 6240 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 - 6252 6253 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 - 6266 6267 6268 6269 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279 - 6280 6281 6282 6283 6284 6285 6286 6287 6288 6289 6290 6291 6292 6293 - 6294 6295 6296 6297 6298 6299 6300 6301 6302 6303 6304 6305 6306 6307 - 6308 6309 6310 6311 6312 6313 6314 6315 6316 6317 6318 6319 6320 6321 - 6322 6323 6324 6325 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335 - 6336 6337 6338 6339 6340 6341 6342 6343 6344 6345 6346 6347 6348 6349 - 6350 6351 6352 6353 6354 6355 6356 6357 6358 6359 6360 6361 6362 6363 - 6364 6365 6366 6367 6368 6369 6370 6371 6372 6373 6374 6375 6376 6377 - 6378 6379 6380 6381 6382 6383 6384 6385 6386 6387 6388 6389 6390 6391 - 6392 6393 6394 6395 6396 6397 6398 6399 6400 6401 6402 6403 6404 6405 - 6406 6407 6408 6409 6410 6411 6412 6413 6414 6415 6416 6417 6418 6419 - 6420 6421 6422 6423 6424 6425 6426 6427 6428 6429 6430 6431 6432 6433 - 6434 6435 6436 6437 6438 6439 6440 6441 6442 6443 6444 6445 6446 6447 - 6448 6449 6450 6451 6452 6453 6454 6455 6456 6457 6458 6459 6460 6461 - 6462 6463 6464 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474 6475 - 6476 6477 6478 6479 6480 6481 6482 6483 6484 6485 6486 6487 6488 6489 - 6490 6491 6492 6493 6494 6495 6496 6497 6498 6499 6500 6501 6502 6503 - 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513 6514 6515 6516 6517 - 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527 6528 6529 6530 6531 - 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 6543 6544 6545 - 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555 6556 6557 6558 6559 - 6560 6561 6562 6563 6564 6565 6566 6567 6568 6569 6570 6571 6572 6573 - 6574 6575 6576 6577 6578 6579 6580 6581 6582 6583 6584 6585 6586 6587 - 6588 6589 6590 6591 6592 6593 6594 6595 6596 6597 6598 6599 6600 6601 - 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611 6612 6613 6614 6615 - 6616 6617 6618 6619 6620 6621 6622 6623 6624 6625 6626 6627 6628 6629 - 6630 6631 6632 6633 6634 6635 6636 6637 6638 6639 6640 6641 6642 6643 - 6644 6645 6646 6647 6648 6649 6650 6651 6652 6653 6654 6655 6656 6657 - 6658 6659 6660 6661 6662 6663 6664 6665 6666 6667 6668 6669 6670 6671 - 6672 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683 6684 6685 - 6686 6687 6688 6689 6690 6691 6692 6693 6694 6695 6696 6697 6698 6699 - 6700 6701 6702 6703 6704 6705 6706 6707 6708 6709 6710 6711 6712 6713 - 6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727 - 6728 6729 6730 6731 6732 6733 6734 6735 6736 6737 6738 6739 6740 6741 - 6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755 - 6756 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768 6769 - 6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780 6781 6782 6783 - 6784 6785 6786 6787 6788 6789 6790 6791 6792 6793 6794 6795 6796 6797 - 6798 6799 6800 6801 6802 6803 6804 6805 6806 6807 6808 6809 6810 6811 - 6812 6813 6814 6815 6816 6817 6818 6819 6820 6821 6822 6823 6824 6825 - 6826 6827 6828 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839 - 6840 6841 6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852 6853 - 6854 6855 6856 6857 6858 6859 6860 6861 6862 6863 6864 6865 6866 6867 - 6868 6869 6870 6871 6872 6873 6874 6875 6876 6877 6878 6879 6880 6881 - 6882 6883 6884 6885 6886 6887 6888 6889 6890 6891 6892 6893 6894 6895 - 6896 6897 6898 6899 6900 6901 6902 6903 6904 6905 6906 6907 6908 6909 - 6910 6911 6912 6913 6914 6915 6916 6917 6918 6919 6920 6921 6922 6923 - 6924 6925 6926 6927 6928 6929 6930 6931 6932 6933 6934 6935 6936 6937 - 6938 6939 6940 6941 6942 6943 6944 6945 6946 6947 6948 6949 6950 6951 - 6952 6953 6954 6955 6956 6957 6958 6959 6960 6961 6962 6963 6964 6965 - 6966 6967 6968 6969 6970 6971 6972 6973 6974 6975 6976 6977 6978 6979 - 6980 6981 6982 6983 6984 6985 6986 6987 6988 6989 6990 6991 6992 6993 - 6994 6995 6996 6997 6998 6999] -offset: [ 0 65034 129447 ... 68673035 68740323 68812327] - -2023-05-10 18:21:18,144 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:21:18,145 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:21:18,149 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:21:18,149 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:21:18,152 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:21:18,152 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:21:18,266 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:21:18,268 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:21:18,268 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:34921. Reason: scheduler-restart -2023-05-10 18:21:18,270 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:21:18,271 - distributed.nanny - INFO - Worker closed -sources: [4000096 4000098 4000098 ... 1553298 1553298 1553298] -destinations: [4124038 160075 1866771 ... 1876656 4235938 2792120] -batch: [8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013 - 8014 8015 8016 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027 - 8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041 - 8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052 8053 8054 8055 - 8056 8057 8058 8059 8060 8061 8062 8063 8064 8065 8066 8067 8068 8069 - 8070 8071 8072 8073 8074 8075 8076 8077 8078 8079 8080 8081 8082 8083 - 8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097 - 8098 8099 8100 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111 - 8112 8113 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124 8125 - 8126 8127 8128 8129 8130 8131 8132 8133 8134 8135 8136 8137 8138 8139 - 8140 8141 8142 8143 8144 8145 8146 8147 8148 8149 8150 8151 8152 8153 - 8154 8155 8156 8157 8158 8159 8160 8161 8162 8163 8164 8165 8166 8167 - 8168 8169 8170 8171 8172 8173 8174 8175 8176 8177 8178 8179 8180 8181 - 8182 8183 8184 8185 8186 8187 8188 8189 8190 8191 8192 8193 8194 8195 - 8196 8197 8198 8199 8200 8201 8202 8203 8204 8205 8206 8207 8208 8209 - 8210 8211 8212 8213 8214 8215 8216 8217 8218 8219 8220 8221 8222 8223 - 8224 8225 8226 8227 8228 8229 8230 8231 8232 8233 8234 8235 8236 8237 - 8238 8239 8240 8241 8242 8243 8244 8245 8246 8247 8248 8249 8250 8251 - 8252 8253 8254 8255 8256 8257 8258 8259 8260 8261 8262 8263 8264 8265 - 8266 8267 8268 8269 8270 8271 8272 8273 8274 8275 8276 8277 8278 8279 - 8280 8281 8282 8283 8284 8285 8286 8287 8288 8289 8290 8291 8292 8293 - 8294 8295 8296 8297 8298 8299 8300 8301 8302 8303 8304 8305 8306 8307 - 8308 8309 8310 8311 8312 8313 8314 8315 8316 8317 8318 8319 8320 8321 - 8322 8323 8324 8325 8326 8327 8328 8329 8330 8331 8332 8333 8334 8335 - 8336 8337 8338 8339 8340 8341 8342 8343 8344 8345 8346 8347 8348 8349 - 8350 8351 8352 8353 8354 8355 8356 8357 8358 8359 8360 8361 8362 8363 - 8364 8365 8366 8367 8368 8369 8370 8371 8372 8373 8374 8375 8376 8377 - 8378 8379 8380 8381 8382 8383 8384 8385 8386 8387 8388 8389 8390 8391 - 8392 8393 8394 8395 8396 8397 8398 8399 8400 8401 8402 8403 8404 8405 - 8406 8407 8408 8409 8410 8411 8412 8413 8414 8415 8416 8417 8418 8419 - 8420 8421 8422 8423 8424 8425 8426 8427 8428 8429 8430 8431 8432 8433 - 8434 8435 8436 8437 8438 8439 8440 8441 8442 8443 8444 8445 8446 8447 - 8448 8449 8450 8451 8452 8453 8454 8455 8456 8457 8458 8459 8460 8461 - 8462 8463 8464 8465 8466 8467 8468 8469 8470 8471 8472 8473 8474 8475 - 8476 8477 8478 8479 8480 8481 8482 8483 8484 8485 8486 8487 8488 8489 - 8490 8491 8492 8493 8494 8495 8496 8497 8498 8499 8500 8501 8502 8503 - 8504 8505 8506 8507 8508 8509 8510 8511 8512 8513 8514 8515 8516 8517 - 8518 8519 8520 8521 8522 8523 8524 8525 8526 8527 8528 8529 8530 8531 - 8532 8533 8534 8535 8536 8537 8538 8539 8540 8541 8542 8543 8544 8545 - 8546 8547 8548 8549 8550 8551 8552 8553 8554 8555 8556 8557 8558 8559 - 8560 8561 8562 8563 8564 8565 8566 8567 8568 8569 8570 8571 8572 8573 - 8574 8575 8576 8577 8578 8579 8580 8581 8582 8583 8584 8585 8586 8587 - 8588 8589 8590 8591 8592 8593 8594 8595 8596 8597 8598 8599 8600 8601 - 8602 8603 8604 8605 8606 8607 8608 8609 8610 8611 8612 8613 8614 8615 - 8616 8617 8618 8619 8620 8621 8622 8623 8624 8625 8626 8627 8628 8629 - 8630 8631 8632 8633 8634 8635 8636 8637 8638 8639 8640 8641 8642 8643 - 8644 8645 8646 8647 8648 8649 8650 8651 8652 8653 8654 8655 8656 8657 - 8658 8659 8660 8661 8662 8663 8664 8665 8666 8667 8668 8669 8670 8671 - 8672 8673 8674 8675 8676 8677 8678 8679 8680 8681 8682 8683 8684 8685 - 8686 8687 8688 8689 8690 8691 8692 8693 8694 8695 8696 8697 8698 8699 - 8700 8701 8702 8703 8704 8705 8706 8707 8708 8709 8710 8711 8712 8713 - 8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724 8725 8726 8727 - 8728 8729 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739 8740 8741 - 8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755 - 8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769 - 8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783 - 8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796 8797 - 8798 8799 8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811 - 8812 8813 8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825 - 8826 8827 8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839 - 8840 8841 8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853 - 8854 8855 8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 - 8868 8869 8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880 8881 - 8882 8883 8884 8885 8886 8887 8888 8889 8890 8891 8892 8893 8894 8895 - 8896 8897 8898 8899 8900 8901 8902 8903 8904 8905 8906 8907 8908 8909 - 8910 8911 8912 8913 8914 8915 8916 8917 8918 8919 8920 8921 8922 8923 - 8924 8925 8926 8927 8928 8929 8930 8931 8932 8933 8934 8935 8936 8937 - 8938 8939 8940 8941 8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 - 8952 8953 8954 8955 8956 8957 8958 8959 8960 8961 8962 8963 8964 8965 - 8966 8967 8968 8969 8970 8971 8972 8973 8974 8975 8976 8977 8978 8979 - 8980 8981 8982 8983 8984 8985 8986 8987 8988 8989 8990 8991 8992 8993 - 8994 8995 8996 8997 8998 8999] -offset: [ 0 67687 133835 ... 68683498 68757293 68826457] - -2023-05-10 18:21:18,299 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:34921 -2023-05-10 18:21:18,299 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37861. Reason: scheduler-restart -2023-05-10 18:21:18,301 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:21:18,302 - distributed.nanny - INFO - Worker closed -sources: [ 353244 353244 353244 ... 4500234 4500234 4500234] -destinations: [3411338 3407598 4106750 ... 3134371 2892048 833324] -batch: [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013 - 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027 - 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041 - 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055 - 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069 - 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083 - 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097 - 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 - 9112 9113 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 - 9126 9127 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 - 9140 9141 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 - 9154 9155 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 - 9168 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 - 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 - 9196 9197 9198 9199 9200 9201 9202 9203 9204 9205 9206 9207 9208 9209 - 9210 9211 9212 9213 9214 9215 9216 9217 9218 9219 9220 9221 9222] -offset: [ 0 66248 132977 200660 266073 334996 404752 474494 - 540358 611772 679897 748503 816547 881878 953491 1021527 - 1091869 1161535 1227410 1296856 1365248 1433642 1502416 1570250 - 1641803 1715228 1783071 1853620 1922381 1991152 2062909 2129937 - 2195854 2266440 2334284 2404708 2471783 2542872 2609076 2679937 - 2747579 2821278 2890582 2961965 3032593 3099371 3167408 3233080 - 3297586 3371671 3439292 3506136 3578277 3644955 3712045 3779969 - 3849900 3919907 3986829 4055591 4126227 4199224 4271867 4342639 - 4409516 4478431 4545512 4615438 4683915 4757013 4825573 4891739 - 4958875 5030503 5097090 5167417 5237782 5302198 5374634 5440807 - 5510885 5578873 5648998 5717646 5781815 5848094 5919437 5983291 - 6049210 6117458 6189059 6259761 6329314 6399001 6468245 6534717 - 6605590 6673240 6738404 6805106 6874095 6941093 7013070 7082614 - 7152057 7222533 7290484 7360512 7426702 7494713 7558860 7629649 - 7697727 7766308 7837473 7905948 7974878 8041665 8113383 8178400 - 8249555 8320093 8388187 8454475 8521790 8591865 8662210 8729796 - 8801210 8870815 8940387 9009935 9078263 9145301 9218947 9292235 - 9364312 9429575 9500543 9568209 9641456 9713013 9783996 9852730 - 9917607 9986270 10051795 10121178 10188353 10257887 10324048 10396663 - 10465609 10537305 10605756 10676672 10747354 10813036 10883384 10950818 - 11022238 11090554 11163020 11233659 11302325 11371921 11438276 11509432 - 11576001 11645200 11712464 11780221 11848147 11918770 11989722 12055864 - 12121835 12191535 12260565 12328238 12397383 12466484 12539062 12612880 - 12680897 12749216 12816156 12885991 12953726 13024408 13094641 13163236 - 13229531 13295976 13363230 13431418 13500314 13564030 13634194 13700883 - 13768963 13837907 13907474 13976363 14045058 14114365 14179580 14250688 - 14316994 14388444 14455673 14522872 14592855 14659462 14731840 14798318 - 14867011 14934514 15003358 15075139 15141735 15210896 15280872 15282499] - -2023-05-10 18:21:20,211 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:21:20,478 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:21:21,469 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:21:21,470 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:21:21,654 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:21:21,655 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:21:21,740 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:21:21,912 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:21:22,593 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:37045 -2023-05-10 18:21:22,594 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:37045 -2023-05-10 18:21:22,594 - distributed.worker - INFO - dashboard at: 10.120.104.11:38993 -2023-05-10 18:21:22,594 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:21:22,594 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:22,594 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:21:22,594 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:21:22,594 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-ru07ko5g -2023-05-10 18:21:22,594 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-e87a5689-86fb-496f-9926-1010208c04d4 -2023-05-10 18:21:22,596 - distributed.worker - INFO - Starting Worker plugin PreImport-9dddbba2-a8c4-401a-b3c6-edc849bb7417 -2023-05-10 18:21:22,596 - distributed.worker - INFO - Starting Worker plugin RMMSetup-6ccdc2ef-a38b-4bf9-bbbb-6b1cd203d9c4 -2023-05-10 18:21:22,786 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:22,797 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:21:22,797 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:22,799 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:21:22,955 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:40247 -2023-05-10 18:21:22,955 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:40247 -2023-05-10 18:21:22,955 - distributed.worker - INFO - dashboard at: 10.120.104.11:39891 -2023-05-10 18:21:22,955 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:21:22,955 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:22,955 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:21:22,955 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:21:22,956 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-n2gur77n -2023-05-10 18:21:22,956 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-c3cee309-df29-4528-936d-6f64a21d480f -2023-05-10 18:21:22,956 - distributed.worker - INFO - Starting Worker plugin RMMSetup-25eee593-d2df-4e66-8686-1b04ad78e878 -2023-05-10 18:21:23,076 - distributed.worker - INFO - Starting Worker plugin PreImport-6440e4d9-f937-466d-b4c9-b47602454e6e -2023-05-10 18:21:23,077 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:23,085 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:21:23,085 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:21:23,087 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:21:31,132 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:21:31,133 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.05s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:21:31,202 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:21:31,203 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.13s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:21:31,207 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:31,208 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:21:31,212 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:21:31,212 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:21:37,383 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.60s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:21:37,527 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.74s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:21:37,530 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:21:37,532 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:22:17,052 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:17,053 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:17,715 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:22:17,716 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:22:18,998 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:18,999 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:24,209 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:24,209 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:45,046 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:45,046 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:46,616 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:22:46,616 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:22:46,619 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:22:46,619 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:22:46,622 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:22:46,622 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:22:46,736 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:22:46,738 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:22:46,776 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:37045. Reason: scheduler-restart -2023-05-10 18:22:46,778 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:22:46,778 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:40247. Reason: scheduler-restart -2023-05-10 18:22:46,779 - distributed.nanny - INFO - Worker closed -2023-05-10 18:22:46,779 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:37045 -sources: [ 498 498 498 ... 3499043 3499043 3499043] -destinations: [2635286 40553 4459323 ... 2179131 2147358 3967418] -batch: [ 0 1 2 ... 1997 1998 1999] -offset: [ 0 70419 136927 ... 137455044 137520691 137589183] - -sources: [2000320 2000321 2000321 ... 2528936 2528936 2528936] -destinations: [4605167 1908584 2577623 ... 964686 111297 1911773] -batch: [4000 4001 4002 ... 5997 5998 5999] -offset: [ 0 65558 134876 ... 137425737 137494878 137563830] - -sources: [4000096 4000098 4000098 ... 1322689 1322689 1322689] -destinations: [4124038 2907526 3584833 ... 3341187 904853 2966264] -batch: [8000 8001 8002 ... 9220 9221 9222] -offset: [ 0 67697 133541 ... 84041893 84111662 84113298] - -2023-05-10 18:22:46,780 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:22:46,782 - distributed.nanny - INFO - Worker closed -sources: [1000097 1000097 1000097 ... 261476 261476 261476] -destinations: [1158198 2457900 2147393 ... 3911980 3206366 4263871] -batch: [2000 2001 2002 ... 3997 3998 3999] -offset: [ 0 69817 136164 ... 137431018 137500095 137567719] - -sources: [3000482 3000483 3000483 ... 1752925 1752925 1752925] -destinations: [4274300 1498441 3942694 ... 2305443 584555 538665] -batch: [6000 6001 6002 ... 7997 7998 7999] -offset: [ 0 64866 129344 ... 137541133 137612982 137678909] - -sources: [] -destinations: [] -batch: [] -offset: [0] - -2023-05-10 18:22:48,550 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:22:48,835 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:22:49,760 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:22:49,761 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:22:50,019 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:22:50,079 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:22:50,079 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:22:50,367 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:22:50,813 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:33561 -2023-05-10 18:22:50,813 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:33561 -2023-05-10 18:22:50,813 - distributed.worker - INFO - dashboard at: 10.120.104.11:39275 -2023-05-10 18:22:50,813 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:22:50,813 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:50,814 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:22:50,814 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:22:50,814 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-64pqmdos -2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-221c8562-790c-4b5b-99f3-fe95bcd5f481 -2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin PreImport-ccb2f45e-7f8e-4a11-beb5-c6c5ec3656f2 -2023-05-10 18:22:50,814 - distributed.worker - INFO - Starting Worker plugin RMMSetup-fd7fbc32-2177-4671-bd43-fd76ad7ba619 -2023-05-10 18:22:50,988 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:51,019 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:22:51,019 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:51,021 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:22:51,262 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:45751 -2023-05-10 18:22:51,262 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:45751 -2023-05-10 18:22:51,262 - distributed.worker - INFO - dashboard at: 10.120.104.11:42099 -2023-05-10 18:22:51,262 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:22:51,262 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:51,262 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:22:51,262 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:22:51,262 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-whwl07vs -2023-05-10 18:22:51,262 - distributed.worker - INFO - Starting Worker plugin RMMSetup-c9ffcc58-86c1-4182-81be-b374abd36260 -2023-05-10 18:22:51,384 - distributed.worker - INFO - Starting Worker plugin PreImport-4668b8be-505a-44b7-bc90-a39a3b6b6b89 -2023-05-10 18:22:51,384 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-f298efeb-ea1e-4fb5-ab23-73cc367e7ed4 -2023-05-10 18:22:51,385 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:51,395 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:22:51,395 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:22:51,397 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:22:59,284 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:22:59,285 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.84s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:22:59,411 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:22:59,412 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.98s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:22:59,416 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:59,417 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:22:59,421 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:22:59,421 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:23:05,615 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.62s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:23:05,688 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.69s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:23:05,688 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:23:05,689 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:23:24,839 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:24,840 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:25,529 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:23:25,530 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:23:26,318 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:26,319 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:29,326 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:29,326 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 880 880 880 ... 2379593 2379593 2379593] -destinations: [2854843 1915172 274740 ... 3951768 1566329 2807515] -batch: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 - 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 - 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 - 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 - 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 - 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 - 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 - 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 - 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 - 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 - 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 - 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 - 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 - 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 - 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 - 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 - 288 289 290 291 292 293 294 295 296 297 298 299] -offset: [ 0 137135 276644 411328 547321 686815 824164 964896 - 1104005 1241801 1376656 1507963 1643509 1775151 1917607 2054089 - 2194973 2331358 2472729 2611370 2748363 2886037 3026779 3162485 - 3299013 3437600 3576821 3715350 3856001 3993629 4129016 4257194 - 4397739 4535724 4674615 4814102 4954416 5090361 5222449 5356292 - 5495165 5636877 5773055 5910313 6049247 6183552 6321317 6457782 - 6593278 6731755 6870459 7004965 7141050 7279578 7415857 7555783 - 7693321 7826976 7964040 8098103 8237460 8375547 8518929 8657136 - 8790987 8926778 9065278 9199097 9340678 9485537 9625543 9756312 - 9890630 10034826 10175063 10311394 10450701 10584564 10722212 10861871 - 10996333 11132111 11268450 11408518 11546811 11688978 11824598 11961369 - 12096268 12235478 12375051 12506191 12643327 12779102 12919322 13052150 - 13192808 13333576 13475251 13617189 13751290 13890561 14023699 14163111 - 14297669 14435135 14573035 14711138 14846631 14982517 15122376 15256407 - 15392358 15533096 15669006 15802831 15939334 16083830 16226069 16365720 - 16504686 16645046 16779779 16920631 17057303 17192533 17330804 17468958 - 17607499 17743602 17879914 18022403 18163581 18303096 18438381 18572836 - 18706736 18840159 18983375 19116691 19257289 19396399 19536518 19672538 - 19808344 19945707 20084973 20221191 20357886 20492290 20632866 20774676 - 20913302 21054758 21189043 21328043 21463800 21601402 21736235 21879340 - 22014728 22151282 22290098 22426471 22562349 22702898 22839954 22979384 - 23116714 23258630 23396934 23536081 23672421 23809290 23948326 24090013 - 24230749 24364052 24501472 24640019 24779239 24923917 25063422 25198793 - 25330784 25467173 25605485 25742549 25875427 26007256 26143069 26275280 - 26415514 26552830 26689615 26828160 26966424 27104332 27241714 27383062 - 27517502 27662296 27801638 27940603 28073080 28209555 28342269 28480809 - 28616260 28754810 28893449 29032365 29168899 29304957 29438162 29575415 - 29709032 29848705 29984089 30119141 30252888 30390049 30527059 30666437 - 30809047 30944743 31083634 31217840 31360296 31495241 31631485 31768486 - 31902771 32040798 32178879 32318362 32453466 32591756 32734845 32869506 - 33009688 33144543 33284336 33419884 33559423 33692895 33830255 33968317 - 34109560 34251111 34385302 34520289 34660784 34797821 34937400 35076431 - 35210210 35346821 35483477 35620011 35759902 35896636 36031772 36172983 - 36310842 36451779 36586545 36722573 36860743 37001425 37139789 37269638 - 37406539 37541498 37681561 37814963 37952330 38091968 38230724 38371506 - 38508426 38649016 38784076 38919781 39056180 39195187 39334084 39472316 - 39605720 39745480 39881674 40018863 40161178 40294978 40434387 40569599 - 40702289 40836503 40976661 41108453 41246657] - -sources: [2903896 2903896 2903896 ... 301824 301824 301824] -destinations: [1668660 4341008 3957807 ... 1257637 2223778 4165190] -batch: [300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 - 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 - 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 - 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 - 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 - 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 - 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 - 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 - 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 - 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 - 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 - 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 - 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 - 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 - 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 - 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 - 588 589 590 591 592 593 594 595 596 597 598 599] -offset: [ 0 135265 270678 407597 538301 680672 816219 953505 - 1093194 1232985 1370762 1506657 1643747 1781288 1917116 2055227 - 2198403 2333751 2474730 2607619 2743634 2878911 3013024 3153711 - 3287660 3429768 3567392 3705836 3842952 3981004 4115256 4253900 - 4388108 4527299 4668215 4809238 4946913 5083096 5221725 5359915 - 5494353 5635105 5767604 5903138 6041091 6180849 6325051 6458875 - 6600377 6737970 6871770 7009569 7145416 7278594 7420659 7559513 - 7700761 7835196 7971843 8111392 8251018 8391369 8528308 8668141 - 8809792 8943693 9081891 9220939 9357654 9494424 9634515 9770113 - 9913132 10047581 10184700 10320900 10452481 10593417 10727777 10863637 - 10999166 11138874 11276017 11408492 11543177 11681213 11810299 11950557 - 12094041 12228607 12368026 12510634 12647142 12784409 12915750 13056762 - 13192842 13327056 13462900 13606840 13743849 13882166 14017264 14151515 - 14289753 14431420 14574962 14710894 14851764 14988209 15121043 15257789 - 15396908 15540931 15677688 15810687 15942841 16080559 16215256 16355173 - 16495906 16635596 16772198 16909687 17043192 17180706 17323447 17463038 - 17599513 17734258 17871030 18007499 18147289 18282657 18419194 18561387 - 18696978 18835206 18973826 19114040 19248188 19384898 19526265 19668267 - 19810719 19948127 20086338 20221433 20355568 20496540 20634449 20773157 - 20909872 21046430 21186398 21323740 21465222 21606079 21744458 21884846 - 22022231 22156580 22289947 22427962 22562954 22702608 22840260 22974234 - 23108380 23248022 23386228 23523942 23656190 23791216 23932471 24067756 - 24206094 24346383 24485532 24618454 24755025 24898404 25031542 25165490 - 25302435 25441700 25582070 25716658 25854763 25992951 26123854 26261730 - 26398669 26538745 26671176 26813270 26952249 27094101 27225599 27363654 - 27504267 27643496 27777319 27916728 28054392 28192567 28330366 28464602 - 28600933 28742748 28880785 29017744 29160710 29296872 29437441 29575199 - 29713871 29849327 29986633 30125347 30264260 30401974 30538923 30674374 - 30814387 30950484 31089138 31227643 31370114 31507039 31644728 31781893 - 31917829 32055858 32190848 32329984 32471765 32610433 32746466 32883027 - 33023633 33164798 33299303 33439951 33574156 33710063 33847404 33986791 - 34126193 34267434 34406642 34544266 34682692 34821013 34957871 35099777 - 35231600 35370057 35505487 35641678 35779349 35913305 36052526 36189864 - 36332520 36466547 36603704 36743375 36884295 37017344 37155548 37293765 - 37429727 37567626 37703792 37842381 37979910 38118036 38254842 38389252 - 38526378 38664357 38793821 38932281 39068934 39209118 39344410 39481289 - 39624013 39758132 39895163 40036016 40172181 40310387 40449848 40583882 - 40721890 40861715 40998747 41136833 41269643] - -2023-05-10 18:23:35,836 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:35,836 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [ 600400 600400 600400 ... 3670192 3670192 3670192] -destinations: [3422011 2140887 4059839 ... 4293014 400440 221352] -batch: [600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 - 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 - 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 - 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 - 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 - 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 - 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 - 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 - 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 - 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 - 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 - 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 - 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 - 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 - 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 - 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 - 888 889 890 891 892 893 894 895 896 897 898 899] -offset: [ 0 138613 277245 417548 554972 690981 832706 968804 - 1110731 1244557 1383180 1515009 1652247 1787967 1924158 2059357 - 2201467 2340554 2479207 2614741 2754912 2890384 3030454 3166863 - 3306773 3445530 3582769 3722549 3857384 3994008 4130638 4266709 - 4402037 4542197 4682112 4817716 4958301 5094752 5232321 5369710 - 5507876 5645401 5781792 5917327 6054064 6191822 6330135 6473954 - 6606473 6746074 6887998 7022373 7154628 7291661 7429485 7559536 - 7698692 7836337 7978135 8112871 8250820 8388947 8523299 8666866 - 8804460 8940542 9077768 9214286 9350631 9486155 9624765 9764070 - 9895770 10033524 10172724 10312089 10456853 10594867 10735012 10871626 - 11008246 11147150 11281451 11418810 11556699 11694137 11832902 11971146 - 12107039 12243632 12380864 12524847 12658017 12792759 12928975 13064078 - 13202925 13341061 13481039 13623570 13762455 13903831 14042439 14185291 - 14329443 14460389 14600077 14741084 14875952 15006705 15145155 15282562 - 15423062 15559207 15694515 15827096 15968334 16111801 16252574 16387236 - 16531393 16662425 16803011 16945245 17085880 17225031 17362002 17497863 - 17632474 17773956 17909539 18053234 18191039 18329788 18466426 18601651 - 18741676 18872431 19004936 19141751 19278392 19411350 19545820 19682760 - 19823648 19962850 20099282 20233656 20365852 20501728 20641205 20781030 - 20917977 21054439 21188497 21325439 21462430 21601627 21741067 21874336 - 22013102 22152999 22286343 22423250 22565284 22708935 22846393 22983137 - 23121519 23260300 23395790 23535618 23672003 23808900 23943750 24081982 - 24217965 24358502 24499048 24635268 24772290 24905250 25047591 25185323 - 25319876 25457839 25597978 25735470 25871333 26008385 26142729 26281494 - 26423151 26559854 26700346 26832999 26968511 27109036 27250695 27389620 - 27529396 27667449 27809771 27949006 28085631 28223045 28360563 28494549 - 28634696 28774196 28913138 29051617 29188382 29322351 29457114 29593873 - 29736865 29874099 30013633 30151814 30292405 30431418 30563701 30703108 - 30841910 30974889 31114614 31248127 31388266 31524015 31664587 31799120 - 31935787 32074321 32209966 32345459 32484433 32620948 32760815 32902390 - 33037727 33173357 33311339 33451848 33593125 33729138 33867909 34006637 - 34141862 34274094 34411585 34546913 34689059 34826244 34960549 35092453 - 35231532 35367543 35509140 35645265 35783831 35917034 36054826 36195746 - 36333680 36468404 36607640 36746370 36882989 37019581 37163501 37296428 - 37436849 37573026 37710696 37849467 37990144 38128978 38266145 38406056 - 38543222 38676589 38814790 38954872 39092383 39231228 39371887 39509138 - 39650796 39786551 39932248 40067130 40203322 40341030 40481494 40618967 - 40756382 40893896 41023775 41163809 41302517] - -sources: [1199408 1199408 1199408 ... 901461 901461 901461] -destinations: [1637710 2254120 799209 ... 3391984 2482357 2803543] -batch: [ 900 901 902 903 904 905 906 907 908 909 910 911 912 913 - 914 915 916 917 918 919 920 921 922 923 924 925 926 927 - 928 929 930 931 932 933 934 935 936 937 938 939 940 941 - 942 943 944 945 946 947 948 949 950 951 952 953 954 955 - 956 957 958 959 960 961 962 963 964 965 966 967 968 969 - 970 971 972 973 974 975 976 977 978 979 980 981 982 983 - 984 985 986 987 988 989 990 991 992 993 994 995 996 997 - 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 - 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 - 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 - 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 - 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 - 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 - 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 - 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 - 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 - 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 - 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 - 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 - 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 - 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 - 1194 1195 1196 1197 1198 1199] -offset: [ 0 132463 269647 405521 546438 683342 821161 962920 - 1100295 1239059 1375510 1510376 1646503 1781365 1925490 2063832 - 2206363 2342643 2479367 2618288 2753130 2890253 3029620 3167877 - 3305994 3440203 3577152 3716013 3853459 3986779 4129224 4270524 - 4405136 4535682 4670462 4807987 4945771 5083622 5220737 5360992 - 5500266 5637591 5771496 5911134 6052564 6189952 6321804 6459576 - 6599575 6733286 6871724 7003952 7148792 7281362 7418954 7556197 - 7689564 7832858 7970545 8111701 8245978 8382904 8520074 8662720 - 8802044 8935462 9074692 9208119 9349466 9484729 9625926 9759793 - 9895652 10034879 10171289 10307332 10447016 10585847 10723345 10863886 - 11002031 11138826 11275655 11413470 11553423 11692279 11826161 11965942 - 12101182 12239036 12380643 12516721 12655822 12796474 12936043 13071940 - 13211332 13352814 13491765 13625924 13760576 13896893 14032326 14175773 - 14312848 14453681 14590534 14723497 14859964 14994879 15133631 15265464 - 15404708 15539604 15677105 15807416 15944031 16081491 16221492 16357159 - 16500332 16638469 16775456 16907624 17043469 17184865 17323470 17453678 - 17592061 17729037 17868018 18006921 18145948 18282585 18420238 18556186 - 18697588 18836037 18974238 19111881 19246905 19393006 19529518 19664737 - 19802173 19944118 20081870 20218161 20355818 20498136 20631314 20770080 - 20913149 21050206 21187281 21320488 21454462 21589600 21728527 21867896 - 22006523 22143567 22281811 22427091 22563521 22698340 22834894 22970115 - 23100816 23239889 23378270 23518885 23656209 23788703 23929696 24062800 - 24203003 24338857 24480686 24621608 24754554 24891328 25026355 25164586 - 25301582 25438401 25577705 25715404 25853066 25991007 26130353 26264988 - 26400702 26541268 26674383 26814689 26951657 27092545 27227717 27365398 - 27498088 27641695 27783270 27921075 28056533 28195034 28330216 28468263 - 28604262 28738541 28878312 29013473 29153670 29292401 29424659 29561124 - 29703860 29839598 29981367 30119905 30262361 30396980 30534035 30670312 - 30813497 30943799 31079798 31211694 31346189 31483819 31625964 31767020 - 31905789 32046321 32179014 32315048 32448811 32582553 32723490 32861444 - 32996155 33136030 33268924 33405650 33543683 33685788 33816836 33954833 - 34092223 34230924 34367260 34505419 34642105 34780295 34914762 35049107 - 35183442 35332689 35467420 35609608 35745445 35886130 36024070 36158288 - 36301333 36432170 36570025 36711597 36844792 36979203 37117183 37259357 - 37400619 37531160 37671620 37803798 37938273 38081582 38217029 38354132 - 38495438 38625572 38765143 38901411 39040724 39179889 39318856 39459452 - 39593811 39730667 39866663 39998479 40136279 40274555 40415779 40553883 - 40691863 40827791 40966768 41106111 41241983] - -2023-05-10 18:23:36,990 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:36,990 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1799977 1799977 1799978 ... 1501864 1501860 1501857] -destinations: [ 67253 683918 3462447 ... 4126744 2225873 678067] -batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 - 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 - 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 - 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 - 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 - 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 - 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 - 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 - 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 - 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 - 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 - 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40731888 40871706 41005049 41139987 41276652] - -2023-05-10 18:23:40,396 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:40,399 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [3000688 3000688 3000692 ... 1177195 1177195 1177195] -destinations: [4136722 1472295 1269526 ... 1057264 1200679 3027879] -batch: [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 - 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 - 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 - 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 - 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 - 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 - 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 - 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 - 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 - 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3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 - 3594 3595 3596 3597 3598 3599] -offset: [ 0 135714 276788 411119 550483 687685 824941 960991 - 1101771 1241164 1378563 1517295 1655803 1791537 1927892 2070608 - 2212780 2345354 2485345 2621830 2760672 2898464 3040569 3176432 - 3317252 3454493 3589260 3723173 3865766 4002657 4138672 4272353 - 4412552 4551783 4689920 4826332 4964960 5094517 5235521 5372233 - 5505333 5643911 5782767 5918585 6056807 6192751 6328018 6467101 - 6607328 6745473 6880026 7020917 7158928 7295950 7440262 7579365 - 7717976 7852472 7988545 8131820 8272567 8408812 8543166 8679926 - 8819844 8956553 9092126 9230923 9365957 9503639 9643398 9782494 - 9915754 10048842 10189272 10330981 10464569 10603002 10739147 10872369 - 11007632 11145981 11290863 11427660 11568719 11703679 11845682 11980009 - 12121195 12259261 12400582 12535284 12669898 12808724 12947252 13085972 - 13230082 13366397 13502927 13643377 13779906 13919662 14056315 14197734 - 14336180 14467476 14606031 14748657 14887427 15021188 15156467 15297035 - 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30845153 30988441 31124845 31260348 31404955 31540372 31676543 31811948 - 31950151 32085241 32228560 32363789 32501737 32639985 32778750 32915710 - 33051796 33193300 33330150 33467516 33599062 33735223 33873736 34012523 - 34153422 34290449 34431584 34568428 34700151 34841043 34978187 35112156 - 35254192 35391466 35522030 35660610 35800082 35938446 36076306 36217282 - 36349725 36484117 36619735 36756075 36890440 37028394 37165562 37300545 - 37435125 37569762 37710835 37850296 37987207 38123497 38259296 38394390 - 38532857 38668424 38804567 38943347 39084408 39223442 39359817 39499360 - 39639214 39775788 39911939 40049223 40185074 40320870 40460640 40594205 - 40739378 40877606 41010357 41148254 41281652] - -2023-05-10 18:23:41,497 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:41,497 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [3600609 3600609 3600616 ... 3494138 3494138 3494138] -destinations: [ 890270 2261968 4522975 ... 2983774 104114 449569] -batch: [3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 - 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25342457 25478976 25617154 25754629 25894411 26039559 26174372 26308590 - 26448371 26588014 26725316 26865090 27001762 27143208 27280134 27421474 - 27561402 27698640 27838230 27974803 28114727 28252005 28391216 28526787 - 28664036 28801953 28940800 29075742 29211211 29345324 29483119 29627478 - 29761556 29897778 30033475 30174600 30307187 30448510 30586598 30724944 - 30864019 31001223 31138423 31272063 31415728 31549431 31689313 31820205 - 31956631 32094023 32232796 32372780 32513588 32652733 32785600 32924154 - 33059618 33201418 33332888 33474266 33608965 33744639 33881131 34013972 - 34153234 34293569 34433767 34570762 34711371 34847444 34986871 35124103 - 35267124 35404168 35542404 35675574 35812004 35948166 36085075 36224914 - 36360351 36503155 36638489 36774098 36916545 37048146 37186968 37326511 - 37465086 37605611 37742783 37881305 38018723 38160273 38299723 38441635 - 38579014 38720084 38854601 38988041 39126112 39261706 39402006 39538799 - 39682295 39823000 39959496 40097846 40231536 40368531 40506057 40649255 - 40783669 40921304 41058056 41194324 41335995] - -2023-05-10 18:23:42,340 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:23:42,340 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:23:42,344 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:23:42,344 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:23:42,346 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:23:42,346 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:23:42,468 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:23:42,469 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:23:42,470 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:33561. Reason: scheduler-restart -2023-05-10 18:23:42,470 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:45751. Reason: scheduler-restart -2023-05-10 18:23:42,471 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:23:42,471 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:23:42,473 - distributed.nanny - INFO - Worker closed -2023-05-10 18:23:42,473 - distributed.nanny - INFO - Worker closed -sources: [4200915 4200915 4200915 ... 4285919 4285919 4285919] -destinations: [2594935 4296605 1792376 ... 2546237 3693783 3652788] -batch: [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 - 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 - 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 - 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 - 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 - 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 - 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 - 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 - 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 - 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 - 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 - 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 - 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 - 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 - 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 - 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 - 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 - 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 - 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 - 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 - 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 - 4494 4495 4496 4497 4498 4499] -offset: [ 0 135419 271535 411518 548999 693191 828931 971570 - 1112616 1251583 1385012 1519461 1661543 1799516 1939530 2073331 - 2204847 2342223 2477567 2615192 2755814 2887261 3026755 3159562 - 3301717 3438354 3572895 3710281 3844461 3984668 4121235 4260320 - 4397969 4537178 4678476 4820048 4956527 5090747 5234043 5369758 - 5506746 5641012 5783778 5919320 6058201 6197339 6337901 6477812 - 6612681 6748810 6884576 7026887 7168032 7304136 7436729 7570856 - 7706565 7847803 7990892 8127006 8264181 8399124 8536321 8674224 - 8812403 8953327 9091184 9229206 9362973 9499260 9634254 9776779 - 9915507 10055020 10192272 10325844 10464342 10604318 10742433 10883785 - 11024637 11162289 11300525 11444301 11582137 11720291 11855505 11990758 - 12130577 12269052 12409010 12545050 12682069 12821503 12959811 13100012 - 13231567 13367136 13504718 13644753 13783303 13914945 14053167 14188054 - 14327580 14467297 14608485 14747818 14884310 15020003 15156944 15292349 - 15428042 15564903 15701358 15838582 15971801 16109135 16244239 16384391 - 16526486 16657159 16796218 16931714 17068343 17202810 17337374 17470827 - 17608318 17745231 17880207 18014914 18150284 18283784 18423184 18563137 - 18697735 18837912 18970806 19108721 19248865 19382358 19518578 19655260 - 19792906 19929686 20071139 20206836 20343956 20480035 20616200 20752488 - 20888131 21025154 21160789 21298249 21436134 21575133 21714827 21852552 - 21989325 22128658 22261523 22405494 22543531 22677710 22815793 22952082 - 23092586 23227157 23370786 23509528 23648556 23780990 23922101 24059264 - 24199221 24339787 24479456 24615850 24751622 24882082 25021224 25164216 - 25298802 25438138 25574180 25711997 25855445 25991933 26132843 26265093 - 26403748 26541436 26678117 26810934 26948150 27089774 27226222 27367909 - 27500065 27630637 27767203 27905292 28049295 28196421 28335875 28473540 - 28613193 28751757 28891467 29031982 29169704 29303825 29441002 29580122 - 29720420 29859525 30001101 30136965 30272966 30413152 30548273 30684270 - 30820387 30958616 31093475 31230780 31372450 31509020 31643887 31788461 - 31929406 32070955 32206261 32348709 32486119 32626188 32760260 32898714 - 33037314 33174899 33311664 33452622 33584275 33722637 33860988 34002125 - 34138274 34279856 34419087 34556305 34691882 34829242 34969300 35105509 - 35242929 35379536 35520561 35652824 35790244 35921286 36062326 36197494 - 36337799 36472104 36607762 36744470 36880716 37023969 37161388 37301212 - 37437341 37571701 37708710 37843603 37984570 38121372 38258046 38393090 - 38538139 38672166 38810523 38946286 39082106 39222852 39359217 39498412 - 39630757 39768225 39903582 40043364 40180555 40323126 40457428 40594954 - 40738384 40872884 41012199 41150751 41293420] - -sources: [1674536 1674536 1674536 ... 313511 313511 313511] -destinations: [3147711 3693947 4450635 ... 2028447 4293604 3445606] -batch: [3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 - 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 - 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 - 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 - 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 - 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 - 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 - 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 - 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 - 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 - 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 - 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 - 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 - 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 - 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 - 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 - 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 - 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 - 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 - 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 - 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 - 4194 4195 4196 4197 4198 4199] -offset: [ 0 139497 280630 419440 557828 691983 824226 960997 - 1099267 1237898 1371237 1506868 1651316 1791012 1925996 2067527 - 2205129 2337173 2476397 2611185 2746493 2884693 3025979 3166543 - 3300601 3439136 3578881 3721777 3862777 3996957 4135627 4269160 - 4404502 4543377 4684723 4823212 4956757 5091188 5232739 5369807 - 5511590 5652109 5790217 5926570 6065113 6202306 6342064 6474345 - 6619687 6753211 6885799 7022784 7162427 7300550 7436324 7578151 - 7719439 7856307 7995858 8132767 8270399 8408606 8546174 8681819 - 8818105 8955122 9091143 9234283 9369785 9509078 9650263 9781549 - 9919062 10058964 10194751 10328983 10468864 10604857 10742314 10875431 - 11016973 11159520 11297374 11435267 11574461 11709865 11856836 11988703 - 12128692 12259752 12397293 12533260 12666716 12807893 12942081 13086657 - 13229552 13370107 13508449 13649081 13786259 13920139 14057976 14192534 - 14326389 14464852 14598372 14738240 14876988 15023383 15166820 15304013 - 15443486 15581509 15716930 15855094 15989976 16128532 16264561 16402834 - 16542821 16677166 16814892 16956261 17093355 17235011 17376066 17509358 - 17646436 17780800 17918472 18060338 18193787 18329735 18468881 18605011 - 18738997 18874714 19011716 19146349 19285959 19420461 19557677 19694966 - 19834643 19968985 20107356 20243210 20384294 20524476 20662207 20806657 - 20944801 21088990 21222054 21358062 21493907 21634635 21766877 21903496 - 22039059 22177749 22316628 22454197 22595053 22729445 22864368 23002447 - 23136383 23280426 23416797 23552569 23691201 23824424 23964089 24104005 - 24236587 24378051 24511956 24648379 24787665 24927219 25066819 25202527 - 25341044 25479893 25620605 25754595 25893972 26031478 26168954 26307378 - 26444052 26581493 26723469 26860363 26996867 27135379 27270688 27412009 - 27548270 27687408 27825795 27961852 28097291 28235427 28378469 28511711 - 28646679 28785952 28922222 29058275 29195761 29334257 29475470 29615325 - 29753053 29884072 30018505 30158015 30293606 30429286 30565094 30702187 - 30846873 30985592 31119753 31261200 31405371 31540962 31677425 31813407 - 31949487 32084762 32225551 32358375 32495875 32633625 32775413 32916777 - 33051149 33188355 33324862 33459058 33594924 33736597 33872161 34009257 - 34144817 34284732 34420502 34558715 34691353 34831497 34970312 35114090 - 35252909 35389794 35523837 35665965 35807487 35942456 36082373 36220701 - 36354772 36492670 36629489 36770738 36903547 37045708 37185596 37324103 - 37456203 37595550 37736465 37878942 38018124 38153833 38286669 38425624 - 38566034 38703170 38843188 38978211 39111242 39249299 39381484 39522560 - 39657111 39796679 39936424 40076692 40215697 40350479 40486583 40622221 - 40762872 40899568 41033824 41170810 41313249] - -sources: [4610697 4610697 4610700 ... 4500573 4500573 4500572] -destinations: [1187165 3211298 4223301 ... 3463338 3671315 3056813] -batch: [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 - 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 - 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 - 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 - 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 - 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 - 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 - 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611] -offset: [ 0 133228 266058 404964 540173 679579 815883 952718 - 1091201 1227376 1365395 1502296 1641551 1783067 1922692 2063630 - 2196102 2334694 2472020 2609138 2747632 2890415 3032613 3167366 - 3297351 3439018 3578149 3712104 3849825 3986889 4126227 4271834 - 4409445 4545320 4683775 4825260 4958699 5096930 5237972 5374761 - 5511122 5649026 5782492 5919921 6049562 6189707 6330137 6469509 - 6606739 6739541 6875077 7013852 7153449 7291685 7428165 7560613 - 7699173 7838323 7976089 8114582 8250673 8389246 8522851 8663164 - 8801985 8941046 9079152 9219795 9365178 9501141 9642065 9784642 - 9918792 10053138 10190043 10326011 10467614 10607785 10749313 10885558 - 11024282 11164836 11304297 11440088 11577603 11714081 11849799 11991208 - 12123706 12262218 12399492 12541161 12683347 12818643 12956039 13097231 - 13232651 13366210 13503568 13637399 13772377 13910967 14048643 14183189 - 14320469 14458719 14595814 14734913 14870317 15006893 15145398 15284450 - 15286055] - -2023-05-10 18:23:44,624 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:23:44,627 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:23:45,848 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:23:45,848 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:23:45,848 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:23:45,848 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:23:46,131 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:23:46,140 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:23:47,257 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36171 -2023-05-10 18:23:47,257 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36171 -2023-05-10 18:23:47,257 - distributed.worker - INFO - dashboard at: 10.120.104.11:42317 -2023-05-10 18:23:47,257 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:23:47,257 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,257 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:23:47,257 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:23:47,257 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-1quet7lg -2023-05-10 18:23:47,258 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-5123bb3b-7637-45a5-a564-d26cd0c05d74 -2023-05-10 18:23:47,258 - distributed.worker - INFO - Starting Worker plugin RMMSetup-690f26d5-e63a-4d34-b09e-c32d2703ba02 -2023-05-10 18:23:47,307 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:46001 -2023-05-10 18:23:47,308 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:46001 -2023-05-10 18:23:47,308 - distributed.worker - INFO - dashboard at: 10.120.104.11:37321 -2023-05-10 18:23:47,308 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:23:47,308 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,308 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:23:47,308 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:23:47,308 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-p_e7q_5k -2023-05-10 18:23:47,309 - distributed.worker - INFO - Starting Worker plugin RMMSetup-81c6c7a9-3861-46e8-a602-9192d937ca02 -2023-05-10 18:23:47,382 - distributed.worker - INFO - Starting Worker plugin PreImport-7abfc3b1-d474-49a0-87a0-6b7f1d4616eb -2023-05-10 18:23:47,383 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,393 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:23:47,393 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,395 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:23:47,429 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-22296338-f086-4de6-b2e6-56411accd318 -2023-05-10 18:23:47,430 - distributed.worker - INFO - Starting Worker plugin PreImport-d7c0db34-8ba0-467b-a85d-eb4a8cffcc1c -2023-05-10 18:23:47,430 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,438 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:23:47,438 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:23:47,440 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:23:55,307 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:23:55,308 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.87s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:23:55,416 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:23:55,417 - distributed.core - INFO - Event loop was unresponsive in Worker for 7.98s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:23:55,420 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:55,421 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:23:55,425 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:23:55,425 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:24:01,536 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.52s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:24:01,663 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.64s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:24:01,666 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:24:01,668 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:24:21,117 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:21,118 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:21,808 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:24:21,809 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:24:22,669 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:22,670 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:26,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:26,275 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [1775325 1775325 1775325 ... 1269982 1269982 1269982] -destinations: [1431345 158227 3349855 ... 4275548 2044741 633003] -batch: [500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 - 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 - 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 - 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 - 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 - 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 - 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 - 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 - 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 - 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 - 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 - 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68201813 68343911 68475680 68613822 68754251] - -2023-05-10 18:24:36,998 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:37,000 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -sources: [4459822 4459822 4459822 ... 1501063 1501063 1501063] -destinations: [3829966 2207127 261070 ... 4103847 4267563 3343647] -batch: [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 - 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 - 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 - 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 - 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 - 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 - 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 - 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 - 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 - 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55047262 55186273 55322845 55464421 55603138 55734116 55872536 56014983 - 56154031 56288143 56423507 56564169 56699802 56836093 56967957 57107036 - 57242319 57382686 57522490 57654953 57791632 57928800 58070172 58209470 - 58346998 58493161 58632305 58769837 58907712 59047279 59184316 59322134 - 59462311 59596221 59731014 59866826 60003550 60138300 60285891 60427899 - 60559736 60699068 60835273 60973318 61106281 61248464 61387152 61523920 - 61662920 61796462 61932762 62070954 62211707 62345417 62481849 62616967 - 62754713 62889498 63027469 63164062 63299130 63436489 63571251 63706162 - 63841058 63975837 64114270 64248956 64389559 64524710 64662682 64798103 - 64942202 65078365 65215650 65352091 65494004 65634408 65768759 65907074 - 66047577 66190019 66323762 66460635 66598184 66737566 66881260 67018306 - 67155245 67290345 67431180 67566356 67703948 67841792 67979091 68123396 - 68261650 68396150 68536456 68672272 68811722] - -2023-05-10 18:24:41,804 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:24:41,804 - distributed.worker - INFO - Run out-of-band function '_get_allocation_counts' -2023-05-10 18:24:41,807 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:24:41,807 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:24:41,810 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:24:41,810 - distributed.worker - INFO - Run out-of-band function '_func_destroy_all' -2023-05-10 18:24:41,925 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:24:41,926 - distributed.nanny - INFO - Nanny asking worker to close. Reason: scheduler-restart -2023-05-10 18:24:41,927 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:36171. Reason: scheduler-restart -2023-05-10 18:24:41,927 - distributed.worker - INFO - Stopping worker at tcp://10.120.104.11:46001. Reason: scheduler-restart -2023-05-10 18:24:41,928 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:24:41,929 - distributed.core - INFO - Connection to tcp://10.120.104.11:8786 has been closed. -2023-05-10 18:24:41,929 - distributed.nanny - INFO - Worker closed -sources: [4000512 4000513 4000513 ... 4187130 4187130 4187130] -destinations: [3575264 27291 2931938 ... 2728522 441286 1700138] -batch: [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 - 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 - 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 - 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 - 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 - 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 - 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 - 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 - 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 - 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 - 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 - 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 - 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 - 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 - 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 - 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 - 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 - 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 - 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 - 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 - 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 - 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 - 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 - 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 - 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 - 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 - 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 - 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 - 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 - 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 - 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 - 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 - 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 - 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 - 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 - 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499] -offset: [ 0 134054 271990 406432 540508 679745 813486 953735 - 1092368 1238723 1381814 1518832 1657885 1796209 1931404 2069463 - 2204530 2343026 2478772 2617112 2757302 2891689 3029210 3170314 - 3307364 3449231 3590364 3724250 3861002 3995224 4132730 4274491 - 4408209 4544009 4683132 4819140 4952295 5087876 5224788 5359692 - 5499055 5633546 5770630 5907805 6047376 6181710 6320432 6456540 - 6597526 6737972 6875351 7020061 7158285 7302641 7435877 7572363 - 7708098 7849090 7981358 8117886 8253481 8392357 8531197 8669063 - 8809765 8944241 9079486 9217662 9351413 9495295 9631568 9766938 - 9905486 10038549 10178457 10318471 10451303 10593019 10726514 10862823 - 11002269 11141779 11281369 11417134 11555724 11694504 11835015 11968776 - 12108341 12245835 12383602 12522091 12658296 12795735 12937478 13074192 - 13210545 13349539 13484585 13626082 13762546 13901749 14040580 14176568 - 14312178 14450203 14593230 14726300 14860982 15000449 15136819 15272851 - 15410212 15548432 15689604 15829260 15967071 16098193 16232874 16372347 - 16508152 16643647 16779385 16916327 17060966 17199464 17333749 17474784 - 17618691 17754282 17891145 18026979 18163259 18298527 18439878 18572569 - 18709861 18847292 18989050 19131304 19265875 19402879 19539317 19673623 - 19809726 19951523 20086484 20223518 20358865 20498437 20634070 20772109 - 20904891 21045139 21183663 21327063 21465684 21602426 21736275 21878299 - 22020189 22154853 22295199 22433883 22568335 22706531 22843451 22984401 - 23117514 23259571 23399535 23538142 23670526 23809953 23950726 24093522 - 24232757 24368717 24501916 24640992 24781336 24918874 25058814 25193985 - 25327327 25465376 25597593 25738740 25873124 26012694 26152331 26292896 - 26432487 26567499 26703658 26839546 26980091 27116951 27251439 27388145 - 27530259 27665629 27801641 27941839 28079375 28224234 28360078 28502730 - 28643265 28782297 28915494 29050290 29192087 29329879 29470380 29604340 - 29736076 29873241 30008648 30146576 30287509 30418773 30557887 30690893 - 30833027 30969883 31104478 31241981 31376804 31516815 31653639 31792524 - 31929953 32069308 32210798 32352445 32489141 32622899 32766405 32902353 - 33039239 33173925 33316891 33452528 33591420 33730718 33871161 34010763 - 34145659 34281546 34417612 34559692 34701125 34837285 34970267 35104576 - 35240686 35382092 35525345 35661102 35798303 35933185 36070555 36208443 - 36346897 36487736 36625576 36763935 36897703 37033843 37169029 37312028 - 37450766 37590422 37727607 37861221 37999391 38139292 38277113 38418494 - 38559441 38696806 38835128 38978924 39116476 39254135 39389623 39525087 - 39665199 39803709 39943825 40079854 40216428 40356202 40494018 40634357 - 40765774 40901642 41039447 41179347 41317667 41449289 41587198 41722300 - 41862059 42001481 42142298 42282222 42418720 42554521 42691987 42827504 - 42963155 43099902 43236088 43373276 43506595 43643967 43779229 43919528 - 44061451 44192076 44331039 44466467 44602949 44737219 44871989 45005406 - 45142728 45280191 45415270 45549670 45684993 45818570 45957455 46096815 - 46231578 46371632 46504706 46642603 46782579 46915794 47051814 47188646 - 47326249 47463011 47604745 47740191 47877648 48013358 48149674 48285281 - 48420705 48557846 48693706 48831027 48969010 49107847 49247283 49385170 - 49522163 49661641 49794621 49938566 50076370 50210631 50348359 50485035 - 50625826 50760102 50903282 51041760 51180952 51313786 51455079 51592197 - 51732044 51872479 52011827 52148434 52284105 52415258 52554120 52697359 - 52832079 52970998 53107130 53245155 53388903 53525654 53666121 53798454 - 53937028 54074687 54210967 54344254 54481718 54623063 54759839 54901757 - 55033796 55164365 55300838 55439330 55583174 55730193 55869691 56007811 - 56147822 56286309 56426121 56566412 56703739 56837997 56975301 57114493 - 57254931 57394170 57535930 57671512 57807093 57947431 58082630 58218816 - 58354872 58493001 58627903 58765193 58907141 59043703 59178903 59324155 - 59465152 59606482 59741726 59883821 60021284 60161293 60295462 60434046 - 60572502 60710197 60846607 60987543 61119459 61257661 61395962 61537061 - 61673117 61814662 61953585 62090983 62226606 62363939 62503955 62640256 - 62778015 62914836 63055978 63188497 63325961 63457256 63598201 63733212 - 63873391 64007645 64143415 64280323 64416756 64560350 64697909 64837213 - 64973530 65108091 65245043 65380037 65520891 65657425 65793987 65929192 - 66074345 66208441 66346588 66482274 66618171 66758737 66895318 67033908 - 67166504 67304066 67439079 67578875 67715786 67858234 67992215 68129439 - 68272724 68407534 68546408 68684557 68827158] - -2023-05-10 18:24:41,930 - distributed.nanny - INFO - Worker closed -sources: [4235513 4235513 4235513 ... 4500005 4500005 4500005] -destinations: [2130107 400795 2585337 ... 4276265 4029929 3520657] -batch: [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 - 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 - 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 - 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 - 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 - 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 - 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 - 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611] -offset: [ 0 133076 266465 405182 540249 679830 816321 953373 - 1092170 1228094 1365979 1503036 1642248 1783936 1923416 2064354 - 2197256 2335851 2473204 2610687 2749143 2891946 3034118 3168885 - 3299138 3441094 3579971 3713710 3851519 3988532 4127275 4272700 - 4410401 4546394 4685189 4827113 4960458 5098567 5239231 5376443 - 5512797 5651377 5784872 5922440 6052195 6191750 6331904 6471034 - 6608400 6741299 6876946 7016230 7155240 7293499 7429660 7561798 - 7700493 7840323 7977808 8116464 8252757 8391190 8524600 8664872 - 8804047 8943262 9081315 9222073 9367340 9503657 9644442 9787243 - 9921092 10055644 10192038 10327680 10469322 10609326 10750908 10886944 - 11025891 11166459 11305833 11441913 11579762 11716107 11851887 11993688 - 12126090 12264783 12401745 12542948 12684978 12820521 12958219 13099273 - 13234776 13368194 13505201 13639141 13773592 13911799 14049225 14184025 - 14321320 14459971 14597333 14735935 14871159 15007764 15145973 15284737 - 15286371] - -2023-05-10 18:24:43,600 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:24:43,909 - distributed.nanny - WARNING - Restarting worker -2023-05-10 18:24:44,837 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:24:44,837 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:24:45,103 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:24:45,111 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize -2023-05-10 18:24:45,111 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize -2023-05-10 18:24:45,389 - distributed.preloading - INFO - Run preload setup: dask_cuda.initialize -2023-05-10 18:24:45,963 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:35339 -2023-05-10 18:24:45,963 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:35339 -2023-05-10 18:24:45,963 - distributed.worker - INFO - dashboard at: 10.120.104.11:37449 -2023-05-10 18:24:45,963 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:24:45,963 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:45,963 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:24:45,963 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:24:45,963 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-czt_ysng -2023-05-10 18:24:45,964 - distributed.worker - INFO - Starting Worker plugin RMMSetup-bf2d6ef0-3255-4d93-8d14-16a0595067cd -2023-05-10 18:24:46,140 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-235f11ac-77a4-4d1f-9037-f3b8b74babff -2023-05-10 18:24:46,140 - distributed.worker - INFO - Starting Worker plugin PreImport-4a8d27e2-7651-41b3-9bc3-46f6fb81a287 -2023-05-10 18:24:46,141 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:46,153 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:24:46,153 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:46,156 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:24:46,382 - distributed.worker - INFO - Start worker at: tcp://10.120.104.11:36565 -2023-05-10 18:24:46,383 - distributed.worker - INFO - Listening to: tcp://10.120.104.11:36565 -2023-05-10 18:24:46,383 - distributed.worker - INFO - dashboard at: 10.120.104.11:42337 -2023-05-10 18:24:46,383 - distributed.worker - INFO - Waiting to connect to: tcp://10.120.104.11:8786 -2023-05-10 18:24:46,383 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:46,383 - distributed.worker - INFO - Threads: 1 -2023-05-10 18:24:46,383 - distributed.worker - INFO - Memory: 755.28 GiB -2023-05-10 18:24:46,383 - distributed.worker - INFO - Local Directory: /tmp/dask-worker-space/worker-wkkkpxxz -2023-05-10 18:24:46,384 - distributed.worker - INFO - Starting Worker plugin RMMSetup-907f7e00-244b-47ac-9e67-b83c849788ee -2023-05-10 18:24:46,504 - distributed.worker - INFO - Starting Worker plugin PreImport-02ec5a93-71b1-424e-a16f-8c3e2614cc5c -2023-05-10 18:24:46,504 - distributed.worker - INFO - Starting Worker plugin CPUAffinity-07b82603-46d5-4e89-b008-0965403ccc5b -2023-05-10 18:24:46,505 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:46,513 - distributed.worker - INFO - Registered to: tcp://10.120.104.11:8786 -2023-05-10 18:24:46,513 - distributed.worker - INFO - ------------------------------------------------- -2023-05-10 18:24:46,516 - distributed.core - INFO - Starting established connection to tcp://10.120.104.11:8786 -2023-05-10 18:24:54,581 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:24:54,582 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.07s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:24:54,903 - distributed.worker - INFO - Run out-of-band function 'enable_spilling' -2023-05-10 18:24:54,905 - distributed.core - INFO - Event loop was unresponsive in Worker for 8.39s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:24:54,909 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:54,910 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:24:54,914 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:24:54,914 - distributed.worker - INFO - Run out-of-band function '_func_init_all' -2023-05-10 18:25:01,014 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.51s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:25:01,155 - distributed.core - INFO - Event loop was unresponsive in Worker for 5.66s. This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability. -2023-05-10 18:25:01,157 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:25:01,158 - distributed.worker - INFO - Run out-of-band function '_subcomm_init' -2023-05-10 18:25:20,341 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:20,341 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:21,044 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:25:21,045 - distributed.worker - INFO - Run out-of-band function 'set_statistics_adaptor' -2023-05-10 18:25:22,327 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:22,328 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:27,493 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:27,493 - distributed.worker - INFO - Run out-of-band function '_func_ucp_listener_port' -2023-05-10 18:25:28,068 - distributed.worker - WARNING - Compute Failed -Key: _call_plc_uniform_neighbor_sample-311a2cc0-dd67-4f2c-8686-db64d7a6c7f1 -Function: _call_plc_uniform_neighbor_sample -args: (b'\xc1\x93Al]\xf3A\xe7\x99\x99\x01gr\xd2&\xcd', , _START_ _BATCH_ -2007008 2007008 2007 -2007009 2007009 2007 -2007010 2007010 2007 -2007011 2007011 2007 -2007012 2007012 2007 -... ... ... -2996187 2996187 2996 -2996188 2996188 2996 -2996189 2996189 2996 -2996190 2996190 2996 -2996191 2996191 2996 - -[1000000 rows x 2 columns], array([2000, 2001, 2002, ..., 3997, 3998, 3999], dtype=int32), 0 0 -1 0 -2 0 -3 0 -4 0 - .. -1995 1 -1996 1 -1997 1 -1998 1 -1999 1 -Length: 2000, dtype: int32, array([10, 25], dtype=int32), False) -kwargs: {'weight_t': 'float32', 'with_edge_properties': True, 'random_state': 3720334982212921840, 'return_offsets': True} -Exception: "RuntimeError('non-success value returned from cugraph_uniform_neighbor_sample_with_edge_properties: CUGRAPH_UNKNOWN_ERROR std::bad_alloc: out_of_memory: RMM failure at:/opt/conda/envs/rapids/include/rmm/mr/device/pool_memory_resource.hpp:196: Maximum pool size exceeded')" - -2023-05-10 18:33:34,883 - distributed.core - INFO - Removing comms to tcp://10.120.104.11:36565 From cd4034c5ac27abcff9e3a00d23ebcea37f996fbf Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 19 May 2023 19:25:00 +0000 Subject: [PATCH 042/347] fix reverse edges arg --- benchmarks/cugraph/standalone/cugraph_bulk_sampling.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index 4c3c15b7950..a6b44c3b71d 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -463,6 +463,14 @@ def get_args(): required=False, default='500000,1000000,2000000', ) + + parser.add_argument( + '--reverse_edges', + action='store_true', + help='Whether to reverse the edges for DGL (defaults to False). Should be True for DGL, False for PyG.', + required=False, + default=False, + ) parser.add_argument( '--dask_worker_devices', @@ -529,7 +537,7 @@ def get_args(): seeds_per_call=seeds_per_call, fanout=fanout, dataset_dir=args.dataset_root, - reverse_edges=False, + reverse_edges=args.reverse_edges, replication_factor=replication_factor ) stats_d["dataset"] = dataset From eb10b7a972e7507c65c2b74dcc47e494c6698939 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 19 May 2023 19:25:36 +0000 Subject: [PATCH 043/347] style --- .../cugraph/gnn/data_loading/bulk_sampler.py | 30 +++++++++---------- .../gnn/data_loading/bulk_sampler_io.py | 6 +--- python/cugraph/cugraph/testing/mg_utils.py | 14 ++++----- 3 files changed, 23 insertions(+), 27 deletions(-) diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 4a083e90a12..a89c677aaf2 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -206,9 +206,9 @@ def flush(self) -> None: end_time_calc_batches = time.perf_counter() self.__logger.info( - f'Calculated batches to sample; min = {min_batch_id}' - f' and max = {max_batch_id};' - f' took {end_time_calc_batches - start_time_calc_batches:.4f} s' + f"Calculated batches to sample; min = {min_batch_id}" + f" and max = {max_batch_id};" + f" took {end_time_calc_batches - start_time_calc_batches:.4f} s" ) if isinstance(self.__graph._plc_graph, pylibcugraph.graphs.SGGraph): @@ -228,7 +228,7 @@ def flush(self) -> None: ) start_time_sample_call = time.perf_counter() - + # Call uniform neighbor sample samples, offsets = sample_fn( self.__graph, @@ -242,22 +242,22 @@ def flush(self) -> None: end_time_sample_call = time.perf_counter() sample_runtime = end_time_sample_call - start_time_sample_call self.__logger.info( - f'Called uniform neighbor sample, took {sample_runtime:.4f} s' - f' ({(sample_runtime) / (max_batch_id - min_batch_id):.4f} s' - ' per batch)' + f"Called uniform neighbor sample, took {sample_runtime:.4f} s" + f" ({(sample_runtime) / (max_batch_id - min_batch_id):.4f} s" + " per batch)" ) start_time_filter_batches = time.perf_counter() # Filter batches to remove those already processed self.__batches = self.__batches[~batch_id_filter] - if hasattr(self.__batches, 'compute'): + if hasattr(self.__batches, "compute"): self.__batches = self.__batches.persist() end_time_filter_batches = time.perf_counter() self.__logger.info( - 'Filtered batches, took ' - f'{end_time_filter_batches - start_time_filter_batches} s' + "Filtered batches, took " + f"{end_time_filter_batches - start_time_filter_batches} s" ) start_time_write = time.perf_counter() @@ -268,15 +268,15 @@ def flush(self) -> None: end_time_write = time.perf_counter() write_runtime = end_time_write - start_time_write self.__logger.info( - f'Wrote samples to parquet, took {write_runtime} seconds' - f' ({(write_runtime) / (max_batch_id - min_batch_id):.4f} s' - ' per batch)' + f"Wrote samples to parquet, took {write_runtime} seconds" + f" ({(write_runtime) / (max_batch_id - min_batch_id):.4f} s" + " per batch)" ) if self.size > 0: self.__logger.info( - f'There are still {self.size} samples remaining, ' - 'calling flush() again...' + f"There are still {self.size} samples remaining, " + "calling flush() again..." ) self.flush() diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py index 34683b46a9c..0295a02b280 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -69,11 +69,7 @@ def _write_samples_to_parquet( results_p["batch_id"] = offsets_p.batch_id.repeat( cupy.diff(offsets_p.offsets.values, append=end_ix) ).values - results_p.to_parquet( - full_output_path, - compression=None, - index=False - ) + results_p.to_parquet(full_output_path, compression=None, index=False) def write_samples( diff --git a/python/cugraph/cugraph/testing/mg_utils.py b/python/cugraph/cugraph/testing/mg_utils.py index 51414961693..04c7658da2f 100644 --- a/python/cugraph/cugraph/testing/mg_utils.py +++ b/python/cugraph/cugraph/testing/mg_utils.py @@ -195,14 +195,14 @@ def persist_dask_object(arg): # Function to convert bytes into human readable format def sizeof_fmt(num, suffix="B"): if isinstance(num, str): - if num[-2:] == 'GB': - return num[:-2] + 'G' - elif num[-2:] == 'MB': - return num[:-2] + 'M' - elif num[-2:] == 'KB': - return num[:-2] + 'K' + if num[-2:] == "GB": + return num[:-2] + "G" + elif num[-2:] == "MB": + return num[:-2] + "M" + elif num[-2:] == "KB": + return num[:-2] + "K" else: - raise ValueError('unknown unit') + raise ValueError("unknown unit") for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: if abs(num) < 1024.0: return "%3.1f%s%s" % (num, unit, suffix) From 9f0b31d78e0fe1f334eb56b9dd87415b3d7798e6 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 23 May 2023 15:00:56 +0000 Subject: [PATCH 044/347] loader fix --- .../standalone/cugraph_bulk_sampling.py | 10 +- .../standalone/cugraph_pyg_graph_sage.py | 247 ++++++++++++++++++ .../cugraph_pyg/loader/cugraph_node_loader.py | 45 ++-- .../cugraph_pyg/tests/test_cugraph_loader.py | 89 +++++++ 4 files changed, 372 insertions(+), 19 deletions(-) create mode 100644 benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index a6b44c3b71d..cd2a56855dc 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -41,7 +41,7 @@ import re import os import gc -from time import sleep +from time import sleep, perf_counter from math import ceil import pandas as pd @@ -178,9 +178,12 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal print('created batches') + start_time = perf_counter() sampler.add_batches(batch_df, start_col_name='node', batch_col_name='batch') sampler.flush() + end_time = perf_counter() print('flushed all batches') + return (end_time - start_time) def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_factor=1): @@ -355,18 +358,21 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed output_sample_path = os.path.join(output_subdir, 'samples') os.makedirs(output_sample_path) + execution_time, allocation_counts = sample_graph(G, dask_label_df, output_sample_path, seed, batch_size, seeds_per_call, fanout) + output_meta = { 'dataset': dataset, + 'dataset_dir': dataset_dir, 'seed': seed, 'batch_size': batch_size, 'seeds_per_call': seeds_per_call, 'fanout': fanout, 'replication_factor': replication_factor, + 'execution_time': execution_time, } with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: json.dump(output_meta, f) - _, allocation_counts = sample_graph(G, dask_label_df, output_sample_path, seed, batch_size, seeds_per_call, fanout) print('allocation counts b:') print(allocation_counts.values()) diff --git a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py new file mode 100644 index 00000000000..173d79d03a1 --- /dev/null +++ b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py @@ -0,0 +1,247 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import json +import time +import argparse +import gc +import os + +import torch +import numpy as np + +from torch_geometric.nn import CuGraphSAGEConv + +import torch.nn as nn +import torch.nn.functional as F + +from typing import Union + +def load_disk_features(meta, node_type, replication_factor=1): + node_type_path = os.path.join(meta['dataset_dir'], meta['dataset'], 'npy', node_type) + + if replication_factor == 1: + return np.load( + os.path.join(node_type_path, 'node_feat.npy'), + mmap_mode='r' + ) + + else: + return np.load( + os.path.join(node_type_path, f'node_feat_{replication_factor}x.npy'), + mmap_mode='r' + ) + + +class CuGraphSAGE(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, num_layers): + super().__init__() + + self.convs = torch.nn.ModuleList() + self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels)) + for _ in range(num_layers - 1): + conv = CuGraphSAGEConv(hidden_channels, hidden_channels) + self.convs.append(conv) + + self.lin = nn.Linear(hidden_channels, out_channels) + + def forward(self, x, edge, size): + edge_csc = CuGraphSAGEConv.to_csc(edge, (size[0], size[0])) + for conv in self.convs: + x = conv(x, edge_csc)[: size[1]] + x = F.relu(x) + x = F.dropout(x, p=0.5) + + return self.lin(x) + + +def init_pytorch_worker(device_id: int) -> None: + import cupy + import rmm + + rmm.reinitialize( + devices=[device_id], + pool_allocator=True, + ) + + cupy.cuda.Device(device_id).use() + torch.cuda.set_device(device_id) + + from rmm.allocators.torch import rmm_torch_allocator + torch.cuda.change_current_allocator(rmm_torch_allocator) + + from rmm.allocators.cupy import rmm_cupy_allocator + cupy.cuda.set_allocator(rmm_cupy_allocator) + + +def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: + pass + +def train(device: int, meta: dict, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: + """ + Parameters + ---------- + device: int + The CUDA device where the model, graph data, and node labels will be stored. + features_device: Union[str, int] + The device (CUDA device or CPU) where features will be stored. + """ + + init_pytorch_worker(device) + + import cudf + import cugraph + from cugraph_pyg.data import CuGraphStore + from cugraph_pyg.loader import CuGraphNeighborLoader + + dataset_path = os.path.join(meta['dataset_dir'], meta['dataset']) + with open(os.path.join(dataset_path, 'meta.json'), 'r') as f: + input_meta = json.load(f) + + replication_factor = meta['replication_factor'] + G = {tuple(edge_type.split('__')): t * replication_factor for edge_type, t in input_meta['num_edges'].items()} + N = {node_type: t * replication_factor for node_type, t in input_meta['num_nodes'].items()} + + fs = cugraph.gnn.FeatureStore(backend="torch") + + for node_type in os.listdir(dataset_path, 'npy'): + feature_data = load_disk_features(meta, node_type, replication_factor=replication_factor) + fs.add_data( + torch.as_tensor(feature_data, device=features_device), + node_type, + "x", + ) + + label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') + if os.path.exists(label_path): + node_label = cudf.read_parquet(label_path) + node_label_tensor = torch.full(N[node_type], -1, dtype=torch.float32) + node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ + torch.as_tensor(node_label.label.values, device='cuda') + + del node_label + gc.collect() + + fs.add_data(torch.as_tensor(node_label, device=device), node_type, 'y') + + cugraph_store = CuGraphStore(fs, G, N) + + model = ( + CuGraphSAGE(in_channels=128, hidden_channels=64, out_channels=349, num_layers=3) + .to(torch.float32) + .to(device) + ) + + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + for epoch in range(num_epochs): + start_time_train = time.perf_counter_ns() + model.train() + + cugraph_bulk_loader = CuGraphNeighborLoader( + cugraph_store, train_nodes, batch_size=500, num_neighbors=[10, 25] + ) + + total_loss = 0 + num_batches = 0 + + # This context manager will handle different # batches per rank + # barrier() cannot do this since the number of ops per rank is + # different. It essentially acts like barrier would if the + # number of ops per rank was the same. + for epoch in range(num_epochs): + for iter_i, hetero_data in enumerate(cugraph_bulk_loader): + num_batches += 1 + if iter_i % 20 == 0: + print(f"iteration {iter_i}") + + # train + train_mask = hetero_data.train_dict["paper"] + y_true = hetero_data.y_dict["paper"] + + y_pred = model( + hetero_data.x_dict["paper"].to(device).to(torch.float32), + hetero_data.edge_index_dict[("paper", "cites", "paper")].to(device), + (len(y_true), len(y_true)), + ) + + y_true = F.one_hot( + y_true[train_mask].to(torch.int64), num_classes=349 + ).to(torch.float32) + + y_pred = y_pred[train_mask] + + loss = F.cross_entropy(y_pred, y_true) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + total_loss += loss.item() + + del y_true + del y_pred + del loss + del hetero_data + gc.collect() + + end_time_train = time.perf_counter_ns() + print( + f"epoch {epoch} time: " + f"{(end_time_train - start_time_train) / 1e9:3.4f} s" + ) + print(f"loss after epoch {epoch}: {total_loss / num_batches}") + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--device", + type=int, + default=0, + help="GPU to allocate to pytorch for model, graph data, and node label storage", + required=False, + ) + + parser.add_argument( + "--features_device", + type=str, + default="0", + help="Device to allocate to pytorch for feature storage", + required=False, + ) + + parser.add_argument( + "--num_epochs", + type=int, + default=1, + help="Number of training epochs", + required=False, + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + try: + features_device = int(args.features_device) + except ValueError: + features_device = args.features_device + + train(args.device, features_device, args.num_epochs) + + +if __name__ == "__main__": + main() diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index b6e9fc9b809..3a79793d2b9 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -44,13 +44,14 @@ def __init__( self, feature_store: CuGraphStore, graph_store: CuGraphStore, - input_nodes: Union[InputNodes, int] = None, + input_nodes: InputNodes = None, batch_size: int = 0, - shuffle=False, + shuffle: bool = False, edge_types: Sequence[Tuple[str]] = None, - directory=None, - starting_batch_id=0, - batches_per_partition=100, + directory: Union[str, tempfile.TemporaryDirectory] = None, + input_files: List[str]=None, + starting_batch_id: int = 0, + batches_per_partition: int = 100, # Sampler args num_neighbors: Union[List[int], Dict[Tuple[str, str, str], List[int]]] = None, replace: bool = True, @@ -69,9 +70,9 @@ def __init__( graph_store: CuGraphStore The graph store containing the graph structure. - input_nodes: Union[InputNodes, int] + input_nodes: InputNodes The input nodes associated with this sampler. - If this is an integer N, this loader will load N batches + If None, this loader will load batches from disk rather than performing sampling in memory. batch_size: int @@ -92,10 +93,13 @@ def __init__( The path of the directory to write samples to. Defaults to a new generated temporary directory. + input_files: List[str] (optional, default=None) + The input files to read from the directory containing + samples. This argument is only used when loading + alread-sampled batches from disk. + starting_batch_id: int (optional, default=0) The starting id for each batch. Defaults to 0. - Generally used when loading previously-sampled - batches from disk. batches_per_partition: int (optional, default=100) The number of batches in each output partition. @@ -121,11 +125,17 @@ def __init__( self.__batches_per_partition = batches_per_partition self.__starting_batch_id = starting_batch_id - if isinstance(input_nodes, int): + if input_nodes is None: # Will be loading from disk self.__num_batches = input_nodes self.__directory = directory - iter(os.listdir(self.__directory)) + if input_files is None: + if isinstance(self.__directory, str): + self.__input_files = iter(os.listdir(self.__directory)) + else: + self.__input_files = iter(os.listdir(self.__directory.name)) + else: + self.__input_files = iter(input_files) return input_type, input_nodes = torch_geometric.loader.utils.get_input_nodes( @@ -201,7 +211,13 @@ def __next__(self): ) # Will raise StopIteration if there are no files left - fname = next(self.__input_files) + try: + fname = next(self.__input_files) + except StopIteration as ex: + # Won't delete a non-temp dir (since it would just be deleting a string) + del self.__directory + self.__directory = None + raise StopIteration(ex) m = self.__ex_parquet_file.match(fname) if m is None: @@ -234,12 +250,7 @@ def __next__(self): ) # Get ready for next iteration - # If there is no next iteration, make sure results are deleted self.__next_batch += 1 - if self.__next_batch >= self.__num_batches + self.__starting_batch_id: - # Won't delete a non-temp dir (since it would just be deleting a string) - del self.__directory - self.__directory = None # Get and return the sampled subgraph if isinstance(torch_geometric, MissingModule): diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 9a32cda2661..d244ff4dea5 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -13,8 +13,16 @@ import pytest +import tempfile +import os + +import cudf +import cupy + from cugraph_pyg.loader import CuGraphNeighborLoader +from cugraph_pyg.loader import BulkSampleLoader from cugraph_pyg.data import CuGraphStore +from cugraph.gnn import FeatureStore from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") @@ -70,3 +78,84 @@ def test_cugraph_loader_hetero(karate_gnn): if "type1" in sample: for prop in sample["type1"]["prop0"].tolist(): assert prop % 41 == 0 + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_from_disk(): + F = FeatureStore() + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + + G = {('t0','knows','t0'): 7} + N = {'t0': 7} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame({ + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), + }) + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + bogus_samples['batch_id'] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + ) + + num_samples = 0 + for sample in loader: + num_samples += 1 + assert sample['t0']['num_nodes'] == 7 + # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] + assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + + assert num_samples == 256 + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_from_disk_subset(): + F = FeatureStore() + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + + G = {('t0','knows','t0'): 7} + N = {'t0': 7} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame({ + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), + }) + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + bogus_samples['batch_id'] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + input_files=list(os.listdir(tempdir.name))[100:200] + ) + + num_samples = 0 + for sample in loader: + num_samples += 1 + assert sample['t0']['num_nodes'] == 7 + # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] + assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + + assert num_samples == 100 \ No newline at end of file From acb99fa94b8bc868a6b616f66e0d399c7a89fa58 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 23 May 2023 15:03:10 +0000 Subject: [PATCH 045/347] pull in loader fix --- .../cugraph_pyg/loader/cugraph_node_loader.py | 45 ++++++---- .../cugraph_pyg/tests/test_cugraph_loader.py | 89 +++++++++++++++++++ 2 files changed, 117 insertions(+), 17 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index b6e9fc9b809..3a79793d2b9 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -44,13 +44,14 @@ def __init__( self, feature_store: CuGraphStore, graph_store: CuGraphStore, - input_nodes: Union[InputNodes, int] = None, + input_nodes: InputNodes = None, batch_size: int = 0, - shuffle=False, + shuffle: bool = False, edge_types: Sequence[Tuple[str]] = None, - directory=None, - starting_batch_id=0, - batches_per_partition=100, + directory: Union[str, tempfile.TemporaryDirectory] = None, + input_files: List[str]=None, + starting_batch_id: int = 0, + batches_per_partition: int = 100, # Sampler args num_neighbors: Union[List[int], Dict[Tuple[str, str, str], List[int]]] = None, replace: bool = True, @@ -69,9 +70,9 @@ def __init__( graph_store: CuGraphStore The graph store containing the graph structure. - input_nodes: Union[InputNodes, int] + input_nodes: InputNodes The input nodes associated with this sampler. - If this is an integer N, this loader will load N batches + If None, this loader will load batches from disk rather than performing sampling in memory. batch_size: int @@ -92,10 +93,13 @@ def __init__( The path of the directory to write samples to. Defaults to a new generated temporary directory. + input_files: List[str] (optional, default=None) + The input files to read from the directory containing + samples. This argument is only used when loading + alread-sampled batches from disk. + starting_batch_id: int (optional, default=0) The starting id for each batch. Defaults to 0. - Generally used when loading previously-sampled - batches from disk. batches_per_partition: int (optional, default=100) The number of batches in each output partition. @@ -121,11 +125,17 @@ def __init__( self.__batches_per_partition = batches_per_partition self.__starting_batch_id = starting_batch_id - if isinstance(input_nodes, int): + if input_nodes is None: # Will be loading from disk self.__num_batches = input_nodes self.__directory = directory - iter(os.listdir(self.__directory)) + if input_files is None: + if isinstance(self.__directory, str): + self.__input_files = iter(os.listdir(self.__directory)) + else: + self.__input_files = iter(os.listdir(self.__directory.name)) + else: + self.__input_files = iter(input_files) return input_type, input_nodes = torch_geometric.loader.utils.get_input_nodes( @@ -201,7 +211,13 @@ def __next__(self): ) # Will raise StopIteration if there are no files left - fname = next(self.__input_files) + try: + fname = next(self.__input_files) + except StopIteration as ex: + # Won't delete a non-temp dir (since it would just be deleting a string) + del self.__directory + self.__directory = None + raise StopIteration(ex) m = self.__ex_parquet_file.match(fname) if m is None: @@ -234,12 +250,7 @@ def __next__(self): ) # Get ready for next iteration - # If there is no next iteration, make sure results are deleted self.__next_batch += 1 - if self.__next_batch >= self.__num_batches + self.__starting_batch_id: - # Won't delete a non-temp dir (since it would just be deleting a string) - del self.__directory - self.__directory = None # Get and return the sampled subgraph if isinstance(torch_geometric, MissingModule): diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 9a32cda2661..d244ff4dea5 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -13,8 +13,16 @@ import pytest +import tempfile +import os + +import cudf +import cupy + from cugraph_pyg.loader import CuGraphNeighborLoader +from cugraph_pyg.loader import BulkSampleLoader from cugraph_pyg.data import CuGraphStore +from cugraph.gnn import FeatureStore from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") @@ -70,3 +78,84 @@ def test_cugraph_loader_hetero(karate_gnn): if "type1" in sample: for prop in sample["type1"]["prop0"].tolist(): assert prop % 41 == 0 + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_from_disk(): + F = FeatureStore() + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + + G = {('t0','knows','t0'): 7} + N = {'t0': 7} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame({ + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), + }) + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + bogus_samples['batch_id'] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + ) + + num_samples = 0 + for sample in loader: + num_samples += 1 + assert sample['t0']['num_nodes'] == 7 + # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] + assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + + assert num_samples == 256 + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_from_disk_subset(): + F = FeatureStore() + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + + G = {('t0','knows','t0'): 7} + N = {'t0': 7} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame({ + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), + }) + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + bogus_samples['batch_id'] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + input_files=list(os.listdir(tempdir.name))[100:200] + ) + + num_samples = 0 + for sample in loader: + num_samples += 1 + assert sample['t0']['num_nodes'] == 7 + # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] + assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + + assert num_samples == 100 \ No newline at end of file From 2e344f7d295fb707992a61e32bae4af2e130b595 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 23 May 2023 15:06:13 +0000 Subject: [PATCH 046/347] style --- .../cugraph_pyg/loader/cugraph_node_loader.py | 2 +- .../cugraph_pyg/tests/test_cugraph_loader.py | 74 ++++++++++--------- 2 files changed, 40 insertions(+), 36 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 3a79793d2b9..e0d3b0a9fca 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -49,7 +49,7 @@ def __init__( shuffle: bool = False, edge_types: Sequence[Tuple[str]] = None, directory: Union[str, tempfile.TemporaryDirectory] = None, - input_files: List[str]=None, + input_files: List[str] = None, starting_batch_id: int = 0, batches_per_partition: int = 100, # Sampler args diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index d244ff4dea5..e0a943aeca3 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -83,26 +83,28 @@ def test_cugraph_loader_hetero(karate_gnn): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_from_disk(): F = FeatureStore() - F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), "t0", "x") - G = {('t0','knows','t0'): 7} - N = {'t0': 7} + G = {("t0", "knows", "t0"): 7} + N = {"t0": 7} cugraph_store = CuGraphStore(F, G, N) - bogus_samples = cudf.DataFrame({ - "sources": [0, 1, 2, 3, 4, 5, 6], - "destinations": [6, 4, 3, 2, 2, 1, 5], - "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), - "edge_id": [5, 10, 15, 20, 25, 30, 35], - "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), - }) + bogus_samples = cudf.DataFrame( + { + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype="int32"), + } + ) tempdir = tempfile.TemporaryDirectory() for s in range(256): - bogus_samples['batch_id'] = cupy.int32(s) - bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) - + bogus_samples["batch_id"] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f"batch={s}-{s}.parquet")) + loader = BulkSampleLoader( feature_store=cugraph_store, graph_store=cugraph_store, @@ -112,50 +114,52 @@ def test_cugraph_loader_from_disk(): num_samples = 0 for sample in loader: num_samples += 1 - assert sample['t0']['num_nodes'] == 7 + assert sample["t0"]["num_nodes"] == 7 # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] - assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] - assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + assert sample["t0"]["x"].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[("t0", "knows", "t0")]["edge_index"].shape) == [2, 7] assert num_samples == 256 - + @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_from_disk_subset(): F = FeatureStore() - F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), 't0', 'x') + F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), "t0", "x") - G = {('t0','knows','t0'): 7} - N = {'t0': 7} + G = {("t0", "knows", "t0"): 7} + N = {"t0": 7} cugraph_store = CuGraphStore(F, G, N) - bogus_samples = cudf.DataFrame({ - "sources": [0, 1, 2, 3, 4, 5, 6], - "destinations": [6, 4, 3, 2, 2, 1, 5], - "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype='int32'), - "edge_id": [5, 10, 15, 20, 25, 30, 35], - "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype='int32'), - }) + bogus_samples = cudf.DataFrame( + { + "sources": [0, 1, 2, 3, 4, 5, 6], + "destinations": [6, 4, 3, 2, 2, 1, 5], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype="int32"), + } + ) tempdir = tempfile.TemporaryDirectory() for s in range(256): - bogus_samples['batch_id'] = cupy.int32(s) - bogus_samples.to_parquet(os.path.join(tempdir.name, f'batch={s}-{s}.parquet')) - + bogus_samples["batch_id"] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f"batch={s}-{s}.parquet")) + loader = BulkSampleLoader( feature_store=cugraph_store, graph_store=cugraph_store, directory=tempdir, - input_files=list(os.listdir(tempdir.name))[100:200] + input_files=list(os.listdir(tempdir.name))[100:200], ) num_samples = 0 for sample in loader: num_samples += 1 - assert sample['t0']['num_nodes'] == 7 + assert sample["t0"]["num_nodes"] == 7 # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] - assert sample['t0']['x'].tolist() == [1, 2, 3, 7, 5, 4, 6] - assert list(sample[('t0', 'knows','t0')]['edge_index'].shape) == [2,7] + assert sample["t0"]["x"].tolist() == [1, 2, 3, 7, 5, 4, 6] + assert list(sample[("t0", "knows", "t0")]["edge_index"].shape) == [2, 7] - assert num_samples == 100 \ No newline at end of file + assert num_samples == 100 From 44fd029e3ab5816389e8d188add16998546ff580 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 23 May 2023 15:39:57 +0000 Subject: [PATCH 047/347] bulk sampler fix --- python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py index 0295a02b280..d7f1c136484 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -43,7 +43,7 @@ def _write_samples_to_parquet( """ # Required by dask; need to skip dummy partitions. - if partition_info is None: + if partition_info is None or len(results) == 0: return if partition_info != "sg" and (not isinstance(partition_info, dict)): raise ValueError("Invalid value of partition_info") From 2e96d5cca19d3addba0bf4b8a55b857889a9a5ad Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 23 May 2023 19:55:44 +0000 Subject: [PATCH 048/347] perf testing --- .../standalone/cugraph_bulk_sampling.py | 18 +++- .../standalone/cugraph_pyg_graph_sage.py | 95 +++++++++++++------ 2 files changed, 80 insertions(+), 33 deletions(-) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index cd2a56855dc..d4893cc373f 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -149,7 +149,7 @@ def _replicate_df(df: cudf.DataFrame, replication_factor: int, col_offsets:Dict[ @get_allocation_counts_dask_lazy(return_allocations=True, logging=True) -def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_call=200000, fanout=[5, 5, 5]): +def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_call=200000, batches_per_partition=100, fanout=[5, 5, 5]): cupy.random.seed(seed) sampler = BulkSampler( @@ -160,7 +160,7 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal with_replacement=False, random_state=seed, seeds_per_call=seeds_per_call, - batches_per_partition=200_000 // batch_size, + batches_per_partition=batches_per_partition, log_level = 'INFO' ) @@ -358,7 +358,17 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed output_sample_path = os.path.join(output_subdir, 'samples') os.makedirs(output_sample_path) - execution_time, allocation_counts = sample_graph(G, dask_label_df, output_sample_path, seed, batch_size, seeds_per_call, fanout) + batches_per_partition = 200_000 // batch_size + execution_time, allocation_counts = sample_graph( + G, + dask_label_df, + output_sample_path, + seed=seed, + batch_size=batch_size, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + fanout=fanout + ) output_meta = { 'dataset': dataset, @@ -366,8 +376,10 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'seed': seed, 'batch_size': batch_size, 'seeds_per_call': seeds_per_call, + 'batches_per_partition': batches_per_partition, 'fanout': fanout, 'replication_factor': replication_factor, + 'num_sampling_gpus': len(G._plc_graph), 'execution_time': execution_time, } with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: diff --git a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py index 173d79d03a1..40f8bef2f6f 100644 --- a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py @@ -22,6 +22,7 @@ import numpy as np from torch_geometric.nn import CuGraphSAGEConv +from torch_geometric.nn import to_hetero import torch.nn as nn import torch.nn.functional as F @@ -49,21 +50,38 @@ def __init__(self, in_channels, hidden_channels, out_channels, num_layers): super().__init__() self.convs = torch.nn.ModuleList() - self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels)) - for _ in range(num_layers - 1): - conv = CuGraphSAGEConv(hidden_channels, hidden_channels) + self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels, aggr='mean')) + for _ in range(num_layers - 2): + conv = CuGraphSAGEConv(hidden_channels, hidden_channels, aggr='mean') self.convs.append(conv) + + self.convs.append(CuGraphSAGEConv(hidden_channels, out_channels, aggr='mean')) - self.lin = nn.Linear(hidden_channels, out_channels) + def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): + if len(num_sampled_nodes) != len(self.convs) + 1: + raise KeyError("Num sampled nodes length did not match # layers") + if len(num_sampled_edges) != len(self.convs): + raise KeyError("Num sampled edges did not match # layers") - def forward(self, x, edge, size): - edge_csc = CuGraphSAGEConv.to_csc(edge, (size[0], size[0])) for conv in self.convs: - x = conv(x, edge_csc)[: size[1]] + if not edge.is_cuda: + edge = edge.cuda() + + edge_csc = CuGraphSAGEConv.to_csc(edge[-num_sampled_edges[-1]:], (num_sampled_nodes[-2], num_sampled_nodes[-1])) + edge = edge[:-num_sampled_edges[-1]] + num_sampled_edges = num_sampled_edges[:-1] + num_sampled_nodes = num_sampled_nodes[:-1] + + if not x.is_cuda: + x = x.cuda() + if x.dtype != torch.float32: + x = x.to(torch.float32) + + x = conv(x, edge_csc) x = F.relu(x) x = F.dropout(x, p=0.5) - return self.lin(x) + return x def init_pytorch_worker(device_id: int) -> None: @@ -88,7 +106,7 @@ def init_pytorch_worker(device_id: int) -> None: def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: pass -def train(device: int, meta: dict, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: +def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ Parameters ---------- @@ -103,25 +121,32 @@ def train(device: int, meta: dict, features_device: Union[str, int] = "cpu", num import cudf import cugraph from cugraph_pyg.data import CuGraphStore - from cugraph_pyg.loader import CuGraphNeighborLoader + from cugraph_pyg.loader import BulkSampleLoader + + with open(os.path.join(bulk_samples_dir, 'output_meta.json'), 'r') as f: + output_meta = json.load(f) - dataset_path = os.path.join(meta['dataset_dir'], meta['dataset']) + dataset_path = os.path.join(output_meta['dataset_dir'], output_meta['dataset']) with open(os.path.join(dataset_path, 'meta.json'), 'r') as f: input_meta = json.load(f) - replication_factor = meta['replication_factor'] + replication_factor = output_meta['replication_factor'] G = {tuple(edge_type.split('__')): t * replication_factor for edge_type, t in input_meta['num_edges'].items()} N = {node_type: t * replication_factor for node_type, t in input_meta['num_nodes'].items()} fs = cugraph.gnn.FeatureStore(backend="torch") + num_input_features = 0 + num_output_features = 0 for node_type in os.listdir(dataset_path, 'npy'): - feature_data = load_disk_features(meta, node_type, replication_factor=replication_factor) + feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) fs.add_data( torch.as_tensor(feature_data, device=features_device), node_type, "x", ) + if feature_data.shape[1] > num_input_features: + num_input_features = feature_data.shape[1] label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') if os.path.exists(label_path): @@ -134,14 +159,16 @@ def train(device: int, meta: dict, features_device: Union[str, int] = "cpu", num gc.collect() fs.add_data(torch.as_tensor(node_label, device=device), node_type, 'y') + if len(node_label) > num_output_features: + num_output_features = len(node_label) - cugraph_store = CuGraphStore(fs, G, N) - - model = ( - CuGraphSAGE(in_channels=128, hidden_channels=64, out_channels=349, num_layers=3) + model = to_hetero( + CuGraphSAGE(in_channels=num_input_features, hidden_channels=64, out_channels=num_output_features, num_layers=len(output_meta['fanout'])) .to(torch.float32) .to(device) ) + + cugraph_store = CuGraphStore(fs, G, N) optimizer = torch.optim.Adam(model.parameters(), lr=0.01) @@ -149,33 +176,34 @@ def train(device: int, meta: dict, features_device: Union[str, int] = "cpu", num start_time_train = time.perf_counter_ns() model.train() - cugraph_bulk_loader = CuGraphNeighborLoader( - cugraph_store, train_nodes, batch_size=500, num_neighbors=[10, 25] + cugraph_loader = BulkSampleLoader( + cugraph_store, + cugraph_store, + input_nodes=None, + directory=os.path.join(bulk_samples_dir, 'samples'), ) total_loss = 0 num_batches = 0 - # This context manager will handle different # batches per rank - # barrier() cannot do this since the number of ops per rank is - # different. It essentially acts like barrier would if the - # number of ops per rank was the same. for epoch in range(num_epochs): - for iter_i, hetero_data in enumerate(cugraph_bulk_loader): + for iter_i, hetero_data in enumerate(cugraph_loader): num_batches += 1 if iter_i % 20 == 0: print(f"iteration {iter_i}") # train - train_mask = hetero_data.train_dict["paper"] - y_true = hetero_data.y_dict["paper"] + train_mask = hetero_data.train_dict + y_true = hetero_data.y_dict y_pred = model( - hetero_data.x_dict["paper"].to(device).to(torch.float32), - hetero_data.edge_index_dict[("paper", "cites", "paper")].to(device), - (len(y_true), len(y_true)), + hetero_data.x_dict, + hetero_data.edge_index_dict, + hetero_data.num_sampled_nodes, ) + print('y pred: ', y_pred) + y_true = F.one_hot( y_true[train_mask].to(torch.int64), num_classes=349 ).to(torch.float32) @@ -229,6 +257,13 @@ def parse_args(): required=False, ) + parser.add_argument( + "--sample_dir", + type=str, + help="Directory with stored bulk samples", + required=True, + ) + return parser.parse_args() @@ -240,7 +275,7 @@ def main(): except ValueError: features_device = args.features_device - train(args.device, features_device, args.num_epochs) + train(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) if __name__ == "__main__": From 7292ab109e6d6a7850b4e1de87809ddd6a95bb1f Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 00:43:02 +0000 Subject: [PATCH 049/347] t --- .../standalone/cugraph_pyg_graph_sage.py | 118 ++++++++++++------ .../cugraph_pyg/data/cugraph_store.py | 2 +- .../cugraph_pyg/loader/cugraph_node_loader.py | 9 +- 3 files changed, 85 insertions(+), 44 deletions(-) diff --git a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py index 40f8bef2f6f..025e649043d 100644 --- a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py @@ -22,7 +22,7 @@ import numpy as np from torch_geometric.nn import CuGraphSAGEConv -from torch_geometric.nn import to_hetero +from torch_geometric.utils.trim_to_layer import TrimToLayer import torch.nn as nn import torch.nn.functional as F @@ -57,25 +57,37 @@ def __init__(self, in_channels, hidden_channels, out_channels, num_layers): self.convs.append(CuGraphSAGEConv(hidden_channels, out_channels, aggr='mean')) - def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): - if len(num_sampled_nodes) != len(self.convs) + 1: - raise KeyError("Num sampled nodes length did not match # layers") - if len(num_sampled_edges) != len(self.convs): - raise KeyError("Num sampled edges did not match # layers") + self._trim = TrimToLayer() - for conv in self.convs: - if not edge.is_cuda: - edge = edge.cuda() + def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): + print(num_sampled_nodes) + print(num_sampled_edges) + + for i, conv in enumerate(self.convs): + edge = edge.cuda() + x = x.cuda().to(torch.float32) + + _, edge, _ = self._trim( + i, + num_sampled_nodes, + num_sampled_edges, + x, + edge, + None + ) + print(edge.shape) + print(edge) + + s = len(edge.unique()) + edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) - edge_csc = CuGraphSAGEConv.to_csc(edge[-num_sampled_edges[-1]:], (num_sampled_nodes[-2], num_sampled_nodes[-1])) - edge = edge[:-num_sampled_edges[-1]] - num_sampled_edges = num_sampled_edges[:-1] - num_sampled_nodes = num_sampled_nodes[:-1] + print('x shape', x.shape) + print('s: ', s) + x = x[:s] - if not x.is_cuda: - x = x.cuda() - if x.dtype != torch.float32: - x = x.to(torch.float32) + print(x.shape) + print(edge.shape) + print('----------------') x = conv(x, edge_csc) x = F.relu(x) @@ -90,11 +102,9 @@ def init_pytorch_worker(device_id: int) -> None: rmm.reinitialize( devices=[device_id], - pool_allocator=True, + pool_allocator=False, ) - cupy.cuda.Device(device_id).use() - torch.cuda.set_device(device_id) from rmm.allocators.torch import rmm_torch_allocator torch.cuda.change_current_allocator(rmm_torch_allocator) @@ -102,6 +112,10 @@ def init_pytorch_worker(device_id: int) -> None: from rmm.allocators.cupy import rmm_cupy_allocator cupy.cuda.set_allocator(rmm_cupy_allocator) + cupy.cuda.Device(device_id).use() + torch.cuda.set_device(device_id) + + def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: pass @@ -138,7 +152,7 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = num_input_features = 0 num_output_features = 0 - for node_type in os.listdir(dataset_path, 'npy'): + for node_type in os.listdir(os.path.join(dataset_path, 'npy')): feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) fs.add_data( torch.as_tensor(feature_data, device=features_device), @@ -151,24 +165,32 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') if os.path.exists(label_path): node_label = cudf.read_parquet(label_path) - node_label_tensor = torch.full(N[node_type], -1, dtype=torch.float32) + node_label_tensor = torch.full((N[node_type],), -1, dtype=torch.float32, device='cuda') node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ torch.as_tensor(node_label.label.values, device='cuda') del node_label gc.collect() - fs.add_data(torch.as_tensor(node_label, device=device), node_type, 'y') - if len(node_label) > num_output_features: - num_output_features = len(node_label) + fs.add_data((node_label_tensor > -1), node_type, 'train') + fs.add_data(node_label_tensor, node_type, 'y') + num_classes = int(node_label_tensor.max()) + 1 + if num_classes > num_output_features: + num_output_features = num_classes + print('done loading data') - model = to_hetero( - CuGraphSAGE(in_channels=num_input_features, hidden_channels=64, out_channels=num_output_features, num_layers=len(output_meta['fanout'])) - .to(torch.float32) - .to(device) - ) + print(num_input_features, num_output_features, len(output_meta['fanout'])) + + model = CuGraphSAGE( + in_channels=num_input_features, + hidden_channels=64, + out_channels=num_output_features, + num_layers=len(output_meta['fanout']) + ).to(torch.float32).to(device) + print('done creating model') cugraph_store = CuGraphStore(fs, G, N) + print('done creating store') optimizer = torch.optim.Adam(model.parameters(), lr=0.01) @@ -182,33 +204,44 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = input_nodes=None, directory=os.path.join(bulk_samples_dir, 'samples'), ) + print('done creating loader') total_loss = 0 num_batches = 0 for epoch in range(num_epochs): - for iter_i, hetero_data in enumerate(cugraph_loader): + t = time.perf_counter() + for iter_i, data in enumerate(cugraph_loader): + print(time.perf_counter() - t) + print(len(data.edge_index_dict['paper','cites','paper'][0].unique())) + print(len(data.edge_index_dict['paper','cites','paper'][1].unique())) + print('*********************************************************') + data = data.to_homogeneous() + num_batches += 1 if iter_i % 20 == 0: print(f"iteration {iter_i}") # train - train_mask = hetero_data.train_dict - y_true = hetero_data.y_dict + y_true = data.y y_pred = model( - hetero_data.x_dict, - hetero_data.edge_index_dict, - hetero_data.num_sampled_nodes, + data.x, + data.edge_index, + data.num_sampled_nodes, + data.num_sampled_edges, ) - print('y pred: ', y_pred) + if y_pred.shape[0] > len(y_true): + raise ValueError(f"illegal shape: {y_pred.shape}; {y_true.shape}") + + y_true = y_true[:y_pred.shape[0]] y_true = F.one_hot( - y_true[train_mask].to(torch.int64), num_classes=349 + y_true.to(torch.int64), num_classes=y_pred.shape[1] ).to(torch.float32) - - y_pred = y_pred[train_mask] + print('shape: ', y_true.shape) + """ loss = F.cross_entropy(y_pred, y_true) @@ -216,12 +249,15 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = loss.backward() optimizer.step() total_loss += loss.item() + del y_true del y_pred del loss - del hetero_data + del data gc.collect() + """ + t = time.perf_counter() end_time_train = time.perf_counter_ns() print( diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 59428c7d998..fa7835d0ca6 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -696,7 +696,7 @@ def _get_vertex_groups_from_sample( vtypes = cudf.Series(self.__vertex_type_offsets["type"]) if len(vtypes) == 1: - noi_index[vtypes[0]] = nodes_of_interest + noi_index[vtypes.iloc[0]] = nodes_of_interest else: noi_type_indices = torch.searchsorted( torch.as_tensor(self.__vertex_type_offsets["stop"], device="cuda"), diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index e0d3b0a9fca..551233f617a 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -213,6 +213,7 @@ def __next__(self): # Will raise StopIteration if there are no files left try: fname = next(self.__input_files) + print('fname:', fname) except StopIteration as ex: # Won't delete a non-temp dir (since it would just be deleting a string) del self.__directory @@ -243,10 +244,11 @@ def __next__(self): self.__data = self.__data[list(columns.keys())].astype(columns) # Pull the next set of sampling results out of the dataframe in memory - f = self.__data["batch_id"] == self.__next_batch + f = (self.__data["batch_id"] == self.__next_batch) + print('batch:', self.__next_batch) sampler_output = _sampler_output_from_sampling_results( - self.__data[f], self.__graph_store + self.__data.loc[f], self.__graph_store ) # Get ready for next iteration @@ -273,6 +275,9 @@ def __next__(self): sampler_output.edge, ) + out.set_value_dict('num_sampled_nodes', sampler_output.num_sampled_nodes) + out.set_value_dict('num_sampled_edges', sampler_output.num_sampled_edges) + return out def __iter__(self): From 20e9c63303a48700e6a634e9db3f9b831b34eaa4 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 03:33:36 +0000 Subject: [PATCH 050/347] testing --- .../cugraph_pyg/data/cugraph_store.py | 28 +++++++++++++------ .../cugraph_pyg/sampler/cugraph_sampler.py | 26 ++++++++--------- 2 files changed, 31 insertions(+), 23 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index fa7835d0ca6..d89114bfe84 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -788,17 +788,27 @@ def _get_renumbered_edge_groups_from_sample( t_pyg_type = list(self.__edge_types_to_attrs.values())[0].edge_type src_type, _, dst_type = t_pyg_type + dst_id_table = noi_index[dst_type] + dst_id_map = ( + cudf.Series(cupy.asarray(dst_id_table), name="dst") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("dst") + ) + dst = dst_id_map["new_id"].loc[sampling_results.destinations] + col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") + sources = torch.as_tensor(sampling_results.sources.values, device="cuda") src_id_table = noi_index[src_type] - src = torch.searchsorted(src_id_table, sources) - row_dict[t_pyg_type] = src - - destinations = torch.as_tensor( - sampling_results.destinations.values, device="cuda" - ) - dst_id_table = noi_index[dst_type] - dst = torch.searchsorted(dst_id_table, destinations) - col_dict[t_pyg_type] = dst + src_id_map = ( + cudf.Series(cupy.asarray(src_id_table), name="src") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("src") + ) + src = src_id_map["new_id"].loc[sampling_results.sources] + row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") + else: # This will retrieve the single string representation. # It needs to be converted to a tuple in the for loop below. diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 655edd27f65..b47f6f261bc 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -133,23 +133,21 @@ def _sampler_output_from_sampling_results( # Use hop 0 sources since those are the only ones not included in destinations # Use torch.concat based on benchmark performance (vs. cudf.concat) nodes_of_interest = ( - cudf.Series( - torch.concat( - [ - torch.as_tensor( - sampling_results_hop_0.sources.values, device="cuda" - ), - torch.as_tensor( - sampling_results.destinations.values, device="cuda" - ), - ] - ), - name="nodes_of_interest", - ) + cudf.concat([ + sampling_results_hop_0.sources, + sampling_results.destinations, + ]) .drop_duplicates() .sort_index() ) - del sampling_results_hop_0 + nodes_of_interest.name = "nodes_of_interest" + # del sampling_results_hop_0 + + print(hops) + print(sampling_results_hop_0.destinations.sort_values()) + print(sampling_results.sources.loc[sampling_results.hop_id==1].sort_values()) + print((sampling_results.sources.loc[sampling_results.hop_id==1].isin(sampling_results[sampling_results.hop_id==0].destinations)).sum()) + assert len(nodes_of_interest) == cudf.concat([sampling_results.sources, sampling_results.destinations]).nunique() # Get the grouped node index (for creating the renumbered grouped edge index) noi_index = graph_store._get_vertex_groups_from_sample( From 272bf87531491205669dba45d17253eff588ad90 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 03:36:05 +0000 Subject: [PATCH 051/347] add store --- .../cugraph_pyg/data/cugraph_store.py | 29 ++++++++++++------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 59428c7d998..e3eb4a85a85 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -696,7 +696,7 @@ def _get_vertex_groups_from_sample( vtypes = cudf.Series(self.__vertex_type_offsets["type"]) if len(vtypes) == 1: - noi_index[vtypes[0]] = nodes_of_interest + noi_index[vtypes.iloc[0]] = nodes_of_interest else: noi_type_indices = torch.searchsorted( torch.as_tensor(self.__vertex_type_offsets["stop"], device="cuda"), @@ -788,17 +788,26 @@ def _get_renumbered_edge_groups_from_sample( t_pyg_type = list(self.__edge_types_to_attrs.values())[0].edge_type src_type, _, dst_type = t_pyg_type - sources = torch.as_tensor(sampling_results.sources.values, device="cuda") - src_id_table = noi_index[src_type] - src = torch.searchsorted(src_id_table, sources) - row_dict[t_pyg_type] = src + dst_id_table = noi_index[dst_type] + dst_id_map = ( + cudf.Series(cupy.asarray(dst_id_table), name="dst") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("dst") + ) + dst = dst_id_map["new_id"].loc[sampling_results.destinations] + col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") - destinations = torch.as_tensor( - sampling_results.destinations.values, device="cuda" + src_id_table = noi_index[src_type] + src_id_map = ( + cudf.Series(cupy.asarray(src_id_table), name="src") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("src") ) - dst_id_table = noi_index[dst_type] - dst = torch.searchsorted(dst_id_table, destinations) - col_dict[t_pyg_type] = dst + src = src_id_map["new_id"].loc[sampling_results.sources] + row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") + else: # This will retrieve the single string representation. # It needs to be converted to a tuple in the for loop below. From 765eb86ac8dfaac08fa7c8fd36fae4750f0b2ccf Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 16:59:12 +0000 Subject: [PATCH 052/347] T --- .../cugraph_pyg/data/cugraph_store.py | 54 ++++++++++--------- .../cugraph_pyg/loader/cugraph_node_loader.py | 1 + .../cugraph_pyg/sampler/cugraph_sampler.py | 18 ++++--- .../cugraph-pyg/cugraph_pyg/tests/conftest.py | 13 +++-- 4 files changed, 50 insertions(+), 36 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index e3eb4a85a85..728f7806160 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -818,39 +818,21 @@ def _get_renumbered_edge_groups_from_sample( ) eoi_types = cudf.Series(eoi_types, name="t").groupby("t").groups + print('eoi types:') + print(eoi_types) + print('\n') for pyg_can_edge_type_str, ix in eoi_types.items(): pyg_can_edge_type = tuple(pyg_can_edge_type_str.split("__")) src_type, _, dst_type = pyg_can_edge_type - # Get the de-offsetted sources - sources = torch.as_tensor( - sampling_results.sources.iloc[ix].values, device="cuda" - ) - sources_ix = torch.searchsorted( - self.__vertex_type_offsets["stop"], sources - ) - sources -= self.__vertex_type_offsets["start"][sources_ix] - - # Create the row entry for this type - src_id_table = noi_index[src_type] - src_id_map = ( - cudf.Series(cupy.asarray(src_id_table), name="src") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("src") - ) - src = src_id_map["new_id"].loc[cupy.asarray(sources)] - row_dict[pyg_can_edge_type] = torch.as_tensor(src.values, device="cuda") - # Get the de-offsetted destinations + dst_num_type = self._numeric_vertex_type_from_name(dst_type) destinations = torch.as_tensor( sampling_results.destinations.iloc[ix].values, device="cuda" ) - destinations_ix = torch.searchsorted( - self.__vertex_type_offsets["stop"], destinations - ) - destinations -= self.__vertex_type_offsets["start"][destinations_ix] + destinations -= self.__vertex_type_offsets["start"][dst_num_type] + print(dst_type, dst_num_type, self.__vertex_type_offsets["start"][dst_num_type]) # Create the col entry for this type dst_id_table = noi_index[dst_type] @@ -860,9 +842,33 @@ def _get_renumbered_edge_groups_from_sample( .rename(columns={"index": "new_id"}) .set_index("dst") ) + print('dst id map:') + print(dst_id_map) + print('\n') + print('destinations:') + print(destinations) + print('\n') dst = dst_id_map["new_id"].loc[cupy.asarray(destinations)] col_dict[pyg_can_edge_type] = torch.as_tensor(dst.values, device="cuda") + # Get the de-offsetted sources + src_num_type = self._numeric_vertex_type_from_name(src_type) + sources = torch.as_tensor( + sampling_results.sources.iloc[ix].values, device="cuda" + ) + sources -= self.__vertex_type_offsets["start"][src_num_type] + + # Create the row entry for this type + src_id_table = noi_index[src_type] + src_id_map = ( + cudf.Series(cupy.asarray(src_id_table), name="src") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("src") + ) + src = src_id_map["new_id"].loc[cupy.asarray(sources)] + row_dict[pyg_can_edge_type] = torch.as_tensor(src.values, device="cuda") + return row_dict, col_dict def put_tensor(self, tensor, attr) -> None: diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 551233f617a..259fd43b2da 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -246,6 +246,7 @@ def __next__(self): # Pull the next set of sampling results out of the dataframe in memory f = (self.__data["batch_id"] == self.__next_batch) print('batch:', self.__next_batch) + print(self.__data.loc[f]) sampler_output = _sampler_output_from_sampling_results( self.__data.loc[f], self.__graph_store diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index b47f6f261bc..06727dd1a4f 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -132,21 +132,23 @@ def _sampler_output_from_sampling_results( # Calculate nodes of interest based on unique nodes in order of appearance # Use hop 0 sources since those are the only ones not included in destinations # Use torch.concat based on benchmark performance (vs. cudf.concat) + import cupy nodes_of_interest = ( - cudf.concat([ - sampling_results_hop_0.sources, - sampling_results.destinations, - ]) + cudf.Series(cupy.asarray(torch.concat([ + torch.as_tensor(sampling_results_hop_0.sources, device="cuda"), + torch.as_tensor(sampling_results.destinations, device="cuda"), + ]))) .drop_duplicates() .sort_index() ) nodes_of_interest.name = "nodes_of_interest" + print(nodes_of_interest) # del sampling_results_hop_0 - print(hops) - print(sampling_results_hop_0.destinations.sort_values()) - print(sampling_results.sources.loc[sampling_results.hop_id==1].sort_values()) - print((sampling_results.sources.loc[sampling_results.hop_id==1].isin(sampling_results[sampling_results.hop_id==0].destinations)).sum()) + #print('hops:', hops) + #print(sampling_results_hop_0.destinations.sort_values()) + #print(sampling_results.sources.loc[sampling_results.hop_id==1].sort_values()) + #print((sampling_results.sources.loc[sampling_results.hop_id==1].isin(sampling_results[sampling_results.hop_id==0].destinations)).sum()) assert len(nodes_of_interest) == cudf.concat([sampling_results.sources, sampling_results.destinations]).nunique() # Get the grouped node index (for creating the renumbered grouped edge index) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py index bb5aaf98f0d..d6e2ab702a9 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py @@ -79,7 +79,7 @@ def karate_gnn(): el = karate.get_edgelist().reset_index(drop=True) el.src = el.src.astype("int64") el.dst = el.dst.astype("int64") - all_vertices = np.array_split(cudf.concat([el.src, el.dst]).unique().values_host, 2) + all_vertices = np.array_split(np.arange(34), 2) F = FeatureStore(backend="torch") F.add_data( @@ -97,22 +97,23 @@ def karate_gnn(): "type0": len(all_vertices[0]), "type1": len(all_vertices[1]), } + print('all_vertices:', all_vertices) offsets = {"type0": 0, "type1": N["type0"]} G = { ("type0", "et01", "type1"): el[ el.src.isin(all_vertices[0]) & el.dst.isin(all_vertices[1]) - ], + ].reset_index(drop=True), ("type1", "et10", "type0"): el[ el.src.isin(all_vertices[1]) & el.dst.isin(all_vertices[0]) - ], + ].reset_index(drop=True), ("type0", "et00", "type0"): el[ el.src.isin(all_vertices[0]) & el.dst.isin(all_vertices[0]) ], ("type1", "et11", "type1"): el[ el.src.isin(all_vertices[1]) & el.dst.isin(all_vertices[1]) - ], + ].reset_index(drop=True), } G = { @@ -123,6 +124,10 @@ def karate_gnn(): for (src_type, edge_type, dst_type), elx in G.items() } + print('offset:', offsets) + print('G:', G) + + return F, G, N From f49edf5d05a8d95c4be3b5e10248b34acbb13542 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 17:04:37 +0000 Subject: [PATCH 053/347] fix ci failure --- python/cugraph-pyg/cugraph_pyg/tests/conftest.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py index bb5aaf98f0d..9c54d81fae9 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py @@ -79,7 +79,7 @@ def karate_gnn(): el = karate.get_edgelist().reset_index(drop=True) el.src = el.src.astype("int64") el.dst = el.dst.astype("int64") - all_vertices = np.array_split(cudf.concat([el.src, el.dst]).unique().values_host, 2) + all_vertices = np.array_split(np.arange(34), 2) F = FeatureStore(backend="torch") F.add_data( @@ -103,16 +103,16 @@ def karate_gnn(): G = { ("type0", "et01", "type1"): el[ el.src.isin(all_vertices[0]) & el.dst.isin(all_vertices[1]) - ], + ].reset_index(drop=True), ("type1", "et10", "type0"): el[ el.src.isin(all_vertices[1]) & el.dst.isin(all_vertices[0]) - ], + ].reset_index(drop=True), ("type0", "et00", "type0"): el[ el.src.isin(all_vertices[0]) & el.dst.isin(all_vertices[0]) ], ("type1", "et11", "type1"): el[ el.src.isin(all_vertices[1]) & el.dst.isin(all_vertices[1]) - ], + ].reset_index(drop=True), } G = { @@ -122,6 +122,7 @@ def karate_gnn(): ) for (src_type, edge_type, dst_type), elx in G.items() } + return F, G, N From c1aa7c7fc75b5f6100435b379135168d93cbe4b5 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 24 May 2023 17:09:45 +0000 Subject: [PATCH 054/347] fix style --- python/cugraph-pyg/cugraph_pyg/tests/conftest.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py index 9c54d81fae9..3270dd0bf93 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py @@ -23,7 +23,6 @@ import torch import numpy as np -import cudf from cugraph.gnn import FeatureStore from cugraph.experimental.datasets import karate @@ -122,7 +121,6 @@ def karate_gnn(): ) for (src_type, edge_type, dst_type), elx in G.items() } - return F, G, N From 00a886c591aa061eb950c1737bf547424dc35c97 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Tue, 30 May 2023 19:59:10 +0000 Subject: [PATCH 055/347] work --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 390 ++++++++++++++++++ benchmarks/cugraph-pyg/models_cugraph.py | 57 +++ benchmarks/cugraph-pyg/models_native.py | 0 .../standalone/cugraph_bulk_sampling.py | 10 +- .../standalone/cugraph_pyg_graph_sage.py | 318 -------------- 5 files changed, 455 insertions(+), 320 deletions(-) create mode 100644 benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py create mode 100644 benchmarks/cugraph-pyg/models_cugraph.py create mode 100644 benchmarks/cugraph-pyg/models_native.py delete mode 100644 benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py new file mode 100644 index 00000000000..72aa133f8de --- /dev/null +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -0,0 +1,390 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import re +import json +import time +import argparse +import gc +import os + +import torch +import numpy as np + +import torch.nn.functional as F + +from typing import Union + +from models_cugraph import CuGraphSAGE + +def load_disk_features(meta: dict, node_type: str, replication_factor: int = 1): + node_type_path = os.path.join(meta['dataset_dir'], meta['dataset'], 'npy', node_type) + + if replication_factor == 1: + return np.load( + os.path.join(node_type_path, 'node_feat.npy'), + mmap_mode='r' + ) + + else: + return np.load( + os.path.join(node_type_path, f'node_feat_{replication_factor}x.npy'), + mmap_mode='r' + ) + + +def init_pytorch_worker(device_id: int) -> None: + import cupy + import rmm + + rmm.reinitialize( + devices=[device_id], + pool_allocator=False, + ) + + + from rmm.allocators.torch import rmm_torch_allocator + torch.cuda.change_current_allocator(rmm_torch_allocator) + + from rmm.allocators.cupy import rmm_cupy_allocator + cupy.cuda.set_allocator(rmm_cupy_allocator) + + cupy.cuda.Device(device_id).use() + torch.cuda.set_device(device_id) + + +def train_epoch(model, loader, optimizer): + total_loss = 0.0 + num_batches = 0 + + t = time.perf_counter() + for iter_i, data in enumerate(loader): + print(time.perf_counter() - t) + print(len(data.edge_index_dict['paper','cites','paper'][0].unique())) + print(len(data.edge_index_dict['paper','cites','paper'][1].unique())) + print('*********************************************************') + data = data.to_homogeneous() + + num_batches += 1 + if iter_i % 20 == 0: + print(f"iteration {iter_i}") + + # train + y_true = data.y + + y_pred = model( + data.x, + data.edge_index, + data.num_sampled_nodes, + data.num_sampled_edges, + ) + + if y_pred.shape[0] > len(y_true): + raise ValueError(f"illegal shape: {y_pred.shape}; {y_true.shape}") + + y_true = y_true[:y_pred.shape[0]] + + y_true = F.one_hot( + y_true.to(torch.int64), num_classes=y_pred.shape[1] + ).to(torch.float32) + print('shape: ', y_true.shape) + """ + + loss = F.cross_entropy(y_pred, y_true) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + total_loss += loss.item() + + + del y_true + del y_pred + del loss + del data + gc.collect() + """ + t = time.perf_counter() + + return total_loss, num_batches + + +def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: + from models_native import GraphSAGE + from torch_geometric.data import HeteroData + from torch_geometric.loader import NeighborLoader + + import cudf + import pandas + + with open(os.path.join(bulk_samples_dir, 'output_meta.json'), 'r') as f: + output_meta = json.load(f) + + dataset_path = os.path.join(output_meta['dataset_dir'], output_meta['dataset']) + with open(os.path.join(dataset_path, 'meta.json'), 'r') as f: + input_meta = json.load(f) + + replication_factor = output_meta['replication_factor'] + + num_edges_dict = {tuple(edge_type.split('__')): t * replication_factor for edge_type, t in input_meta['num_edges'].items()} + num_nodes_dict = {node_type: t * replication_factor for node_type, t in input_meta['num_nodes'].items()} + + hetero_data = HeteroData() + num_input_features = 0 + num_output_features = 0 + for node_type in os.listdir(os.path.join(dataset_path, 'npy')): + feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) + hetero_data[node_type].x = feature_data.to(features_device) + + if feature_data.shape[1] > num_input_features: + num_input_features = feature_data.shape[1] + + label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') + if os.path.exists(label_path): + node_label = cudf.read_parquet(label_path) + node_label_tensor = torch.full((num_nodes_dict[node_type],), -1, dtype=torch.float32, device='cuda') + node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ + torch.as_tensor(node_label.label.values, device='cuda') + + del node_label + gc.collect() + + hetero_data[node_type]['train'] = (node_label_tensor > -1) + hetero_data[node_type]['y'] = node_label_tensor + + num_classes = int(node_label_tensor.max()) + 1 + if num_classes > num_output_features: + num_output_features = num_classes + + print('done loading feature data') + + # Have to load graph data for native PyG + parquet_path = os.path.join( + output_meta['dataset_dir'], + output_meta['dataset'], + 'parquet' + ) + + for edge_type in os.listdir(parquet_path): + if re.match(r'[a-z]+__[a-z]+__[a-z]+', edge_type): + print(f'Loading edge index for edge type {edge_type}') + + can_edge_type = tuple(edge_type.split('__')) + ei = pandas.read_parquet(os.path.join(os.path.join(parquet_path, edge_type), 'edge_index.parquet')) + ei = { + 'src': torch.as_tensor(ei['src']), + 'dst': torch.as_tensor(ei['dst']), + } + gc.collect() + + if replication_factor > 1: + for r in range(1, replication_factor): + ei['src'] = torch.concat([ + ei['src'], + ei['src'] + int(r * output_meta['num_nodes'][edge_type[0]]), + ]) + + ei['dst'] = torch.concat([ + ei['dst'], + ei['dst'] + int(r * output_meta['num_nodes'][edge_type[2]]), + ]) + gc.collect() + + ei = torch.stack([ + ei['src'], + ei['dst'], + ]) + gc.collect() + + hetero_data[can_edge_type]['edge_index'] = ei + gc.collect() + + print('done loading graph data') + print(num_input_features, num_output_features, len(output_meta['fanout'])) + + model = GraphSAGE( + in_channels=num_input_features, + hidden_channels=64, + out_channels=num_output_features, + num_layers=len(output_meta['fanout']) + ).to(torch.float32).to(device) + print('done creating model') + + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + for epoch in range(num_epochs): + start_time_train = time.perf_counter_ns() + model.train() + + loader = NeighborLoader( + hetero_data, + hetero_data, + input_nodes=None, + ) + print('done creating loader') + + total_loss, num_batches = train_epoch(model, loader, optimizer) + + end_time_train = time.perf_counter_ns() + print( + f"epoch {epoch} time: " + f"{(end_time_train - start_time_train) / 1e9:3.4f} s" + ) + print(f"loss after epoch {epoch}: {total_loss / num_batches}") + +def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: + """ + Parameters + ---------- + device: int + The CUDA device where the model, graph data, and node labels will be stored. + features_device: Union[str, int] + The device (CUDA device or CPU) where features will be stored. + """ + + init_pytorch_worker(device) + + import cudf + import cugraph + from cugraph_pyg.data import CuGraphStore + from cugraph_pyg.loader import BulkSampleLoader + + with open(os.path.join(bulk_samples_dir, 'output_meta.json'), 'r') as f: + output_meta = json.load(f) + + dataset_path = os.path.join(output_meta['dataset_dir'], output_meta['dataset']) + with open(os.path.join(dataset_path, 'meta.json'), 'r') as f: + input_meta = json.load(f) + + replication_factor = output_meta['replication_factor'] + G = {tuple(edge_type.split('__')): t * replication_factor for edge_type, t in input_meta['num_edges'].items()} + N = {node_type: t * replication_factor for node_type, t in input_meta['num_nodes'].items()} + + fs = cugraph.gnn.FeatureStore(backend="torch") + + num_input_features = 0 + num_output_features = 0 + for node_type in os.listdir(os.path.join(dataset_path, 'npy')): + feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) + fs.add_data( + torch.as_tensor(feature_data, device=features_device), + node_type, + "x", + ) + if feature_data.shape[1] > num_input_features: + num_input_features = feature_data.shape[1] + + label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') + if os.path.exists(label_path): + node_label = cudf.read_parquet(label_path) + node_label_tensor = torch.full((N[node_type],), -1, dtype=torch.float32, device='cuda') + node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ + torch.as_tensor(node_label.label.values, device='cuda') + + del node_label + gc.collect() + + fs.add_data((node_label_tensor > -1), node_type, 'train') + fs.add_data(node_label_tensor, node_type, 'y') + num_classes = int(node_label_tensor.max()) + 1 + if num_classes > num_output_features: + num_output_features = num_classes + print('done loading data') + + print(num_input_features, num_output_features, len(output_meta['fanout'])) + + model = CuGraphSAGE( + in_channels=num_input_features, + hidden_channels=64, + out_channels=num_output_features, + num_layers=len(output_meta['fanout']) + ).to(torch.float32).to(device) + print('done creating model') + + cugraph_store = CuGraphStore(fs, G, N) + print('done creating store') + + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + for epoch in range(num_epochs): + start_time_train = time.perf_counter_ns() + model.train() + + cugraph_loader = BulkSampleLoader( + cugraph_store, + cugraph_store, + input_nodes=None, + directory=os.path.join(bulk_samples_dir, 'samples'), + ) + print('done creating loader') + + total_loss, num_batches = train_epoch(model, cugraph_loader, optimizer) + + end_time_train = time.perf_counter_ns() + print( + f"epoch {epoch} time: " + f"{(end_time_train - start_time_train) / 1e9:3.4f} s" + ) + print(f"loss after epoch {epoch}: {total_loss / num_batches}") + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--device", + type=int, + default=0, + help="GPU to allocate to pytorch for model, graph data, and node label storage", + required=False, + ) + + parser.add_argument( + "--features_device", + type=str, + default="0", + help="Device to allocate to pytorch for feature storage", + required=False, + ) + + parser.add_argument( + "--num_epochs", + type=int, + default=1, + help="Number of training epochs", + required=False, + ) + + parser.add_argument( + "--sample_dir", + type=str, + help="Directory with stored bulk samples", + required=True, + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + try: + features_device = int(args.features_device) + except ValueError: + features_device = args.features_device + + train(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/cugraph-pyg/models_cugraph.py b/benchmarks/cugraph-pyg/models_cugraph.py new file mode 100644 index 00000000000..a42417368fa --- /dev/null +++ b/benchmarks/cugraph-pyg/models_cugraph.py @@ -0,0 +1,57 @@ +import torch + +from torch_geometric.nn import CuGraphSAGEConv +from torch_geometric.utils.trim_to_layer import TrimToLayer + +import torch.nn as nn +import torch.nn.functional as F + +class CuGraphSAGE(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, num_layers): + super().__init__() + + self.convs = torch.nn.ModuleList() + self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels, aggr='mean')) + for _ in range(num_layers - 2): + conv = CuGraphSAGEConv(hidden_channels, hidden_channels, aggr='mean') + self.convs.append(conv) + + self.convs.append(CuGraphSAGEConv(hidden_channels, out_channels, aggr='mean')) + + self._trim = TrimToLayer() + + def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): + print(num_sampled_nodes) + print(num_sampled_edges) + + for i, conv in enumerate(self.convs): + edge = edge.cuda() + x = x.cuda().to(torch.float32) + + _, edge, _ = self._trim( + i, + num_sampled_nodes, + num_sampled_edges, + x, + edge, + None + ) + print(edge.shape) + print(edge) + + s = len(edge.unique()) + edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) + + print('x shape', x.shape) + print('s: ', s) + x = x[:s] + + print(x.shape) + print(edge.shape) + print('----------------') + + x = conv(x, edge_csc) + x = F.relu(x) + x = F.dropout(x, p=0.5) + + return x diff --git a/benchmarks/cugraph-pyg/models_native.py b/benchmarks/cugraph-pyg/models_native.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index 0a41adc196f..3b2092ac3b3 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -201,6 +201,7 @@ def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor:int=1): return node_offsets, node_offsets_replicated, count_replicated + def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_factor=1): path = os.path.join(dataset_dir, dataset) parquet_path = os.path.join(path, 'parquet') @@ -241,8 +242,12 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_ edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].rename({'src':'dst','dst':'src'}).persist() # cuGraph-PyG assigns numeric edge type ids based on lexicographic order + edge_offsets = {} + edge_count = 0 for num_edge_type, can_edge_type in enumerate(sorted(edge_index_dict.keys())): edge_index_dict[can_edge_type]['etp'] = cupy.int32(num_edge_type) + edge_offsets[can_edge_type] += edge_count + edge_count += len(edge_index_dict[can_edge_type]) all_edges_df = dask_cudf.concat( list(edge_index_dict.values()) @@ -279,7 +284,7 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_ del node_labels gc.collect() - return all_edges_df, node_labels_df, node_offsets_replicated, total_num_nodes + return all_edges_df, node_labels_df, node_offsets_replicated, edge_offsets, total_num_nodes def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seeds_per_call, fanout, reverse_edges=True, dataset_dir='.', replication_factor=1, num_labels=256, labeled_percentage=0.001): @@ -342,7 +347,7 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) else: - dask_edgelist_df, dask_label_df, node_offsets, total_num_nodes = \ + dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ load_disk_dataset(dataset, dataset_dir=dataset_dir, reverse_edges=reverse_edges, replication_factor=replication_factor) num_input_edges = len(dask_edgelist_df) @@ -383,6 +388,7 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'dataset_dir': dataset_dir, 'seed': seed, 'node_offsets': node_offsets, + 'edge_offsets': edge_offsets, 'total_num_nodes': total_num_nodes, 'total_num_edges': num_input_edges, 'batch_size': batch_size, diff --git a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py b/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py deleted file mode 100644 index 025e649043d..00000000000 --- a/benchmarks/cugraph/standalone/cugraph_pyg_graph_sage.py +++ /dev/null @@ -1,318 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import json -import time -import argparse -import gc -import os - -import torch -import numpy as np - -from torch_geometric.nn import CuGraphSAGEConv -from torch_geometric.utils.trim_to_layer import TrimToLayer - -import torch.nn as nn -import torch.nn.functional as F - -from typing import Union - -def load_disk_features(meta, node_type, replication_factor=1): - node_type_path = os.path.join(meta['dataset_dir'], meta['dataset'], 'npy', node_type) - - if replication_factor == 1: - return np.load( - os.path.join(node_type_path, 'node_feat.npy'), - mmap_mode='r' - ) - - else: - return np.load( - os.path.join(node_type_path, f'node_feat_{replication_factor}x.npy'), - mmap_mode='r' - ) - - -class CuGraphSAGE(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, num_layers): - super().__init__() - - self.convs = torch.nn.ModuleList() - self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels, aggr='mean')) - for _ in range(num_layers - 2): - conv = CuGraphSAGEConv(hidden_channels, hidden_channels, aggr='mean') - self.convs.append(conv) - - self.convs.append(CuGraphSAGEConv(hidden_channels, out_channels, aggr='mean')) - - self._trim = TrimToLayer() - - def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): - print(num_sampled_nodes) - print(num_sampled_edges) - - for i, conv in enumerate(self.convs): - edge = edge.cuda() - x = x.cuda().to(torch.float32) - - _, edge, _ = self._trim( - i, - num_sampled_nodes, - num_sampled_edges, - x, - edge, - None - ) - print(edge.shape) - print(edge) - - s = len(edge.unique()) - edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) - - print('x shape', x.shape) - print('s: ', s) - x = x[:s] - - print(x.shape) - print(edge.shape) - print('----------------') - - x = conv(x, edge_csc) - x = F.relu(x) - x = F.dropout(x, p=0.5) - - return x - - -def init_pytorch_worker(device_id: int) -> None: - import cupy - import rmm - - rmm.reinitialize( - devices=[device_id], - pool_allocator=False, - ) - - - from rmm.allocators.torch import rmm_torch_allocator - torch.cuda.change_current_allocator(rmm_torch_allocator) - - from rmm.allocators.cupy import rmm_cupy_allocator - cupy.cuda.set_allocator(rmm_cupy_allocator) - - cupy.cuda.Device(device_id).use() - torch.cuda.set_device(device_id) - - - -def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: - pass - -def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: - """ - Parameters - ---------- - device: int - The CUDA device where the model, graph data, and node labels will be stored. - features_device: Union[str, int] - The device (CUDA device or CPU) where features will be stored. - """ - - init_pytorch_worker(device) - - import cudf - import cugraph - from cugraph_pyg.data import CuGraphStore - from cugraph_pyg.loader import BulkSampleLoader - - with open(os.path.join(bulk_samples_dir, 'output_meta.json'), 'r') as f: - output_meta = json.load(f) - - dataset_path = os.path.join(output_meta['dataset_dir'], output_meta['dataset']) - with open(os.path.join(dataset_path, 'meta.json'), 'r') as f: - input_meta = json.load(f) - - replication_factor = output_meta['replication_factor'] - G = {tuple(edge_type.split('__')): t * replication_factor for edge_type, t in input_meta['num_edges'].items()} - N = {node_type: t * replication_factor for node_type, t in input_meta['num_nodes'].items()} - - fs = cugraph.gnn.FeatureStore(backend="torch") - - num_input_features = 0 - num_output_features = 0 - for node_type in os.listdir(os.path.join(dataset_path, 'npy')): - feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) - fs.add_data( - torch.as_tensor(feature_data, device=features_device), - node_type, - "x", - ) - if feature_data.shape[1] > num_input_features: - num_input_features = feature_data.shape[1] - - label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') - if os.path.exists(label_path): - node_label = cudf.read_parquet(label_path) - node_label_tensor = torch.full((N[node_type],), -1, dtype=torch.float32, device='cuda') - node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ - torch.as_tensor(node_label.label.values, device='cuda') - - del node_label - gc.collect() - - fs.add_data((node_label_tensor > -1), node_type, 'train') - fs.add_data(node_label_tensor, node_type, 'y') - num_classes = int(node_label_tensor.max()) + 1 - if num_classes > num_output_features: - num_output_features = num_classes - print('done loading data') - - print(num_input_features, num_output_features, len(output_meta['fanout'])) - - model = CuGraphSAGE( - in_channels=num_input_features, - hidden_channels=64, - out_channels=num_output_features, - num_layers=len(output_meta['fanout']) - ).to(torch.float32).to(device) - print('done creating model') - - cugraph_store = CuGraphStore(fs, G, N) - print('done creating store') - - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - - for epoch in range(num_epochs): - start_time_train = time.perf_counter_ns() - model.train() - - cugraph_loader = BulkSampleLoader( - cugraph_store, - cugraph_store, - input_nodes=None, - directory=os.path.join(bulk_samples_dir, 'samples'), - ) - print('done creating loader') - - total_loss = 0 - num_batches = 0 - - for epoch in range(num_epochs): - t = time.perf_counter() - for iter_i, data in enumerate(cugraph_loader): - print(time.perf_counter() - t) - print(len(data.edge_index_dict['paper','cites','paper'][0].unique())) - print(len(data.edge_index_dict['paper','cites','paper'][1].unique())) - print('*********************************************************') - data = data.to_homogeneous() - - num_batches += 1 - if iter_i % 20 == 0: - print(f"iteration {iter_i}") - - # train - y_true = data.y - - y_pred = model( - data.x, - data.edge_index, - data.num_sampled_nodes, - data.num_sampled_edges, - ) - - if y_pred.shape[0] > len(y_true): - raise ValueError(f"illegal shape: {y_pred.shape}; {y_true.shape}") - - y_true = y_true[:y_pred.shape[0]] - - y_true = F.one_hot( - y_true.to(torch.int64), num_classes=y_pred.shape[1] - ).to(torch.float32) - print('shape: ', y_true.shape) - """ - - loss = F.cross_entropy(y_pred, y_true) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - total_loss += loss.item() - - - del y_true - del y_pred - del loss - del data - gc.collect() - """ - t = time.perf_counter() - - end_time_train = time.perf_counter_ns() - print( - f"epoch {epoch} time: " - f"{(end_time_train - start_time_train) / 1e9:3.4f} s" - ) - print(f"loss after epoch {epoch}: {total_loss / num_batches}") - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--device", - type=int, - default=0, - help="GPU to allocate to pytorch for model, graph data, and node label storage", - required=False, - ) - - parser.add_argument( - "--features_device", - type=str, - default="0", - help="Device to allocate to pytorch for feature storage", - required=False, - ) - - parser.add_argument( - "--num_epochs", - type=int, - default=1, - help="Number of training epochs", - required=False, - ) - - parser.add_argument( - "--sample_dir", - type=str, - help="Directory with stored bulk samples", - required=True, - ) - - return parser.parse_args() - - -def main(): - args = parse_args() - - try: - features_device = int(args.features_device) - except ValueError: - features_device = args.features_device - - train(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) - - -if __name__ == "__main__": - main() From 71846cdb4457227df89e951b10efc87b76d82d02 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 31 May 2023 00:07:16 +0000 Subject: [PATCH 056/347] making things happen --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 41 +++++++++++---- benchmarks/cugraph-pyg/models_cugraph.py | 20 ++------ .../standalone/cugraph_bulk_sampling.py | 10 ++-- .../all_cuda-118_arch-x86_64.yaml | 2 +- .../cugraph-service/conda_build_config.yaml | 2 +- conda/recipes/cugraph/conda_build_config.yaml | 2 +- .../pylibcugraph/conda_build_config.yaml | 2 +- cpp/cmake/thirdparty/get_libcugraphops.cmake | 2 +- dependencies.yaml | 2 +- python/cugraph-dgl/pyproject.toml | 2 +- .../cugraph_pyg/loader/cugraph_node_loader.py | 2 - .../cugraph_pyg/sampler/cugraph_sampler.py | 51 ++++++++++++------- python/cugraph-pyg/pyproject.toml | 2 +- python/pylibcugraph/pyproject.toml | 12 ++--- 14 files changed, 90 insertions(+), 62 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index 72aa133f8de..df0a491c1a4 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -18,6 +18,7 @@ import argparse import gc import os +import socket import torch import numpy as np @@ -70,10 +71,8 @@ def train_epoch(model, loader, optimizer): t = time.perf_counter() for iter_i, data in enumerate(loader): - print(time.perf_counter() - t) - print(len(data.edge_index_dict['paper','cites','paper'][0].unique())) - print(len(data.edge_index_dict['paper','cites','paper'][1].unique())) - print('*********************************************************') + #print(data.edge_index_dict['paper','cites','paper'].shape) + #print('*********************************************************') data = data.to_homogeneous() num_batches += 1 @@ -98,7 +97,7 @@ def train_epoch(model, loader, optimizer): y_true = F.one_hot( y_true.to(torch.int64), num_classes=y_pred.shape[1] ).to(torch.float32) - print('shape: ', y_true.shape) + #print('shape: ', y_true.shape) """ loss = F.cross_entropy(y_pred, y_true) @@ -243,7 +242,7 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") -def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: +def train(bulk_samples_dir: str, output_dir:str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ Parameters ---------- @@ -302,11 +301,12 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = num_output_features = num_classes print('done loading data') - print(num_input_features, num_output_features, len(output_meta['fanout'])) + print(f"num input features: {num_input_features}; num output features: {num_output_features}; fanout: {output_meta['fanout']}") + num_hidden_channels = 64 model = CuGraphSAGE( in_channels=num_input_features, - hidden_channels=64, + hidden_channels=num_hidden_channels, out_channels=num_output_features, num_layers=len(output_meta['fanout']) ).to(torch.float32).to(device) @@ -337,6 +337,22 @@ def train(bulk_samples_dir: str, device: int, features_device: Union[str, int] = f"{(end_time_train - start_time_train) / 1e9:3.4f} s" ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") + + output_result_filename = 'results.csv' + with open(os.path.join(output_dir, output_result_filename)) as f: + results = { + 'Machine': socket.gethostname(), + 'Comms': output_meta['comms'] if 'comms' in output_meta else 'tcp', + 'Dataset': output_meta['dataset'], + 'Model': 'GraphSAGE', + '# Layers': len(model.convs), + '# Input Channels': num_input_features, + '# Output Channels': num_output_features, + '# Hidden Channels': num_hidden_channels, + '# Vertices': output_meta['total_num_nodes'], + '# Edges': output_meta['total_num_edges'], + } + def parse_args(): @@ -372,6 +388,13 @@ def parse_args(): required=True, ) + parser.add_argument( + "--output_dir", + type=str, + help="Directory to store results", + required=True + ) + return parser.parse_args() @@ -383,7 +406,7 @@ def main(): except ValueError: features_device = args.features_device - train(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + train(args.sample_dir, args.output_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) if __name__ == "__main__": diff --git a/benchmarks/cugraph-pyg/models_cugraph.py b/benchmarks/cugraph-pyg/models_cugraph.py index a42417368fa..9e2df150b76 100644 --- a/benchmarks/cugraph-pyg/models_cugraph.py +++ b/benchmarks/cugraph-pyg/models_cugraph.py @@ -21,14 +21,12 @@ def __init__(self, in_channels, hidden_channels, out_channels, num_layers): self._trim = TrimToLayer() def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): - print(num_sampled_nodes) - print(num_sampled_edges) - + s = x.shape[0] for i, conv in enumerate(self.convs): edge = edge.cuda() x = x.cuda().to(torch.float32) - _, edge, _ = self._trim( + x, edge, _ = self._trim( i, num_sampled_nodes, num_sampled_edges, @@ -36,19 +34,9 @@ def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): edge, None ) - print(edge.shape) - print(edge) - - s = len(edge.unique()) - edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) - print('x shape', x.shape) - print('s: ', s) - x = x[:s] - - print(x.shape) - print(edge.shape) - print('----------------') + s = x.shape[0] + edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) x = conv(x, edge_csc) x = F.relu(x) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index 3b2092ac3b3..3a48623445f 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -246,7 +246,7 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_ edge_count = 0 for num_edge_type, can_edge_type in enumerate(sorted(edge_index_dict.keys())): edge_index_dict[can_edge_type]['etp'] = cupy.int32(num_edge_type) - edge_offsets[can_edge_type] += edge_count + edge_offsets[can_edge_type] = edge_count edge_count += len(edge_index_dict[can_edge_type]) all_edges_df = dask_cudf.concat( @@ -388,7 +388,7 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'dataset_dir': dataset_dir, 'seed': seed, 'node_offsets': node_offsets, - 'edge_offsets': edge_offsets, + 'edge_offsets': {'__'.join(k): v for k, v in edge_offsets.items()}, 'total_num_nodes': total_num_nodes, 'total_num_edges': num_input_edges, 'batch_size': batch_size, @@ -400,7 +400,11 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'execution_time': execution_time, } with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: - json.dump(output_meta, f) + json.dump( + output_meta, + f, + indent='\t' + ) print('allocation counts b:') print(allocation_counts.values()) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index cffb4364ba7..2a542cb71f3 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -64,5 +64,5 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.33.* +- ucx-py==0.32.* name: all_cuda-118_arch-x86_64 diff --git a/conda/recipes/cugraph-service/conda_build_config.yaml b/conda/recipes/cugraph-service/conda_build_config.yaml index af1d362141a..ab90a8af2a4 100644 --- a/conda/recipes/cugraph-service/conda_build_config.yaml +++ b/conda/recipes/cugraph-service/conda_build_config.yaml @@ -1,2 +1,2 @@ ucx_py_version: - - "0.33.*" + - "0.32.*" diff --git a/conda/recipes/cugraph/conda_build_config.yaml b/conda/recipes/cugraph/conda_build_config.yaml index 798948aa634..20194c031f4 100644 --- a/conda/recipes/cugraph/conda_build_config.yaml +++ b/conda/recipes/cugraph/conda_build_config.yaml @@ -14,7 +14,7 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.33.*" + - "0.32.*" # The CTK libraries below are missing from the conda-forge::cudatoolkit # package. The "*_host_*" version specifiers correspond to `11.8` packages. diff --git a/conda/recipes/pylibcugraph/conda_build_config.yaml b/conda/recipes/pylibcugraph/conda_build_config.yaml index 798948aa634..20194c031f4 100644 --- a/conda/recipes/pylibcugraph/conda_build_config.yaml +++ b/conda/recipes/pylibcugraph/conda_build_config.yaml @@ -14,7 +14,7 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.33.*" + - "0.32.*" # The CTK libraries below are missing from the conda-forge::cudatoolkit # package. The "*_host_*" version specifiers correspond to `11.8` packages. diff --git a/cpp/cmake/thirdparty/get_libcugraphops.cmake b/cpp/cmake/thirdparty/get_libcugraphops.cmake index b10af300a98..240a88a3bf6 100644 --- a/cpp/cmake/thirdparty/get_libcugraphops.cmake +++ b/cpp/cmake/thirdparty/get_libcugraphops.cmake @@ -25,7 +25,7 @@ if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_BRANCH) endif() if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_REPOSITORY) - set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "git@github.com:rapidsai/cugraph-ops.git") + set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "https://github.com/alexbarghi-nv/cugraph-ops.git") endif() function(find_and_configure_cugraph_ops) diff --git a/dependencies.yaml b/dependencies.yaml index 8ec9936ac97..73be8c6882d 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -306,7 +306,7 @@ dependencies: - &numba numba>=0.57 - raft-dask==23.6.* - *rmm - - &ucx_py ucx-py==0.33.* + - &ucx_py ucx-py==0.32.* - output_types: conda packages: - &cupy cupy>=12.0.0 diff --git a/python/cugraph-dgl/pyproject.toml b/python/cugraph-dgl/pyproject.toml index effdfedd912..d3e4c102662 100644 --- a/python/cugraph-dgl/pyproject.toml +++ b/python/cugraph-dgl/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-dgl" -version = "23.08.00" +version = "23.06.00" description = "cugraph extensions for DGL" readme = { file = "README.md", content-type = "text/markdown" } authors = [ diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 3dab94ec844..d46c7483a27 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -244,8 +244,6 @@ def __next__(self): # Pull the next set of sampling results out of the dataframe in memory f = (self.__data["batch_id"] == self.__next_batch) - print('batch:', self.__next_batch) - print(self.__data.loc[f]) sampler_output = _sampler_output_from_sampling_results( self.__data.loc[f], self.__graph_store diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 06727dd1a4f..2e86f20d374 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -31,11 +31,11 @@ ) -def _count_unique_nodes( +def _get_unique_nodes( sampling_results: cudf.DataFrame, graph_store: CuGraphStore, node_type: str, - node_position: str, + node_position: str ) -> int: """ Counts the number of unique nodes of a given node type. @@ -54,8 +54,8 @@ def _count_unique_nodes( Returns ------- - int - The number of unique nodes of the given node type. + cudf.Series + The unique nodes of the given node type. """ if node_position == "src": edge_index = "sources" @@ -78,9 +78,9 @@ def _count_unique_nodes( sampling_results_node = sampling_results[f] else: - return 0 + return cudf.Series([], dtype='int64') - return sampling_results_node[edge_index].nunique() + return sampling_results_node[edge_index] def _sampler_output_from_sampling_results( @@ -119,9 +119,9 @@ def _sampler_output_from_sampling_results( if len(graph_store.node_types) == 1: num_unique_nodes = sampling_results_hop_0.sources.nunique() else: - num_unique_nodes = _count_unique_nodes( + num_unique_nodes = _get_unique_nodes( sampling_results_hop_0, graph_store, node_type, "src" - ) + ).nunique() if num_unique_nodes > 0: num_nodes_per_hop_dict[node_type] = torch.zeros( @@ -142,14 +142,13 @@ def _sampler_output_from_sampling_results( .sort_index() ) nodes_of_interest.name = "nodes_of_interest" - print(nodes_of_interest) # del sampling_results_hop_0 #print('hops:', hops) #print(sampling_results_hop_0.destinations.sort_values()) #print(sampling_results.sources.loc[sampling_results.hop_id==1].sort_values()) #print((sampling_results.sources.loc[sampling_results.hop_id==1].isin(sampling_results[sampling_results.hop_id==0].destinations)).sum()) - assert len(nodes_of_interest) == cudf.concat([sampling_results.sources, sampling_results.destinations]).nunique() + #assert len(nodes_of_interest) == cudf.concat([sampling_results.sources, sampling_results.destinations]).nunique() # Get the grouped node index (for creating the renumbered grouped edge index) noi_index = graph_store._get_vertex_groups_from_sample( @@ -166,15 +165,33 @@ def _sampler_output_from_sampling_results( for hop in range(len(hops)): hop_ix_start = hops[hop] hop_ix_end = hops[hop + 1] if hop < len(hops) - 1 else len(sampling_results) - sampling_results_hop = sampling_results.iloc[hop_ix_start:hop_ix_end] + sampling_results_to_hop = sampling_results.iloc[0:hop_ix_end] for node_type in graph_store.node_types: if len(graph_store.node_types) == 1: - num_unique_nodes = sampling_results_hop.destinations.nunique() + num_unique_nodes = cudf.concat([ + sampling_results_hop_0.sources, + sampling_results_to_hop.destinations, + ]).nunique() else: - num_unique_nodes = _count_unique_nodes( - sampling_results_hop, graph_store, node_type, "dst" + unique_nodes_hop = _get_unique_nodes( + sampling_results_to_hop, + graph_store, + node_type, + "dst" + ) + + unique_nodes_0 = _get_unique_nodes( + sampling_results_hop_0, + graph_store, + node_type, + "src" ) + + num_unique_nodes = cudf.concat([ + unique_nodes_0, + unique_nodes_hop + ]).nunique() if num_unique_nodes > 0: if node_type not in num_nodes_per_hop_dict: @@ -189,12 +206,10 @@ def _sampler_output_from_sampling_results( num_edges_per_hop_dict[edge_type] = torch.zeros( len(hops), dtype=torch.int64 ) - num_edges_per_hop_dict[graph_store.edge_types[0]][hop] = len( - sampling_results_hop - ) + num_edges_per_hop_dict[graph_store.edge_types[0]][hop] = hop_ix_end - hop_ix_start else: numeric_etypes, counts = torch.unique( - torch.as_tensor(sampling_results_hop.edge_type.values, device="cuda"), + torch.as_tensor(sampling_results.iloc[hop_ix_start:hop_ix_end].edge_type.values, device="cuda"), return_counts=True, ) numeric_etypes = list(numeric_etypes) diff --git a/python/cugraph-pyg/pyproject.toml b/python/cugraph-pyg/pyproject.toml index 9dcdd351220..a62d32d4278 100644 --- a/python/cugraph-pyg/pyproject.toml +++ b/python/cugraph-pyg/pyproject.toml @@ -12,7 +12,7 @@ testpaths = ["cugraph_pyg/tests"] [project] name = "cugraph_pyg" -version = "23.08.00" +version = "23.06.00" description = "cugraph_pyg - PyG support for cuGraph massive-scale, ultra-fast GPU graph analytics." authors = [ { name = "NVIDIA Corporation" }, diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index 592f11937d5..982c435efb5 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -6,8 +6,8 @@ requires = [ "cmake>=3.23.1,!=3.25.0", "cython>=0.29,<0.30", "ninja", - "pylibraft==23.8.*", - "rmm==23.8.*", + "pylibraft==23.6.*", + "rmm==23.6.*", "scikit-build>=0.13.1,<0.17.2", "setuptools", "wheel", @@ -19,7 +19,7 @@ testpaths = ["pylibcugraph/tests"] [project] name = "pylibcugraph" -version = "23.08.00" +version = "23.06.00" description = "pylibcugraph - Python bindings for the libcugraph cuGraph C/C++/CUDA library" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -28,8 +28,8 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "pylibraft==23.8.*", - "rmm==23.8.*", + "pylibraft==23.6.*", + "rmm==23.6.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", @@ -40,7 +40,7 @@ classifiers = [ [project.optional-dependencies] test = [ - "cudf==23.8.*", + "cudf==23.6.*", "networkx>=2.5.1", "numpy>=1.21,<1.24", "pandas", From 12766fac3231a4c57dadb7433807b4f27490fd52 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 31 May 2023 07:52:54 +0000 Subject: [PATCH 057/347] bleh --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 124 ++++++++++++------ benchmarks/cugraph-pyg/models_cugraph.py | 5 +- benchmarks/cugraph-pyg/models_native.py | 42 ++++++ .../cugraph_pyg/sampler/cugraph_sampler.py | 2 +- 4 files changed, 131 insertions(+), 42 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index df0a491c1a4..5b3477b1861 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -22,6 +22,7 @@ import torch import numpy as np +import pandas import torch.nn.functional as F @@ -73,6 +74,8 @@ def train_epoch(model, loader, optimizer): for iter_i, data in enumerate(loader): #print(data.edge_index_dict['paper','cites','paper'].shape) #print('*********************************************************') + num_sampled_nodes = data['paper']['num_sampled_nodes'] + num_sampled_edges = data['paper','cites','paper']['num_sampled_edges'] data = data.to_homogeneous() num_batches += 1 @@ -85,8 +88,8 @@ def train_epoch(model, loader, optimizer): y_pred = model( data.x, data.edge_index, - data.num_sampled_nodes, - data.num_sampled_edges, + num_sampled_nodes, + num_sampled_edges, ) if y_pred.shape[0] > len(y_true): @@ -119,13 +122,12 @@ def train_epoch(model, loader, optimizer): return total_loss, num_batches -def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: +def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: from models_native import GraphSAGE from torch_geometric.data import HeteroData from torch_geometric.loader import NeighborLoader import cudf - import pandas with open(os.path.join(bulk_samples_dir, 'output_meta.json'), 'r') as f: output_meta = json.load(f) @@ -144,7 +146,7 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs num_output_features = 0 for node_type in os.listdir(os.path.join(dataset_path, 'npy')): feature_data = load_disk_features(output_meta, node_type, replication_factor=replication_factor) - hetero_data[node_type].x = feature_data.to(features_device) + hetero_data[node_type].x = torch.as_tensor(feature_data, device=features_device) if feature_data.shape[1] > num_input_features: num_input_features = feature_data.shape[1] @@ -152,6 +154,14 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs label_path = os.path.join(dataset_path, 'parquet', node_type, 'node_label.parquet') if os.path.exists(label_path): node_label = cudf.read_parquet(label_path) + if replication_factor > 1: + base_num_nodes = input_meta['num_nodes'][node_type] + dfr = cudf.DataFrame({ + 'node': cudf.concat([node_label.node + (r * base_num_nodes) for r in range(1, replication_factor)]), + 'label': cudf.concat([node_label.label for r in range(1, replication_factor)]), + }) + node_label = cudf.concat([node_label, dfr]).reset_index(drop=True) + node_label_tensor = torch.full((num_nodes_dict[node_type],), -1, dtype=torch.float32, device='cuda') node_label_tensor[torch.as_tensor(node_label.node.values, device='cuda')] = \ torch.as_tensor(node_label.label.values, device='cuda') @@ -161,6 +171,7 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs hetero_data[node_type]['train'] = (node_label_tensor > -1) hetero_data[node_type]['y'] = node_label_tensor + hetero_data[node_type]['num_nodes'] = num_nodes_dict[node_type] num_classes = int(node_label_tensor.max()) + 1 if num_classes > num_output_features: @@ -182,31 +193,36 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs can_edge_type = tuple(edge_type.split('__')) ei = pandas.read_parquet(os.path.join(os.path.join(parquet_path, edge_type), 'edge_index.parquet')) ei = { - 'src': torch.as_tensor(ei['src']), - 'dst': torch.as_tensor(ei['dst']), + 'src': torch.from_numpy(ei.src.values), + 'dst': torch.from_numpy(ei.dst.values), } + print('sorting edge index...') + ei['dst'], ix = torch.sort(ei['dst']) + ei['src'] = ei['src'][ix] + del ix gc.collect() if replication_factor > 1: for r in range(1, replication_factor): ei['src'] = torch.concat([ ei['src'], - ei['src'] + int(r * output_meta['num_nodes'][edge_type[0]]), - ]) + ei['src'] + int(r * input_meta['num_nodes'][can_edge_type[0]]), + ]).contiguous() ei['dst'] = torch.concat([ ei['dst'], - ei['dst'] + int(r * output_meta['num_nodes'][edge_type[2]]), - ]) + ei['dst'] + int(r * input_meta['num_nodes'][can_edge_type[2]]), + ]).contiguous() gc.collect() - ei = torch.stack([ - ei['src'], - ei['dst'], - ]) - gc.collect() - - hetero_data[can_edge_type]['edge_index'] = ei + hetero_data.put_edge_index( + layout='coo', + edge_index=[ei['src'], ei['dst']], + edge_type=can_edge_type, + size=(num_nodes_dict[can_edge_type[0]], num_nodes_dict[can_edge_type[2]]), + is_sorted=True + ) + #hetero_data[can_edge_type]['edge_index'] = ei gc.collect() print('done loading graph data') @@ -216,7 +232,7 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs in_channels=num_input_features, hidden_channels=64, out_channels=num_output_features, - num_layers=len(output_meta['fanout']) + num_layers=len(output_meta['fanout']) + 1 ).to(torch.float32).to(device) print('done creating model') @@ -226,10 +242,16 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs start_time_train = time.perf_counter_ns() model.train() + input_nodes = hetero_data['paper']['train'] + print('input nodes:', input_nodes.nonzero().shape) loader = NeighborLoader( hetero_data, - hetero_data, - input_nodes=None, + input_nodes=('paper', input_nodes.cpu()), + batch_size=output_meta['batch_size'], + num_neighbors={('paper','cites','paper'):[10,25]}, + replace=False, + is_sorted=True, + disjoint=True, ) print('done creating loader') @@ -242,7 +264,7 @@ def train_native(device:int, features_device:Union[str, int] = "cpu", num_epochs ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") -def train(bulk_samples_dir: str, output_dir:str, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: +def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ Parameters ---------- @@ -252,8 +274,6 @@ def train(bulk_samples_dir: str, output_dir:str, device: int, features_device: U The device (CUDA device or CPU) where features will be stored. """ - init_pytorch_worker(device) - import cudf import cugraph from cugraph_pyg.data import CuGraphStore @@ -308,7 +328,7 @@ def train(bulk_samples_dir: str, output_dir:str, device: int, features_device: U in_channels=num_input_features, hidden_channels=num_hidden_channels, out_channels=num_output_features, - num_layers=len(output_meta['fanout']) + num_layers=len(output_meta['fanout']) + 1 ).to(torch.float32).to(device) print('done creating model') @@ -332,26 +352,46 @@ def train(bulk_samples_dir: str, output_dir:str, device: int, features_device: U total_loss, num_batches = train_epoch(model, cugraph_loader, optimizer) end_time_train = time.perf_counter_ns() + train_time = (end_time_train - start_time_train) / 1e9 print( f"epoch {epoch} time: " - f"{(end_time_train - start_time_train) / 1e9:3.4f} s" + f"{train_time:3.4f} s" ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") output_result_filename = 'results.csv' - with open(os.path.join(output_dir, output_result_filename)) as f: - results = { - 'Machine': socket.gethostname(), - 'Comms': output_meta['comms'] if 'comms' in output_meta else 'tcp', - 'Dataset': output_meta['dataset'], - 'Model': 'GraphSAGE', - '# Layers': len(model.convs), - '# Input Channels': num_input_features, - '# Output Channels': num_output_features, - '# Hidden Channels': num_hidden_channels, - '# Vertices': output_meta['total_num_nodes'], - '# Edges': output_meta['total_num_edges'], - } + results = { + 'Machine': socket.gethostname(), + 'Comms': output_meta['comms'] if 'comms' in output_meta else 'tcp', + 'Dataset': output_meta['dataset'], + 'Model': 'GraphSAGE', + '# Layers': len(model.convs), + '# Input Channels': num_input_features, + '# Output Channels': num_output_features, + '# Hidden Channels': num_hidden_channels, + '# Vertices': output_meta['total_num_nodes'], + '# Edges': output_meta['total_num_edges'], + '# Vertex Types': len(N.keys()), + '# Edge Types': len(G.keys()), + 'Sampling # GPUs': output_meta['num_sampling_gpus'], + 'Seeds Per Call': output_meta['seeds_per_call'], + 'Batch Size': output_meta['batch_size'], + '# Train Batches': num_batches, + 'Batches Per Partition': output_meta['batches_per_partition'], + 'Fanout': str(output_meta['fanout']), + 'Training # GPUs': 1, + 'Feature Storage': 'cpu' if features_device == 'cpu' else 'gpu', + 'Memory Type': 'Device', # could be managed if configured + 'Sampling Time': output_meta['execution_time'], + 'Sampling Time Per Batch': output_meta['execution_time'] / num_batches, + 'Training Time': train_time, + 'Training Time Per Batch': train_time / num_batches, + 'Total Time': train_time + output_meta['execution_time'], + 'Native Equivalent Time': native_time, + 'Speedup': native_time / (train_time + output_meta['execution_time']), + } + df = pandas.DataFrame(results, index=[0]) + df.to_csv(os.path.join(output_dir, output_result_filename),header=False, sep=',', index=False, mode='a') @@ -406,7 +446,11 @@ def main(): except ValueError: features_device = args.features_device - train(args.sample_dir, args.output_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + #init_pytorch_worker(args.device) + + #native_time = train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + native_time = 3600 + train(args.sample_dir, args.output_dir, native_time, device=args.device, features_device=features_device, num_epochs=args.num_epochs) if __name__ == "__main__": diff --git a/benchmarks/cugraph-pyg/models_cugraph.py b/benchmarks/cugraph-pyg/models_cugraph.py index 9e2df150b76..ac6c089dcd6 100644 --- a/benchmarks/cugraph-pyg/models_cugraph.py +++ b/benchmarks/cugraph-pyg/models_cugraph.py @@ -21,7 +21,6 @@ def __init__(self, in_channels, hidden_channels, out_channels, num_layers): self._trim = TrimToLayer() def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): - s = x.shape[0] for i, conv in enumerate(self.convs): edge = edge.cuda() x = x.cuda().to(torch.float32) @@ -34,6 +33,10 @@ def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): edge, None ) + print(num_sampled_nodes) + print(num_sampled_edges) + print('x:', x.shape) + print('edge:', edge.shape) s = x.shape[0] edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) diff --git a/benchmarks/cugraph-pyg/models_native.py b/benchmarks/cugraph-pyg/models_native.py index e69de29bb2d..976b6e5cab7 100644 --- a/benchmarks/cugraph-pyg/models_native.py +++ b/benchmarks/cugraph-pyg/models_native.py @@ -0,0 +1,42 @@ +import torch + +from torch_geometric.nn import SAGEConv +from torch_geometric.utils.trim_to_layer import TrimToLayer + +import torch.nn as nn +import torch.nn.functional as F + +class GraphSAGE(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, num_layers): + super().__init__() + + self.convs = torch.nn.ModuleList() + self.convs.append(SAGEConv(in_channels, hidden_channels, aggr='mean')) + for _ in range(num_layers - 2): + conv = SAGEConv(hidden_channels, hidden_channels, aggr='mean') + self.convs.append(conv) + + self.convs.append(SAGEConv(hidden_channels, out_channels, aggr='mean')) + + self._trim = TrimToLayer() + + def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): + for i, conv in enumerate(self.convs): + edge = edge.cuda() + x = x.cuda().to(torch.float32) + + x, edge, _ = self._trim( + i, + num_sampled_nodes, + num_sampled_edges, + x, + edge, + None + ) + + s = x.shape[0] + x = conv(x, edge, size=(s, s)) + x = F.relu(x) + x = F.dropout(x, p=0.5) + + return x diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 2e86f20d374..9b1ea83a086 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -198,7 +198,7 @@ def _sampler_output_from_sampling_results( num_nodes_per_hop_dict[node_type] = torch.zeros( len(hops) + 1, dtype=torch.int64 ) - num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes + num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes - num_nodes_per_hop_dict[node_type][hop] if len(graph_store.edge_types) == 1: edge_type = graph_store.edge_types[0] From a6e2403b2b7b311512ccdcefad7ec4ae0037290f Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 31 May 2023 17:12:09 +0000 Subject: [PATCH 058/347] work --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 29 ++++++--- benchmarks/cugraph-pyg/models_cugraph.py | 12 ++-- benchmarks/cugraph-pyg/models_native.py | 7 +++ .../cugraph_pyg/data/cugraph_store.py | 63 ++++++++++--------- .../cugraph_pyg/sampler/cugraph_sampler.py | 18 +++++- 5 files changed, 86 insertions(+), 43 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index 5b3477b1861..9c312bb2f74 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -100,7 +100,7 @@ def train_epoch(model, loader, optimizer): y_true = F.one_hot( y_true.to(torch.int64), num_classes=y_pred.shape[1] ).to(torch.float32) - #print('shape: ', y_true.shape) + """ loss = F.cross_entropy(y_pred, y_true) @@ -169,8 +169,8 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i del node_label gc.collect() - hetero_data[node_type]['train'] = (node_label_tensor > -1) - hetero_data[node_type]['y'] = node_label_tensor + hetero_data[node_type]['train'] = (node_label_tensor > -1).contiguous() + hetero_data[node_type]['y'] = node_label_tensor.contiguous() hetero_data[node_type]['num_nodes'] = num_nodes_dict[node_type] num_classes = int(node_label_tensor.max()) + 1 @@ -232,7 +232,7 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i in_channels=num_input_features, hidden_channels=64, out_channels=num_output_features, - num_layers=len(output_meta['fanout']) + 1 + num_layers=len(output_meta['fanout']) ).to(torch.float32).to(device) print('done creating model') @@ -263,6 +263,8 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i f"{(end_time_train - start_time_train) / 1e9:3.4f} s" ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") + + return (end_time_train - start_time_train / 1e9) def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ @@ -328,7 +330,7 @@ def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, in_channels=num_input_features, hidden_channels=num_hidden_channels, out_channels=num_output_features, - num_layers=len(output_meta['fanout']) + 1 + num_layers=len(output_meta['fanout']) ).to(torch.float32).to(device) print('done creating model') @@ -435,6 +437,14 @@ def parse_args(): required=True ) + parser.add_argument( + "--native_time", + type=float, + help="Input the native runtime to avoid doing a native run", + required=False, + default=-1.0 + ) + return parser.parse_args() @@ -446,10 +456,13 @@ def main(): except ValueError: features_device = args.features_device - #init_pytorch_worker(args.device) + init_pytorch_worker(args.device) - #native_time = train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) - native_time = 3600 + if args.native_time < 0: + native_time = train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + else: + native_time = args.native_time + train(args.sample_dir, args.output_dir, native_time, device=args.device, features_device=features_device, num_epochs=args.num_epochs) diff --git a/benchmarks/cugraph-pyg/models_cugraph.py b/benchmarks/cugraph-pyg/models_cugraph.py index ac6c089dcd6..b9908fdd2cf 100644 --- a/benchmarks/cugraph-pyg/models_cugraph.py +++ b/benchmarks/cugraph-pyg/models_cugraph.py @@ -33,10 +33,6 @@ def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): edge, None ) - print(num_sampled_nodes) - print(num_sampled_edges) - print('x:', x.shape) - print('edge:', edge.shape) s = x.shape[0] edge_csc = CuGraphSAGEConv.to_csc(edge, (s, s)) @@ -45,4 +41,12 @@ def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): x = F.relu(x) x = F.dropout(x, p=0.5) + x = x.narrow( + dim=0, + start=0, + length=x.shape[0] - num_sampled_nodes[1] + ) + + assert x.shape[0] == num_sampled_nodes[0] return x + diff --git a/benchmarks/cugraph-pyg/models_native.py b/benchmarks/cugraph-pyg/models_native.py index 976b6e5cab7..58f7bc35851 100644 --- a/benchmarks/cugraph-pyg/models_native.py +++ b/benchmarks/cugraph-pyg/models_native.py @@ -39,4 +39,11 @@ def forward(self, x, edge, num_sampled_nodes, num_sampled_edges): x = F.relu(x) x = F.dropout(x, p=0.5) + x = x.narrow( + dim=0, + start=0, + length=x.shape[0] - num_sampled_nodes[1] + ) + + assert x.shape[0] == num_sampled_nodes[0] return x diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 728f7806160..dc8363c67ba 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -787,26 +787,41 @@ def _get_renumbered_edge_groups_from_sample( if len(self.__edge_types_to_attrs) == 1: t_pyg_type = list(self.__edge_types_to_attrs.values())[0].edge_type src_type, _, dst_type = t_pyg_type + + if len(self.__vertex_type_offsets["type"]) == 1: + vtype = src_type + id_table = noi_index[vtype] + id_map = cudf.Series( + cupy.arange(id_table.shape[0], dtype='int32'), + name='new_id', + index=cupy.asarray(id_table) + ).sort_index() + + ix_r = torch.searchsorted(torch.as_tensor(id_map.index.values, device='cuda'), torch.as_tensor(sampling_results.sources.values, device='cuda')) + row_dict[t_pyg_type] = torch.as_tensor(id_map.values, device='cuda')[ix_r] + + ix_c = torch.searchsorted(torch.as_tensor(id_map.index.values, device='cuda'), torch.as_tensor(sampling_results.destinations.values, device='cuda')) + col_dict[t_pyg_type] = torch.as_tensor(id_map.values, device='cuda')[ix_c] + else: + dst_id_table = noi_index[dst_type] + dst_id_map = ( + cudf.DataFrame({ + 'dst': cupy.asarray(dst_id_table), + 'new_id': cupy.arange(dst_id_table.shape[0]) + }).set_index('dst') + ) + dst = dst_id_map["new_id"].loc[sampling_results.destinations] + col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") - dst_id_table = noi_index[dst_type] - dst_id_map = ( - cudf.Series(cupy.asarray(dst_id_table), name="dst") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("dst") - ) - dst = dst_id_map["new_id"].loc[sampling_results.destinations] - col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") - - src_id_table = noi_index[src_type] - src_id_map = ( - cudf.Series(cupy.asarray(src_id_table), name="src") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("src") - ) - src = src_id_map["new_id"].loc[sampling_results.sources] - row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") + src_id_table = noi_index[src_type] + src_id_map = ( + cudf.DataFrame({ + 'src': cupy.asarray(src_id_table), + 'new_id': cupy.arange(src_id_table.shape[0]) + }).set_index('src') + ) + src = src_id_map["new_id"].loc[sampling_results.sources] + row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") else: # This will retrieve the single string representation. @@ -818,9 +833,6 @@ def _get_renumbered_edge_groups_from_sample( ) eoi_types = cudf.Series(eoi_types, name="t").groupby("t").groups - print('eoi types:') - print(eoi_types) - print('\n') for pyg_can_edge_type_str, ix in eoi_types.items(): pyg_can_edge_type = tuple(pyg_can_edge_type_str.split("__")) @@ -832,7 +844,6 @@ def _get_renumbered_edge_groups_from_sample( sampling_results.destinations.iloc[ix].values, device="cuda" ) destinations -= self.__vertex_type_offsets["start"][dst_num_type] - print(dst_type, dst_num_type, self.__vertex_type_offsets["start"][dst_num_type]) # Create the col entry for this type dst_id_table = noi_index[dst_type] @@ -842,12 +853,6 @@ def _get_renumbered_edge_groups_from_sample( .rename(columns={"index": "new_id"}) .set_index("dst") ) - print('dst id map:') - print(dst_id_map) - print('\n') - print('destinations:') - print(destinations) - print('\n') dst = dst_id_map["new_id"].loc[cupy.asarray(destinations)] col_dict[pyg_can_edge_type] = torch.as_tensor(dst.values, device="cuda") diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 9b1ea83a086..35f056023ad 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -13,6 +13,7 @@ from typing import Sequence +from time import perf_counter from cugraph_pyg.data import CuGraphStore @@ -103,18 +104,22 @@ def _sampler_output_from_sampling_results( HeteroSamplerOutput """ + time_hop_start = perf_counter() hops = torch.arange(sampling_results.hop_id.max() + 1, device="cuda") hops = torch.searchsorted( torch.as_tensor(sampling_results.hop_id.values, device="cuda"), hops ) + print(f'calc hop pos: {perf_counter() - time_hop_start} s') num_nodes_per_hop_dict = {} num_edges_per_hop_dict = {} + time_calc_hop_0_start = perf_counter() # Fill out hop 0 in num_nodes_per_hop_dict, which is based on src instead of dst sampling_results_hop_0 = sampling_results.iloc[ 0 : (hops[1] if len(hops) > 1 else len(sampling_results)) ] + for node_type in graph_store.node_types: if len(graph_store.node_types) == 1: num_unique_nodes = sampling_results_hop_0.sources.nunique() @@ -128,10 +133,12 @@ def _sampler_output_from_sampling_results( len(hops) + 1, dtype=torch.int64 ) num_nodes_per_hop_dict[node_type][0] = num_unique_nodes + print(f'calc hop 0: {perf_counter() - time_calc_hop_0_start} s') # Calculate nodes of interest based on unique nodes in order of appearance # Use hop 0 sources since those are the only ones not included in destinations # Use torch.concat based on benchmark performance (vs. cudf.concat) + time_noi_start = perf_counter() import cupy nodes_of_interest = ( cudf.Series(cupy.asarray(torch.concat([ @@ -142,6 +149,8 @@ def _sampler_output_from_sampling_results( .sort_index() ) nodes_of_interest.name = "nodes_of_interest" + print(f'calc noi: {perf_counter() - time_noi_start} s') + # del sampling_results_hop_0 #print('hops:', hops) @@ -151,17 +160,21 @@ def _sampler_output_from_sampling_results( #assert len(nodes_of_interest) == cudf.concat([sampling_results.sources, sampling_results.destinations]).nunique() # Get the grouped node index (for creating the renumbered grouped edge index) + time_vertex_groups_start = perf_counter() noi_index = graph_store._get_vertex_groups_from_sample( torch.as_tensor(nodes_of_interest.values, device="cuda") ) + print(f'vertex groups: {perf_counter() - time_vertex_groups_start} s') del nodes_of_interest # Get the new edge index (by type as expected for HeteroData) - # FIXME handle edge ids/types after the C++ updates + time_edge_groups_start = perf_counter() row_dict, col_dict = graph_store._get_renumbered_edge_groups_from_sample( sampling_results, noi_index ) + print(f'edge groups: {perf_counter() - time_edge_groups_start} s') + time_nodes_per_hop_start = perf_counter() for hop in range(len(hops)): hop_ix_start = hops[hop] hop_ix_end = hops[hop + 1] if hop < len(hops) - 1 else len(sampling_results) @@ -198,7 +211,7 @@ def _sampler_output_from_sampling_results( num_nodes_per_hop_dict[node_type] = torch.zeros( len(hops) + 1, dtype=torch.int64 ) - num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes - num_nodes_per_hop_dict[node_type][hop] + num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes - int(num_nodes_per_hop_dict[node_type][:hop+1].sum(0)) if len(graph_store.edge_types) == 1: edge_type = graph_store.edge_types[0] @@ -221,6 +234,7 @@ def _sampler_output_from_sampling_results( len(hops), dtype=torch.int64 ) num_edges_per_hop_dict[can_etype][hop] = count + print(f'nodes/edges per hop: {perf_counter() - time_nodes_per_hop_start} s') if HeteroSamplerOutput is None: raise ImportError("Error importing from pyg") From 8ed2977c40a416cd9920fbdf94a2a77477f2bf69 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 31 May 2023 18:32:04 +0000 Subject: [PATCH 059/347] x --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 55 +++++++++++-------- .../cugraph_pyg/sampler/cugraph_sampler.py | 20 +++---- 2 files changed, 42 insertions(+), 33 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index 9c312bb2f74..b72410cfee1 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -26,7 +26,7 @@ import torch.nn.functional as F -from typing import Union +from typing import Union, List from models_cugraph import CuGraphSAGE @@ -70,7 +70,8 @@ def train_epoch(model, loader, optimizer): total_loss = 0.0 num_batches = 0 - t = time.perf_counter() + time_forward = 0.0 + time_backward = 0.0 for iter_i, data in enumerate(loader): #print(data.edge_index_dict['paper','cites','paper'].shape) #print('*********************************************************') @@ -85,12 +86,15 @@ def train_epoch(model, loader, optimizer): # train y_true = data.y + start_time_forward = time.perf_counter() y_pred = model( data.x, data.edge_index, num_sampled_nodes, num_sampled_edges, ) + end_time_forward = time.perf_counter() + time_forward += end_time_forward - start_time_forward if y_pred.shape[0] > len(y_true): raise ValueError(f"illegal shape: {y_pred.shape}; {y_true.shape}") @@ -100,15 +104,16 @@ def train_epoch(model, loader, optimizer): y_true = F.one_hot( y_true.to(torch.int64), num_classes=y_pred.shape[1] ).to(torch.float32) - - """ + start_time_backward = time.perf_counter() loss = F.cross_entropy(y_pred, y_true) optimizer.zero_grad() loss.backward() optimizer.step() total_loss += loss.item() + end_time_backward = time.perf_counter() + time_backward += end_time_backward - start_time_backward del y_true @@ -116,10 +121,8 @@ def train_epoch(model, loader, optimizer): del loss del data gc.collect() - """ - t = time.perf_counter() - return total_loss, num_batches + return total_loss, num_batches, (time_forward / num_batches), (time_backward / num_batches) def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: @@ -255,7 +258,8 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i ) print('done creating loader') - total_loss, num_batches = train_epoch(model, loader, optimizer) + total_loss, num_batches, mean_forward_time, mean_backward_time = \ + train_epoch(model, loader, optimizer) end_time_train = time.perf_counter_ns() print( @@ -264,9 +268,9 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") - return (end_time_train - start_time_train / 1e9) + return (end_time_train - start_time_train / 1e9), mean_forward_time, mean_backward_time -def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: +def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ Parameters ---------- @@ -351,7 +355,7 @@ def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, ) print('done creating loader') - total_loss, num_batches = train_epoch(model, cugraph_loader, optimizer) + total_loss, num_batches, mean_time_fw, mean_time_bw = train_epoch(model, cugraph_loader, optimizer) end_time_train = time.perf_counter_ns() train_time = (end_time_train - start_time_train) / 1e9 @@ -389,11 +393,16 @@ def train(bulk_samples_dir: str, output_dir:str, native_time:float, device: int, 'Training Time': train_time, 'Training Time Per Batch': train_time / num_batches, 'Total Time': train_time + output_meta['execution_time'], - 'Native Equivalent Time': native_time, - 'Speedup': native_time / (train_time + output_meta['execution_time']), + 'Mean Foward Time': mean_time_fw, + 'Native Mean Forward Time': native_times[1], + 'Mean Backward Time': mean_time_bw, + 'Native Mean Backward Time': native_times[2], + 'Native Equivalent Time': native_times[0], + 'Sample/Load Speedup': (native_times[0] - native_times[1]*num_batches - native_times[2]*num_batches) / (output_meta['execution_time'] - mean_time_fw*num_batches - mean_time_bw*num_batches), + 'Total Speedup': native_times[0] / (train_time + output_meta['execution_time']), } df = pandas.DataFrame(results, index=[0]) - df.to_csv(os.path.join(output_dir, output_result_filename),header=False, sep=',', index=False, mode='a') + df.to_csv(os.path.join(output_dir, output_result_filename),header=True, sep=',', index=False, mode='a') @@ -438,11 +447,11 @@ def parse_args(): ) parser.add_argument( - "--native_time", - type=float, - help="Input the native runtime to avoid doing a native run", + "--native_times", + type=str, + help="Input the native runtimes (total, fw, bw) to avoid doing a native run", required=False, - default=-1.0 + default="-1.0,-1.0,-1.0" ) return parser.parse_args() @@ -458,12 +467,12 @@ def main(): init_pytorch_worker(args.device) - if args.native_time < 0: - native_time = train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) - else: - native_time = args.native_time + native_time, native_mean_fw_time, native_mean_bw_time = [float(x) for x in args.native_times.split(',')] + if native_time < 0: + native_time, native_mean_fw_time, native_mean_bw_time = \ + train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) - train(args.sample_dir, args.output_dir, native_time, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + train(args.sample_dir, args.output_dir, (native_time, native_mean_fw_time, native_mean_bw_time), device=args.device, features_device=features_device, num_epochs=args.num_epochs) if __name__ == "__main__": diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 35f056023ad..054b58f2ef2 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -109,7 +109,7 @@ def _sampler_output_from_sampling_results( hops = torch.searchsorted( torch.as_tensor(sampling_results.hop_id.values, device="cuda"), hops ) - print(f'calc hop pos: {perf_counter() - time_hop_start} s') + #print(f'calc hop pos: {perf_counter() - time_hop_start} s') num_nodes_per_hop_dict = {} num_edges_per_hop_dict = {} @@ -133,7 +133,7 @@ def _sampler_output_from_sampling_results( len(hops) + 1, dtype=torch.int64 ) num_nodes_per_hop_dict[node_type][0] = num_unique_nodes - print(f'calc hop 0: {perf_counter() - time_calc_hop_0_start} s') + #print(f'calc hop 0: {perf_counter() - time_calc_hop_0_start} s') # Calculate nodes of interest based on unique nodes in order of appearance # Use hop 0 sources since those are the only ones not included in destinations @@ -149,7 +149,7 @@ def _sampler_output_from_sampling_results( .sort_index() ) nodes_of_interest.name = "nodes_of_interest" - print(f'calc noi: {perf_counter() - time_noi_start} s') + #print(f'calc noi: {perf_counter() - time_noi_start} s') # del sampling_results_hop_0 @@ -164,7 +164,7 @@ def _sampler_output_from_sampling_results( noi_index = graph_store._get_vertex_groups_from_sample( torch.as_tensor(nodes_of_interest.values, device="cuda") ) - print(f'vertex groups: {perf_counter() - time_vertex_groups_start} s') + #print(f'vertex groups: {perf_counter() - time_vertex_groups_start} s') del nodes_of_interest # Get the new edge index (by type as expected for HeteroData) @@ -172,7 +172,7 @@ def _sampler_output_from_sampling_results( row_dict, col_dict = graph_store._get_renumbered_edge_groups_from_sample( sampling_results, noi_index ) - print(f'edge groups: {perf_counter() - time_edge_groups_start} s') + #print(f'edge groups: {perf_counter() - time_edge_groups_start} s') time_nodes_per_hop_start = perf_counter() for hop in range(len(hops)): @@ -182,10 +182,10 @@ def _sampler_output_from_sampling_results( for node_type in graph_store.node_types: if len(graph_store.node_types) == 1: - num_unique_nodes = cudf.concat([ - sampling_results_hop_0.sources, - sampling_results_to_hop.destinations, - ]).nunique() + num_unique_nodes = torch.concat([ + torch.as_tensor(sampling_results_hop_0.sources.values, device='cuda'), + torch.as_tensor(sampling_results_to_hop.destinations.values, device='cuda'), + ]).unique().shape[0] else: unique_nodes_hop = _get_unique_nodes( sampling_results_to_hop, @@ -234,7 +234,7 @@ def _sampler_output_from_sampling_results( len(hops), dtype=torch.int64 ) num_edges_per_hop_dict[can_etype][hop] = count - print(f'nodes/edges per hop: {perf_counter() - time_nodes_per_hop_start} s') + #print(f'nodes/edges per hop: {perf_counter() - time_nodes_per_hop_start} s') if HeteroSamplerOutput is None: raise ImportError("Error importing from pyg") From 100f00053f8db765e053df428664d07488830ce0 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 31 May 2023 19:21:20 +0000 Subject: [PATCH 060/347] minor correction --- benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index b72410cfee1..0320bcf0033 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -268,7 +268,7 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") - return (end_time_train - start_time_train / 1e9), mean_forward_time, mean_backward_time + return ((end_time_train - start_time_train) / 1e9), mean_forward_time, mean_backward_time def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ @@ -402,7 +402,7 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic 'Total Speedup': native_times[0] / (train_time + output_meta['execution_time']), } df = pandas.DataFrame(results, index=[0]) - df.to_csv(os.path.join(output_dir, output_result_filename),header=True, sep=',', index=False, mode='a') + df.to_csv(os.path.join(output_dir, output_result_filename),header=False, sep=',', index=False, mode='a') From 1c3bd662f3c20c77c97d9d7acccd1232633639cd Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Thu, 1 Jun 2023 16:52:31 +0000 Subject: [PATCH 061/347] reorg imports --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 77 +++++++++++++------ .../standalone/cugraph_bulk_sampling.py | 7 +- .../cugraph_pyg/loader/cugraph_node_loader.py | 16 +++- 3 files changed, 71 insertions(+), 29 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index 0320bcf0033..4a459f8169e 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -52,7 +52,8 @@ def init_pytorch_worker(device_id: int) -> None: rmm.reinitialize( devices=[device_id], - pool_allocator=False, + pool_allocator=True, + maximum_pool_size=28e9, ) @@ -72,16 +73,22 @@ def train_epoch(model, loader, optimizer): time_forward = 0.0 time_backward = 0.0 + start_time = time.perf_counter() for iter_i, data in enumerate(loader): #print(data.edge_index_dict['paper','cites','paper'].shape) #print('*********************************************************') num_sampled_nodes = data['paper']['num_sampled_nodes'] num_sampled_edges = data['paper','cites','paper']['num_sampled_edges'] + + start_time_to_homogeneous = time.perf_counter() data = data.to_homogeneous() + end_time_to_homogeneous = time.perf_counter() + time_to_homogeneous = (end_time_to_homogeneous - start_time_to_homogeneous) num_batches += 1 if iter_i % 20 == 0: print(f"iteration {iter_i}") + print('homogeneous time: ', time_to_homogeneous) # train y_true = data.y @@ -115,14 +122,20 @@ def train_epoch(model, loader, optimizer): end_time_backward = time.perf_counter() time_backward += end_time_backward - start_time_backward - + """ + start_time_delete = time.perf_counter() del y_true del y_pred del loss del data gc.collect() + end_time_delete = time.perf_counter() + + print('delete:', end_time_delete - start_time_delete) + """ - return total_loss, num_batches, (time_forward / num_batches), (time_backward / num_batches) + end_time = time.perf_counter() + return total_loss, num_batches, ((end_time - start_time) / num_batches), (time_forward / num_batches), (time_backward / num_batches) def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, int] = "cpu", num_epochs=1) -> None: @@ -246,29 +259,29 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i model.train() input_nodes = hetero_data['paper']['train'] - print('input nodes:', input_nodes.nonzero().shape) loader = NeighborLoader( hetero_data, input_nodes=('paper', input_nodes.cpu()), batch_size=output_meta['batch_size'], - num_neighbors={('paper','cites','paper'):[10,25]}, + num_neighbors={('paper','cites','paper'):output_meta['fanout']}, replace=False, is_sorted=True, disjoint=True, ) print('done creating loader') - total_loss, num_batches, mean_forward_time, mean_backward_time = \ + total_loss, num_batches, mean_total_time, mean_forward_time, mean_backward_time = \ train_epoch(model, loader, optimizer) end_time_train = time.perf_counter_ns() print( f"epoch {epoch} time: " f"{(end_time_train - start_time_train) / 1e9:3.4f} s" + f"\n trained {num_batches} batches" ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") - return ((end_time_train - start_time_train) / 1e9), mean_forward_time, mean_backward_time + return mean_total_time, mean_forward_time, mean_backward_time def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ @@ -355,21 +368,24 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic ) print('done creating loader') - total_loss, num_batches, mean_time_fw, mean_time_bw = train_epoch(model, cugraph_loader, optimizer) + total_loss, num_batches, mean_total_time, mean_time_fw, mean_time_bw = train_epoch(model, cugraph_loader, optimizer) end_time_train = time.perf_counter_ns() train_time = (end_time_train - start_time_train) / 1e9 print( f"epoch {epoch} time: " f"{train_time:3.4f} s" + f"\n trained {num_batches} batches" ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") + train_time = mean_total_time * num_batches output_result_filename = 'results.csv' results = { 'Machine': socket.gethostname(), 'Comms': output_meta['comms'] if 'comms' in output_meta else 'tcp', 'Dataset': output_meta['dataset'], + 'Replication Factor': replication_factor, 'Model': 'GraphSAGE', '# Layers': len(model.convs), '# Input Channels': num_input_features, @@ -388,21 +404,34 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic 'Training # GPUs': 1, 'Feature Storage': 'cpu' if features_device == 'cpu' else 'gpu', 'Memory Type': 'Device', # could be managed if configured - 'Sampling Time': output_meta['execution_time'], - 'Sampling Time Per Batch': output_meta['execution_time'] / num_batches, - 'Training Time': train_time, - 'Training Time Per Batch': train_time / num_batches, + 'Total Time': train_time + output_meta['execution_time'], - 'Mean Foward Time': mean_time_fw, - 'Native Mean Forward Time': native_times[1], - 'Mean Backward Time': mean_time_bw, - 'Native Mean Backward Time': native_times[2], - 'Native Equivalent Time': native_times[0], - 'Sample/Load Speedup': (native_times[0] - native_times[1]*num_batches - native_times[2]*num_batches) / (output_meta['execution_time'] - mean_time_fw*num_batches - mean_time_bw*num_batches), - 'Total Speedup': native_times[0] / (train_time + output_meta['execution_time']), + 'Native Equivalent Time': native_times[0] * num_batches, + 'Total Speedup': (native_times[0] * num_batches) / (train_time + output_meta['execution_time']), + + 'Bulk Sampling Time': output_meta['execution_time'], + 'Bulk Sampling Time Per Batch': output_meta['execution_time'] / num_batches, + + 'Parquet Read Time': cugraph_loader._total_read_time, + 'Parquet Read Time Per Batch': cugraph_loader._total_read_time / num_batches, + + 'Minibatch Conversion Time': cugraph_loader._total_convert_time, + 'Minibatch Conversion Time Per Batch': cugraph_loader._total_convert_time / num_batches, + + 'Foward Time': mean_time_fw * num_batches, + 'Native Forward Time': native_times[1] * num_batches, + + 'Forward Time Per Batch': mean_time_fw, + 'Native Forward Time Per Batch': native_times[1], + + 'Backward Time': mean_time_bw * num_batches, + 'Native Backward Time': native_times[2] * num_batches, + + 'Backward Time Per Batch': mean_time_bw, + 'Native Backward Time Per Batch': native_times[2], } df = pandas.DataFrame(results, index=[0]) - df.to_csv(os.path.join(output_dir, output_result_filename),header=False, sep=',', index=False, mode='a') + df.to_csv(os.path.join(output_dir, output_result_filename),header=True, sep=',', index=False, mode='a') @@ -467,12 +496,12 @@ def main(): init_pytorch_worker(args.device) - native_time, native_mean_fw_time, native_mean_bw_time = [float(x) for x in args.native_times.split(',')] - if native_time < 0: - native_time, native_mean_fw_time, native_mean_bw_time = \ + native_mean_time, native_mean_fw_time, native_mean_bw_time = [float(x) for x in args.native_times.split(',')] + if native_mean_time < 0: + native_mean_time, native_mean_fw_time, native_mean_bw_time = \ train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) - train(args.sample_dir, args.output_dir, (native_time, native_mean_fw_time, native_mean_bw_time), device=args.device, features_device=features_device, num_epochs=args.num_epochs) + train(args.sample_dir, args.output_dir, (native_mean_time, native_mean_fw_time, native_mean_bw_time), device=args.device, features_device=features_device, num_epochs=args.num_epochs) if __name__ == "__main__": diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index 3a48623445f..f96fc234a54 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -46,12 +46,14 @@ import pandas as pd import numpy as np +import cupy import cudf + import dask_cudf import dask.dataframe as ddf -import cupy +from dask.distributed import default_client -from typing import Optional, Union, List, Dict +from typing import Optional, Union, Dict def construct_graph(dask_dataframe): @@ -164,7 +166,6 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal log_level = 'INFO' ) - from dask.distributed import wait, default_client n_workers = len(default_client().scheduler_info()['workers']) meta = cudf.DataFrame({ diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index d46c7483a27..53b359a3188 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -125,6 +125,9 @@ def __init__( self.__batches_per_partition = batches_per_partition self.__starting_batch_id = starting_batch_id + self._total_read_time = 0.0 + self._total_convert_time = 0.0 + if input_nodes is None: # Will be loading from disk self.__num_batches = input_nodes @@ -198,6 +201,9 @@ def __init__( self.__input_files = iter(os.listdir(self.__directory.name)) def __next__(self): + from time import perf_counter + start_time_read_data = perf_counter() + # Load the next set of sampling results if necessary if self.__next_batch >= self.__end_exclusive: if self.__directory is None: @@ -242,7 +248,11 @@ def __next__(self): self.__data = cudf.read_parquet(parquet_path) self.__data = self.__data[list(columns.keys())].astype(columns) + end_time_read_data = perf_counter() + self._total_read_time += (end_time_read_data - start_time_read_data) + # Pull the next set of sampling results out of the dataframe in memory + start_time_convert = perf_counter() f = (self.__data["batch_id"] == self.__next_batch) sampler_output = _sampler_output_from_sampling_results( @@ -255,7 +265,7 @@ def __next__(self): # Get and return the sampled subgraph if isinstance(torch_geometric, MissingModule): noi_index, row_dict, col_dict, edge_dict = sampler_output["out"] - return _filter_cugraph_store( + out = _filter_cugraph_store( self.__feature_store, self.__graph_store, noi_index, @@ -276,7 +286,9 @@ def __next__(self): out.set_value_dict('num_sampled_nodes', sampler_output.num_sampled_nodes) out.set_value_dict('num_sampled_edges', sampler_output.num_sampled_edges) - return out + end_time_convert = perf_counter() + self._total_convert_time += (end_time_convert - start_time_convert) + return out def __iter__(self): return self From 4d51fae794151f953e124f85ed18b9d2183f9a54 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 2 Jun 2023 03:52:29 +0000 Subject: [PATCH 062/347] work --- .../cugraph-pyg/cugraph_pyg_graph_sage.py | 70 ++++++++++++++++--- .../cugraph_pyg/loader/cugraph_node_loader.py | 10 ++- 2 files changed, 68 insertions(+), 12 deletions(-) diff --git a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py index 4a459f8169e..fa98e8ffb36 100644 --- a/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py +++ b/benchmarks/cugraph-pyg/cugraph_pyg_graph_sage.py @@ -29,6 +29,7 @@ from typing import Union, List from models_cugraph import CuGraphSAGE +from cugraph.testing.mg_utils import enable_spilling def load_disk_features(meta: dict, node_type: str, replication_factor: int = 1): node_type_path = os.path.join(meta['dataset_dir'], meta['dataset'], 'npy', node_type) @@ -80,15 +81,11 @@ def train_epoch(model, loader, optimizer): num_sampled_nodes = data['paper']['num_sampled_nodes'] num_sampled_edges = data['paper','cites','paper']['num_sampled_edges'] - start_time_to_homogeneous = time.perf_counter() data = data.to_homogeneous() - end_time_to_homogeneous = time.perf_counter() - time_to_homogeneous = (end_time_to_homogeneous - start_time_to_homogeneous) num_batches += 1 if iter_i % 20 == 0: print(f"iteration {iter_i}") - print('homogeneous time: ', time_to_homogeneous) # train y_true = data.y @@ -269,6 +266,7 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i disjoint=True, ) print('done creating loader') + # loader was patched to record the feature extraction time total_loss, num_batches, mean_total_time, mean_forward_time, mean_backward_time = \ train_epoch(model, loader, optimizer) @@ -281,7 +279,7 @@ def train_native(bulk_samples_dir: str, device:int, features_device:Union[str, i ) print(f"loss after epoch {epoch}: {total_loss / num_batches}") - return mean_total_time, mean_forward_time, mean_backward_time + return mean_total_time, mean_forward_time, mean_backward_time, (loader._feature_time / num_batches) def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], device: int, features_device: Union[str, int] = "cpu", num_epochs=1) -> None: """ @@ -381,6 +379,38 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic train_time = mean_total_time * num_batches output_result_filename = 'results.csv' + results_native = { + 'Dataset': f"{output_meta['dataset']} x {replication_factor}", + 'Framework': 'PyG', + 'Setup Details': f"GraphSAGE, {len(model.convs)} layers", + 'Batch Size': output_meta['batch_size'], + 'Fanout': str(output_meta['fanout']), + 'Machine Details': socket.gethostname(), + 'Bulk Sampling per epoch': 'n/a', + 'MFG Creation': 0.0, + 'Feature Loading': native_times[3] * num_batches, + 'Model FWD': native_times[1] * num_batches, + 'Model BWD': native_times[2] * num_batches, + 'Time Per Epoch': native_times[0] * num_batches, + 'Time Per Batch': native_times[0], + 'Speedup': 1, + } + results_cugraph = { + 'Dataset': f"{output_meta['dataset']} x {replication_factor}", + 'Framework': 'cuGraph-PyG', + 'Setup Details': f"GraphSAGE, {len(model.convs)} layers", + 'Batch Size': output_meta['batch_size'], + 'Fanout': str(output_meta['fanout']), + 'Machine Details': socket.gethostname(), + 'Bulk Sampling per epoch': output_meta['execution_time'], + 'MFG Creation': cugraph_loader._total_convert_time + cugraph_loader._total_read_time, + 'Feature Loading': cugraph_loader._total_feature_time, + 'Model FWD': mean_time_fw * num_batches, + 'Model BWD': mean_time_bw * num_batches, + 'Time Per Epoch': train_time + output_meta['execution_time'], + 'Time Per Batch': (train_time + output_meta['execution_time']) / num_batches, + 'Speedup': (native_times[0] * num_batches) / (train_time + output_meta['execution_time']), + } results = { 'Machine': socket.gethostname(), 'Comms': output_meta['comms'] if 'comms' in output_meta else 'tcp', @@ -418,6 +448,9 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic 'Minibatch Conversion Time': cugraph_loader._total_convert_time, 'Minibatch Conversion Time Per Batch': cugraph_loader._total_convert_time / num_batches, + 'Feature Fetch Time': cugraph_loader._total_feature_time, + 'Feature Fetch Time Per Batch': cugraph_loader._total_feature_time / num_batches, + 'Foward Time': mean_time_fw * num_batches, 'Native Forward Time': native_times[1] * num_batches, @@ -432,6 +465,10 @@ def train(bulk_samples_dir: str, output_dir:str, native_times:List[float], devic } df = pandas.DataFrame(results, index=[0]) df.to_csv(os.path.join(output_dir, output_result_filename),header=True, sep=',', index=False, mode='a') + + df_n = pandas.DataFrame(results_native, index=[0]) + df_c = pandas.DataFrame(results_cugraph, index=[1]) + pandas.concat([df_n, df_c]).to_csv(os.path.join(output_dir, output_result_filename),header=True, sep=',', index=False, mode='a') @@ -480,7 +517,7 @@ def parse_args(): type=str, help="Input the native runtimes (total, fw, bw) to avoid doing a native run", required=False, - default="-1.0,-1.0,-1.0" + default="-1.0,-1.0,-1.0,-1.0" ) return parser.parse_args() @@ -495,13 +532,26 @@ def main(): features_device = args.features_device init_pytorch_worker(args.device) + enable_spilling() - native_mean_time, native_mean_fw_time, native_mean_bw_time = [float(x) for x in args.native_times.split(',')] + native_mean_time, native_mean_fw_time, native_mean_bw_time, native_mean_feature_time = [float(x) for x in args.native_times.split(',')] if native_mean_time < 0: - native_mean_time, native_mean_fw_time, native_mean_bw_time = \ - train_native(args.sample_dir, device=args.device, features_device=features_device, num_epochs=args.num_epochs) + native_mean_time, native_mean_fw_time, native_mean_bw_time, native_mean_feature_time = \ + train_native( + args.sample_dir, + device=args.device, + features_device=features_device, + num_epochs=args.num_epochs + ) - train(args.sample_dir, args.output_dir, (native_mean_time, native_mean_fw_time, native_mean_bw_time), device=args.device, features_device=features_device, num_epochs=args.num_epochs) + train( + args.sample_dir, + args.output_dir, + (native_mean_time, native_mean_fw_time, native_mean_bw_time, native_mean_feature_time), + device=args.device, + features_device=features_device, + num_epochs=args.num_epochs + ) if __name__ == "__main__": diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 53b359a3188..6fc51645957 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -127,6 +127,7 @@ def __init__( self._total_read_time = 0.0 self._total_convert_time = 0.0 + self._total_feature_time = 0.0 if input_nodes is None: # Will be loading from disk @@ -259,9 +260,13 @@ def __next__(self): self.__data.loc[f], self.__graph_store ) + end_time_convert = perf_counter() + self._total_convert_time += (end_time_convert - start_time_convert) + # Get ready for next iteration self.__next_batch += 1 + start_time_feature = perf_counter() # Get and return the sampled subgraph if isinstance(torch_geometric, MissingModule): noi_index, row_dict, col_dict, edge_dict = sampler_output["out"] @@ -285,9 +290,10 @@ def __next__(self): out.set_value_dict('num_sampled_nodes', sampler_output.num_sampled_nodes) out.set_value_dict('num_sampled_edges', sampler_output.num_sampled_edges) + end_time_feature = perf_counter() - end_time_convert = perf_counter() - self._total_convert_time += (end_time_convert - start_time_convert) + self._total_feature_time += (end_time_feature - start_time_feature) + return out def __iter__(self): From 20c366aca1884da7a5be7dad813ccfb9c1c1575a Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 2 Jun 2023 03:57:32 +0000 Subject: [PATCH 063/347] pull in changes from other branch --- .../standalone/cugraph_bulk_sampling.py | 618 ++++++++++++++++++ 1 file changed, 618 insertions(+) create mode 100644 benchmarks/cugraph/standalone/cugraph_bulk_sampling.py diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py new file mode 100644 index 00000000000..f96fc234a54 --- /dev/null +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -0,0 +1,618 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import warnings +import argparse + +from cugraph.testing.mg_utils import ( + generate_edgelist_rmat, + # get_allocation_counts_dask_persist, + get_allocation_counts_dask_lazy, + sizeof_fmt, + get_peak_output_ratio_across_workers, + restart_client, +) + +from cugraph.testing.mg_utils import ( + start_dask_client, + stop_dask_client, + enable_spilling, +) + +from cugraph.structure.symmetrize import symmetrize +from cugraph.experimental.gnn import BulkSampler + +import cugraph + +from datetime import datetime + +import json +import re +import os +import gc +from time import sleep, perf_counter +from math import ceil + +import pandas as pd +import numpy as np +import cupy +import cudf + +import dask_cudf +import dask.dataframe as ddf +from dask.distributed import default_client + +from typing import Optional, Union, Dict + + +def construct_graph(dask_dataframe): + """ + Args: + dask_dataframe: + dask_dataframe contains weighted and undirected edges with self + loops. Multiple edges will likely be present as well. + directed: + If True, the graph will be directed. + renumber: + If True, the graph will be renumbered. + Returns: + G: cugraph.Graph + """ + assert dask_dataframe['src'].dtype == 'int64' + assert dask_dataframe['dst'].dtype == 'int64' + assert dask_dataframe['etp'].dtype == 'int32' + + G = cugraph.MultiGraph(directed=True) + G.from_dask_cudf_edgelist( + dask_dataframe, source="src", destination="dst", edge_type='etp', renumber=False + ) + return G + + +def symmetrize_ddf(dask_dataframe): + source_col, dest_col = symmetrize( + dask_dataframe, + 'src', + 'dst', + multi=True, + symmetrize=True, + ) + + new_ddf = source_col.to_frame() + new_ddf['dst'] = dest_col + + return new_ddf + +def renumber_ddf(dask_df): + vertices = dask_cudf.concat([dask_df['src'], dask_df['dst']]).unique().reset_index(drop=True).persist() + vertices.name = 'v' + vertices = vertices.reset_index().set_index('v').rename(columns={'index': 'm'}).persist() + + src = dask_df.merge(vertices, left_on='src', right_on='v', how='left').m.rename('src') + dst = dask_df.merge(vertices, left_on='dst', right_on='v', how='left').m.rename('dst') + df = src.to_frame() + df['dst'] = dst + + return df.reset_index(drop=True) + +def _make_batch_ids(bdf: cudf.DataFrame, batch_size: int, num_workers: int, partition_info: Optional[Union[dict, str]] = None): + # Required by dask; need to skip dummy partitions. + if partition_info is None: + return cudf.DataFrame({ + 'batch': cudf.Series(dtype='int32'), + 'start': cudf.Series(dtype='int64') + }) + + partition = partition_info['number'] + if partition is None: + raise ValueError('division is absent') + + num_batches = int(ceil(len(bdf) / batch_size)) + + batch_ids = cupy.repeat( + cupy.arange(num_batches * partition, num_batches * (partition + 1), dtype='int32'), + batch_size + )[:len(bdf)] + + bdf = bdf.reset_index(drop=True) + bdf['batch'] = cudf.Series(batch_ids) + + return bdf + + +def _replicate_df(df: cudf.DataFrame, replication_factor: int, col_offsets:Dict[str, int], partition_info: Optional[Union[dict, str]] = None): + # Required by dask; need to skip dummy partitions. + if partition_info is None: + return cudf.DataFrame({ + 'batch': cudf.Series(dtype='int32'), + 'start': cudf.Series(dtype='int64') + }) + + if replication_factor > 1: + for r in range(1, replication_factor): + df_replicated = df + for col, offset in col_offsets.items(): + df_replicated[col] += offset * r + + df = cudf.concat([df, df_replicated], ignore_index=True) + + return df + + +@get_allocation_counts_dask_lazy(return_allocations=True, logging=True) +def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_call=200000, batches_per_partition=100, fanout=[5, 5, 5]): + cupy.random.seed(seed) + + sampler = BulkSampler( + batch_size=batch_size, + output_path=output_path, + graph=G, + fanout_vals=fanout, + with_replacement=False, + random_state=seed, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + log_level = 'INFO' + ) + + n_workers = len(default_client().scheduler_info()['workers']) + + meta = cudf.DataFrame({ + 'node': cudf.Series(dtype='int64'), + 'batch': cudf.Series(dtype='int32') + }) + + + batch_df = label_df.map_partitions(_make_batch_ids, batch_size, n_workers, meta=meta).persist() + del label_df + print('created batches') + + + start_time = perf_counter() + sampler.add_batches(batch_df, start_col_name='node', batch_col_name='batch') + sampler.flush() + end_time = perf_counter() + print('flushed all batches') + return (end_time - start_time) + + +def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor:int=1): + # cuGraph-PyG assigns offsets based on lexicographic order + node_offsets = {} + node_offsets_replicated = {} + count = 0 + count_replicated = 0 + for node_type in sorted(node_counts.keys()): + node_offsets[node_type] = count + node_offsets_replicated[node_type] = count_replicated + + count += node_counts[node_type] + count_replicated += node_counts[node_type] * replication_factor + + return node_offsets, node_offsets_replicated, count_replicated + + +def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_factor=1): + path = os.path.join(dataset_dir, dataset) + parquet_path = os.path.join(path, 'parquet') + + with open(os.path.join(path, 'meta.json')) as meta_file: + meta = json.load(meta_file) + + # cuGraph-PyG assigns offsets based on lexicographic order + node_offsets, node_offsets_replicated, total_num_nodes = \ + assign_offsets_pyg(meta['num_nodes'], replication_factor=replication_factor) + + edge_index_dict = {} + for edge_type in os.listdir(parquet_path): + if re.match(r'[a-z]+__[a-z]+__[a-z]+', edge_type): + print(f'Loading edge index for edge type {edge_type}') + + can_edge_type = tuple(edge_type.split('__')) + edge_index_dict[can_edge_type] = dask_cudf.read_parquet(os.path.join(os.path.join(parquet_path, edge_type), 'edge_index.parquet')) + + edge_index_dict[can_edge_type]['src'] += node_offsets_replicated[can_edge_type[0]] + edge_index_dict[can_edge_type]['dst'] += node_offsets_replicated[can_edge_type[-1]] + edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].persist() + + if replication_factor > 1: + edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].map_partitions( + _replicate_df, + replication_factor, + { + 'src': node_offsets[can_edge_type[0]], + 'dst': node_offsets[can_edge_type[2]], + }, + meta=cudf.DataFrame({'src':cudf.Series(dtype='int64'), 'dst':cudf.Series(dtype='int64')}) + ).persist() + + gc.collect() + + if reverse_edges: + edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].rename({'src':'dst','dst':'src'}).persist() + + # cuGraph-PyG assigns numeric edge type ids based on lexicographic order + edge_offsets = {} + edge_count = 0 + for num_edge_type, can_edge_type in enumerate(sorted(edge_index_dict.keys())): + edge_index_dict[can_edge_type]['etp'] = cupy.int32(num_edge_type) + edge_offsets[can_edge_type] = edge_count + edge_count += len(edge_index_dict[can_edge_type]) + + all_edges_df = dask_cudf.concat( + list(edge_index_dict.values()) + ).persist() + + del edge_index_dict + gc.collect() + + node_labels = {} + for node_type, offset in node_offsets_replicated.items(): + print(f'Loading node labels for node type {node_type} (offset={offset})') + node_label_path = os.path.join(os.path.join(parquet_path, node_type), 'node_label.parquet') + if os.path.exists(node_label_path): + node_labels[node_type] = dask_cudf.read_parquet(node_label_path).drop('label',axis=1).persist() + node_labels[node_type]['node'] += offset + node_labels[node_type] = node_labels[node_type].persist() + + if replication_factor > 1: + node_labels[node_type] = node_labels[node_type].map_partitions( + _replicate_df, + replication_factor, + { + 'node': node_offsets[node_type] + }, + meta=cudf.DataFrame({'node':cudf.Series(dtype='int64')}) + ).persist() + + gc.collect() + + node_labels_df = dask_cudf.concat( + list(node_labels.values()) + ).persist() + + del node_labels + gc.collect() + + return all_edges_df, node_labels_df, node_offsets_replicated, edge_offsets, total_num_nodes + + +def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seeds_per_call, fanout, reverse_edges=True, dataset_dir='.', replication_factor=1, num_labels=256, labeled_percentage=0.001): + """ + Entry point for the benchmark. + + Parameters + ---------- + dataset: str + The dataset to sample. Can be rmat_{scale}_{edgefactor}, or the name of an ogb dataset. + output_path: str + The output path, where samples and metadata will be stored. + seed: int + The random seed. + batch_size: int + The batch size (number of input seeds in a single sampling batch). + seeds_per_call: int + The number of input seeds in a single sampling call. + fanout: list[int] + The fanout. + reverse_edges: bool + Whether to reverse edges when constructing the graph. + dataset_dir: str + The directory where datasets are stored (only for ogb datasets) + replication_factor: int + The number of times to replicate the dataset. + num_labels: int + The number of random labels to generate (only for rmat datasets) + labeled_percentage: float + The percentage of the data that is labeled (only for rmat datasets) + Defaults to 0.001 to match papers100M + """ + if dataset[0:5] == 'rmat': + dataset = dataset.split('_') + scale = int(dataset[1]) + edgefactor = int(dataset[2]) + + dask_edgelist_df = generate_edgelist_rmat( + scale=scale, edgefactor=edgefactor, seed=seed, unweighted=True, mg=True, + ) + dask_edgelist_df = dask_edgelist_df.astype("int64") + dask_edgelist_df = dask_edgelist_df.reset_index(drop=True) + + + dask_edgelist_df = renumber_ddf(dask_edgelist_df).persist() + dask_edgelist_df = symmetrize_ddf(dask_edgelist_df).persist() + dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really + + # generator = np.random.default_rng(seed=seed) + num_labeled_nodes = int(2**(scale+1) * labeled_percentage) + label_df = pd.DataFrame({ + 'node': np.arange(num_labeled_nodes), + # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') + }) + + dask_label_df = ddf.from_pandas(label_df) + del label_df + gc.collect() + + dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) + + else: + dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ + load_disk_dataset(dataset, dataset_dir=dataset_dir, reverse_edges=reverse_edges, replication_factor=replication_factor) + + num_input_edges = len(dask_edgelist_df) + print( + f"Number of input edges = {num_input_edges:,}" + ) + + G = construct_graph( + dask_edgelist_df + ) + print('constructed graph') + + input_memory = G.edgelist.edgelist_df.memory_usage().sum().compute() + print(f'input memory: {input_memory}') + + now = datetime.now() + timestring = datetime.strftime(now, '%Y-%m-%d-%H:%M:%S') + output_subdir = os.path.join(output_path, f'{dataset}[{replication_factor}]_b{batch_size}_f{fanout}_{timestring}') + os.makedirs(output_subdir) + + output_sample_path = os.path.join(output_subdir, 'samples') + os.makedirs(output_sample_path) + + batches_per_partition = 200_000 // batch_size + execution_time, allocation_counts = sample_graph( + G, + dask_label_df, + output_sample_path, + seed=seed, + batch_size=batch_size, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + fanout=fanout + ) + + output_meta = { + 'dataset': dataset, + 'dataset_dir': dataset_dir, + 'seed': seed, + 'node_offsets': node_offsets, + 'edge_offsets': {'__'.join(k): v for k, v in edge_offsets.items()}, + 'total_num_nodes': total_num_nodes, + 'total_num_edges': num_input_edges, + 'batch_size': batch_size, + 'seeds_per_call': seeds_per_call, + 'batches_per_partition': batches_per_partition, + 'fanout': fanout, + 'replication_factor': replication_factor, + 'num_sampling_gpus': len(G._plc_graph), + 'execution_time': execution_time, + } + with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: + json.dump( + output_meta, + f, + indent='\t' + ) + + print('allocation counts b:') + print(allocation_counts.values()) + + ( + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) = get_memory_statistics( + allocation_counts=allocation_counts, input_memory=input_memory + ) + print(f"Number of edges in final graph = {G.number_of_edges():,}") + print("-" * 80) + return ( + num_input_edges, + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) + + +def get_memory_statistics(allocation_counts, input_memory): + """ + Get memory statistics for the benchmark. + """ + output_to_peak_ratio = get_peak_output_ratio_across_workers(allocation_counts) + peak_allocation_across_workers = max( + [a["peak_bytes"] for a in allocation_counts.values()] + ) + input_memory_per_worker = input_memory / len(allocation_counts.keys()) + input_to_peak_ratio = peak_allocation_across_workers / input_memory_per_worker + print(f"Edge List Memory = {sizeof_fmt(input_memory_per_worker)}") + print(f"Peak Memory across workers = {sizeof_fmt(peak_allocation_across_workers)}") + print(f"Max Peak to output graph ratio across workers = {output_to_peak_ratio:.2f}") + print( + f"Max Peak to avg input graph ratio across workers = {input_to_peak_ratio:.2f}" + ) + return ( + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + '--output_root', + type=str, + help='The output root directory. File/folder names are auto-generated.', + required=True, + ) + + parser.add_argument( + '--dataset_root', + type=str, + help='The dataset root directory containing ogb datasets.', + required=True, + ) + + parser.add_argument( + '--datasets', + type=str, + help=( + 'Comma separated list of datasets; can specify ogb or rmat (i.e. ogb_papers100M[2],rmat_22_16).' + ' For ogb datasets, can provide replication factor using brackets.' + ), + required=True, + ) + + parser.add_argument( + '--fanouts', + type=str, + help='Comma separated list of fanouts (i.e. 10_25,5_5_5)', + required=False, + default='10_25', + ) + + parser.add_argument( + '--batch_sizes', + type=str, + help='Comma separated list of batch sizes (i.e. 500,1000)', + required=False, + default='500,1000' + ) + + parser.add_argument( + '--seeds_per_call_opts', + type=str, + help='Comma separated list of seeds per call (i.e. 1000000,2000000)', + required=False, + default='500000,1000000,2000000', + ) + + parser.add_argument( + '--reverse_edges', + action='store_true', + help='Whether to reverse the edges for DGL (defaults to False). Should be True for DGL, False for PyG.', + required=False, + default=False, + ) + + parser.add_argument( + '--dask_worker_devices', + type=str, + help='Comma separated list of dask worker devices', + required=False, + default=[0,1] + ) + + parser.add_argument( + '--random_seed', + type=int, + help='Random seed', + required=False, + default=62 + ) + + return parser.parse_args() + + +# call __main__ function +if __name__ == "__main__": + logging.basicConfig() + + args = get_args() + fanouts = [[int(f) for f in fanout.split('_')] for fanout in args.fanouts.split(',')] + datasets = args.datasets.split(',') + batch_sizes = [int(b) for b in args.batch_sizes.split(',')] + seeds_per_call_opts = [int(s) for s in args.seeds_per_call_opts.split(',')] + dask_worker_devices = [int(d) for d in args.dask_worker_devices.split(',')] + + client, cluster = start_dask_client(dask_worker_devices=dask_worker_devices, jit_unspill=False, rmm_pool_size=28e9, rmm_async=True) + enable_spilling() + stats_ls = [] + client.run(enable_spilling) + for dataset in datasets: + for fanout in fanouts: + for batch_size in batch_sizes: + for seeds_per_call in seeds_per_call_opts: + print(f'dataset: {dataset}') + print(f'batch size: {batch_size}') + print(f'fanout: {fanout}') + print(f'seeds_per_call: {seeds_per_call}') + + if re.match(r'([A-z]|[0-9])+\[[0-9]+\]', dataset): + replication_factor = int(dataset[-2]) + dataset = dataset[:-3] + else: + replication_factor = 1 + + try: + stats_d = {} + ( + num_input_edges, + input_to_peak_ratio, + output_to_peak_ratio, + input_memory_per_worker, + peak_allocation_across_workers, + ) = benchmark_cugraph_bulk_sampling( + dataset=dataset, + output_path=args.output_root, + seed=args.random_seed, + batch_size=batch_size, + seeds_per_call=seeds_per_call, + fanout=fanout, + dataset_dir=args.dataset_root, + reverse_edges=args.reverse_edges, + replication_factor=replication_factor + ) + stats_d["dataset"] = dataset + stats_d["num_input_edges"] = num_input_edges + stats_d["batch_size"] = batch_size + stats_d["fanout"] = fanout + stats_d["seeds_per_call"] = seeds_per_call + stats_d["input_memory_per_worker"] = sizeof_fmt(input_memory_per_worker) + stats_d["peak_allocation_across_workers"] = sizeof_fmt( + peak_allocation_across_workers + ) + stats_d["input_to_peak_ratio"] = input_to_peak_ratio + stats_d["output_to_peak_ratio"] = output_to_peak_ratio + stats_ls.append(stats_d) + except Exception as e: + warnings.warn('An Exception Occurred!') + print(e) + restart_client(client) + sleep(10) + + stats_df = pd.DataFrame( + stats_ls, + columns=[ + "dataset", + "num_input_edges", + "directed", + "renumber", + "input_memory_per_worker", + "peak_allocation_across_workers", + "input_to_peak_ratio", + "output_to_peak_ratio", + ], + ) + stats_df.to_csv("cugraph_graph_creation_stats.csv") + print("-" * 40 + f"dataset = {dataset} completed" + "-" * 40) + + # Cleanup Dask Cluster + stop_dask_client(client, cluster) From 48e84f4c74efeae70b48adcbe5e8d8b023260812 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 2 Jun 2023 04:03:44 +0000 Subject: [PATCH 064/347] run file generator --- python/cugraph-service/server/pyproject.toml | 2 +- python/cugraph/pyproject.toml | 20 ++++++++++---------- python/pylibcugraph/pyproject.toml | 10 +++++----- 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index 6c34a60f7ab..6901fa67459 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -30,7 +30,7 @@ dependencies = [ "numpy>=1.21,<1.24", "rmm==23.6.*", "thriftpy2", - "ucx-py==0.32.*", + "ucx-py==0.33.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index 731f32b52ce..95a1c2473f3 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -6,9 +6,9 @@ requires = [ "cmake>=3.23.1,!=3.25.0", "cython>=0.29,<0.30", "ninja", - "pylibcugraph==23.8.*", - "pylibraft==23.8.*", - "rmm==23.8.*", + "pylibcugraph==23.6.*", + "pylibraft==23.6.*", + "rmm==23.6.*", "scikit-build>=0.13.1,<0.17.2", "setuptools", "wheel", @@ -29,17 +29,17 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.8.*", + "cudf==23.6.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.8.*", - "dask-cudf==23.8.*", + "dask-cuda==23.6.*", + "dask-cudf==23.6.*", "dask==2023.3.2", "distributed==2023.3.2.1", "numba>=0.56.4,<0.57", - "pylibcugraph==23.8.*", - "raft-dask==23.8.*", - "rmm==23.8.*", - "ucx-py==0.32.*", + "pylibcugraph==23.6.*", + "raft-dask==23.6.*", + "rmm==23.6.*", + "ucx-py==0.33.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index 592f11937d5..14d68c8fa63 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -6,8 +6,8 @@ requires = [ "cmake>=3.23.1,!=3.25.0", "cython>=0.29,<0.30", "ninja", - "pylibraft==23.8.*", - "rmm==23.8.*", + "pylibraft==23.6.*", + "rmm==23.6.*", "scikit-build>=0.13.1,<0.17.2", "setuptools", "wheel", @@ -28,8 +28,8 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "pylibraft==23.8.*", - "rmm==23.8.*", + "pylibraft==23.6.*", + "rmm==23.6.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", @@ -40,7 +40,7 @@ classifiers = [ [project.optional-dependencies] test = [ - "cudf==23.8.*", + "cudf==23.6.*", "networkx>=2.5.1", "numpy>=1.21,<1.24", "pandas", From 838431cb6a4d3f804922b720fe91911e3b3f808c Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 2 Jun 2023 15:11:38 +0000 Subject: [PATCH 065/347] revert repository change in cmake --- cpp/cmake/thirdparty/get_libcugraphops.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/cmake/thirdparty/get_libcugraphops.cmake b/cpp/cmake/thirdparty/get_libcugraphops.cmake index 240a88a3bf6..0f9138bb8ae 100644 --- a/cpp/cmake/thirdparty/get_libcugraphops.cmake +++ b/cpp/cmake/thirdparty/get_libcugraphops.cmake @@ -25,7 +25,7 @@ if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_BRANCH) endif() if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_REPOSITORY) - set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "https://github.com/alexbarghi-nv/cugraph-ops.git") + set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "https://github.com/rapidsai/cugraph-ops.git") endif() function(find_and_configure_cugraph_ops) From ccbc0a0c7b174dfccff0c4474f58fc7a57c6f50f Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Fri, 2 Jun 2023 15:13:46 +0000 Subject: [PATCH 066/347] set cugraph ops version in cmake --- cpp/cmake/thirdparty/get_libcugraphops.cmake | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/cpp/cmake/thirdparty/get_libcugraphops.cmake b/cpp/cmake/thirdparty/get_libcugraphops.cmake index 0f9138bb8ae..f7bf311ec43 100644 --- a/cpp/cmake/thirdparty/get_libcugraphops.cmake +++ b/cpp/cmake/thirdparty/get_libcugraphops.cmake @@ -16,9 +16,11 @@ set(CUGRAPH_MIN_VERSION_cugraph_ops "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}.00") -if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_VERSION) - set(CUGRAPH_CUGRAPH_OPS_VERSION "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}") -endif() +# if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_VERSION) +# set(CUGRAPH_CUGRAPH_OPS_VERSION "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}") +# endif() + +set(CUGRAPH_CUGRAPH_OPS_VERSION "23.06") if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_BRANCH) set(CUGRAPH_CUGRAPH_OPS_BRANCH "branch-${CUGRAPH_CUGRAPH_OPS_VERSION}") From f17c609cf6bbe5818ee812b05ecb69b7abddc784 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 16:08:19 +0000 Subject: [PATCH 067/347] fix bad merge --- python/cugraph-service/server/pyproject.toml | 2 +- python/cugraph/pyproject.toml | 12 ++++++------ python/pylibcugraph/pyproject.toml | 10 +++++----- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index 7d86b01126f..baf9d0049fa 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -31,7 +31,7 @@ dependencies = [ "numpy>=1.21", "rmm==23.6.*", "thriftpy2", - "ucx-py==0.33.*", + "ucx-py==0.32.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index 1f288b40abe..2bb311ec634 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -6,9 +6,9 @@ requires = [ "cmake>=3.23.1,!=3.25.0", "cython>=0.29,<0.30", "ninja", - "pylibcugraph==23.6.*", - "pylibraft==23.6.*", - "rmm==23.6.*", + "pylibcugraph==23.8.*", + "pylibraft==23.8.*", + "rmm==23.8.*", "scikit-build>=0.13.1,<0.17.2", "setuptools", "wheel", @@ -29,10 +29,10 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.6.*", + "cudf==23.8.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.6.*", - "dask-cudf==23.6.*", + "dask-cuda==23.8.*", + "dask-cudf==23.8.*", "dask==2023.3.2", "distributed==2023.3.2.1", "numba>=0.57", diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index 2e02fca443a..868527d6cbe 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -6,8 +6,8 @@ requires = [ "cmake>=3.23.1,!=3.25.0", "cython>=0.29,<0.30", "ninja", - "pylibraft==23.6.*", - "rmm==23.6.*", + "pylibraft==23.8.*", + "rmm==23.8.*", "scikit-build>=0.13.1,<0.17.2", "setuptools", "wheel", @@ -28,8 +28,8 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "pylibraft==23.6.*", - "rmm==23.6.*", + "pylibraft==23.8.*", + "rmm==23.8.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", @@ -40,7 +40,7 @@ classifiers = [ [project.optional-dependencies] test = [ - "cudf==23.6.*", + "cudf==23.8.*", "networkx>=2.5.1", "numpy>=1.21", "pandas", From 7c036102b92031a7b27e480f19c6e466ced5bdbb Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 16:20:33 +0000 Subject: [PATCH 068/347] improve rmat generation --- .../standalone/cugraph_bulk_sampling.py | 78 ++++++++++++------- 1 file changed, 51 insertions(+), 27 deletions(-) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index f96fc234a54..f9ed0a5c37d 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -202,6 +202,55 @@ def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor:int=1): return node_offsets, node_offsets_replicated, count_replicated +def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels=256, reverse_edges=False): + """ + Generates an rmat dataset. Currently does not support heterogeneous datasets. + + Parameters + ---------- + dataset: The specifier of the rmat dataset (i.e. rmat_20_16) + seed: The seed to use for random number generation + num_labels: The number of classes for the labeled nodes + reverse_edges: Whether to reverse the edges in the edgelist (should be True for DGL, False, for PyG) + """ + + dataset = dataset.split('_') + scale = int(dataset[1]) + edgefactor = int(dataset[2]) + + dask_edgelist_df = generate_edgelist_rmat( + scale=scale, edgefactor=edgefactor, seed=seed, unweighted=True, mg=True, + ) + dask_edgelist_df = dask_edgelist_df.astype("int64") + dask_edgelist_df = dask_edgelist_df.reset_index(drop=True) + + + dask_edgelist_df = renumber_ddf(dask_edgelist_df).persist() + dask_edgelist_df = symmetrize_ddf(dask_edgelist_df).persist() + dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really + + generator = np.random.default_rng(seed=seed) + num_labeled_nodes = int(2**(scale+1) * labeled_percentage) + label_df = pd.DataFrame({ + 'node': np.arange(num_labeled_nodes), + 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') + }) + + dask_label_df = ddf.from_pandas(label_df) + del label_df + gc.collect() + + dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) + + node_offsets = {'paper': 0} + edge_offsets = {('paper','cites','paper'):0}, + total_num_nodes = dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique() + + if reverse_edges: + dask_edgelist_df = dask_edgelist_df.rename(columns={'src':'dst', 'dst':'src'}) + + return dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes + def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_factor=1): path = os.path.join(dataset_dir, dataset) @@ -319,33 +368,8 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed Defaults to 0.001 to match papers100M """ if dataset[0:5] == 'rmat': - dataset = dataset.split('_') - scale = int(dataset[1]) - edgefactor = int(dataset[2]) - - dask_edgelist_df = generate_edgelist_rmat( - scale=scale, edgefactor=edgefactor, seed=seed, unweighted=True, mg=True, - ) - dask_edgelist_df = dask_edgelist_df.astype("int64") - dask_edgelist_df = dask_edgelist_df.reset_index(drop=True) - - - dask_edgelist_df = renumber_ddf(dask_edgelist_df).persist() - dask_edgelist_df = symmetrize_ddf(dask_edgelist_df).persist() - dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really - - # generator = np.random.default_rng(seed=seed) - num_labeled_nodes = int(2**(scale+1) * labeled_percentage) - label_df = pd.DataFrame({ - 'node': np.arange(num_labeled_nodes), - # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') - }) - - dask_label_df = ddf.from_pandas(label_df) - del label_df - gc.collect() - - dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) + dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ + generate_rmat_dataset(dataset, reverse_edges=reverse_edges, seed=seed, labeled_percentage=labeled_percentage, num_labels=num_labels) else: dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ From 8af2b2e8cae8d31a1b181d7e0e097c1aa70ef24c Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 16:25:51 +0000 Subject: [PATCH 069/347] revert some unintentional changes --- conda/environments/all_cuda-118_arch-x86_64.yaml | 2 +- conda/recipes/cugraph-service/conda_build_config.yaml | 2 +- conda/recipes/cugraph/conda_build_config.yaml | 2 +- conda/recipes/pylibcugraph/conda_build_config.yaml | 2 +- python/cugraph-pyg/cugraph_pyg/tests/conftest.py | 5 ----- 5 files changed, 4 insertions(+), 9 deletions(-) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index f3d2afd2e24..12f9138960c 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -64,5 +64,5 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.32.* +- ucx-py==0.33.* name: all_cuda-118_arch-x86_64 diff --git a/conda/recipes/cugraph-service/conda_build_config.yaml b/conda/recipes/cugraph-service/conda_build_config.yaml index ab90a8af2a4..af1d362141a 100644 --- a/conda/recipes/cugraph-service/conda_build_config.yaml +++ b/conda/recipes/cugraph-service/conda_build_config.yaml @@ -1,2 +1,2 @@ ucx_py_version: - - "0.32.*" + - "0.33.*" diff --git a/conda/recipes/cugraph/conda_build_config.yaml b/conda/recipes/cugraph/conda_build_config.yaml index 20194c031f4..798948aa634 100644 --- a/conda/recipes/cugraph/conda_build_config.yaml +++ b/conda/recipes/cugraph/conda_build_config.yaml @@ -14,7 +14,7 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.32.*" + - "0.33.*" # The CTK libraries below are missing from the conda-forge::cudatoolkit # package. The "*_host_*" version specifiers correspond to `11.8` packages. diff --git a/conda/recipes/pylibcugraph/conda_build_config.yaml b/conda/recipes/pylibcugraph/conda_build_config.yaml index 20194c031f4..798948aa634 100644 --- a/conda/recipes/pylibcugraph/conda_build_config.yaml +++ b/conda/recipes/pylibcugraph/conda_build_config.yaml @@ -14,7 +14,7 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.32.*" + - "0.33.*" # The CTK libraries below are missing from the conda-forge::cudatoolkit # package. The "*_host_*" version specifiers correspond to `11.8` packages. diff --git a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py index a153613341b..3270dd0bf93 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py @@ -96,7 +96,6 @@ def karate_gnn(): "type0": len(all_vertices[0]), "type1": len(all_vertices[1]), } - print('all_vertices:', all_vertices) offsets = {"type0": 0, "type1": N["type0"]} @@ -123,10 +122,6 @@ def karate_gnn(): for (src_type, edge_type, dst_type), elx in G.items() } - print('offset:', offsets) - print('G:', G) - - return F, G, N From 6b19ee6e9dbfca0ea81ae6537b29a2fadb9385b4 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 16:26:19 +0000 Subject: [PATCH 070/347] revert cmake change --- cpp/cmake/thirdparty/get_libcugraphops.cmake | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/cpp/cmake/thirdparty/get_libcugraphops.cmake b/cpp/cmake/thirdparty/get_libcugraphops.cmake index f7bf311ec43..b10af300a98 100644 --- a/cpp/cmake/thirdparty/get_libcugraphops.cmake +++ b/cpp/cmake/thirdparty/get_libcugraphops.cmake @@ -16,18 +16,16 @@ set(CUGRAPH_MIN_VERSION_cugraph_ops "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}.00") -# if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_VERSION) -# set(CUGRAPH_CUGRAPH_OPS_VERSION "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}") -# endif() - -set(CUGRAPH_CUGRAPH_OPS_VERSION "23.06") +if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_VERSION) + set(CUGRAPH_CUGRAPH_OPS_VERSION "${CUGRAPH_VERSION_MAJOR}.${CUGRAPH_VERSION_MINOR}") +endif() if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_BRANCH) set(CUGRAPH_CUGRAPH_OPS_BRANCH "branch-${CUGRAPH_CUGRAPH_OPS_VERSION}") endif() if(NOT DEFINED CUGRAPH_CUGRAPH_OPS_REPOSITORY) - set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "https://github.com/rapidsai/cugraph-ops.git") + set(CUGRAPH_CUGRAPH_OPS_REPOSITORY "git@github.com:rapidsai/cugraph-ops.git") endif() function(find_and_configure_cugraph_ops) From a4ab110bd1a5154dfd4b5acf982a091a09938f8a Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 16:27:48 +0000 Subject: [PATCH 071/347] revert dependencies --- dependencies.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dependencies.yaml b/dependencies.yaml index 53da972c87e..d5c1ddc8a4e 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -306,7 +306,7 @@ dependencies: - &numba numba>=0.57 - raft-dask==23.6.* - *rmm - - &ucx_py ucx-py==0.32.* + - &ucx_py ucx-py==0.33.* - output_types: conda packages: - &cupy cupy>=12.0.0 From 73258e1b180549aebae41dfe2f441afa4f0676fa Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 19:36:42 +0000 Subject: [PATCH 072/347] some fixes for rmat, will still require data generation (which is ok) --- .../cugraph/standalone/cugraph_bulk_sampling.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index f9ed0a5c37d..c84f185aa73 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -14,6 +14,7 @@ import logging import warnings import argparse +import traceback from cugraph.testing.mg_utils import ( generate_edgelist_rmat, @@ -229,22 +230,23 @@ def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels= dask_edgelist_df = symmetrize_ddf(dask_edgelist_df).persist() dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really - generator = np.random.default_rng(seed=seed) + # generator = np.random.default_rng(seed=seed) num_labeled_nodes = int(2**(scale+1) * labeled_percentage) label_df = pd.DataFrame({ 'node': np.arange(num_labeled_nodes), - 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') + # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') }) - dask_label_df = ddf.from_pandas(label_df) + n_workers = len(default_client().scheduler_info()['workers']) + dask_label_df = ddf.from_pandas(label_df, npartitions=n_workers*2) del label_df gc.collect() dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) node_offsets = {'paper': 0} - edge_offsets = {('paper','cites','paper'):0}, - total_num_nodes = dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique() + edge_offsets = {('paper','cites','paper'):0} + total_num_nodes = int(dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique().compute()) if reverse_edges: dask_edgelist_df = dask_edgelist_df.rename(columns={'src':'dst', 'dst':'src'}) @@ -367,7 +369,8 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed The percentage of the data that is labeled (only for rmat datasets) Defaults to 0.001 to match papers100M """ - if dataset[0:5] == 'rmat': + print(dataset) + if dataset[0:4] == 'rmat': dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ generate_rmat_dataset(dataset, reverse_edges=reverse_edges, seed=seed, labeled_percentage=labeled_percentage, num_labels=num_labels) @@ -424,6 +427,7 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'num_sampling_gpus': len(G._plc_graph), 'execution_time': execution_time, } + with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: json.dump( output_meta, @@ -619,6 +623,7 @@ def get_args(): except Exception as e: warnings.warn('An Exception Occurred!') print(e) + traceback.print_exc() restart_client(client) sleep(10) From d8648d2ce0fa5e7132600be5b2ead51f8991d0db Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Mon, 5 Jun 2023 19:38:49 +0000 Subject: [PATCH 073/347] updates to rmat --- .../cugraph/standalone/cugraph_bulk_sampling.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py index f9ed0a5c37d..c84f185aa73 100644 --- a/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/cugraph_bulk_sampling.py @@ -14,6 +14,7 @@ import logging import warnings import argparse +import traceback from cugraph.testing.mg_utils import ( generate_edgelist_rmat, @@ -229,22 +230,23 @@ def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels= dask_edgelist_df = symmetrize_ddf(dask_edgelist_df).persist() dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really - generator = np.random.default_rng(seed=seed) + # generator = np.random.default_rng(seed=seed) num_labeled_nodes = int(2**(scale+1) * labeled_percentage) label_df = pd.DataFrame({ 'node': np.arange(num_labeled_nodes), - 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') + # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') }) - dask_label_df = ddf.from_pandas(label_df) + n_workers = len(default_client().scheduler_info()['workers']) + dask_label_df = ddf.from_pandas(label_df, npartitions=n_workers*2) del label_df gc.collect() dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) node_offsets = {'paper': 0} - edge_offsets = {('paper','cites','paper'):0}, - total_num_nodes = dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique() + edge_offsets = {('paper','cites','paper'):0} + total_num_nodes = int(dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique().compute()) if reverse_edges: dask_edgelist_df = dask_edgelist_df.rename(columns={'src':'dst', 'dst':'src'}) @@ -367,7 +369,8 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed The percentage of the data that is labeled (only for rmat datasets) Defaults to 0.001 to match papers100M """ - if dataset[0:5] == 'rmat': + print(dataset) + if dataset[0:4] == 'rmat': dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ generate_rmat_dataset(dataset, reverse_edges=reverse_edges, seed=seed, labeled_percentage=labeled_percentage, num_labels=num_labels) @@ -424,6 +427,7 @@ def benchmark_cugraph_bulk_sampling(dataset, output_path, seed, batch_size, seed 'num_sampling_gpus': len(G._plc_graph), 'execution_time': execution_time, } + with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: json.dump( output_meta, @@ -619,6 +623,7 @@ def get_args(): except Exception as e: warnings.warn('An Exception Occurred!') print(e) + traceback.print_exc() restart_client(client) sleep(10) From cf2e32359793e3980af703b95ca5a121f07a9a07 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 7 Jun 2023 19:26:26 +0000 Subject: [PATCH 074/347] commit --- .../pytest-based/bench_algos_pyg.py | 328 ++++++++++++++++++ .../cugraph/pytest-based/bench_algos.py | 35 ++ .../shared/python/cugraph_benchmarking/gnn.py | 16 + ci/test_python.sh | 2 + .../cugraph_pyg/tests/test_cugraph_sampler.py | 66 ++++ 5 files changed, 447 insertions(+) create mode 100644 benchmarks/cugraph-pyg/pytest-based/bench_algos_pyg.py create mode 100644 benchmarks/shared/python/cugraph_benchmarking/gnn.py diff --git a/benchmarks/cugraph-pyg/pytest-based/bench_algos_pyg.py b/benchmarks/cugraph-pyg/pytest-based/bench_algos_pyg.py new file mode 100644 index 00000000000..2e507a6699a --- /dev/null +++ b/benchmarks/cugraph-pyg/pytest-based/bench_algos_pyg.py @@ -0,0 +1,328 @@ +import pytest + +import re +import os +import tempfile + +import cupy +import cudf +import torch + +import pytest +import tempfile +import numpy as np +import cupy +import cudf +import pytest_benchmark +# FIXME: Remove this when rapids_pytest_benchmark.gpubenchmark is available +# everywhere +try: + from rapids_pytest_benchmark import setFixtureParamNames +except ImportError: + print("\n\nWARNING: rapids_pytest_benchmark is not installed, " + "falling back to pytest_benchmark fixtures.\n") + + # if rapids_pytest_benchmark is not available, just perfrom time-only + # benchmarking and replace the util functions with nops + gpubenchmark = pytest_benchmark.plugin.benchmark + + def setFixtureParamNames(*args, **kwargs): + pass + +import rmm +import dask_cudf +from pylibcugraph.testing import gen_fixture_params_product + +import cugraph +import cugraph.dask as dask_cugraph +from cugraph.structure.number_map import NumberMap +from cugraph.generators import rmat +from cugraph.testing import utils, mg_utils +from cugraph.utilities.utils import is_device_version_less_than + +from cugraph_pyg.data import CuGraphStore +from cugraph_pyg.loader.cugraph_node_loader import _sampler_output_from_sampling_results + +from cugraph_benchmarking.params import ( + directed_datasets, + undirected_datasets, + managed_memory, + pool_allocator, +) + +from cugraph_benchmarking.gnn import bulk_sample + +ex_parquet_file = re.compile(r"batch=([0-9]+)\-([0-9]+)\.parquet") + +# duck-type compatible Dataset for RMAT data +class RmatDataset: + def __init__(self, scale=4, edgefactor=2, mg=False): + self._scale = scale + self._edgefactor = edgefactor + self._edgelist = None + + self.mg = mg + + def __str__(self): + mg_str = "mg" if self.mg else "sg" + return f"rmat_{mg_str}_{self._scale}_{self._edgefactor}" + + def get_edgelist(self, fetch=False): + seed = 42 + if self._edgelist is None: + self._edgelist = rmat( + self._scale, + (2**self._scale)*self._edgefactor, + 0.57, # from Graph500 + 0.19, # from Graph500 + 0.19, # from Graph500 + seed or 42, + clip_and_flip=False, + scramble_vertex_ids=True, + create_using=None, # return edgelist instead of Graph instance + mg=self.mg + ) + rng = np.random.default_rng(seed) + if self.mg: + self._edgelist["weight"] = self._edgelist.map_partitions( + lambda df: rng.random(size=len(df))) + else: + self._edgelist["weight"] = rng.random(size=len(self._edgelist)) + + return self._edgelist + + def get_graph(self, + fetch=False, + create_using=cugraph.Graph, + ignore_weights=False, + store_transposed=False): + if isinstance(create_using, cugraph.Graph): + # what about BFS if trnaposed is True + attrs = {"directed": create_using.is_directed()} + G = type(create_using)(**attrs) + elif type(create_using) is type: + G = create_using() + + edge_attr = None if ignore_weights else "weight" + df = self.get_edgelist() + if isinstance(df, dask_cudf.DataFrame): + G.from_dask_cudf_edgelist(df, + source="src", + destination="dst", + edge_attr=edge_attr, + store_transposed=store_transposed) + else: + G.from_cudf_edgelist(df, + source="src", + destination="dst", + edge_attr=edge_attr, + store_transposed=store_transposed) + return G + + def get_path(self): + """ + (this is likely not needed for use with pytest-benchmark, just added for + API completeness with Dataset.) + """ + return str(self) + + def unload(self): + self._edgelist = None + + +_rmat_scale = getattr(pytest, "_rmat_scale", 20) # ~1M vertices +_rmat_edgefactor = getattr(pytest, "_rmat_edgefactor", 16) # ~17M edges +rmat_sg_dataset = pytest.param(RmatDataset(scale=_rmat_scale, + edgefactor=_rmat_edgefactor, + mg=False), + marks=[pytest.mark.rmat_data, + pytest.mark.sg, + ]) +rmat_mg_dataset = pytest.param(RmatDataset(scale=_rmat_scale, + edgefactor=_rmat_edgefactor, + mg=True), + marks=[pytest.mark.rmat_data, + pytest.mark.mg, + ]) + +rmm_fixture_params = gen_fixture_params_product( + (managed_memory, "mm"), + (pool_allocator, "pa")) +dataset_fixture_params = gen_fixture_params_product( + (directed_datasets + + undirected_datasets + + [rmat_sg_dataset, rmat_mg_dataset], "ds")) + +# Record the current RMM settings so reinitialize() will be called only when a +# change is needed (RMM defaults both values to False). The --allow-rmm-reinit +# option is required to allow the RMM options to be set by the pytest user +# directly, in order to prevent reinitialize() from being called more than once +# (see conftest.py for details). +# The defaults for managed_mem (False) and pool_alloc (True) are set in +# conftest.py +RMM_SETTINGS = {"managed_mem": False, + "pool_alloc": False} + +# FIXME: this only changes the RMM config in a SG environment. The dask config +# that applies to RMM in an MG environment is not changed by this! +def reinitRMM(managed_mem, pool_alloc): + """ + Reinitializes RMM to the value of managed_mem and pool_alloc, but only if + those values are different that the current configuration. + """ + if (managed_mem != RMM_SETTINGS["managed_mem"]) or \ + (pool_alloc != RMM_SETTINGS["pool_alloc"]): + + rmm.reinitialize( + managed_memory=managed_mem, + pool_allocator=pool_alloc, + initial_pool_size=2 << 27 + ) + RMM_SETTINGS.update(managed_mem=managed_mem, + pool_alloc=pool_alloc) + + +############################################################################### +# Fixtures +# +# Executed automatically when specified on a test/benchmark, and the return +# value is made available to the test/benchmark for use. Fixtures can use other +# fixtures to chain their execution. +# +# For benchmarks, the operations performed in fixtures are not measured as part +# of the benchmark. + +@pytest.fixture(scope="module", + params=rmm_fixture_params) +def rmm_config(request): + # Since parameterized fixtures do not assign param names to param values, + # manually call the helper to do so. Ensure the order of the name list + # passed to it matches if there are >1 params. + # If the request only contains n params, only the first n names are set. + setFixtureParamNames(request, ["managed_mem", "pool_allocator"]) + reinitRMM(request.param[0], request.param[1]) + + +@pytest.fixture(scope="module", + params=dataset_fixture_params) +def dataset(request, rmm_config): + + """ + Fixture which provides a Dataset instance, setting up a Dask cluster and + client if necessary for MG, to tests and other fixtures. When all + tests/fixtures are done with the Dataset, it has the Dask cluster and + client torn down (if MG) and all data loaded is freed. + """ + setFixtureParamNames(request, ["dataset"]) + dataset = request.param[0] + client = cluster = None + # For now, only RmatDataset instanaces support MG and have a "mg" attr. + if hasattr(dataset, "mg") and dataset.mg: + (client, cluster) = mg_utils.start_dask_client() + + yield dataset + + dataset.unload() + if client is not None: + mg_utils.stop_dask_client(client, cluster) + + +@pytest.fixture(scope="module") +def edgelist(request, dataset): + df = dataset.get_edgelist() + return df + + +@pytest.fixture(scope="module") +def graph(request, dataset): + G = dataset.get_graph() + return G + + +@pytest.fixture(scope="module") +def unweighted_graph(request, dataset): + G = dataset.get_graph(ignore_weights=True) + return G + + +@pytest.fixture(scope="module") +def directed_graph(request, dataset): + G = dataset.get_graph(create_using=cugraph.Graph(directed=True)) + return G + + +@pytest.fixture(scope="module") +def transposed_graph(request, dataset): + G = dataset.get_graph(store_transposed=True) + return G + + +############################################################################### +def is_graph_distributed(graph): + """ + Return True if graph is distributed (for use with cugraph.dask APIs) + """ + return isinstance(graph.edgelist.edgelist_df, dask_cudf.DataFrame) + + +@pytest.fixture(scope="module") +def graph(request, dataset): + G = dataset.get_graph() + return G + +@pytest.mark.parametrize('batch_size', [500, 1000]) +@pytest.mark.parametrize('fanout', [[10, 25], [10, 10, 10]]) +def bench_pyg_mfg_create(gpubenchmark, graph, batch_size, fanout): + seed = 62 + seeds_per_call = 100_000 + generator = cupy.random.default_rng(seed=seed) + + tempdir = tempfile.TemporaryDirectory() + batches_per_partition = 200_000 // batch_size + + num_vertices = graph.number_of_vertices() + training_percentage = 0.1 + train_nodes = generator.integers(0, int(training_percentage * num_vertices)) + train_batches = cupy.arange(num_vertices // batch_size + 1).repeat(batch_size)[:len(train_nodes)] + batch_df = cudf.DataFrame({ + 'node': train_nodes, + 'batch': train_batches, + }) + + bulk_sample( + graph, + batch_size, + fanout, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + batch_df=batch_df, + samples_dir=tempdir, + seed=seed + ) + + G = {('vt1','et1','vt1'): graph.number_of_edges()} + N = {'vt1': graph.number_of_vertices()} + cugraph_store = CuGraphStore(None, G, N) + + def read_batches(samples_dir, graph_store): + for file in os.listdir(samples_dir): + df = cudf.read_parquet(file) + m = ex_parquet_file.match(file) + if m is None: + raise ValueError(f"Invalid parquet filename {file}") + + next_batch, end_inclusive = [int(g) for g in m.groups()] + + while next_batch <= end_inclusive: + df_i = df[df.batch_id==next_batch] + _sampler_output_from_sampling_results( + df_i, + graph_store, + None + ) + + gpubenchmark( + read_batches, + tempdir.name, + cugraph_store + ) \ No newline at end of file diff --git a/benchmarks/cugraph/pytest-based/bench_algos.py b/benchmarks/cugraph/pytest-based/bench_algos.py index d7fcb7812e4..e8da67b8e5d 100644 --- a/benchmarks/cugraph/pytest-based/bench_algos.py +++ b/benchmarks/cugraph/pytest-based/bench_algos.py @@ -12,7 +12,10 @@ # limitations under the License. import pytest +import tempfile import numpy as np +import cupy +import cudf import pytest_benchmark # FIXME: Remove this when rapids_pytest_benchmark.gpubenchmark is available # everywhere @@ -39,6 +42,7 @@ def setFixtureParamNames(*args, **kwargs): from cugraph.generators import rmat from cugraph.testing import utils, mg_utils from cugraph.utilities.utils import is_device_version_less_than +from cugraph_benchmarking.gnn import bulk_sample from cugraph_benchmarking.params import ( directed_datasets, @@ -410,3 +414,34 @@ def bench_egonet(gpubenchmark, graph): n = 1 radius = 2 gpubenchmark(egonet, graph, n, radius=radius) + +@pytest.mark.parametrize('batch_size', [250, 500, 1000]) +@pytest.mark.parametrize('fanout', [[10,25], [10,10,10], [25,25]]) +@pytest.mark.parametrize('seeds_per_call', [50_000, 200_000, 800_000, 2_000_000]) +def bench_bulk_sampler(gpubenchmark, graph, batch_size, fanout, seeds_per_call): + seed = 62 + generator = cupy.random.default_rng(seed=seed) + + tempdir = tempfile.TemporaryDirectory() + batches_per_partition = 200_000 // batch_size + + num_vertices = graph.number_of_vertices() + training_percentage = 0.1 + train_nodes = generator.integers(0, int(training_percentage * num_vertices)) + train_batches = cupy.arange(num_vertices // batch_size + 1).repeat(batch_size)[:len(train_nodes)] + batch_df = cudf.DataFrame({ + 'node': train_nodes, + 'batch': train_batches, + }) + + gpubenchmark( + bulk_sample, + graph, + batch_size, + fanout, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + batch_df=batch_df, + samples_dir=tempdir, + seed=seed + ) diff --git a/benchmarks/shared/python/cugraph_benchmarking/gnn.py b/benchmarks/shared/python/cugraph_benchmarking/gnn.py new file mode 100644 index 00000000000..09f813dc305 --- /dev/null +++ b/benchmarks/shared/python/cugraph_benchmarking/gnn.py @@ -0,0 +1,16 @@ +from cugraph.experimental.gnn import BulkSampler + +def bulk_sample(graph, batch_size, fanout, seeds_per_call, batches_per_partition, batch_df, samples_dir, seed): + sampler = BulkSampler( + batch_size=batch_size, + output_path=samples_dir, + graph=graph, + fanout_vals=fanout, + with_replacement=False, + random_state=seed, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + ) + + sampler.add_batches(batch_df, start_col_name='node', batch_col_name='batch') + sampler.flush() \ No newline at end of file diff --git a/ci/test_python.sh b/ci/test_python.sh index 3a23f521734..28a25e6cf88 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -192,6 +192,8 @@ if [[ "${RAPIDS_CUDA_VERSION}" == "11.8.0" ]]; then pushd python/cugraph-pyg/cugraph_pyg # rmat is not tested because of multi-GPU testing pytest \ + -v \ + --benchmark-disable \ --cache-clear \ --ignore=tests/int \ --ignore=tests/mg \ diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py index b4057727582..41385b11b95 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py @@ -171,3 +171,69 @@ def test_neighbor_sample_mock_sampling_results(abc_graph): assert out.num_sampled_edges[("A", "ab", "B")].tolist() == [3, 0, 1, 0] assert out.num_sampled_edges[("B", "ba", "A")].tolist() == [0, 1, 0, 1] assert out.num_sampled_edges[("B", "bc", "C")].tolist() == [0, 2, 0, 2] + +""" +Add benchmark results capture +Disable benchmark results capture when running in CI + + +1. update test_python.sh +2. update dockerfile in graph_dl +3. add new sg srun script (coordinate with Rick first) +""" +def test_mfg_creation(): + seed = 62 + seeds_per_call = 100_000 + generator = cupy.random.default_rng(seed=seed) + + tempdir = tempfile.TemporaryDirectory() + batches_per_partition = 200_000 // batch_size + + num_vertices = graph.number_of_vertices() + training_percentage = 0.1 + train_nodes = generator.integers(0, int(training_percentage * num_vertices)) + train_batches = cupy.arange(num_vertices // batch_size + 1).repeat(batch_size)[:len(train_nodes)] + batch_df = cudf.DataFrame({ + 'node': train_nodes, + 'batch': train_batches, + }) + + bulk_sample( + graph, + batch_size, + fanout, + seeds_per_call=seeds_per_call, + batches_per_partition=batches_per_partition, + batch_df=batch_df, + samples_dir=tempdir, + seed=seed + ) + + # todo read from file instead of running sampler + + G = {('vt1','et1','vt1'): graph.number_of_edges()} + N = {'vt1': graph.number_of_vertices()} + cugraph_store = CuGraphStore(None, G, N) + + def read_batches(samples_dir, graph_store): + for file in os.listdir(samples_dir): + df = cudf.read_parquet(file) + m = ex_parquet_file.match(file) + if m is None: + raise ValueError(f"Invalid parquet filename {file}") + + next_batch, end_inclusive = [int(g) for g in m.groups()] + + while next_batch <= end_inclusive: + df_i = df[df.batch_id==next_batch] + _sampler_output_from_sampling_results( + df_i, + graph_store, + None + ) + + gpubenchmark( + read_batches, + tempdir.name, + cugraph_store + ) \ No newline at end of file From 5f97b899ed5fe31a9f0c5df3891ab4db180151d7 Mon Sep 17 00:00:00 2001 From: Alexandria Barghi Date: Wed, 7 Jun 2023 19:52:09 +0000 Subject: [PATCH 075/347] add samples file --- .../files/papers100M_1000_10_10_10.parquet | Bin 0 -> 4367104 bytes .../tests/papers100M_1000_10_10_10.parquet | Bin 0 -> 4367104 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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