Drop support for NetworkX 3.0 and 3.1 (#27)

I successfully ran tests locally for networkx versions 3.3, 3.4, 3.4.1, and 3.4.2. I'm seeing failures for 3.2 and 3.2.1, but these failures are not introduced by this PR. I'll investigate further, but getting these to pass are probably "nice to have" for this release.

We should consider adding tests (perhaps nightly tests) to test against different networkx versions, such as minimum supported version and dev versions.

Closes rapidsai/cugraph#4760

Authors:
  - Erik Welch (https://github.com/eriknw)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)
  - Ray Douglass (https://github.com/raydouglass)

URL: #27

README.md

@@ -10,7 +10,7 @@ nx-cugraph requires the following:
  * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+
  * CUDA 11.4-11.8 or 12.0-12.5
  * Python version 3.10, 3.11, or 3.12
- * NetworkX >= version 3.0 (version 3.4 or higher recommended)
+ * NetworkX >= version 3.2 (version 3.4 or higher recommended)
 
 More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).
 

_nx_cugraph/__init__.py

@@ -270,7 +270,7 @@
 
 
 def get_info():
-    """Target of ``networkx.plugin_info`` entry point.
+    """Target of ``networkx.backend_info`` entry point.
 
     This tells NetworkX about the cugraph backend without importing nx_cugraph.
     """
@@ -330,7 +330,6 @@ def update_env_var(varname):
             update_env_var("NETWORKX_BACKEND_PRIORITY")
         # And for older NetworkX versions
         update_env_var("NETWORKX_AUTOMATIC_BACKENDS")  # For NetworkX 3.2
-        update_env_var("NETWORKX_GRAPH_CONVERT")  # For NetworkX 3.0 and 3.1
         # Automatically create nx-cugraph Graph from graph generators
         update_env_var("NETWORKX_BACKEND_PRIORITY_GENERATORS")
         # Run default NetworkX implementation (in >=3.4) if not implemented by nx-cugraph

benchmarks/nx-cugraph/pytest-based/bench_algos.py

@@ -20,15 +20,6 @@
 
 import nx_cugraph as nxcg
 
-# Attempt to import the NetworkX dispatching module, which is only needed when
-# testing with NX <3.2 in order to dynamically switch backends. NX >=3.2 allows
-# the backend to be specified directly in the API call.
-try:
-    from networkx.classes import backends  # NX <3.2
-except ImportError:
-    backends = None
-
-
 ################################################################################
 # Fixtures and params
 
@@ -133,45 +124,10 @@ def nx_graph_from_dataset(dataset_obj):
     return G
 
 
-def get_legacy_backend_wrapper(backend_name):
-    """
-    Returns a callable that wraps an algo function with either the default
-    dispatcher (which dispatches based on input graph type), or the "testing"
-    dispatcher (which autoconverts and unconditionally dispatches).
-    This is only supported for NetworkX <3.2
-    """
-    backends.plugin_name = "cugraph"
-    orig_dispatch = backends._dispatch
-    testing_dispatch = backends.test_override_dispatch
-
-    if backend_name == "cugraph":
-        dispatch = testing_dispatch
-    else:
-        dispatch = orig_dispatch
-
-    def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False):
-        # Networkx <3.2 registers functions when the dispatch decorator is
-        # applied (called) and errors if re-registered, so clear bookkeeping to
-        # allow it to be called repeatedly.
-        backends._registered_algorithms = {}
-        actual_func = dispatch(func)  # returns the func the dispatcher picks
-
-        def wrapper(*args, **kwargs):
-            retval = actual_func(*args, **kwargs)
-            if exhaust_returned_iterator:
-                retval = list(retval)
-            return retval
-
-        return wrapper
-
-    return wrap_callable_for_dispatch
-
-
 def get_backend_wrapper(backend_name):
     """
     Returns a callable that wraps an algo function in order to set the
     "backend" kwarg on it.
-    This is only supported for NetworkX >= 3.2
     """
 
     def wrap_callable_for_dispatch(func, exhaust_returned_iterator=False):
@@ -210,12 +166,7 @@ def backend_wrapper(request):
     if backend_name == "cugraph-preconverted":
         actual_backend_name = "cugraph"
 
-    # NX <3.2 does not support the backends= kwarg, so the backend must be
-    # enabled differently
-    if backends is not None:
-        wrapper = get_legacy_backend_wrapper(actual_backend_name)
-    else:
-        wrapper = get_backend_wrapper(actual_backend_name)
+    wrapper = get_backend_wrapper(actual_backend_name)
 
     wrapper.backend_name = backend_name
     return wrapper

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -12,7 +12,7 @@ dependencies:
 - graphviz
 - ipython
 - nbsphinx
-- networkx>=3.0
+- networkx>=3.2
 - numpy>=1.23,<3.0a0
 - numpydoc
 - pandas

conda/environments/all_cuda-125_arch-x86_64.yaml

@@ -12,7 +12,7 @@ dependencies:
 - graphviz
 - ipython
 - nbsphinx
-- networkx>=3.0
+- networkx>=3.2
 - numpy>=1.23,<3.0a0
 - numpydoc
 - pandas

conda/recipes/nx-cugraph/meta.yaml

@@ -23,7 +23,7 @@ requirements:
     - setuptools>=61.0.0
   run:
     - pylibcugraph ={{ minor_version }}
-    - networkx >=3.0
+    - networkx >=3.2
     - cupy >=12.0.0
     - python
 

dependencies.yaml

@@ -177,7 +177,7 @@ dependencies:
     common:
       - output_types: [conda, pyproject]
         packages:
-          - networkx>=3.0
+          - networkx>=3.2
           - &numpy numpy>=1.23,<3.0a0
   test_python_nx_cugraph:
     common:

docs/nx-cugraph/source/index.rst

@@ -23,7 +23,7 @@ Required hardware/software for cuGraph and `RAPIDS <https://docs.rapids.ai/user-
  * NVIDIA GPU, Volta architecture or later, with `compute capability <https://developer.nvidia.com/cuda-gpus> 7.0+`_
  * CUDA 11.2-11.8, 12.0-12.5
  * Python version 3.10, 3.11, or 3.12
- * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. NetworkX version 3.4 or newer is recommended. (`see below <#cugraph-using-networkx-code>`).
+ * NetworkX version 3.2 or newer in order to use use the nx-cuGraph backend. NetworkX version 3.4 or newer is recommended. (`see below <#cugraph-using-networkx-code>`).
 
 Installation
 The latest RAPIDS System Requirements documentation is located `here <https://docs.rapids.ai/install#system-req>`_.

docs/nx-cugraph/source/nx_cugraph/installation.md

@@ -10,7 +10,7 @@ This guide describes how to install ``nx-cugraph`` and use it in your workflows.
  - **Volta architecture or later NVIDIA GPU, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+**
  - **[CUDA](https://docs.nvidia.com/cuda/index.html) 11.2, 11.4, 11.5, 11.8, 12.0, 12.2, or 12.5**
  - **Python >= 3.10**
- - **[NetworkX](https://networkx.org/documentation/stable/install.html#) >= 3.0 (version 3.2 or higher recommended)**
+ - **[NetworkX](https://networkx.org/documentation/stable/install.html#) >= 3.2 (version 3.4 or higher recommended)**
 
 More details about system requirements can be found in the [RAPIDS System Requirements Documentation](https://docs.rapids.ai/install#system-req).
 

nx_cugraph/classes/graph.py

@@ -86,8 +86,7 @@ def clear(self) -> None:
 
 class Graph(nx.Graph):
     # Tell networkx to dispatch calls with this object to nx-cugraph
-    __networkx_backend__: ClassVar[str] = "cugraph"  # nx >=3.2
-    __networkx_plugin__: ClassVar[str] = "cugraph"  # nx <3.2
+    __networkx_backend__: ClassVar[str] = "cugraph"
 
     # Core attributes of NetowkrX graphs that will be copied and cleared as appropriate.
     # These attributes comprise the edge and node data model for NetworkX graphs.
@@ -513,8 +512,7 @@ def from_dcsc(
 
 class CudaGraph:
     # Tell networkx to dispatch calls with this object to nx-cugraph
-    __networkx_backend__: ClassVar[str] = "cugraph"  # nx >=3.2
-    __networkx_plugin__: ClassVar[str] = "cugraph"  # nx <3.2
+    __networkx_backend__: ClassVar[str] = "cugraph"
 
     # Allow networkx dispatch machinery to cache conversions.
     # This means we should clear the cache if we ever mutate the object!