Enhanced monitor with system metrics logging and wandb support

enviroment/docker_files/federatedscope-torch1.10-application.Dockerfile

@@ -39,8 +39,12 @@ RUN conda install -y numpy=1.21.2 scikit-learn=1.0.2 scipy=1.7.3 pandas=1.4.1 -c
 RUN conda install -y pytorch=1.10.1 torchvision=0.11.2 torchaudio=0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge \
     && conda clean -a -y
 
+# torch helper package
+RUN conda install -y fvcore iopath -c fvcore -c iopath -c conda-forge \
+    && conda clean -a -y
+
 # auxiliaries (communications, monitoring, etc.)
-RUN conda install -y wandb tensorboard tensorboardX -c conda-forge \
+RUN conda install -y wandb tensorboard tensorboardX pympler -c conda-forge \
     && pip install grpcio grpcio-tools protobuf==3.19.4 setuptools==61.2.0 \
     && conda clean -a -y
 

enviroment/docker_files/federatedscope-torch1.10.Dockerfile

@@ -39,7 +39,11 @@ RUN conda install -y numpy=1.21.2 scikit-learn=1.0.2 scipy=1.7.3 pandas=1.4.1 -c
 RUN conda install -y pytorch=1.10.1 torchvision=0.11.2 torchaudio=0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge \
     && conda clean -a -y
 
+# torch helper package
+RUN conda install -y fvcore iopath -c fvcore -c iopath -c conda-forge \
+    && conda clean -a -y
+
 # auxiliaries (communications, monitoring, etc.)
-RUN conda install -y wandb tensorboard tensorboardX -c conda-forge \
+RUN conda install -y wandb tensorboard tensorboardX pympler -c conda-forge \
     && pip install grpcio grpcio-tools protobuf==3.19.4 setuptools==61.2.0 \
     && conda clean -a -y

enviroment/docker_files/federatedscope-torch1.8-application.Dockerfile

@@ -39,6 +39,10 @@ RUN conda install -y numpy=1.21.2 scikit-learn=1.0.2 scipy=1.7.3 pandas=1.4.1 -c
 RUN conda install -y pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch \
     && conda clean -a -y
 
+# torch helper package
+RUN conda install -y fvcore iopath -c fvcore -c iopath -c conda-forge \
+    && conda clean -a -y
+
 # for graph
 RUN conda install -y pyg==2.0.1 -c pyg  \
     && conda install -y rdkit=2021.09.4 -c conda-forge \
@@ -52,6 +56,6 @@ RUN conda install -y sentencepiece textgrid typeguard -c conda-forge \
     && conda clean -a -y
 
 # auxiliaries (communications, monitoring, etc.)
-RUN conda install -y wandb tensorboard tensorboardX -c conda-forge \
+RUN conda install -y wandb tensorboard tensorboardX pympler -c conda-forge \
     && pip install grpcio grpcio-tools protobuf==3.19.4 setuptools==61.2.0 \
     && conda clean -a -y

enviroment/requirements-torch1.10-application.txt

@@ -24,4 +24,7 @@ transformers==4.16.2
 tokenizers==0.10.3
 torchtext
 datasets
+fvcore
+pympler
+iopath
 

enviroment/requirements-torch1.10.txt

@@ -13,4 +13,7 @@ tensorboardX
 grpcio
 grpcio-tools
 protobuf==3.19.4
-setuptools==61.2.0
+setuptools==61.2.0
+fvcore
+pympler
+iopath

enviroment/requirements-torch1.8-application.txt

@@ -24,5 +24,8 @@ transformers==4.16.2
 tokenizers==0.10.3
 torchtext
 datasets
+fvcore
+pympler
+iopath
 
 

federatedscope/core/auxiliaries/trainer_builder.py

@@ -22,22 +22,25 @@ def get_trainer(model=None,
                 device=None,
                 config=None,
                 only_for_eval=False,
-                is_attacker=False):
+                is_attacker=False,
+                monitor=None):
     if config.trainer.type == 'general':
         if config.backend == 'torch':
             from federatedscope.core.trainers import GeneralTorchTrainer
             trainer = GeneralTorchTrainer(model=model,
                                           data=data,
                                           device=device,
                                           config=config,
-                                          only_for_eval=only_for_eval)
+                                          only_for_eval=only_for_eval,
+                                          monitor=monitor)
         elif config.backend == 'tensorflow':
             from federatedscope.core.trainers.tf_trainer import GeneralTFTrainer
             trainer = GeneralTFTrainer(model=model,
                                        data=data,
                                        device=device,
                                        config=config,
-                                       only_for_eval=only_for_eval)
+                                       only_for_eval=only_for_eval,
+                                       monitor=monitor)
         else:
             raise ValueError
     elif config.trainer.type == 'none':
@@ -70,7 +73,8 @@ def get_trainer(model=None,
                               data=data,
                               device=device,
                               config=config,
-                              only_for_eval=only_for_eval)
+                              only_for_eval=only_for_eval,
+                              monitor=monitor)
     else:
         # try to find user registered trainer
         trainer = None
@@ -81,7 +85,8 @@ def get_trainer(model=None,
                                       data=data,
                                       device=device,
                                       config=config,
-                                      only_for_eval=only_for_eval)
+                                      only_for_eval=only_for_eval,
+                                      monitor=monitor)
         if trainer is None:
             raise ValueError('Trainer {} is not provided'.format(
                 config.trainer.type))

federatedscope/core/auxiliaries/utils.py

@@ -1,3 +1,5 @@
+import copy
+import json
 import logging
 import os
 import random
@@ -11,6 +13,7 @@
 import signal
 
 import numpy as np
+
 # Blind torch
 try:
     import torch
@@ -38,7 +41,6 @@ def setup_seed(seed):
 
 def update_logger(cfg, clear_before_add=False):
     import os
-    import sys
     import logging
 
     root_logger = logging.getLogger("federatedscope")
@@ -62,6 +64,8 @@ def update_logger(cfg, clear_before_add=False):
     # ================ create outdir to save log, exp_config, models, etc,.
     if cfg.outdir == "":
         cfg.outdir = os.path.join(os.getcwd(), "exp")
+    if cfg.expname == "":
+        cfg.expname = f"{cfg.federate.method}_{cfg.model.type}_on_{cfg.data.type}"
     cfg.outdir = os.path.join(cfg.outdir, cfg.expname)
 
     # if exist, make directory with given name and time
@@ -85,10 +89,37 @@ def update_logger(cfg, clear_before_add=False):
         "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
     fh.setFormatter(logger_formatter)
     root_logger.addHandler(fh)
-    #sys.stderr = sys.stdout
+    # sys.stderr = sys.stdout
 
     root_logger.info(f"the output dir is {cfg.outdir}")
 
+    if cfg.wandb.use:
+        init_wandb(cfg)
+
+
+def init_wandb(cfg):
+    try:
+        import wandb
+    except ImportError:
+        logger.error("cfg.wandb.use=True but not install the wandb package")
+        exit()
+    dataset_name = cfg.data.type
+    method_name = cfg.federate.method
+    exp_name = cfg.expname
+
+    tmp_cfg = copy.deepcopy(cfg)
+    tmp_cfg.cfg_check_funcs = []
+    import yaml
+    cfg_yaml = yaml.safe_load(tmp_cfg.dump())
+
+    wandb.init(project=cfg.wandb.name_project,
+               entity=cfg.wandb.name_user,
+               config=cfg_yaml,
+               group=dataset_name,
+               job_type=method_name,
+               name=exp_name,
+               notes=f"{method_name}, {exp_name}")
+
 
 def get_dataset(type, root, transform, target_transform, download=True):
     if isinstance(type, str):
@@ -159,7 +190,6 @@ def get_random(type, sample_shape, params, device):
 
 
 def batch_iter(data, batch_size=64, shuffled=True):
-
     assert 'x' in data and 'y' in data
     data_x = data['x']
     data_y = data['y']
@@ -265,3 +295,75 @@ def block(self):
 
     def exceed_max_failure(self, num_failure):
         return num_failure > self.max_failure
+
+
+def logfile_2_wandb_dict(exp_log_f, raw_out=True):
+    """
+        parse the logfiles [exp_print.log, eval_results.log] into wandb_dict that contains non-nested dicts
+
+    :param exp_log_f: opened exp_log file
+    :param raw_out: True indicates "exp_print.log", otherwise indicates "eval_results.log",
+        the difference is whether contains the logger header such as "2022-05-02 16:55:02,843 (client:197) INFO:"
+
+    :return: tuple including (all_log_res, exp_stop_normal, last_line, log_res_best)
+    """
+    log_res_best = {}
+    exp_stop_normal = False
+    all_log_res = []
+    last_line = None
+    for line in exp_log_f:
+        last_line = line
+        if " Find new best result" in line:
+            # e.g.,
+            # 2022-03-22 10:48:42,562 (server:459) INFO: Find new best result for client_individual.test_acc with value 0.5911787974683544
+            parse_res = line.split("INFO: ")[1].split("with value")
+            best_key, best_val = parse_res[-2], parse_res[-1]
+            # client_individual.test_acc -> client_individual/test_acc
+            best_key = best_key.replace("Find new best result for",
+                                        "").replace(".", "/")
+            log_res_best[best_key.strip()] = float(best_val.strip())
+
+        if "'Role': 'Server #'" in line:
+            if raw_out:
+                line = line.split("INFO: ")[1]
+            res = line.replace("\'", "\"")
+            res = json.loads(s=res)
+            if res['Role'] == 'Server #':
+                cur_round = res['Round']
+            res.pop('Role')
+            if cur_round != "Final" and 'Results_raw' in res:
+                res.pop('Results_raw')
+
+            log_res = {}
+            for key, val in res.items():
+                if not isinstance(val, dict):
+                    log_res[key] = val
+                else:
+                    if cur_round != "Final":
+                        for key_inner, val_inner in val.items():
+                            assert not isinstance(
+                                val_inner, dict), "Un-expected log format"
+                            log_res[f"{key}/{key_inner}"] = val_inner
+
+                    else:
+                        exp_stop_normal = True
+                        if key == "Results_raw":
+                            for final_type, final_type_dict in res[
+                                    "Results_raw"].items():
+                                for inner_key, inner_val in final_type_dict.items(
+                                ):
+                                    log_res_best[
+                                        f"{final_type}/{inner_key}"] = inner_val
+                    #     log_res_best = {}
+                    #     for best_res_type, val_dict in val.items():
+                    #         for key_inner, val_inner in val_dict.items():
+                    #             assert not isinstance(val_inner, dict), "Un-expected log format"
+                    #             log_res_best[f"{best_res_type}/{key_inner}"] = val_inner
+            # if log_res_best is not None and "Results_weighted_avg/val_loss" in log_res and \
+            #         log_res_best["client_summarized_weighted_avg/val_loss"] > \
+            #         log_res["Results_weighted_avg/val_loss"]:
+            #     print("Missing the results of last round, update best results")
+            #     for key, val in log_res.items():
+            #         log_res_best[key.replace("Results", "client_summarized")] = val
+            all_log_res.append(log_res)
+    return all_log_res, exp_stop_normal, last_line, log_res_best

federatedscope/core/communication.py

@@ -11,9 +11,10 @@ class StandaloneCommManager(object):
     """
     The communicator used for standalone mode
     """
-    def __init__(self, comm_queue):
+    def __init__(self, comm_queue, monitor=None):
         self.comm_queue = comm_queue
         self.neighbors = dict()
+        self.monitor = monitor  # used to track the communication related metrics
 
     def receive(self):
         # we don't need receive() in standalone
@@ -37,6 +38,8 @@ def get_neighbors(self, neighbor_id=None):
 
     def send(self, message):
         self.comm_queue.append(message)
+        download_bytes, upload_bytes = message.count_bytes()
+        self.monitor.track_upload_bytes(upload_bytes)
 
 
 class gRPCCommManager(object):
@@ -60,6 +63,7 @@ def __init__(self, host='0.0.0.0', port='50050', client_num=2):
                                       port=port,
                                       options=options)
         self.neighbors = dict()
+        self.monitor = None  # used to track the communication related metrics
 
     def serve(self, max_workers, host, port, options):
         """

federatedscope/core/configs/__init__.py

@@ -13,7 +13,6 @@
 if "config" in all_sub_configs:
     all_sub_configs.remove('config')
 
-
 from federatedscope.core.configs.config import CN, init_global_cfg
 __all__ = __all__ + \
           [
@@ -28,4 +27,3 @@
 for base_config in base_configs:
     all_sub_configs.pop(all_sub_configs.index(base_config))
     all_sub_configs.insert(0, base_config)
-

federatedscope/core/configs/cfg_data.py

@@ -13,9 +13,11 @@ def extend_data_cfg(cfg):
     cfg.data.args = []  # args for external dataset, eg. [{'download': True}]
     cfg.data.splitter = ''
     cfg.data.splitter_args = []  # args for splitter, eg. [{'alpha': 0.5}]
-    cfg.data.transform = []  # transform for x, eg. [['ToTensor'], ['Normalize', {'mean': [0.1307], 'std': [0.3081]}]]
+    cfg.data.transform = [
+    ]  # transform for x, eg. [['ToTensor'], ['Normalize', {'mean': [0.1307], 'std': [0.3081]}]]
     cfg.data.target_transform = []  # target_transform for y, use as above
-    cfg.data.pre_transform = []  # pre_transform for `torch_geometric` dataset, use as above
+    cfg.data.pre_transform = [
+    ]  # pre_transform for `torch_geometric` dataset, use as above
     cfg.data.batch_size = 64
     cfg.data.drop_last = False
     cfg.data.sizes = [10, 5]

federatedscope/core/configs/config.py

@@ -1,10 +1,13 @@
 import copy
+import logging
 import os
 
 from yacs.config import CfgNode
 
 import federatedscope.register as register
 
+logger = logging.getLogger(__name__)
+
 
 class CN(CfgNode):
     """
@@ -83,7 +86,9 @@ def clean_unused_sub_cfgs(self):
 
     def freeze(self):
         """
-            make the cfg attributes immutable, and save the freezed cfg_check_funcs into "self.outdir/config.yaml" for better reproducibility
+            1) make the cfg attributes immutable;
+            2) save the frozen cfg_check_funcs into "self.outdir/config.yaml" for better reproducibility;
+            3) if self.wandb.use=True, update the frozen config
 
         :return:
         """
@@ -96,6 +101,20 @@ def freeze(self):
                 tmp_cfg = copy.deepcopy(self)
                 tmp_cfg.cfg_check_funcs = []
                 print(tmp_cfg.dump())
+            if self.wandb.use:
+                # update the frozen config
+                try:
+                    import wandb
+                except ImportError:
+                    logger.error(
+                        "cfg.wandb.use=True but not install the wandb package")
+                    exit()
+
+                import yaml
+                cfg_yaml = yaml.safe_load(tmp_cfg.dump())
+                wandb.config.update(cfg_yaml, allow_val_change=True)
+
+            logger.info("the used configs are: \n" + str(tmp_cfg))
 
         super(CN, self).freeze()