Periodic update

.env/pyproject.toml

@@ -21,12 +21,12 @@ numpy = "1.26.4"
 pandas = "^2.2.3"
 visdom = "^0.2.4"
 Pillow = "^10.4.0"
-scipy = "^1.13.1"
+scipy = "^1.14.1"
 matplotlib = "^3.9.0"
 scikit-learn = "^1.5.2"
-faiss-cpu = "^1.7.4"
-pynvml = "^11.5.0"
-PyYAML = "^6.0.1"
+faiss-cpu = "^1.8.0"
+pynvml = "^11.5.3"
+PyYAML = "^6.0.2"
 ray = { extras = ["default"], version = "2.36.1" }
 tensorboard = "^2.17.1"
 cvxpy = "^1.5.1"

.env/requirements.txt

@@ -11,7 +11,7 @@ faiss-cpu~=1.7.4
 scikit-learn~=1.5.2
 pynvml~=11.5.0
 PyYAML~=6.0.1
-ray[default]~=2.36.1
+ray[default]~=2.38.0
 tensorboard~=2.17.1
 cvxpy~=1.5.1
 hydra-core~=1.3.2

README.md

@@ -199,7 +199,7 @@ FL-bench supports `visdom` and `tensorboard`.
 # your_config.yaml
 common:
   ...
-  visible: tensorboard # options: [null, visdom, tensorboard]
+  monitor: tensorboard # options: [null, visdom, tensorboard]
 ```
 > \[!NOTE\]
 > You needs to launch `visdom` / `tensorboard` server by yourself.

config/defaults.yaml

@@ -102,7 +102,7 @@ common:
   eval_train: false
 
   verbose_gap: 10 # Interval round of displaying clients training performance on terminal.
-  visible: null # [null, visdom, tensorboard]
+  monitor: null # [null, visdom, tensorboard]
   use_cuda: true # Whether to use cuda for training.
 
   save_log: true # Whether to save log files in out/<method>/<start_time>.

src/server/fedavg.py

@@ -5,8 +5,8 @@
 import pickle
 import random
 import shutil
-import traceback
 import time
+import traceback
 import warnings
 from collections import OrderedDict
 from copy import deepcopy
@@ -19,7 +19,7 @@
 from hydra.core.hydra_config import HydraConfig
 from omegaconf import DictConfig, OmegaConf
 from rich.console import Console
-from rich.json import JSON
+from rich.pretty import pprint as rich_pprint
 from rich.progress import track
 from torchvision import transforms
 
@@ -199,16 +199,16 @@ def __init__(
             console=stdout,
         )
 
-        if self.args.common.visible is not None:
+        if self.args.common.monitor is not None:
             self.monitor_window_name_suffix = (
                 self.args.dataset.monitor_window_name_suffix
             )
 
-        if self.args.common.visible == "visdom":
+        if self.args.common.monitor == "visdom":
             from visdom import Visdom
 
             self.viz = Visdom()
-        elif self.args.common.visible == "tensorboard":
+        elif self.args.common.monitor == "tensorboard":
             from torch.utils.tensorboard import SummaryWriter
 
             self.tensorboard = SummaryWriter(log_dir=self.output_dir)
@@ -613,7 +613,7 @@ def log_info(self):
 
                     self.global_metrics[stage][split].append(global_metrics)
 
-                    if self.args.common.visible == "visdom":
+                    if self.args.common.monitor == "visdom":
                         self.viz.line(
                             [global_metrics.accuracy],
                             [self.current_epoch],
@@ -627,7 +627,7 @@ def log_info(self):
                                 legend=[self.algorithm_name],
                             ),
                         )
-                    elif self.args.common.visible == "tensorboard":
+                    elif self.args.common.monitor == "tensorboard":
                         self.tensorboard.add_scalar(
                             f"Accuracy-{self.monitor_window_name_suffix}/{split}set-{stage}LocalTraining",
                             global_metrics.accuracy,
@@ -688,13 +688,21 @@ def run(self):
         """
         self.logger.log("=" * 20, self.algorithm_name, "=" * 20)
         self.logger.log("Experiment Arguments:")
-        self.logger.log(JSON(json.dumps(OmegaConf.to_object(self.args))))
-        if self.args.common.visible == "tensorboard":
+        rich_pprint(
+            OmegaConf.to_object(self.args), console=self.logger.stdout, expand_all=True
+        )
+        if self.args.common.save_log:
+            rich_pprint(
+                OmegaConf.to_object(self.args),
+                console=self.logger.logfile_logger,
+                expand_all=True,
+            )
+        if self.args.common.monitor == "tensorboard":
             self.tensorboard.add_text(
                 f"ExperimentalArguments-{self.monitor_window_name_suffix}",
-                f"<pre>{self.args}</pre>",
+                f"{json.dumps(OmegaConf.to_object(self.args), indent=4)}",
             )
-
+ 
         begin = time.time()
         try:
             self.train()
@@ -745,7 +753,7 @@ def run(self):
         }
 
         self.logger.log(json.dumps(all_test_results, indent=4))
-        if self.args.common.visible == "tensorboard":
+        if self.args.common.monitor == "tensorboard":
             for epoch, results in all_test_results.items():
                 self.tensorboard.add_text(
                     f"Results-{self.monitor_window_name_suffix}",

src/utils/constants.py

@@ -73,7 +73,7 @@ class MODE(Enum):
         "eval_val": False,
         "eval_train": False,
         "verbose_gap": 10,
-        "visible": None,
+        "monitor": None,
         "use_cuda": True,
         "save_log": True,
         "save_model": False,