Merge branch 'main' into dbe/fixing_some_numpy_randomness_in_aggregation

examples/ae_examples/cvae_dim_example/client.py

@@ -22,8 +22,8 @@
 
 
 class CvaeDimClient(BasicClient):
-    def __init__(self, data_path: Path, metrics: Sequence[Metric], DEVICE: torch.device, condition: torch.Tensor):
-        super().__init__(data_path, metrics, DEVICE)
+    def __init__(self, data_path: Path, metrics: Sequence[Metric], device: torch.device, condition: torch.Tensor):
+        super().__init__(data_path, metrics, device)
         self.condition = condition
 
     def get_data_loaders(self, config: Config) -> Tuple[DataLoader, DataLoader]:
@@ -64,11 +64,11 @@ def get_model(self, config: Config) -> nn.Module:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     set_all_random_seeds(42)
     # Creating the condition vector used for training this CVAE.
     condition_vector = torch.nn.functional.one_hot(torch.tensor(args.condition), num_classes=args.num_conditions)
-    client = CvaeDimClient(data_path, [Accuracy("accuracy")], DEVICE, condition_vector)
+    client = CvaeDimClient(data_path, [Accuracy("accuracy")], device, condition_vector)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/ae_examples/cvae_examples/conv_cvae_example/client.py

@@ -36,8 +36,8 @@ def binary_class_condition_data_converter(
 
 
 class CondConvAutoEncoderClient(BasicClient):
-    def __init__(self, data_path: Path, metrics: Sequence[Metric], DEVICE: torch.device) -> None:
-        super().__init__(data_path, metrics, DEVICE)
+    def __init__(self, data_path: Path, metrics: Sequence[Metric], device: torch.device) -> None:
+        super().__init__(data_path, metrics, device)
         # To train an autoencoder-based model we need to define a data converter that prepares the data
         # for self-supervised learning, concatenates the inputs and condition (packing) to let the data
         # fit into the training pipeline, and unpacks the input from condition for the model inference.
@@ -93,8 +93,8 @@ def get_model(self, config: Config) -> nn.Module:
     parser.add_argument("--dataset_path", action="store", type=str, help="Path to the local dataset")
     args = parser.parse_args()
     set_all_random_seeds(42)
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = CondConvAutoEncoderClient(data_path=data_path, metrics=[], DEVICE=DEVICE)
+    client = CondConvAutoEncoderClient(data_path=data_path, metrics=[], device=device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/ae_examples/cvae_examples/mlp_cvae_example/client.py

@@ -25,9 +25,9 @@
 
 class CondAutoEncoderClient(BasicClient):
     def __init__(
-        self, data_path: Path, metrics: Sequence[Metric], DEVICE: torch.device, condition: torch.Tensor
+        self, data_path: Path, metrics: Sequence[Metric], device: torch.device, condition: torch.Tensor
     ) -> None:
-        super().__init__(data_path, metrics, DEVICE)
+        super().__init__(data_path, metrics, device)
         # In this example, condition is based on client ID.
         self.condition_vector = condition
         # To train an autoencoder-based model we need to define a data converter that prepares the data
@@ -96,13 +96,13 @@ def get_model(self, config: Config) -> nn.Module:
     )
     args = parser.parse_args()
     set_all_random_seeds(42)
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     # Create the condition vector. This creation needs to be "consistent" across clients.
     # In this example, condition is based on client ID.
     # Client should decide how they want to create their condition vector.
     # Here we use simple one_hot_encoding but it can be any vector.
     condition_vector = torch.nn.functional.one_hot(torch.tensor(args.condition), num_classes=args.num_conditions)
-    client = CondAutoEncoderClient(data_path, [], DEVICE, condition_vector)
+    client = CondAutoEncoderClient(data_path, [], device, condition_vector)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/ae_examples/fedprox_vae_example/client.py

@@ -60,8 +60,8 @@ def get_model(self, config: Config) -> nn.Module:
     parser.add_argument("--dataset_path", action="store", type=str, help="Path to the local dataset")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = VaeFedProxClient(data_path, [], DEVICE)
+    client = VaeFedProxClient(data_path, [], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/apfl_example/client.py

@@ -53,12 +53,12 @@ def get_criterion(self, config: Config) -> _Loss:
 
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
 
     # Set the random seed for reproducibility
     set_all_random_seeds(args.seed)
 
-    client = MnistApflClient(data_path, [Accuracy()], DEVICE, reporters=[JsonReporter()])
+    client = MnistApflClient(data_path, [Accuracy()], device, reporters=[JsonReporter()])
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()  # This will tell the JsonReporter to dump data

examples/basic_example/client.py

@@ -43,8 +43,8 @@ def get_model(self, config: Config) -> nn.Module:
     parser.add_argument("--dataset_path", action="store", type=str, help="Path to the local dataset")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = CifarClient(data_path, [Accuracy("accuracy")], DEVICE)
+    client = CifarClient(data_path, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/ditto_example/client.py

@@ -61,15 +61,15 @@ def get_criterion(self, config: Config) -> _Loss:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    log(INFO, f"Device to be used: {DEVICE}")
+    log(INFO, f"Device to be used: {device}")
     log(INFO, f"Server Address: {args.server_address}")
 
     # Set the random seed for reproducibility
     set_all_random_seeds(args.seed)
 
-    client = MnistDittoClient(data_path, [Accuracy()], DEVICE, reporters=[JsonReporter()])
+    client = MnistDittoClient(data_path, [Accuracy()], device, reporters=[JsonReporter()])
     fl.client.start_client(server_address=args.server_address, client=client.to_client())
 
     # Shutdown the client gracefully

examples/docker_basic_example/fl_client/client.py

@@ -39,7 +39,7 @@ def setup_client(self, config: Config) -> None:
     args = parser.parse_args()
 
     # Load model and data
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = CifarClient(data_path, [Accuracy("accuracy")], DEVICE)
+    client = CifarClient(data_path, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="fl_server:8080", client=client.to_client())

examples/dp_fed_examples/client_level_dp/client.py

@@ -40,7 +40,7 @@ def get_criterion(self, config: Config) -> _Loss:
 
     # Load model and data
     data_path = Path(args.dataset_path)
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    client = CifarClient(data_path, [Accuracy("accuracy")], DEVICE)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    client = CifarClient(data_path, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/dp_fed_examples/client_level_dp_weighted/client.py

@@ -40,8 +40,8 @@ def get_criterion(self, config: Config) -> _Loss:
     args = parser.parse_args()
 
     # Load model and data
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = HospitalClient(data_path, [Accuracy("accuracy")], DEVICE)
+    client = HospitalClient(data_path, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/dp_fed_examples/instance_level_dp/client.py

@@ -52,8 +52,8 @@ def get_criterion(self, config: Config) -> _Loss:
 
     # Load model and data
     data_path = Path(args.dataset_path)
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    client = CifarClient(data_path, [Accuracy("accuracy")], DEVICE, checkpointer=checkpointer)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    client = CifarClient(data_path, [Accuracy("accuracy")], device, checkpointer=checkpointer)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
 
     client.shutdown()

examples/dp_scaffold_example/client.py

@@ -41,10 +41,10 @@ def get_criterion(self, config: Config) -> _Loss:
 
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
 
-    client = MnistDPScaffoldClient(data_path=data_path, metrics=[Accuracy()], device=DEVICE)
+    client = MnistDPScaffoldClient(data_path=data_path, metrics=[Accuracy()], device=device)
 
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/dynamic_layer_exchange_example/client.py

@@ -67,8 +67,8 @@ def get_parameter_exchanger(self, config: Config) -> ParameterExchanger:
     parser.add_argument("--dataset_path", action="store", type=str, help="Path to the local dataset")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = CifarDynamicLayerClient(data_path, [Accuracy("accuracy")], DEVICE, store_initial_model=True)
+    client = CifarDynamicLayerClient(data_path, [Accuracy("accuracy")], device, store_initial_model=True)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/ensemble_example/client.py

@@ -52,8 +52,8 @@ def get_criterion(self, config: Config) -> _Loss:
 
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
 
-    client = MnistEnsembleClient(data_path, [Accuracy()], DEVICE)
+    client = MnistEnsembleClient(data_path, [Accuracy()], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())

examples/feature_alignment_example/client.py

@@ -90,15 +90,15 @@ def get_data_frame(self, config: Config) -> pd.DataFrame:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     data_path = Path(args.dataset_path)
 
-    log(INFO, f"Device to be used: {DEVICE}")
+    log(INFO, f"Device to be used: {device}")
     log(INFO, f"Server Address: {args.server_address}")
 
     # ham_id is the id column and LOSgroupNum is the target column.
-    client = Mimic3TabularDataClient(data_path, [Accuracy("accuracy")], DEVICE, "hadm_id", ["LOSgroupNum"])
+    client = Mimic3TabularDataClient(data_path, [Accuracy("accuracy")], device, "hadm_id", ["LOSgroupNum"])
     # This call demonstrates how the user may specify a particular sklearn pipeline for a specific feature.
     client.preset_specific_pipeline("NumNotes", MaxAbsScaler())
     fl.client.start_client(server_address=args.server_address, client=client.to_client())

examples/fedbn_example/client.py

@@ -88,15 +88,15 @@ def get_parameter_exchanger(self, config: Config) -> ParameterExchanger:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    log(INFO, f"Device to be used: {DEVICE}")
+    log(INFO, f"Device to be used: {device}")
     log(INFO, f"Server Address: {args.server_address}")
 
     if args.dataset_name in ["Barcelona", "Rosendahl", "Vienna", "UFES", "Canada"]:
-        client: BasicClient = SkinCancerFedBNClient(data_path, [Accuracy()], DEVICE, args.dataset_name)
+        client: BasicClient = SkinCancerFedBNClient(data_path, [Accuracy()], device, args.dataset_name)
     elif args.dataset_name == "mnist":
-        client = MnistFedBNClient(data_path, [Accuracy()], DEVICE)
+        client = MnistFedBNClient(data_path, [Accuracy()], device)
     else:
         raise ValueError(
             "Unsupported dataset name. Please choose from 'Barcelona', 'Rosendahl', \

examples/feddg_ga_example/client.py

@@ -53,12 +53,12 @@ def get_criterion(self, config: Config) -> _Loss:
 
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
 
     # Set the random seed for reproducibility
     set_all_random_seeds(args.seed)
 
-    client = MnistApflClient(data_path, [Accuracy()], DEVICE, reporters=[JsonReporter()])
+    client = MnistApflClient(data_path, [Accuracy()], device, reporters=[JsonReporter()])
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/federated_eval_example/client.py

@@ -63,12 +63,12 @@ def get_criterion(self, config: Config) -> _Loss:
     data_path = Path(args.dataset_path)
     client_checkpoint_path = Path(args.checkpoint_path) if args.checkpoint_path else None
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
     client = CifarClient(
         data_path=data_path,
         metrics=[Accuracy("accuracy")],
-        device=DEVICE,
+        device=device,
         model_checkpoint_path=client_checkpoint_path,
     )
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())

examples/fedopt_example/client.py

@@ -95,7 +95,7 @@ def predict(
     args = parser.parse_args()
 
     # Load model and data
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    client = NewsClassifierClient(data_path, [CompoundMetric("Compound Metric")], DEVICE)
+    client = NewsClassifierClient(data_path, [CompoundMetric("Compound Metric")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())

examples/fedpca_examples/dim_reduction/client.py

@@ -66,14 +66,14 @@ def get_model(self, config: Config) -> nn.Module:
     parser.add_argument("--seed", action="store", type=int, help="Random seed for this client.")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     seed = args.seed
 
     # If the user wants to ensure that this example uses the same data as
     # the data used in the perform_pca example, then both examples
     # should use the same random seed.
     set_all_random_seeds(seed)
-    client = MnistFedPcaClient(data_path, [Accuracy("accuracy")], DEVICE)
+    client = MnistFedPcaClient(data_path, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/fedpca_examples/perform_pca/client.py

@@ -36,7 +36,7 @@ def get_data_tensor(self, data_loader: DataLoader) -> Tensor:
     parser.add_argument("--seed", action="store", type=int, help="Random seed for this client.")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     components_save_path = Path(args.components_save_path)
     seed = args.seed
@@ -45,5 +45,5 @@ def get_data_tensor(self, data_loader: DataLoader) -> Tensor:
     # the data used in the dim_reduction example, then both examples
     # should use the same random seed.
     set_all_random_seeds(seed)
-    client = MnistFedPCAClient(data_path=data_path, device=DEVICE, model_save_path=components_save_path)
+    client = MnistFedPCAClient(data_path=data_path, device=device, model_save_path=components_save_path)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())

examples/fedper_example/client.py

@@ -62,9 +62,9 @@ def get_criterion(self, config: Config) -> _Loss:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     minority_numbers = {int(number) for number in args.minority_numbers}
-    client = MnistFedPerClient(data_path, [Accuracy("accuracy")], DEVICE, minority_numbers)
+    client = MnistFedPerClient(data_path, [Accuracy("accuracy")], device, minority_numbers)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/fedpm_example/client.py

@@ -54,9 +54,9 @@ def get_criterion(self, config: Config) -> _Loss:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
     minority_numbers = {int(number) for number in args.minority_numbers}
-    client = MnistFedPmClient(data_path, [Accuracy("accuracy")], DEVICE, minority_numbers)
+    client = MnistFedPmClient(data_path, [Accuracy("accuracy")], device, minority_numbers)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()

examples/fedprox_example/client.py

@@ -57,15 +57,15 @@ def get_criterion(self, config: Config) -> _Loss:
     )
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_path = Path(args.dataset_path)
-    log(INFO, f"Device to be used: {DEVICE}")
+    log(INFO, f"Device to be used: {device}")
     log(INFO, f"Server Address: {args.server_address}")
 
     # Set the random seed for reproducibility
     set_all_random_seeds(args.seed)
 
-    client = MnistFedProxClient(data_path, [Accuracy()], DEVICE, reporters=[JsonReporter()])
+    client = MnistFedProxClient(data_path, [Accuracy()], device, reporters=[JsonReporter()])
     fl.client.start_client(server_address=args.server_address, client=client.to_client())
 
     # Shutdown the client gracefully

examples/fedrep_example/client.py

@@ -55,8 +55,8 @@ def get_criterion(self, config: Config) -> _Loss:
     parser.add_argument("--dataset_path", action="store", type=str, help="Path to the local dataset")
     args = parser.parse_args()
 
-    DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     data_dir = Path(args.dataset_path)
-    client = CifarFedRepClient(data_dir, [Accuracy("accuracy")], DEVICE)
+    client = CifarFedRepClient(data_dir, [Accuracy("accuracy")], device)
     fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
     client.shutdown()