Reformat examples

examples/app-pytorch/client.py

@@ -18,7 +18,6 @@
 
 # Define FlowerClient and client_fn
 class FlowerClient(NumPyClient):
-
     def fit(self, parameters, config):
         set_weights(net, parameters)
         results = train(net, trainloader, testloader, epochs=1, device=DEVICE)

examples/custom-mods/client.py

@@ -86,7 +86,6 @@ def wandb_mod(msg: Message, context: Context, app: ClientAppCallable) -> Message
 
         # if the `ClientApp` just processed a "fit" message, let's log some metrics to W&B
         if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
-
             metrics = reply.content.configs_records
 
             results_to_log = dict(metrics.get("fitres.metrics", ConfigsRecord()))

examples/quickstart-mlcube/dev/mnist.py

@@ -36,6 +36,7 @@ def create_directory(path: str) -> None:
 
 def download(task_args: List[str]) -> None:
     """Task: download.
+
     Input parameters:
         --data_dir
     """
@@ -81,6 +82,7 @@ def download(task_args: List[str]) -> None:
 
 def train(task_args: List[str]) -> None:
     """Task: train.
+
     Input parameters:
         --data_dir, --log_dir, --model_dir, --parameters_file
     """
@@ -175,6 +177,7 @@ def train(task_args: List[str]) -> None:
 
 def evaluate(task_args: List[str]) -> None:
     """Task: train.
+
     Input parameters:
         --data_dir, --log_dir, --model_dir, --parameters_file
     """

examples/quickstart-pytorch-lightning/mnist.py

@@ -84,7 +84,9 @@ def load_data(partition):
     # 20 % for on federated evaluation
     partition_full = partition.train_test_split(test_size=0.2, seed=42)
     # 60 % for the federated train and 20 % for the federated validation (both in fit)
-    partition_train_valid = partition_full["train"].train_test_split(train_size=0.75, seed=42)
+    partition_train_valid = partition_full["train"].train_test_split(
+        train_size=0.75, seed=42
+    )
     trainloader = DataLoader(
         partition_train_valid["train"],
         batch_size=32,

examples/vit-finetune/client.py

@@ -8,9 +8,7 @@
 
 
 class FedViTClient(NumPyClient):
-
     def __init__(self, trainset):
-
         self.trainset = trainset
         self.model = get_model()
 

examples/vit-finetune/main.py

@@ -19,7 +19,6 @@
 
 
 def main():
-
     args = parser.parse_args()
 
     # To control the degree of parallelism

examples/whisper-federated-finetuning/utils.py

@@ -107,10 +107,10 @@ def prepare_silences_dataset(train_dataset, ratio_silence: float = 0.1) -> Datas
     """Generate silences for the train set.
 
     One of the classes in the SpeechCommands datatset is `silence`. However, the dataset
-    does not include clips of silence. It does however include 5 long files with different
-    background sounds. The taks of this function is to extract several (defined by `ratio_silence`)
-    one-second long clips from those background audio files. Later, those audio clips will be
-    included into the training set.
+    does not include clips of silence. It does however include 5 long files with
+    different background sounds. The taks of this function is to extract several
+    (defined by `ratio_silence`) one-second long clips from those background audio
+    files. Later, those audio clips will be included into the training set.
     """
     # retrieve original silence audio clips
     silences = [d for d in train_dataset if d["label"] == 35]
@@ -138,9 +138,9 @@ def prepare_silences_dataset(train_dataset, ratio_silence: float = 0.1) -> Datas
 def construct_client_mapping(full_trainset, num_clients: int = 100):
     """Create a mapping to partition the dataset into `num_client` buckets.
 
-    These buckets contain the same number of `spekaer_id` but likely different
-    number of training exampes since each `speaker_id` in SpeechCommands does
-    provide different amounts of data to the dataset.
+    These buckets contain the same number of `spekaer_id` but likely different number of
+    training exampes since each `speaker_id` in SpeechCommands does provide different
+    amounts of data to the dataset.
     """
     client_ids = list(set(full_trainset["speaker_id"]))
     client_ids.remove(
@@ -191,7 +191,7 @@ def set_params(model: torch.nn.ModuleList, params: List[fl.common.NDArrays]):
 
 
 def get_model(device, num_classes, compile: bool = True):
-    """Create model: Whisper-tiny Encoder + classification head"""
+    """Create model: Whisper-tiny Encoder + classification head."""
     encoder = WhisperForConditionalGeneration.from_pretrained(
         "openai/whisper-tiny"
     ).get_encoder()

examples/xgboost-comprehensive/dataset.py

@@ -27,7 +27,9 @@ def instantiate_partitioner(partitioner_type: str, num_partitions: int):
 
 def train_test_split(partition: Dataset, test_fraction: float, seed: int):
     """Split the data into train and validation set given split rate."""
-    train_test = partition.train_test_split(test_size=test_fraction, seed=seed, shuffle=False)
+    train_test = partition.train_test_split(
+        test_size=test_fraction, seed=seed, shuffle=False
+    )
     partition_train = train_test["train"]
     partition_test = train_test["test"]