Formatting

examples/xgboost-comprehensive/README.md

@@ -103,7 +103,6 @@ flwr run . --run-config "train-method='cyclic' partitioner-type='linear' central
 > \[!NOTE\]
 > An update to this example will show how to run this Flower application with the Deployment Engine and TLS certificates, or with Docker.
 
-
 ## Expected Experimental Results
 
 ### Bagging aggregation experiment

examples/xgboost-comprehensive/pyproject.toml

@@ -53,4 +53,3 @@ default = "local-simulation"
 [tool.flwr.federations.local-simulation]
 options.num-supernodes = 5
 options.backend.client-resources.num-cpus = 2
-options.backend.client-resources.num-gpus = 0.0

examples/xgboost-comprehensive/xgboost_comprehensive/client_app.py

@@ -133,8 +133,14 @@ def client_fn(context: Context):
     centralised_eval_client = cfg["centralised_eval_client"]
 
     # Load training and validation data
-    train_dmatrix, valid_dmatrix, num_train, num_val = load_data(partitioner_type, partition_id, num_partitions,
-                                                                 centralised_eval_client, test_fraction, seed)
+    train_dmatrix, valid_dmatrix, num_train, num_val = load_data(
+        partitioner_type,
+        partition_id,
+        num_partitions,
+        centralised_eval_client,
+        test_fraction,
+        seed,
+    )
 
     # Setup learning rate
     if cfg["scaled_lr"]:

examples/xgboost-comprehensive/xgboost_comprehensive/server_app.py

@@ -152,7 +152,9 @@ def server_fn(context: Context):
     config = ServerConfig(num_rounds=num_rounds)
     client_manager = CyclicClientManager() if train_method == "cyclic" else None
 
-    return ServerAppComponents(strategy=strategy, config=config, client_manager=client_manager)
+    return ServerAppComponents(
+        strategy=strategy, config=config, client_manager=client_manager
+    )
 
 
 # Create ServerApp

examples/xgboost-comprehensive/xgboost_comprehensive/task.py

@@ -1,8 +1,6 @@
 """xgboost_comprehensive: A Flower / XGBoost app."""
 
 from logging import INFO
-from typing import Union
-from tqdm import tqdm
 import xgboost as xgb
 from datasets import Dataset, DatasetDict, concatenate_datasets
 
@@ -24,6 +22,7 @@
 
 fds = None  # Cache FederatedDataset
 
+
 def train_test_split(partition, test_fraction, seed):
     """Split the data into train and validation set given split rate."""
     train_test = partition.train_test_split(test_size=test_fraction, seed=seed)
@@ -60,7 +59,14 @@ def instantiate_fds(partitioner_type, num_partitions):
     return fds
 
 
-def load_data(partitioner_type, partition_id, num_partitions, centralised_eval_client, test_fraction, seed):
+def load_data(
+    partitioner_type,
+    partition_id,
+    num_partitions,
+    centralised_eval_client,
+    test_fraction,
+    seed,
+):
     """Load partition data."""
     fds_ = instantiate_fds(partitioner_type, num_partitions)
     partition = fds_.load_partition(partition_id)