fix formatting

src/lerobot/scripts/lerobot_train.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-import os
 import time
 from contextlib import nullcontext
 from pprint import pformat
@@ -143,16 +142,13 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
     # Create Accelerator if not provided
     # It will automatically detect if running in distributed mode or single-process mode
-    # We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting
-    # the lr_scheduler steps based on the num_processes
-    # We set find_unused_parameters=True to handle models with conditional computation paths
+    # We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting the lr_scheduler steps based on the num_processes
+    # We set find_unused_parameters=True to handle models with conditional computation
     if accelerator is None:
         from accelerate.utils import DistributedDataParallelKwargs
+
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
-        accelerator = Accelerator(
-            step_scheduler_with_optimizer=False,
-            kwargs_handlers=[ddp_kwargs]
-        )
+        accelerator = Accelerator(step_scheduler_with_optimizer=False, kwargs_handlers=[ddp_kwargs])
 
     # Determine if this is the main process (for logging and checkpointing)
     # When using accelerate, only the main process should log to avoid duplicate outputs
@@ -183,7 +179,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         logging.info("Creating dataset")
         dataset = make_dataset(cfg)
 
-    # Wait for main process to finish downloading/caching dataset
     accelerator.wait_for_everyone()
 
     # Now all other processes can safely load the dataset
@@ -341,7 +336,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         step += 1
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0 and is_main_process
-        is_saving_step = (step % cfg.save_freq == 0 or step == cfg.steps)
+        is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
         is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
 
         if is_log_step:

src/lerobot/utils/utils.py

@@ -21,7 +21,6 @@ import subprocess
 import sys
 import time
 from collections.abc import Callable
-from accelerate import Accelerator
 from copy import copy, deepcopy
 from datetime import datetime
 from pathlib import Path
@@ -29,6 +28,7 @@ from statistics import mean
 
 import numpy as np
 import torch
+from accelerate import Accelerator
 from datasets.utils.logging import disable_progress_bar, enable_progress_bar
 
 
@@ -128,6 +128,7 @@ def init_logging(
         file_level: Logging level for file output
         accelerator: Optional Accelerator instance (for multi-GPU detection)
     """
+
     def custom_format(record: logging.LogRecord) -> str:
         dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         fnameline = f"{record.pathname}:{record.lineno}"
@@ -159,7 +160,6 @@ def init_logging(
         logger.addHandler(logging.NullHandler())
         logger.setLevel(logging.ERROR)
 
-    # File logging (optional, all processes)
     if log_file is not None:
         file_handler = logging.FileHandler(log_file)
         file_handler.setFormatter(formatter)
@@ -178,6 +178,7 @@ def format_big_number(num, precision=0):
 
     return num
 
+
 def say(text: str, blocking: bool = False):
     system = platform.system()
 

tests/training/test_multi_gpu.py

@@ -25,9 +25,7 @@ The tests automatically generate accelerate configs and launch training
 with subprocess to properly test the distributed training environment.
 """
 
-import json
 import os
-import shutil
 import subprocess
 import tempfile
 from pathlib import Path
@@ -70,15 +68,6 @@ def run_accelerate_training(config_args, num_processes=4, temp_dir=None):
     Returns:
         subprocess.CompletedProcess result
     """
-    # Create accelerate config
-    accelerate_config = {
-        "compute_environment": "LOCAL_MACHINE",
-        "distributed_type": "MULTI_GPU",
-        "mixed_precision": "no",
-        "num_processes": num_processes,
-        "use_cpu": False,
-        "gpu_ids": "all",
-    }
 
     config_path = Path(temp_dir) / "accelerate_config.yaml"