From 771b03c30df3b1ef62de3faeb95be63f52614e63 Mon Sep 17 00:00:00 2001
From: Pepijn <pepijn@huggingface.co>
Date: Fri, 10 Oct 2025 13:35:26 +0200
Subject: [PATCH] fix pre commit

---
 docs/source/multi_gpu_training.mdx   | 2 +-
 src/lerobot/scripts/lerobot_train.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/source/multi_gpu_training.mdx b/docs/source/multi_gpu_training.mdx
index a6cd540bc..1e26e8806 100644
--- a/docs/source/multi_gpu_training.mdx
+++ b/docs/source/multi_gpu_training.mdx
@@ -57,6 +57,7 @@ accelerate launch \
 ```
 
 **Key accelerate parameters:**
+
 - `--multi_gpu`: Enable multi-GPU training
 - `--num_processes=2`: Number of GPUs to use
 - `--mixed_precision=fp16`: Use fp16 mixed precision (or `bf16` if supported)
@@ -97,4 +98,3 @@ For faster training, you can enable mixed precision (fp16 or bf16). This is conf
 - When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
 
 For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate).
-
diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 65e86cb46..1d1f0adc8 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -15,10 +15,10 @@
 # limitations under the License.
 import logging
 import time
+from collections.abc import Callable
 from contextlib import nullcontext
 from pprint import pformat
 from typing import Any
-from collections.abc import Callable
 
 import torch
 from termcolor import colored
@@ -163,7 +163,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Callable | None = None):
         cfg: A `TrainPipelineConfig` object containing all training configurations.
     """
     cfg.validate()
-    
+
     if accelerator and not accelerator.is_main_process:
         # Disable logging on non-main processes.
         cfg.wandb.enable = False
@@ -311,7 +311,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Callable | None = None):
 
     if not accelerator or accelerator.is_main_process:
         logging.info("Start offline training on a fixed dataset")
-        
+
     for _ in range(step, cfg.steps):
         start_time = time.perf_counter()
         batch = next(dl_iter)