fix formatting

.github/workflows/nightly.yml

@@ -186,7 +186,7 @@ jobs:
         run: |
           nvidia-smi
           python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
-      
+
       - name: Run multi-GPU training tests
         run: pytest tests/training/test_multi_gpu.py -vv --maxfail=3
         timeout-minutes: 10

docs/source/multi_gpu_training.mdx

@@ -80,7 +80,7 @@ When you launch training with accelerate:
 
 ### Why No Automatic Scaling?
 
-Many distributed training frameworks automatically scale the learning rate by the number of GPUs (e.g., `lr = base_lr × num_gpus`). 
+Many distributed training frameworks automatically scale the learning rate by the number of GPUs (e.g., `lr = base_lr × num_gpus`).
 However, LeRobot keeps the learning rate exactly as you specify it.
 
 ### When and How to Scale
@@ -104,8 +104,8 @@ Since the effective batch size `bs` increases with multiple GPUs (batch_size ×
 
 ```bash
 # Example: 2 GPUs with effective batch size 2x larger
-# Original: batch_size=8, steps=100000  
-# With 2 GPUs: batch_size=8 (16 in total), steps=50000 
+# Original: batch_size=8, steps=100000
+# With 2 GPUs: batch_size=8 (16 in total), steps=50000
 accelerate launch --num_processes=2 $(which lerobot-train) \
   --batch_size=8 \
   --steps=50000 \

src/lerobot/optim/schedulers.py

@@ -81,7 +81,7 @@ class VQBeTSchedulerConfig(LRSchedulerConfig):
 @dataclass
 class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
     """Used by Physical Intelligence to train Pi0.
-    
+
     Automatically scales warmup and decay steps if num_training_steps < num_decay_steps.
     This ensures the learning rate schedule completes properly even with shorter training runs.
     """
@@ -95,13 +95,13 @@ class CosineDecayWithWarmupSchedulerConfig(LRSchedulerConfig):
         # Auto-scale scheduler parameters if training steps are shorter than configured decay steps
         actual_warmup_steps = self.num_warmup_steps
         actual_decay_steps = self.num_decay_steps
-        
+
         if num_training_steps < self.num_decay_steps:
             # Calculate scaling factor to fit the schedule into the available training steps
             scale_factor = num_training_steps / self.num_decay_steps
             actual_warmup_steps = int(self.num_warmup_steps * scale_factor)
             actual_decay_steps = num_training_steps
-            
+
             logging.info(
                 f"Auto-scaling LR scheduler: "
                 f"num_training_steps ({num_training_steps}) < num_decay_steps ({self.num_decay_steps}). "

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
@@ -86,7 +85,7 @@ def update_policy(
     """
     start_time = time.perf_counter()
     policy.train()
-    
+
     # Let accelerator handle mixed precision
     with accelerator.autocast():
         loss, output_dict = policy.forward(batch)
@@ -94,17 +93,17 @@ def update_policy(
 
     # Use accelerator's backward method
     accelerator.backward(loss)
-    
+
     # Clip gradients if specified
     if grad_clip_norm > 0:
         grad_norm = accelerator.clip_grad_norm_(policy.parameters(), grad_clip_norm)
     else:
         grad_norm = torch.tensor(0.0, device=accelerator.device)
-    
+
     # Optimizer step
     with lock if lock is not None else nullcontext():
         optimizer.step()
-    
+
     optimizer.zero_grad()
 
     # Step through pytorch scheduler at every batch instead of epoch
@@ -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
@@ -182,10 +178,9 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     if is_main_process:
         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
     if not is_main_process:
         dataset = make_dataset(cfg)
@@ -205,7 +200,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         cfg=cfg.policy,
         ds_meta=dataset.meta,
     )
-    
+
     # Wait for all processes to finish policy creation before continuing
     accelerator.wait_for_everyone()
 
@@ -288,7 +283,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         drop_last=False,
         prefetch_factor=2 if cfg.num_workers > 0 else None,
     )
-    
+
     # Prepare everything with accelerator
     accelerator.wait_for_everyone()
     policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
@@ -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:
@@ -431,7 +426,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             unwrapped_policy.push_model_to_hub(cfg)
             preprocessor.push_to_hub(cfg.policy.repo_id)
             postprocessor.push_to_hub(cfg.policy.repo_id)
-    
+
     # Properly clean up the distributed process group
     accelerator.wait_for_everyone()
     accelerator.end_training()

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
 
 
@@ -117,10 +117,10 @@ def init_logging(
     accelerator: Accelerator | None = None,
 ):
     """Initialize logging configuration for LeRobot.
-    
+
     In multi-GPU training, only the main process logs to console to avoid duplicate output.
     Non-main processes have console logging suppressed but can still log to file.
-    
+
     Args:
         log_file: Optional file path to write logs to
         display_pid: Include process ID in log messages (useful for debugging multi-process)
@@ -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}"
@@ -139,7 +140,7 @@ def init_logging(
 
     logger = logging.getLogger()
     logger.setLevel(logging.NOTSET)
-    
+
     # Clear any existing handlers
     logger.handlers.clear()
 
@@ -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
@@ -48,7 +46,7 @@ def get_num_available_gpus():
 def download_dataset(repo_id, episodes):
     """
     Pre-download dataset to avoid race conditions in multi-GPU training.
-    
+
     Args:
         repo_id: HuggingFace dataset repository ID
         episodes: List of episode indices to download
@@ -61,27 +59,18 @@ def download_dataset(repo_id, episodes):
 def run_accelerate_training(config_args, num_processes=4, temp_dir=None):
     """
     Helper function to run training with accelerate launch.
-    
+
     Args:
         config_args: List of config arguments to pass to lerobot_train.py
         num_processes: Number of processes (GPUs) to use
         temp_dir: Temporary directory for outputs
-        
+
     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"
-    
+
     # Write YAML config
     with open(config_path, "w") as f:
         f.write("compute_environment: LOCAL_MACHINE\n")
@@ -96,7 +85,7 @@ def run_accelerate_training(config_args, num_processes=4, temp_dir=None):
         f.write("num_machines: 1\n")
         f.write("rdzv_backend: static\n")
         f.write("same_network: true\n")
-    
+
     cmd = [
         "accelerate",
         "launch",
@@ -105,14 +94,14 @@ def run_accelerate_training(config_args, num_processes=4, temp_dir=None):
         "-m",
         "lerobot.scripts.lerobot_train",
     ] + config_args
-    
+
     result = subprocess.run(
         cmd,
         capture_output=True,
         text=True,
         env={**os.environ, "CUDA_VISIBLE_DEVICES": ",".join(map(str, range(num_processes)))},
     )
-    
+
     return result
 
 
@@ -130,10 +119,10 @@ class TestMultiGPUTraining:
         """
         # Pre-download dataset to avoid race conditions
         download_dataset("lerobot/pusht", episodes=[0])
-        
+
         with tempfile.TemporaryDirectory() as temp_dir:
             output_dir = Path(temp_dir) / "outputs"
-            
+
             config_args = [
                 "--dataset.repo_id=lerobot/pusht",
                 "--dataset.episodes=[0]",
@@ -149,20 +138,20 @@ class TestMultiGPUTraining:
                 "--seed=42",
                 "--num_workers=0",
             ]
-            
+
             result = run_accelerate_training(config_args, num_processes=4, temp_dir=temp_dir)
-            
+
             # Check that training completed successfully
             assert result.returncode == 0, (
                 f"Multi-GPU training failed with return code {result.returncode}\n"
                 f"STDOUT:\n{result.stdout}\n"
                 f"STDERR:\n{result.stderr}"
             )
-            
+
             # Verify checkpoint was saved
             checkpoints_dir = output_dir / "checkpoints"
             assert checkpoints_dir.exists(), "Checkpoints directory was not created"
-            
+
             # Verify that training completed
             assert "End of training" in result.stdout or "End of training" in result.stderr
 
@@ -173,10 +162,10 @@ class TestMultiGPUTraining:
         """
         # Pre-download dataset to avoid race conditions
         download_dataset("lerobot/pusht", episodes=[0])
-        
+
         with tempfile.TemporaryDirectory() as temp_dir:
             output_dir = Path(temp_dir) / "outputs"
-            
+
             config_args = [
                 "--dataset.repo_id=lerobot/pusht",
                 "--dataset.episodes=[0]",
@@ -192,31 +181,31 @@ class TestMultiGPUTraining:
                 "--seed=42",
                 "--num_workers=0",
             ]
-            
+
             result = run_accelerate_training(config_args, num_processes=2, temp_dir=temp_dir)
-            
+
             assert result.returncode == 0, (
                 f"Training failed:\nSTDOUT:\n{result.stdout}\n\nSTDERR:\n{result.stderr}"
             )
-            
+
             # Verify checkpoint directory exists
             checkpoints_dir = output_dir / "checkpoints"
             assert checkpoints_dir.exists(), "Checkpoints directory not created"
-            
+
             # Count checkpoint directories (should have checkpoint at step 10 and 20)
             checkpoint_dirs = [d for d in checkpoints_dir.iterdir() if d.is_dir()]
             assert len(checkpoint_dirs) >= 1, f"Expected at least 1 checkpoint, found {len(checkpoint_dirs)}"
-            
+
             # Verify checkpoint contents
             for checkpoint_dir in checkpoint_dirs:
                 # Check for model files
                 model_files = list(checkpoint_dir.rglob("*.safetensors"))
                 assert len(model_files) > 0, f"No model files in checkpoint {checkpoint_dir}"
-                
+
                 # Check for training state
                 training_state_dir = checkpoint_dir / "training_state"
                 assert training_state_dir.exists(), f"No training state in checkpoint {checkpoint_dir}"
-                
+
                 # Verify optimizer state exists
                 optimizer_state = training_state_dir / "optimizer_state.safetensors"
                 assert optimizer_state.exists(), f"No optimizer state in checkpoint {checkpoint_dir}"