pre download dataset in tests

tests/training/test_multi_gpu.py

@@ -35,6 +35,8 @@ from pathlib import Path
 import pytest
 import torch
 
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
 
 def get_num_available_gpus():
     """Returns the number of available GPUs."""
@@ -43,6 +45,19 @@ def get_num_available_gpus():
     return torch.cuda.device_count()
 
 
+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
+    """
+    # Simply instantiating the dataset will download it
+    _ = LeRobotDataset(repo_id, episodes=episodes)
+    print(f"Dataset {repo_id} downloaded successfully")
+
+
 def run_accelerate_training(config_args, num_processes=4, temp_dir=None):
     """
     Helper function to run training with accelerate launch.
@@ -113,6 +128,9 @@ class TestMultiGPUTraining:
         Test that basic multi-GPU training runs successfully.
         Verifies that the training completes without errors.
         """
+        # 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"
             
@@ -129,6 +147,7 @@ class TestMultiGPUTraining:
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",
+                "--num_workers=0",
             ]
             
             result = run_accelerate_training(config_args, num_processes=4, temp_dir=temp_dir)
@@ -152,6 +171,9 @@ class TestMultiGPUTraining:
         Test that checkpoints are correctly saved during multi-GPU training.
         Only the main process (rank 0) should save checkpoints.
         """
+        # 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"
             
@@ -168,6 +190,7 @@ class TestMultiGPUTraining:
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",
+                "--num_workers=0",
             ]
             
             result = run_accelerate_training(config_args, num_processes=2, temp_dir=temp_dir)