add some debugging

src/lerobot/scripts/lerobot_train.py

@@ -463,7 +463,17 @@ def main():
 
 
 if __name__ == "__main__":
+    import os
+    distributed_env_vars = {
+        "LOCAL_RANK": os.environ.get("LOCAL_RANK", "NOT SET"),
+        "WORLD_SIZE": os.environ.get("WORLD_SIZE", "NOT SET"),
+        "RANK": os.environ.get("RANK", "NOT SET"),
+        "ACCELERATE_MIXED_PRECISION": os.environ.get("ACCELERATE_MIXED_PRECISION", "NOT SET"),
+    }
+    print(f"[PID {os.getpid()}] Distributed env vars: {distributed_env_vars}")
+    
     if is_launched_with_accelerate():
+        print(f"[PID {os.getpid()}] Detected distributed training mode")
         import accelerate
 
         # We set step_scheduler_with_optimizer False to prevent accelerate from

src/lerobot/utils/utils.py

@@ -179,19 +179,17 @@ def format_big_number(num, precision=0):
 
 
 def is_launched_with_accelerate() -> bool:
-    """Check if the script was launched using accelerate.
+    """Check if the script was launched in a distributed training context.
     
-    Accelerate sets several environment variables when launching distributed training.
-    We check for variables that are always set regardless of the configuration.
+    This checks for standard distributed training environment variables that are set
+    by accelerate launch, torchrun, or torch.distributed.launch.
+    
+    Returns:
+        True if running in a distributed context, False otherwise.
     """
-    # Check for environment variables that accelerate always sets
-    accelerate_env_vars = [
-        "ACCELERATE_MIXED_PRECISION",  # Set when mixed precision is configured
-        "LOCAL_RANK",                   # Always set in distributed training
-        "WORLD_SIZE",                   # Always set in distributed training
-        "ACCELERATE_USE_CPU",           # Set by accelerate
-    ]
-    return any(var in os.environ for var in accelerate_env_vars)
+    # Check for LOCAL_RANK which is the standard way to detect distributed training
+    # This is set by accelerate, torchrun, and torch.distributed.launch
+    return "LOCAL_RANK" in os.environ
 
 
 def say(text: str, blocking: bool = False):