chore(scripts): add multiprocessing_context safeguards

src/lerobot/configs/train.py

@@ -14,6 +14,7 @@
 import builtins
 import datetime as dt
 import json
+import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
@@ -102,10 +103,11 @@ class TrainPipelineConfig(HubMixin):
     prefetch_factor: int = 4
     persistent_workers: bool = True
     # DataLoader worker start method. "spawn" is safer than "fork" with
-    # non-fork-safe libs (PyAV / torchcodec / ffmpeg — see #2488), but
+    # non-fork-safe libs, but adds some worker-startup time per run
-    # adds some worker-startup time per run since workers re-import
+    # since workers re-import modules instead of inheriting parent state.
-    # modules instead of inheriting parent state.
+    # Override with `--dataloader_multiprocessing_context=fork` when appropriate,
-    dataloader_multiprocessing_context: str = "spawn"
+    # or set it to `null` to use Python's platform default.
+    dataloader_multiprocessing_context: str | None = "spawn"
     steps: int = 100_000
     # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
     env_eval_freq: int = 20_000
@@ -217,6 +219,17 @@ class TrainPipelineConfig(HubMixin):
             self.reward_model.pretrained_path = str(policy_dir)
 
     def validate(self) -> None:
+        available_contexts = multiprocessing.get_all_start_methods()
+        if (
+            self.dataloader_multiprocessing_context is not None
+            and self.dataloader_multiprocessing_context not in available_contexts
+        ):
+            raise ValueError(
+                "`dataloader_multiprocessing_context` must be None or one of "
+                f"{available_contexts} on this platform, got "
+                f"{self.dataloader_multiprocessing_context!r}."
+            )
+
         self._resolve_pretrained_from_cli()
 
         if self.policy is None and self.reward_model is None:

src/lerobot/scripts/lerobot_train.py

@@ -71,6 +71,16 @@ from lerobot.utils.utils import (
 from .lerobot_eval import eval_policy_all
 
 
+def _dataloader_worker_kwargs(cfg: TrainPipelineConfig) -> dict[str, Any]:
+    """Return worker-only DataLoader options, disabling them for single-process loading."""
+    workers_enabled = cfg.num_workers > 0
+    return {
+        "prefetch_factor": cfg.prefetch_factor if workers_enabled else None,
+        "persistent_workers": cfg.persistent_workers and workers_enabled,
+        "multiprocessing_context": cfg.dataloader_multiprocessing_context if workers_enabled else None,
+    }
+
+
 def update_policy(
     train_metrics: MetricsTracker,
     policy: PreTrainedPolicy,
@@ -473,9 +483,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         pin_memory=device.type == "cuda",
         drop_last=False,
         collate_fn=collate_fn,
-        prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
+        **_dataloader_worker_kwargs(cfg),
-        persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
-        multiprocessing_context=cfg.dataloader_multiprocessing_context if cfg.num_workers > 0 else None,
     )
 
     # Build eval dataloader if a held-out split exists
@@ -501,8 +509,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             pin_memory=device.type == "cuda",
             drop_last=False,
             collate_fn=eval_collate_fn,
-            prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
+            **_dataloader_worker_kwargs(cfg),
-            persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
         )
 
     # Prepare everything with accelerator