fix: use configured multiprocessing context for eval

Prevent evaluation workers from forking memory-heavy distributed training ranks and exhausting host RAM.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/lerobot/scripts/lerobot_train.py

@@ -538,6 +538,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             collate_fn=eval_collate_fn,
             prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
             persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+            multiprocessing_context=mp_context if cfg.num_workers > 0 else None,
         )
 
     # Prepare everything with accelerator