Limits of PyTorch DataLoader multiprocessing start‑method for CUDA workloads
Goal: assess whether the forkserver start method can be employed with PyTorch DataLoader workers to avoid per‑worker CUDA context re‑initialization while maintaining safe GPU usage in distributed training. Constraints: the official documentation warns that fork‑ing a process holding CUDA resources is unsafe, and there is no stable API for forkserver in PyTor
pytorch