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A CPython 3.x service uses the threading module to execute independent numerical kernels concurrently on a multi-core host, yet wall-clock time remains identical to a single-threaded baseline. The GIL serializes execution of Python bytecodes, so CPU-bound work does not achieve true parallelism with threads, while I/O-bound tasks benefit because the lock is r
Goal Identify a start method that allows a library using multiprocessing to run reliably on Linux (default fork ), macOS (default spawn since 3.8), and Windows (only spawn ) without requiring application‑level changes. Constraints and uncertainty Changing the start method after any process creation raises RuntimeError . Third‑party code may implicitly depend
Goal: Achieve deterministic batch ordering when using torch.utils.data.DataLoader with num_workers>0 while preserving the performance benefits of multiprocess data loading. Constraint: Setting a global seed via torch.manual_seed does not propagate uniquely to each worker, so workers may generate identical augmentation sequences unless a worker_init_fn is
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
python
pytorch