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Frameworks & web · #pytorch
Decide when to use a plain DataLoader versus pairing it with DistributedSampler for efficient, non‑overlapping data access across multiple GPUs.
Learn how to wrap a PyTorch model with torch.compile to fuse operators and reduce training epoch time, plus the trade‑offs to watch for.
Learn when to use Map-style vs. Iterable datasets in PyTorch to avoid memory OOMs and I/O bottlenecks, including a guide on preventing data duplication in multi-process loading.
Learn how to correctly enable and implement GPU acceleration in Kaggle Notebooks, including PyTorch device mapping and managing VRAM quotas.
Learn how to properly configure, verify, and utilize GPU acceleration in Google Colab to speed up machine learning workloads and avoid common device placement errors.
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
kaggle
googlecolab