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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.
Learn how PyTorch's define-by-run architecture enables dynamic computational graphs, allowing for flexible model logic and variable-length inputs while managing VRAM efficiently.
The torch.profiler.profile context manager provides detailed operator-level latency and memory consumption data via Kineto integration. While the profiler includes a schedule configuration to mitigate initial warmup overhead, the measurement accuracy for extremely small, high-frequency operators remains a concern. When capturing traces for models with many l
In PyTorch Distributed Data Parallel (DDP), the find_unused_parameters flag is used to handle models where some parameters do not contribute to the loss in every forward pass. When enabled, DDP must traverse the autograd graph to identify these unused parameters and exclude them from the all-reduce synchronization process. While this ensures correctness for
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