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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.
When should I use .eval() ? I understand it is supposed to allow me to "evaluate my model". How do I turn it back off for training? Example training code using .eval() .
I am trying to initialize a tensor on Google Colab with GPU enabled. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') t = torch.tensor([1,2], device=device) But I am getting this strange error. RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktra
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
kaggle
googlecolab
python