pytorch-core
Overview
Core PyTorch provides the fundamental building blocks for deep learning, focusing on tensor computation with strong GPU acceleration and a deep-learning-oriented autograd system. It emphasizes a "define-by-run" approach where models are standard Python objects.
When to Use
Use PyTorch Core when you need granular control over model architecture, custom training loops, or specific hardware optimizations like pinned memory for data transfers.
Decision Tree
- Do you know the input dimensions of your data?
- YES: Use standard layers (e.g.,
nn.Linear). - NO: Use Lazy modules (e.g.,
nn.LazyLinear) to defer initialization.
- YES: Use standard layers (e.g.,
- Is your bottleneck data transfer to the GPU?
- YES: Enable
pin_memory=Truein yourDataLoader. - NO: Standard data loading suffices.
- YES: Enable
- Are you fine-tuning a model?
- YES: Set
requires_grad=Falsefor frozen parameters. - NO: Keep
requires_grad=Truefor full training.
- YES: Set
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