27 lines
1.2 KiB
Markdown
27 lines
1.2 KiB
Markdown
# PyTorch — reference material
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*Placeholder.* This folder will cover PyTorch: the framework most modern AI models are written in. You don't need to know PyTorch to *use* a model, but you'll see it everywhere once you start poking around.
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Planned topics:
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- What a tensor is, and why it's basically a multi-dimensional array
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- Moving tensors between CPU and GPU (and what to do if you don't have a GPU)
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- Loading a pretrained model and running inference
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- The shape of a forward pass, conceptually
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- `torch.no_grad()` — and when it matters
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- Reading a model architecture without panicking
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- A very gentle intro to training and fine-tuning (separate file, optional)
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- CPU-only PyTorch installs vs. CUDA installs — which one you actually want
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## When to dip in
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When you find yourself reading model code and the lines starting with `torch.` are blocking you, or when you want to fine-tune something on your own data.
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## When *not* to dip in
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If you only ever call models through a high-level API (Hugging Face `pipeline()`, an OpenAI-compatible client, etc.), you may never need this.
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## Prerequisites
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Comfort with [`../python/`](../python/), especially classes and basic data structures.
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