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This PR documents the fact that PyTorch does not have visibility into how every CUDA memory allocation happend - it only knows about allocations that went through the pytorch CUDA allocator. It also adds a code snippet showing how to use pynvml to query current GPU memory usage. ## Preview Added a note at the top of "Understanding CUDA Memory Usage" doc: <img width="732" alt="image" src="https://github.com/user-attachments/assets/69e28d2a-841a-4b1b-b886-e96fb5d76582" /> which links to a section below: <img width="733" alt="image" src="https://github.com/user-attachments/assets/cab4f252-9ac2-4fc6-a45d-fdb958fc7dbc" /> Pull Request resolved: https://github.com/pytorch/pytorch/pull/150880 Approved by: https://github.com/kwen2501, https://github.com/ngimel |
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| libtorch.rst | ||
| make.bat | ||
| Makefile | ||
| README.md | ||
| requirements.txt | ||
Please see the Writing documentation section of CONTRIBUTING.md for details on both writing and building the docs.