google-deepmind / google-deepmind/fusion_surrogates
Best practices for distributing NN model weights
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- Python
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Description
If I understand correctly, the model weights for the new QLKNN are in one of these files ?
https://github.com/google-deepmind/fusion_surrogates/tree/main/fusion_surrogates/qlknn/models
As a community, we have growing collection of surrogate models and the distribution and packaging of their weights could do with some standardisation / best practices. Maybe you have thought about this more than the wider fusion community...
Some questions
* Are model weights best distributed via `git`, `git lfs`, or something else ?
* I don't want to be continually downloading unrelated git histories and working trees that I don't really need
* Is it good to use git submodules to pull these weights into my code that uses them? Upside is good provenance tracking, downside is continually cloning other stuff we don't need, and submodules are not well suited to managing optional dependencies
* Do we all agree on ONNX format as best for moving between frameworks?
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