Revamped tuning #130
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Revamped tuning #130
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| def grid_search( | ||
| method: str, | ||
| charges: torch.Tensor, |
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I would turn the logic around and keep the tune_XXX method. Also, grid_search is a very common name. It is not really clear from this that this will find the optimal parameters for the methods.
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| @@ -515,3 +518,82 @@ def forward(self, positions, cell, charges): | |||
| print(f"Evaluation time:\nPytorch: {time_python}ms\nJitted: {time_jit}ms") | |||
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| # %% | |||
| # Other auto-differentiation ideas | |||
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IMHO opinion I wouldn't put this example here - even though I think it is good to have it. The tutorial is already 500 lines and with this super long. I rather vote for smaller examples tackling one specific tasks. Finding solutions is much easier if they are shorter. See also the beloved matplotlib examples.
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This PR introduces two things:
Still more works need to be done, like writing documentations, fixing the pytests and the example, before this PR is ready.
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📚 Documentation preview 📚: https://torch-pme--130.org.readthedocs.build/en/130/