Add save_on_exception option to ModelCheckpoint #20916
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…ining part of callbacks in individal test for better overview
…lidation callback
for more information, see https://pre-commit.ci
…sly defined epoch lenght
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…ntefere with current checkpoint behavoir
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Borda
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Borda
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| def on_fit_end(self): | ||
| raise RuntimeError("Trouble!") | ||
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| models = [ |
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lets have this as test parametrisation
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Should I move the models and callback for testing in a separate file? Because in my opinion they clutter the test file a bit too much
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…onvention Co-authored-by: Jirka Borovec <[email protected]>
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…eights_only to False when loading checkpoint to because default will change in future
…according to documentation
…in ModelCheckpoint
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What does this PR do?
This PR adds a
save_on_exceptionoption to the ModelCheckpoint callback. This some of this functionality is already implemented in theOnExceptionCheckpointcheckpoint, but I believe that bundling all checkpoint options in the ModelCheckpoint is more intuitive. Additionally, this leads to the same naming conventions and directory paths used for the exception checkpoint as for all the others.When enabled, this option serves as a contingency in case of any disruption during training, allowing one to continue from the last step before the exception occurs without losing too much progress. By printing the exception type and message, this also alleviates issue #20187.
Fixes #19686
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📚 Documentation preview 📚: https://pytorch-lightning--20916.org.readthedocs.build/en/20916/