SIGTERM Handling #21
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SIGTERM Handling #21
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Test Results 6 files 6 suites 1h 3m 56s ⏱️ Results for commit 1659d57. |
conorsim
approved these changes
Apr 14, 2024
| @@ -56,7 +90,7 @@ def _upload_logs(self) -> None: | |||
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| def _trainer_fit(self, *args, **kwargs): | |||
| try: | |||
| self.pl_trainer.fit(*args, **kwargs) | |||
| self.pl_trainer.fit(*args, ckpt_path=self.resume, **kwargs) | |||
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Unimportant for this PR, but when we resume from checkpoint, should we consider also resuming the state of some hyperparameters? I'm not sure if that is already abstracted and/or a feature of pytorch lightning.
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Yes, we should probably store all states (optimizer, scheduler, ...) so we can truly continue with the training.
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Yeah, pytorch lightning does this automatically.
kozlov721
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Oct 9, 2024
* handling SIGTERM signal * resume argument takes path
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Added handling of the SIGTERM signal. The current state of the training is saved and can be later resumed using the
--resumeflag.