Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add function to remove checkpoint to allow override for extended classes #16067

Merged
merged 2 commits into from
Dec 15, 2022
Merged

Add function to remove checkpoint to allow override for extended classes #16067

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9952cbe
Add function to remove checkpoint to allow override for extended classes
SeanNaren Dec 15, 2022
51fd1f5
Merge branch 'master' into fix/add_remove_fn
SeanNaren Dec 15, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 7 additions & 3 deletions src/pytorch_lightning/callbacks/model_checkpoint.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -649,7 +649,7 @@ def _save_last_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[
previous, self.last_model_path = self.last_model_path, filepath
self._save_checkpoint(trainer, filepath)
if previous and previous != filepath:
trainer.strategy.remove_checkpoint(previous)
self._remove_checkpoint(trainer, previous)

def _save_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
assert self.monitor
Expand All @@ -668,7 +668,7 @@ def _save_none_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidate
previous, self.best_model_path = self.best_model_path, filepath
self._save_checkpoint(trainer, filepath)
if self.save_top_k == 1 and previous and previous != filepath:
trainer.strategy.remove_checkpoint(previous)
self._remove_checkpoint(trainer, previous)

def _update_best_and_save(
self, current: Tensor, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]
Expand Down Expand Up @@ -710,7 +710,7 @@ def _update_best_and_save(
self._save_checkpoint(trainer, filepath)

if del_filepath is not None and filepath != del_filepath:
trainer.strategy.remove_checkpoint(del_filepath)
self._remove_checkpoint(trainer, del_filepath)

def to_yaml(self, filepath: Optional[_PATH] = None) -> None:
"""Saves the `best_k_models` dict containing the checkpoint paths with the corresponding scores to a YAML
Expand All @@ -727,3 +727,7 @@ def file_exists(self, filepath: _PATH, trainer: "pl.Trainer") -> bool:
state to diverge between ranks."""
exists = self._fs.exists(filepath)
return trainer.strategy.broadcast(exists)

def _remove_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
"""Calls the strategy to remove the checkpoint file."""
trainer.strategy.remove_checkpoint(filepath)