CheckpointHooks — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)
class lightning.pytorch.core.hooks.CheckpointHooks[source]¶
Bases: object
Hooks to be used with Checkpointing.
on_load_checkpoint(checkpoint)[source]¶
Called by Lightning to restore your model. If you saved something with on_save_checkpoint() this is your chance to restore this.
Parameters:
checkpoint¶ (dict[str, Any]) – Loaded checkpoint
Return type:
Example:
def on_load_checkpoint(self, checkpoint): # 99% of the time you don't need to implement this method self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
Note
Lightning auto-restores global step, epoch, and train state including amp scaling. There is no need for you to restore anything regarding training.
on_save_checkpoint(checkpoint)[source]¶
Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.
Parameters:
checkpoint¶ (dict[str, Any]) – The full checkpoint dictionary before it gets dumped to a file. Implementations of this hook can insert additional data into this dictionary.
Return type:
Example:
def on_save_checkpoint(self, checkpoint): # 99% of use cases you don't need to implement this method checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
Note
Lightning saves all aspects of training (epoch, global step, etc…) including amp scaling. There is no need for you to store anything about training.