CheckpointHooks¶
- 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.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.
- Return type
- 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.
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.
- Return type