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.

Parameters:: checkpoint¶ (dict[str, Any]) – Loaded checkpoint
Return type:: None

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:: None

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.