ray.tune.Trainable.save_checkpoint
ray.tune.Trainable.save_checkpoint#
- Trainable.save_checkpoint(checkpoint_dir: str) Optional[Union[str, Dict]] [source]#
Subclasses should override this to implement
save()
.Warning
Do not rely on absolute paths in the implementation of
Trainable.save_checkpoint
andTrainable.load_checkpoint
.Use
validate_save_restore
to catchTrainable.save_checkpoint
/Trainable.load_checkpoint
errors before execution.>>> from ray.tune.utils import validate_save_restore >>> MyTrainableClass = ... >>> validate_save_restore(MyTrainableClass) >>> validate_save_restore( ... MyTrainableClass, use_object_store=True)
New in version 0.8.7.
- Parameters
checkpoint_dir – The directory where the checkpoint file must be stored. In a Tune run, if the trial is paused, the provided path may be temporary and moved.
- Returns
A dict or string. If string, the return value is expected to be prefixed by
checkpoint_dir
. If dict, the return value will be automatically serialized by Tune. In both cases, the return value is exactly what will be passed toTrainable.load_checkpoint()
upon restore.
Example
>>> trainable, trainable1, trainable2 = ... >>> print(trainable1.save_checkpoint("/tmp/checkpoint_1")) "/tmp/checkpoint_1" >>> print(trainable2.save_checkpoint("/tmp/checkpoint_2")) {"some": "data"} >>> trainable.save_checkpoint("/tmp/bad_example") "/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.