Katherine Wu a3e64f721c Add SaveableObjects to SavedModel. ... When objects are loaded from the SavedModel, they don't retain their `_gather_saveables_for_checkpoint` functions, which can result in values not being loaded from the checkpoint. This CL adds a field in the SavedModel proto that stores a save and restore function for each SaveableObject in each node. When loading into Python, the SaveableObjects are restored using the functions. PiperOrigin-RevId: 318549786 Change-Id: I688c72d7658e1bca98abf373a13a0e15a7fb83e2		2020-07-01 14:28:41 -07:00
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BUILD	Wrap save/restore logic in tf.function when in eager mode. This allows parallel saving and restoring when using multiple devices.	2020-06-22 13:23:14 -07:00
checkpoint_options.py	Add an option to choose the I/O Device for saving and loading models.	2020-04-22 11:27:15 -07:00
functional_saver_test.py	Wrap save/restore logic in tf.function when in eager mode. This allows parallel saving and restoring when using multiple devices.	2020-06-22 13:23:14 -07:00
functional_saver.py	Wrap save/restore logic in tf.function when in eager mode. This allows parallel saving and restoring when using multiple devices.	2020-06-22 13:23:14 -07:00
saveable_hook.py	Add SaveableHook, a special SaveableObject which registers callbacks.	2019-11-15 12:18:18 -08:00
saveable_object_util.py	Add SaveableObjects to SavedModel.	2020-07-01 14:28:41 -07:00
saveable_object.py	In all cases, we can't rely on the tensor.device attribute being set. So its better to get the device for a SaveSpec from the device passed in rather. This was an issue with saving iterators because for iterators the resource usually has a device specification but the serialized tensor derived from it might not have it set. As a result, when saving iterators in a sharded fashion all iterators end up on '' device instead which is not what is intended.	2019-12-18 19:11:04 -08:00