|TensorFlow 1 version||View source on GitHub|
Checkpoints input pipeline state every N steps or seconds.
Compat aliases for migration
See Migration guide for more details.
tf.data.experimental.CheckpointInputPipelineHook( estimator, external_state_policy=None )
This hook saves the state of the iterators in the
Graph so that when
training is resumed the input pipeline continues from where it left off.
This could potentially avoid overfitting in certain pipelines where the
number of training steps per eval are small compared to the dataset
size or if the training pipeline is pre-empted.
- Saves only the input pipelines in the "iterators" collection and not the global variables or other saveable objects.
- Does not write the
MetaGraphDefto the summary.
Example of checkpointing the training pipeline:
est = tf.estimator.Estimator(model_fn) while True: est.train( train_input_fn, hooks=[tf.data.experimental.CheckpointInputPipelineHook(est)], steps=train_steps_per_eval) # Note: We do not pass the hook here. metrics = est.evaluate(eval_input_fn) if should_stop_the_training(metrics): break
This hook should be used if the input pipeline state needs to be saved separate from the model checkpoint. Doing so may be useful for a few reasons:
- The input pipeline checkpoint may be large, if there are large shuffle or prefetch buffers for instance, and may bloat the checkpoint size.
- If the input pipeline is shared between training and validation, restoring the checkpoint during validation may override the validation input pipeline.
For saving the input pipeline checkpoint alongside the model weights use
tf.data.experimental.make_saveable_from_iterator directly to create a
SaveableObject and add to the
SAVEABLE_OBJECTS collection. Note, however,
that you will need to be careful not to restore the training iterator during
eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS
collector when building the eval graph.
||A string that identifies how to handle input pipelines that depend on external state. Possible values are 'ignore': The external state is silently ignored. 'warn': The external state is ignored, logging a warning. 'fail': The operation fails upon encountering external state. By default we set it to 'fail'.|
||At most one of saver or scaffold should be set.|
after_create_session( session, coord )
Called when new TensorFlow session is created.
This is called to signal the hooks that a new session has been created. This
has two essential differences with the situation in which
begin is called:
- When this is called, the graph is finalized and ops can no longer be added to the graph.
- This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.
||A TensorFlow Session that has been created.|
||A Coordinator object which keeps track of all threads.|