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Interface for listeners that take action before or after checkpoint save.
CheckpointSaverListener triggers only in steps when
triggered, and provides callbacks at the following points:
- before using the session
- before each call to
- after each call to
- at the end of session
To use a listener, implement a class and pass the listener to a
CheckpointSaverHook, as in this example:
class ExampleCheckpointSaverListener(CheckpointSaverListener): def begin(self): # You can add ops to the graph here. print('Starting the session.') self.your_tensor = ... def before_save(self, session, global_step_value): print('About to write a checkpoint') def after_save(self, session, global_step_value): print('Done writing checkpoint.') if decided_to_stop_training(): return True def end(self, session, global_step_value): print('Done with the session.') ... listener = ExampleCheckpointSaverListener() saver_hook = tf.estimator.CheckpointSaverHook( checkpoint_dir, listeners=[listener]) with tf.compat.v1.train.MonitoredTrainingSession(chief_only_hooks=[saver_hook]): ...
CheckpointSaverListener may simply take some action after every
checkpoint save. It is also possible for the listener to use its own schedule
to act less frequently, e.g. based on global_step_value. In this case,
implementors should implement the
end() method to handle actions related to
the last checkpoint save. But the listener should not act twice if
after_save() already handled this last checkpoint save.
CheckpointSaverListener can request training to be stopped, by returning
after_save. Please note that, in replicated distributed training
chief should use this behavior. Otherwise each worker will do
their own evaluation, which may be wasteful of resources.
after_save( session, global_step_value )
before_save( session, global_step_value )
end( session, global_step_value )