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Interface for listeners that take action before or after checkpoint save.
CheckpointSaverListener triggers only in steps when CheckpointSaverHook is
triggered, and provides callbacks at the following points:
- before using the session
- before each call to
Saver.save() - after each call to
Saver.save() - 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]):
...
A 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.
A CheckpointSaverListener can request training to be stopped, by returning
True in after_save. Please note that, in replicated distributed training
setting, only chief should use this behavior. Otherwise each worker will do
their own evaluation, which may be wasteful of resources.
Methods
after_save
after_save(
session, global_step_value
)
before_save
before_save(
session, global_step_value
)
begin
begin()
end
end(
session, global_step_value
)