# tf.train.CheckpointSaverListener

## Class CheckpointSaverListener

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):

def after_save(self, session, global_step_value):
print('Done writing checkpoint.')

def end(self, session, global_step_value):
print('Done with the session.')

...
listener = ExampleCheckpointSaverListener()
saver_hook = tf.train.CheckpointSaverHook(
checkpoint_dir, listeners=[listener])
with tf.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.

## 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
)