class tf.train.CheckpointSaverHook

See the guide: Training > Training Hooks

Saves checkpoints every N steps or seconds.


__init__(checkpoint_dir, save_secs=None, save_steps=None, saver=None, checkpoint_basename='model.ckpt', scaffold=None, listeners=None)

Initialize CheckpointSaverHook monitor.


  • checkpoint_dir: str, base directory for the checkpoint files.
  • save_secs: int, save every N secs.
  • save_steps: int, save every N steps.
  • saver: Saver object, used for saving.
  • checkpoint_basename: str, base name for the checkpoint files.
  • scaffold: Scaffold, use to get saver object.
  • listeners: List of CheckpointSaverListener subclass instances. Used for callbacks that run immediately after the corresponding CheckpointSaverHook callbacks, only in steps where the CheckpointSaverHook was triggered.


  • ValueError: One of save_steps or save_secs should be set.
  • ValueError: Exactly 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.


  • session: A TensorFlow Session that has been created.
  • coord: A Coordinator object which keeps track of all threads.

after_run(run_context, run_values)




Defined in tensorflow/python/training/