tf.compat.v1.train.MonitoredSession

Session-like object that handles initialization, recovery and hooks.

Example usage:

saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with MonitoredSession(session_creator=ChiefSessionCreator(...),
                      hooks=[saver_hook, summary_hook]) as sess:
  while not sess.should_stop():
    sess.run(train_op)

Initialization: At creation time the monitored session does following things in given order:

  • calls hook.begin() for each given hook
  • finalizes the graph via scaffold.finalize()
  • create session
  • initializes the model via initialization ops provided by Scaffold
  • restores variables if a checkpoint exists
  • launches queue runners
  • calls hook.after_create_session()

Run: When run() is called, the monitored session does following things:

  • calls hook.before_run()
  • calls TensorFlow session.run() with merged fetches and feed_dict
  • calls hook.after_run()
  • returns result of session.run() asked by user
  • if AbortedError or UnavailableError occurs, it recovers or reinitializes the session before executing the run() call again

Exit: At the close(), the monitored session does following things in order:

  • calls hook.end()
  • closes the queue runners and the session
  • suppresses OutOfRange error which indicates that all inputs have been processed if the monitored_session is used as a context

How to set