tf.train.MonitoredSession

class tf.train.MonitoredSession

See the guide: Training > Distributed execution

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

Example usage:

saver_hook = CheckpointSaverHook(...)
summary_hook = SummaryHook(...)
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

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 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 tf.Session arguments:

  • In most cases you can set session arguments as follows:
MonitoredSession(
  session_creator=ChiefSessionCreator(master=..., config=...))
  • In distributed setting for a non-chief worker, you can use following:
MonitoredSession(
  session_creator=WorkerSessionCreator(master=..., config=...))

See MonitoredTrainingSession for an example usage based on chief or worker.

Args:

  • session_creator: A factory object to create session. Typically a ChiefSessionCreator which is the default one.
  • hooks: An iterable of `SessionRunHook' objects.

Returns:

A MonitoredSession object.

Properties

graph

The graph that was launched in this session.

Methods

__init__(session_creator=None, hooks=None)

close()

run(fetches, feed_dict=None, options=None, run_metadata=None)

Run ops in the monitored session.

This method is completely compatible with the tf.Session.run() method.

Args:

  • fetches: Same as tf.Session.run().
  • feed_dict: Same as tf.Session.run().
  • options: Same as tf.Session.run().
  • run_metadata: Same as tf.Session.run().

Returns:

Same as tf.Session.run().

should_stop()

Defined in tensorflow/python/training/monitored_session.py.