tf.train.MonitoredSession

tf.train.MonitoredSession

class tf.train.MonitoredSession

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

See the guide: Training > Distributed execution

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

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 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__

__init__(
    session_creator=None,
    hooks=None,
    stop_grace_period_secs=120
)

__enter__

__enter__()

__exit__

__exit__(
    exception_type,
    exception_value,
    traceback
)

close

close()

run

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

should_stop()