tf.train.MonitoredSession

Class MonitoredSession

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

See the guides: Threading and Queues > Queue usage overview, 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
• 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 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.

Note: This is not a tf.Session. For example, it cannot do following:

• it cannot be set as default session.
• it cannot be sent to saver.save.
• it cannot be sent to tf.train.start_queue_runners.

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.

Child Classes

class StepContext

Properties

graph

The graph that was launched in this session.

Methods

__init__

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

Sets up a Monitored or Hooked Session.

Args:

• session_creator: A factory object to create session. Typically a ChiefSessionCreator or a WorkerSessionCreator.
• hooks: An iterable of `SessionRunHook' objects.
• should_recover: A bool. Indicates whether to recover from AbortedError and UnavailableError or not.
• stop_grace_period_secs: Number of seconds given to threads to stop after close() has been called.

__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().

run_step_fn

run_step_fn(step_fn)

Run ops using a step function.

Args:

• step_fn: A function or a method with a single argument of type StepContext. The function may use methods of the argument to perform computations with access to a raw session.

  The returned value of the step_fn will be returned from run_step_fn, unless a stop is requested. In that case, the next should_stop call will return True.

  Example usage:

  ```python with tf.Graph().as_default(): c = tf.placeholder(dtypes.float32) v = tf.add(c, 4.0) w = tf.add(c, 0.5)

  def step_fn(step_context):
    a = step_context.session.run(fetches=v, feed_dict={c: 0.5})
    if a <= 4.5:
      step_context.request_stop()
    return step_context.run_with_hooks(fetches=w, feed_dict={c: 0.1})
  
  with tf.MonitoredSession() as session:
    while not session.should_stop():
      a = session.run_step_fn(step_fn)
  

  ```

  Hooks interact with the run_with_hooks() call inside the step_fn as they do with a MonitoredSession.run call.

Returns:

Returns the returned value of step_fn.

Raises:

• StopIteration: if step_fn has called request_stop(). It may be caught by with tf.MonitoredSession() to close the session.
• ValueError: if step_fn doesn't have a single argument called step_context. It may also optionally have self for cases when it belongs to an object.

should_stop

should_stop()