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

    hooks=None, scaffold=None, master='', config=None, checkpoint_dir=None,
    stop_grace_period_secs=120, checkpoint_filename_with_path=None

Please note that this utility is not recommended for distributed settings. For distributed settings, please use tf.compat.v1.train.MonitoredSession. The differences between MonitoredSession and SingularMonitoredSession are:

  • MonitoredSession handles AbortedError and UnavailableError for distributed settings, but SingularMonitoredSession does not.
  • MonitoredSession can be created in chief or worker modes. SingularMonitoredSession is always created as chief.
  • You can access the raw tf.compat.v1.Session object used by SingularMonitoredSession, whereas in MonitoredSession the raw session is private. This can be used:
    • To run without hooks.
    • To save and restore.
  • All other functionality is identical.

Example usage:

saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with SingularMonitoredSession(hooks=[saver_hook, summary_hook]) as sess:
  while not sess.should_stop():

Initialization: At creation time the hooked 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 hooked session does following things:

  • calls hook.before_run()
  • calls TensorFlow with merged fetches and feed_dict
  • calls hook.after_run()
  • returns result of asked by user

Exit: At the close(), the hooked 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 SingularMonitoredSession is used as a context.


  • hooks: An iterable of `SessionRunHook' objects.
  • scaffold: A Scaffold used for gathering or building supportive ops. If not specified a default one is created. It's used to finalize the graph.
  • master: String representation of the TensorFlow master to use.
  • config: ConfigProto proto used to configure the session.
  • checkpoint_dir: A string. Optional path to a directory where to restore variables.
  • stop_grace_period_secs: Number of seconds given to threads to stop after close() has been called.
  • checkpoint_filename_with_path: A string. Optional path to a checkpoint file from which to restore variables.


  • graph: The graph that was launched in this session.

Child Classes

class StepContext



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    exception_type, exception_value, traceback


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Returns underlying TensorFlow.Session object.


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    fetches, feed_dict=None, options=None, run_metadata=None

Run ops in the monitored session.

This method is completely compatible with the method.


  • fetches: Same as
  • feed_dict: Same as
  • options: Same as
  • run_metadata: Same as


Same as


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Run ops using a step function.


  • 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:
with tf.Graph().as_default():
  c = tf.compat.v1.placeholder(dtypes.float32)
  v = tf.add(c, 4.0)
  w = tf.add(c, 0.5)
  def step_fn(step_context):
    a =, feed_dict={c: 0.5})
    if a <= 4.5:
      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 `` call.


Returns the returned value of step_fn.


  • 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.


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