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

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. </td> </tr><tr> <td>scaffold</td> <td> AScaffoldused for gathering or building supportive ops. If not specified a default one is created. It's used to finalize the graph. </td> </tr><tr> <td>master</td> <td>Stringrepresentation of the TensorFlow master to use. </td> </tr><tr> <td>config</td> <td>ConfigProtoproto used to configure the session. </td> </tr><tr> <td>checkpoint_dir</td> <td> A string. Optional path to a directory where to restore variables. </td> </tr><tr> <td>stop_grace_period_secs</td> <td> Number of seconds given to threads to stop afterclose()has been called. </td> </tr><tr> <td>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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Returns underlying TensorFlow.Session object.


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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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