tf.train.SingularMonitoredSession

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

tf.train.SingularMonitoredSession(
    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():
    sess.run(train_op)

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 session.run() with merged fetches and feed_dict
  • calls hook.after_run()
  • returns result of session.run() 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.
Args
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.
Attributes
graph The graph that was launched in this session.

Child Classes

class StepContext

Methods

close

View source

close()

raw_session

View source

raw_session()

Returns underlying TensorFlow.Session object.

run

View source

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

View source

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: 
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 = 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

View source

should_stop()

__enter__

View source

__enter__()

__exit__

View source

__exit__(
    exception_type, exception_value, traceback
)

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/SingularMonitoredSession