tf.train.SingularMonitoredSession

class tf.train.SingularMonitoredSession

See the guide: Training > Distributed execution

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.train.MonitoredSession. The differences between MonitoredSession and SingularMonitoredSession are: * MonitoredSession handles AbortedError 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.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: python saver_hook = CheckpointSaverHook(...) summary_hook = SummaryHook(...) 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
  • surpresses OutOfRange error which indicates that all inputs have been processed if the SingularMonitoredSession is used as a context.

Properties

graph

The graph that was launched in this session.

Methods

__init__(hooks=None, scaffold=None, master='', config=None, checkpoint_dir=None)

Creates a SingularMonitoredSession.

Args:

  • 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.

close()

raw_session()

Returns underlying TensorFlow.Session object.

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()

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