tf.compat.v1.train.SingularMonitoredSession

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

tf.compat.v1.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.
• 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.

Attributes:

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

Child Classes

class StepContext

Methods

__enter__

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__enter__()

__exit__

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__exit__(
    exception_type, exception_value, traceback
)

close

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close()

raw_session

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raw_session()

Returns underlying TensorFlow.Session object.

run

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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

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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.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

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should_stop()