tf.compat.v1.train.MonitoredSession

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

tf.compat.v1.train.MonitoredSession(
    session_creator=None, hooks=None, stop_grace_period_secs=120
)

Example usage:

saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with MonitoredSession(session_creator=ChiefSessionCreator(...),
                      hooks=[saver_hook, summary_hook]) as sess:
  while not sess.should_stop():
    sess.run(train_op)

Initialization: At creation time the monitored 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
  • calls hook.after_create_session()

Run: When run() is called, the monitored 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
  • if AbortedError or UnavailableError occurs, it recovers or reinitializes the session before executing the run() call again

Exit: At the close(), the monitored 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 monitored_session is used as a context

How to set tf.compat.v1.Session arguments:

  • In most cases you can set session arguments as follows:
MonitoredSession(
  session_creator=ChiefSessionCreator(master=..., config=...))
  • In distributed setting for a non-chief worker, you can use following:
MonitoredSession(
  session_creator=WorkerSessionCreator(master=..., config=...))

See MonitoredTrainingSession for an example usage based on chief or worker.

  • it cannot be set as default session.
  • it cannot be sent to saver.save.
  • it cannot be sent to tf.train.start_queue_runners.

session_creator A factory object to create session. Typically a ChiefSessionCreator which is the default one.
hooks An iterable of `SessionRunHook' objects.

A MonitoredSession object.

session_creator A factory object to create session. Typically a ChiefSessionCreator or a WorkerSessionCreator.
hooks An iterable of SessionRunHook' objects. </td> </tr><tr> <td>should_recover</td> <td> A bool. Indicates whether to recover fromAbortedErrorandUnavailableErroror not. </td> </tr><tr> <td>stop_grace_period_secs</td> <td> Number of seconds given to threads to stop afterclose()` has been called.

graph The graph that was launched in this session.

Child Classes

class StepContext

Methods

close

View source

close()

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__

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__enter__()

__exit__

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__exit__(
    exception_type, exception_value, traceback
)