tf.compat.v1.train.MonitoredSession

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

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

Migrate to TF2

This API is not compatible with eager execution and tf.function. To migrate to TF2, rewrite the code to be compatible with eager execution. Check the migration guide on replacing Session.run calls. In Keras, session hooks can be replaced by Callbacks e.g. logging hook notebook For more details please read Better performance with tf.function.

Description

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.

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:

```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

View source

should_stop()

__enter__

View source

__enter__()

__exit__

View source

__exit__(
    exception_type, exception_value, traceback
)