tf.compat.v1.train.SingularMonitoredSession

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
)

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

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.

hooks An iterable of SessionRunHook' objects. </td> </tr><tr> <td>scaffold<a id="scaffold"></a> </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<a id="master"></a> </td> <td>Stringrepresentation of the TensorFlow master to use. </td> </tr><tr> <td>config<a id="config"></a> </td> <td>ConfigProtoproto used to configure the session. </td> </tr><tr> <td>checkpoint_dir<a id="checkpoint_dir"></a> </td> <td> A string. Optional path to a directory where to restore variables. </td> </tr><tr> <td>stop_grace_period_secs<a id="stop_grace_period_secs"></a> </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.

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:

```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
)