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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
)

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. </td> </tr><tr> <td>`scaffold`</td> <td> A`Scaffold`used 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`</td> <td>`String`representation of the TensorFlow master to use. </td> </tr><tr> <td>`config`</td> <td>`ConfigProto`proto used to configure the session. </td> </tr><tr> <td>`checkpoint_dir`</td> <td> A string. Optional path to a directory where to restore variables. </td> </tr><tr> <td>`stop_grace_period_secs`</td> <td> Number of seconds given to threads to stop after`close()`has been called. </td> </tr><tr> <td>`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

`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

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.

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`

View source

__enter__()

`exit`

View source

__exit__(
    exception_type, exception_value, traceback
)

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/train/SingularMonitoredSession