tf.train.SingularMonitoredSession
tf.train.SingularMonitoredSession
class tf.train.SingularMonitoredSession
Defined in tensorflow/python/training/monitored_session.py
.
See the guide: Training > Distributed execution
Session-like object that handles initialization, restoring, and hooks.
Please note that this utility is not recommended for distributed settings. For distributed settings, please use tf.train.MonitoredSession
. The differences between MonitoredSession
and SingularMonitoredSession
are:
-
MonitoredSession
handlesAbortedError
andUnavailableError
for distributed settings, butSingularMonitoredSession
does not. -
MonitoredSession
can be created inchief
orworker
modes.SingularMonitoredSession
is always created aschief
. - You can access the raw
tf.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.
- To
- 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 theSingularMonitoredSession
is used as a context.
Properties
graph
The graph that was launched in this session.
Methods
__init__
__init__( hooks=None, scaffold=None, master='', config=None, checkpoint_dir=None, stop_grace_period_secs=120 )
Creates a SingularMonitoredSession.
Args:
-
hooks
: An iterable of `SessionRunHook' objects. -
scaffold
: AScaffold
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 afterclose()
has been called.
__enter__
__enter__()
__exit__
__exit__( exception_type, exception_value, traceback )
close
close()
raw_session
raw_session()
Returns underlying TensorFlow.Session
object.
run
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 astf.Session.run()
. -
feed_dict
: Same astf.Session.run()
. -
options
: Same astf.Session.run()
. -
run_metadata
: Same astf.Session.run()
.
Returns:
Same as tf.Session.run()
.
should_stop
should_stop()
© 2017 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/api_docs/python/tf/train/SingularMonitoredSession