Type SingularMonitoredSession
Namespace tensorflow.train
Parent _MonitoredSession
Interfaces ISingularMonitoredSession
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.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:
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.
Show Example
saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with SingularMonitoredSession(hooks=[saver_hook, summary_hook]) as sess:
while not sess.should_stop():
sess.run(train_op)
Methods
Properties
Public instance methods
object raw_session()
Returns underlying `TensorFlow.Session` object.
object raw_session_dyn()
Returns underlying `TensorFlow.Session` object.
Public static methods
SingularMonitoredSession NewDyn(object hooks, object scaffold, ImplicitContainer<T> master, object config, object checkpoint_dir, ImplicitContainer<T> stop_grace_period_secs, object checkpoint_filename_with_path)
Creates a SingularMonitoredSession.
Parameters
-
objecthooks - An iterable of `SessionRunHook' objects.
-
objectscaffold - A `Scaffold` used for gathering or building supportive ops. If not specified a default one is created. It's used to finalize the graph.
-
ImplicitContainer<T>master - `String` representation of the TensorFlow master to use.
-
objectconfig - `ConfigProto` proto used to configure the session.
-
objectcheckpoint_dir - A string. Optional path to a directory where to restore variables.
-
ImplicitContainer<T>stop_grace_period_secs - Number of seconds given to threads to stop after `close()` has been called.
-
objectcheckpoint_filename_with_path - A string. Optional path to a checkpoint file from which to restore variables.