LostTech.TensorFlow : API Documentation

Type SessionManager

Namespace tensorflow.train

Parent PythonObjectContainer

Interfaces ISessionManager

Training helper that restores from checkpoint and creates session.

This class is a small wrapper that takes care of session creation and checkpoint recovery. It also provides functions that to facilitate coordination among multiple training threads or processes.

* Checkpointing trained variables as the training progresses. * Initializing variables on startup, restoring them from the most recent checkpoint after a crash, or wait for checkpoints to become available.

### Usage: `prepare_session()` initializes or restores a model. It requires `init_op` and `saver` as an argument.

A second process could wait for the model to be ready by doing the following: `wait_for_session()` waits for a model to be initialized by other processes.
Show Example 
with tf.Graph().as_default():
              ...add operations to the graph...
              # Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
              sm = SessionManager()
              sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
              # Use the session to train the graph.
              while True:
                sess.run()

Methods

Properties

Public instance methods

Session prepare_session(string master, IEnumerable<object> init_op, IEnumerable<object> saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IEnumerable<object> init_op
Optional `Operation` used to initialize the model.
IEnumerable<object> saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, object init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
object init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, object init_op, int saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
object init_op
Optional `Operation` used to initialize the model.
int saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, Operation init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
Operation init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, object init_op, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
object init_op
Optional `Operation` used to initialize the model.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, Operation init_op, int saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
Operation init_op
Optional `Operation` used to initialize the model.
int saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IGraphNodeBase init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IGraphNodeBase init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, Operation init_op, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
Operation init_op
Optional `Operation` used to initialize the model.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IGraphNodeBase init_op, int saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IGraphNodeBase init_op
Optional `Operation` used to initialize the model.
int saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, object init_op, IEnumerable<object> saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
object init_op
Optional `Operation` used to initialize the model.
IEnumerable<object> saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IGraphNodeBase init_op, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IGraphNodeBase init_op
Optional `Operation` used to initialize the model.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IEnumerable<object> init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IEnumerable<object> init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IGraphNodeBase init_op, IEnumerable<object> saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IGraphNodeBase init_op
Optional `Operation` used to initialize the model.
IEnumerable<object> saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, int init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
int init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IEnumerable<object> init_op, int saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IEnumerable<object> init_op
Optional `Operation` used to initialize the model.
int saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, int init_op, int saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
int init_op
Optional `Operation` used to initialize the model.
int saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, IEnumerable<object> init_op, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
IEnumerable<object> init_op
Optional `Operation` used to initialize the model.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, int init_op, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
int init_op
Optional `Operation` used to initialize the model.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, Operation init_op, IEnumerable<object> saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
Operation init_op
Optional `Operation` used to initialize the model.
IEnumerable<object> saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

Session prepare_session(string master, int init_op, IEnumerable<object> saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config, IDictionary<object, object> init_feed_dict, PythonFunctionContainer init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
string master
`String` representation of the TensorFlow master to use.
int init_op
Optional `Operation` used to initialize the model.
IEnumerable<object> saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
IDictionary<object, object> init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
PythonFunctionContainer init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
Session
A `Session` object that can be used to drive the model.

object prepare_session_dyn(object master, object init_op, object saver, object checkpoint_dir, object checkpoint_filename_with_path, ImplicitContainer<T> wait_for_checkpoint, ImplicitContainer<T> max_wait_secs, object config, object init_feed_dict, object init_fn)

Creates a `Session`. Makes sure the model is ready to be used.

Creates a `Session` on 'master'. If a `saver` object is passed in, and `checkpoint_dir` points to a directory containing valid checkpoint files, then it will try to recover the model from checkpoint. If no checkpoint files are available, and `wait_for_checkpoint` is `True`, then the process would check every `recovery_wait_secs`, up to `max_wait_secs`, for recovery to succeed.

If the model cannot be recovered successfully then it is initialized by running the `init_op` and calling `init_fn` if they are provided. The `local_init_op` is also run after init_op and init_fn, regardless of whether the model was recovered successfully, but only if `ready_for_local_init_op` passes.

If the model is recovered from a checkpoint it is assumed that all global variables have been initialized, in particular neither `init_op` nor `init_fn` will be executed.

It is an error if the model cannot be recovered and no `init_op` or `init_fn` or `local_init_op` are passed.
Parameters
object master
`String` representation of the TensorFlow master to use.
object init_op
Optional `Operation` used to initialize the model.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
ImplicitContainer<T> wait_for_checkpoint
Whether to wait for checkpoint to become available.
ImplicitContainer<T> max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
object init_feed_dict
Optional dictionary that maps `Tensor` objects to feed values. This feed dictionary is passed to the session `run()` call when running the init op.
object init_fn
Optional callable used to initialize the model. Called after the optional `init_op` is called. The callable must accept one argument, the session being initialized.
Returns
object
A `Session` object that can be used to drive the model.

ValueTuple<Session, bool> recover_session(string master, Saver saver, object checkpoint_dir, object checkpoint_filename_with_path, bool wait_for_checkpoint, int max_wait_secs, object config)

Creates a `Session`, recovering if possible.

Creates a new session on 'master'. If the session is not initialized and can be recovered from a checkpoint, recover it.
Parameters
string master
`String` representation of the TensorFlow master to use.
Saver saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
bool wait_for_checkpoint
Whether to wait for checkpoint to become available.
int max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
Returns
ValueTuple<Session, bool>
A pair (sess, initialized) where 'initialized' is `True` if the session could be recovered and initialized, `False` otherwise.

object recover_session_dyn(object master, object saver, object checkpoint_dir, object checkpoint_filename_with_path, ImplicitContainer<T> wait_for_checkpoint, ImplicitContainer<T> max_wait_secs, object config)

Creates a `Session`, recovering if possible.

Creates a new session on 'master'. If the session is not initialized and can be recovered from a checkpoint, recover it.
Parameters
object master
`String` representation of the TensorFlow master to use.
object saver
A `Saver` object used to restore a model.
object checkpoint_dir
Path to the checkpoint files. The latest checkpoint in the dir will be used to restore.
object checkpoint_filename_with_path
Full file name path to the checkpoint file.
ImplicitContainer<T> wait_for_checkpoint
Whether to wait for checkpoint to become available.
ImplicitContainer<T> max_wait_secs
Maximum time to wait for checkpoints to become available.
object config
Optional `ConfigProto` proto used to configure the session.
Returns
object
A pair (sess, initialized) where 'initialized' is `True` if the session could be recovered and initialized, `False` otherwise.

Session wait_for_session(string master, object config, ImplicitContainer<T> max_wait_secs)

Creates a new `Session` and waits for model to be ready.

Creates a new `Session` on 'master'. Waits for the model to be initialized or recovered from a checkpoint. It's expected that another thread or process will make the model ready, and that this is intended to be used by threads/processes that participate in a distributed training configuration where a different thread/process is responsible for initializing or recovering the model being trained.

NB: The amount of time this method waits for the session is bounded by max_wait_secs. By default, this function will wait indefinitely.
Parameters
string master
`String` representation of the TensorFlow master to use.
object config
Optional ConfigProto proto used to configure the session.
ImplicitContainer<T> max_wait_secs
Maximum time to wait for the session to become available.
Returns
Session
A `Session`. May be None if the operation exceeds the timeout specified by config.operation_timeout_in_ms.

Session wait_for_session(string master, object config, int max_wait_secs)

Creates a new `Session` and waits for model to be ready.

Creates a new `Session` on 'master'. Waits for the model to be initialized or recovered from a checkpoint. It's expected that another thread or process will make the model ready, and that this is intended to be used by threads/processes that participate in a distributed training configuration where a different thread/process is responsible for initializing or recovering the model being trained.

NB: The amount of time this method waits for the session is bounded by max_wait_secs. By default, this function will wait indefinitely.
Parameters
string master
`String` representation of the TensorFlow master to use.
object config
Optional ConfigProto proto used to configure the session.
int max_wait_secs
Maximum time to wait for the session to become available.
Returns
Session
A `Session`. May be None if the operation exceeds the timeout specified by config.operation_timeout_in_ms.

object wait_for_session_dyn(object master, object config, ImplicitContainer<T> max_wait_secs)

Creates a new `Session` and waits for model to be ready.

Creates a new `Session` on 'master'. Waits for the model to be initialized or recovered from a checkpoint. It's expected that another thread or process will make the model ready, and that this is intended to be used by threads/processes that participate in a distributed training configuration where a different thread/process is responsible for initializing or recovering the model being trained.

NB: The amount of time this method waits for the session is bounded by max_wait_secs. By default, this function will wait indefinitely.
Parameters
object master
`String` representation of the TensorFlow master to use.
object config
Optional ConfigProto proto used to configure the session.
ImplicitContainer<T> max_wait_secs
Maximum time to wait for the session to become available.
Returns
object
A `Session`. May be None if the operation exceeds the timeout specified by config.operation_timeout_in_ms.

Public static methods

SessionManager NewDyn(object local_init_op, object ready_op, object ready_for_local_init_op, object graph, ImplicitContainer<T> recovery_wait_secs, object local_init_run_options)

Creates a SessionManager.

The `local_init_op` is an `Operation` that is run always after a new session was created. If `None`, this step is skipped.

The `ready_op` is an `Operation` used to check if the model is ready. The model is considered ready if that operation returns an empty 1D string tensor. If the operation returns a non empty 1D string tensor, the elements are concatenated and used to indicate to the user why the model is not ready.

The `ready_for_local_init_op` is an `Operation` used to check if the model is ready to run local_init_op. The model is considered ready if that operation returns an empty 1D string tensor. If the operation returns a non empty 1D string tensor, the elements are concatenated and used to indicate to the user why the model is not ready.

If `ready_op` is `None`, the model is not checked for readiness.

`recovery_wait_secs` is the number of seconds between checks that the model is ready. It is used by processes to wait for a model to be initialized or restored. Defaults to 30 seconds.
Parameters
object local_init_op
An `Operation` run immediately after session creation. Usually used to initialize tables and local variables.
object ready_op
An `Operation` to check if the model is initialized.
object ready_for_local_init_op
An `Operation` to check if the model is ready to run local_init_op.
object graph
The `Graph` that the model will use.
ImplicitContainer<T> recovery_wait_secs
Seconds between checks for the model to be ready.
object local_init_run_options
RunOptions to be passed to session.run when executing the local_init_op.

Public properties

object PythonObject get;