Python tensorflow.python.pywrap_tensorflow.TF_Reset() Examples

The following are 5 code examples of tensorflow.python.pywrap_tensorflow.TF_Reset(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.pywrap_tensorflow , or try the search function .
Example #1
Source File: session.py    From lambda-packs with MIT License 5 votes vote down vote up 
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config)
Example #2
Source File: session.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config)
Example #3
Source File: session.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config)
Example #4
Source File: session.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config)
Example #5
Source File: session.py    From keras-lambda with MIT License 5 votes vote down vote up 
def reset(target, containers=None, config=None):
    """Resets resource containers on `target`, and close all connected sessions.

    A resource container is distributed across all workers in the
    same cluster as `target`.  When a resource container on `target`
    is reset, resources associated with that container will be cleared.
    In particular, all Variables in the container will become undefined:
    they lose their values and shapes.

    NOTE:
    (i) reset() is currently only implemented for distributed sessions.
    (ii) Any sessions on the master named by `target` will be closed.

    If no resource containers are provided, all containers are reset.

    Args:
      target: The execution engine to connect to.
      containers: A list of resource container name strings, or `None` if all of
        all the containers are to be reset.
      config: (Optional.) Protocol buffer with configuration options.

    Raises:
      tf.errors.OpError: Or one of its subclasses if an error occurs while
        resetting containers.
    """
    if target is not None:
      target = compat.as_bytes(target)
    if containers is not None:
      containers = [compat.as_bytes(c) for c in containers]
    else:
      containers = []
    tf_session.TF_Reset(target, containers, config)