Method __init__

tensorflow/contrib/mpi_collectives/__init__.py:246–275

Creates a new TensorFlow MPI session. Unlike a normal `tf.Session`, an MPI Session may only use a single GPU, which must be specified in advance before the session is initialized. In addition, it only uses a single graph evaluation thread, and initializes MPI immediately upon starti

(self, target='', graph=None, config=None)

  """

  def __init__(self, target='', graph=None, config=None):
    """Creates a new TensorFlow MPI session.

    Unlike a normal `tf.Session`, an MPI Session may only use a single GPU,
    which must be specified in advance before the session is initialized.
    In addition, it only uses a single graph evaluation thread, and
    initializes MPI immediately upon starting.

    If no `graph` argument is specified when constructing the session,
    the default graph will be launched in the session. If you are
    using more than one graph (created with `tf.Graph()` in the same
    process, you will have to use different sessions for each graph,
    but each graph can be used in multiple sessions. In this case, it
    is often clearer to pass the graph to be launched explicitly to
    the session constructor.

    Args:
    target: (Optional.) The execution engine to connect to.
    graph: (Optional.) The `Graph` to be launched (described above).
    config: (Optional.) A `ConfigProto` protocol buffer with configuration
    options for the session.
    """
    super(Session, self).__init__(target, graph, config=config)

    # Initialize MPI on the relevant device.
    # TODO: Move this to library load and eliminate mpi.Session()
    if graph is None:
      graph = tf.get_default_graph()
    with graph.as_default():
      self.run(init())