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tf.distribute.cluster_resolver.SimpleClusterResolver

TensorFlow 2.0 version View source on GitHub

Class SimpleClusterResolver

Simple implementation of ClusterResolver that accepts a ClusterSpec.

Inherits From: ClusterResolver

Aliases:

  • Class tf.compat.v1.distribute.cluster_resolver.SimpleClusterResolver
  • Class tf.compat.v2.distribute.cluster_resolver.SimpleClusterResolver
  • Class tf.contrib.cluster_resolver.SimpleClusterResolver
  • Class tf.contrib.cluster_resolver.python.training.SimpleClusterResolver

__init__

View source

__init__(
    cluster_spec,
    master='',
    task_type=None,
    task_id=None,
    environment='',
    num_accelerators=None,
    rpc_layer=None
)

Creates a SimpleClusterResolver from a ClusterSpec.

Properties

environment

rpc_layer

task_id

task_type

Methods

cluster_spec

View source

cluster_spec()

Returns the ClusterSpec passed into the constructor.

master

View source

master(
    task_type=None,
    task_id=None,
    rpc_layer=None
)

Returns the master address to use when creating a session.

Args:

  • task_type: (Optional) The type of the TensorFlow task of the master.
  • task_id: (Optional) The index of the TensorFlow task of the master.
  • rpc_layer: (Optional) The RPC used by distributed TensorFlow.

Returns:

The name or URL of the session master.

If a task_type and task_id is given, this will override the master string passed into the initialization function.

num_accelerators

View source

num_accelerators(
    task_type=None,
    task_id=None,
    config_proto=None
)

Returns the number of accelerator cores per worker.

The SimpleClusterResolver does not do automatic detection of accelerators, so a TensorFlow session will never be created, and thus all arguments are unused and we simply assume that the type of accelerator is a GPU and return the value in provided to us in the constructor.

Args:

  • task_type: Unused.
  • task_id: Unused.
  • config_proto: Unused.