tf.distribute.cluster_resolver.SimpleClusterResolver

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Class SimpleClusterResolver

Simple implementation of ClusterResolver that accepts a ClusterSpec.

Inherits From: ClusterResolver

Aliases:

__init__

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__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

Returns the current environment which TensorFlow is running in.

There are two possible return values, "google" (when TensorFlow is running in a Google-internal environment) or an empty string (when TensorFlow is running elsewhere).

If you are implementing a ClusterResolver that works in both the Google environment and the open-source world (for instance, a TPU ClusterResolver or similar), you will have to return the appropriate string depending on the environment, which you will have to detect.

Otherwise, if you are implementing a ClusterResolver that will only work in open-source TensorFlow, you do not need to implement this property.

rpc_layer

task_id

task_type

Methods

tf.distribute.cluster_resolver.SimpleClusterResolver.cluster_spec

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cluster_spec()

Returns the ClusterSpec passed into the constructor.

tf.distribute.cluster_resolver.SimpleClusterResolver.master

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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.

tf.distribute.cluster_resolver.SimpleClusterResolver.num_accelerators

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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.