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tf.distribute.cluster_resolver.SimpleClusterResolver

TensorFlow 2 version View source on GitHub

Simple implementation of ClusterResolver that accepts a ClusterSpec.

Inherits From: ClusterResolver

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

cluster_spec

View source

Returns the ClusterSpec passed into the constructor.

master

View source

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

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.