tf.distribute.cluster_resolver.SimpleClusterResolver

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Simple implementation of ClusterResolver that accepts a ClusterSpec.

Inherits From: ClusterResolver

View aliases

Main aliases

`tf.contrib.cluster_resolver.SimpleClusterResolver`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.distribute.cluster_resolver.SimpleClusterResolver, `tf.compat.v2.distribute.cluster_resolver.SimpleClusterResolver`

tf.distribute.cluster_resolver.SimpleClusterResolver(
    cluster_spec, master='', task_type=None, task_id=None, environment='',
    num_accelerators=None, rpc_layer=None
)

Attributes
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

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.