tf.test.create_local_cluster

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Create and start local servers and return the associated Server objects.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.test.create_local_cluster

tf.test.create_local_cluster(
    num_workers, num_ps, protocol='grpc', worker_config=None,
    ps_config=None
)

"PS" stands for "parameter server": a task responsible for storing and updating the model's parameters. Other tasks send updates to these parameters as they work on optimizing the parameters. This particular division of labor between tasks is not required, but is common for distributed training.

Read more at https://www.tensorflow.org/guide/extend/architecture

Figure illustrates the interaction of these components. "/job:worker/task:0" and "/job:ps/task:0" are both tasks with worker services.

Example:

workers, _ = tf.test.create_local_cluster(num_workers=2, num_ps=2)

worker_sessions = [tf.compat.v1.Session(w.target) for w in workers]

with tf.device("/job:ps/task:0"):
  ...
with tf.device("/job:ps/task:1"):
  ...
with tf.device("/job:worker/task:0"):
  ...
with tf.device("/job:worker/task:1"):
  ...

worker_sessions[0].run(...)

Args
num_workers	Number of worker servers to start.
num_ps	Number of PS servers to start.
protocol	Communication protocol. Allowed values are documented in the documentation of tf.distribute.Server.
worker_config	(optional) tf.ConfigProto to initialize workers. Can be used to instantiate multiple devices etc.
ps_config	(optional) tf.ConfigProto to initialize PS servers.

Returns
A tuple (worker_servers, ps_servers). worker_servers is a list of num_workers objects of type tf.distribute.Server (all running locally); and ps_servers is a list of num_ps objects of similar type.

Raises
ImportError	if portpicker module was not found at load time