|TensorFlow 2.0 version||View source on GitHub|
Create and start local servers and return the associated
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.
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.run(...)
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.ConfigPrototo initialize workers. Can be used to instantiate multiple devices etc.
tf.ConfigPrototo initialize PS servers.
worker_servers is a list
num_workers objects of type
tf.distribute.Server (all running
ps_servers is a list of
num_ps objects of similar type.
ImportError: if portpicker module was not found at load time