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tf.data.experimental.service.WorkerServer

An in-process tf.data service worker server.

A tf.data.experimental.service.WorkerServer performs tf.data.Dataset processing for user-defined datasets, and provides the resulting elements over RPC. A worker is associated with a single tf.data.experimental.service.DispatchServer.

dispatcher = tf.data.experimental.service.DispatchServer()
dispatcher_address = dispatcher.target.split("://")[1]
worker = tf.data.experimental.service.WorkerServer(
    tf.data.experimental.service.WorkerConfig(
        dispatcher_address=dispatcher_address))
dataset = tf.data.Dataset.range(10)
dataset = dataset.apply(tf.data.experimental.service.distribute(
    processing_mode="parallel_epochs", service=dispatcher.target))
print(list(dataset.as_numpy_iterator()))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

When starting a dedicated tf.data worker process, use join() to block indefinitely after starting up the server.

worker = tf.data.experimental.service.WorkerServer(
    port=5051, dispatcher_address="localhost:5050")
worker.join()

config A tf.data.experimental.service.WorkerConfig configration.
start (Optional.) Boolean, indicating whether to start the server after creating it. Defaults to True.

Methods

join

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Blocks until the server has shut down.

This is useful when starting a dedicated worker process.

worker_server = tf.data.experimental.service.WorkerServer(
    port=5051, dispatcher_address="localhost:5050")
worker_server.join()

This method currently blocks forever.

Raises
tf.errors.OpError Or one of its subclasses if an error occurs while joining the server.

start

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Starts this server.

Raises
tf.errors.OpError Or one of its subclasses if an error occurs while starting the server.