def __init__(self, config, start=True):
"""Creates a new worker server.
Args:
config: A `tf.data.experimental.service.WorkerConfig` configration.
start: (Optional.) Boolean, indicating whether to start the server after
creating it.
"""
if config.dispatcher_address is None:
raise ValueError("must specify a dispatcher_address")
config_proto = service_config_pb2.WorkerConfig(
dispatcher_address=config.dispatcher_address,
worker_address=config.worker_address,
port=config.port,
protocol=config.protocol)
self._server = _pywrap_server_lib.TF_DATA_NewWorkerServer(
config_proto.SerializeToString())
if start:
self._server.start()
def __init__(self,
port,
dispatcher_address,
worker_address=None,
protocol=None,
start=True):
"""Creates a new worker server.
Args:
port: Specifies the port to bind to. A value of 0 indicates that the
worker can bind to any available port.
dispatcher_address: Specifies the address of the dispatcher.
worker_address: (Optional.) Specifies the address of the worker server.
This address is passed to the dispatcher so that the dispatcher can
tell clients how to connect to this worker. Defaults to
`"localhost:%port%"`, where `%port%` will be replaced with the port used
by the worker.
protocol: (Optional.) Specifies the protocol to be used by the server.
Acceptable values include `"grpc", "grpc+local"`. Defaults to `"grpc"`.
start: (Optional.) Boolean, indicating whether to start the server after
creating it. Defaults to `True`.
Raises:
tf.errors.OpError: Or one of its subclasses if an error occurs while
creating the TensorFlow server.
"""
if worker_address is None:
worker_address = "localhost:%port%"
if protocol is None:
protocol = "grpc"
self._protocol = protocol
config = service_config_pb2.WorkerConfig(
port=port,
protocol=protocol,
dispatcher_address=dispatcher_address,
worker_address=worker_address)
self._server = _pywrap_server_lib.TF_DATA_NewWorkerServer(
config.SerializeToString())
if start:
self._server.start()