old_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Helpers to connect to remote servers."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from tensorflow.core.protobuf.cluster_pb2 import ClusterDef
  from tensorflow.core.protobuf.tensorflow_server_pb2 import ServerDef
  from tensorflow.python.eager import context
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export("config.experimental_connect_to_host")
  def connect_to_remote_host(remote_host=None, job_name="worker"):
    """Connects to a single machine to enable remote execution on it.
  
    Will make devices on the remote host available to use. Note that calling this
    more than once will work, but will invalidate any tensor handles on the old
    remote devices.
  
    Using the default job_name of worker, you can schedule ops to run remotely as
    follows:
    ```python
    # Enable eager execution, and connect to the remote host.
    tf.enable_eager_execution()
    tf.contrib.eager.connect_to_remote_host("exampleaddr.com:9876")
  
    with ops.device("job:worker/replica:0/task:1/device:CPU:0"):
      # The following tensors should be resident on the remote device, and the op
      # will also execute remotely.
      x1 = array_ops.ones([2, 2])
      x2 = array_ops.ones([2, 2])
      y = math_ops.matmul(x1, x2)
    ```
  
    Args:
      remote_host: The addr of the remote server in host-port format.
      job_name: The job name under which the new server will be accessible.
  
    Raises:
      ValueError: if remote_host is None.
    """
    if remote_host is None:
      raise ValueError("Must provide an remote_host")
    cluster_def = ClusterDef()
    job_def = cluster_def.job.add()
    job_def.name = job_name
    job_def.tasks[0] = "127.0.0.1:0"
    job_def.tasks[1] = remote_host
  
    server_def = ServerDef(
        cluster=cluster_def,
        job_name=job_name,
        task_index=0,
        protocol="grpc")
  
    # TODO(nareshmodi): Make this default since it works in more situations.
    os.environ["TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC"] = "1"
    context.set_server_def(server_def)
  
new_contents: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Helpers to connect to remote servers."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from tensorflow.core.protobuf.cluster_pb2 import ClusterDef
  from tensorflow.core.protobuf.tensorflow_server_pb2 import ServerDef
  from tensorflow.python.eager import context
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export("config.experimental_connect_to_host")
  def connect_to_remote_host(remote_host=None, job_name="worker"):
    """Connects to a single machine to enable remote execution on it.
  
    Will make devices on the remote host available to use. Note that calling this
    more than once will work, but will invalidate any tensor handles on the old
    remote devices.
  
    Using the default job_name of worker, you can schedule ops to run remotely as
    follows:
    ```python
    # Enable eager execution, and connect to the remote host.
    tf.enable_eager_execution()
    tf.contrib.eager.connect_to_remote_host("exampleaddr.com:9876")
  
    with ops.device("job:worker/replica:0/task:1/device:CPU:0"):
      # The following tensors should be resident on the remote device, and the op
      # will also execute remotely.
      x1 = array_ops.ones([2, 2])
      x2 = array_ops.ones([2, 2])
      y = math_ops.matmul(x1, x2)
    ```
  
    Args:
      remote_host: The addr of the remote server in host-port format.
      job_name: The job name under which the new server will be accessible.
  
    Raises:
      ValueError: if remote_host is None.
    """
    if remote_host is None:
      raise ValueError("Must provide an remote_host")
  
    grpc_prefix = "grpc://"
    if remote_host.startswith(grpc_prefix):
      remote_host = remote_host[len(grpc_prefix):]
  
    cluster_def = ClusterDef()
    job_def = cluster_def.job.add()
    job_def.name = job_name
    job_def.tasks[0] = "127.0.0.1:0"
    job_def.tasks[1] = remote_host
  
    server_def = ServerDef(
        cluster=cluster_def,
        job_name=job_name,
        task_index=0,
        protocol="grpc")
  
    # TODO(nareshmodi): Make this default since it works in more situations.
    os.environ["TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC"] = "1"
    context.set_server_def(server_def)
  
subject: Support hosts passed with grpc://