def testRemote(self):
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
context.ensure_initialized()
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
for gpu in gpus:
self.assertIsNotNone(gpu.name)
context.ensure_initialized()
job_name = 'test'
cluster_def = cluster_pb2.ClusterDef()
job_def = cluster_def.job.add()
job_def.name = job_name
job_def.tasks[0] = 'localhost:0'
server_def = tensorflow_server_pb2.ServerDef(cluster=cluster_def,
job_name=job_name,
task_index=0,
protocol='grpc')
context.set_server_def(server_def)
gpus = config.list_logical_devices('GPU')
for gpu in gpus:
self.assertIsNotNone(gpu.name)
def testRemote(self):
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
context.ensure_initialized()
gpus = config.list_logical_devices('GPU')
self.assertNotEqual(len(gpus), 0)
for gpu in gpus:
self.assertIsNotNone(gpu.name)
context.ensure_initialized()
job_name = 'test'
cluster_def = cluster_pb2.ClusterDef()
job_def = cluster_def.job.add()
job_def.name = job_name
job_def.tasks[0] = 'localhost:0'
server_def = tensorflow_server_pb2.ServerDef(
cluster=cluster_def, job_name=job_name, task_index=0, protocol='grpc')
context.set_server_def(server_def)
gpus = config.list_logical_devices('GPU')
for gpu in gpus:
self.assertIsNotNone(gpu.name)
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.compat.v1.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: a single or a list the remote server addr 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 not remote_host:
raise ValueError("Must provide at least one remote_host")
remote_host = nest.flatten(remote_host)
grpc_prefix = "grpc://"
local_port = pywrap_tensorflow.TF_PickUnusedPortOrDie()
cluster_def = ClusterDef()
job_def = cluster_def.job.add()
job_def.name = "localhost"
# TODO(fishx): Update this to make sure remote worker has valid ip address
# to connect with local.
job_def.tasks[0] = "localhost:{}".format(local_port)
job_def = cluster_def.job.add()
job_def.name = job_name
for i in range(len(remote_host)):
if remote_host[i].startswith(grpc_prefix):
job_def.tasks[i] = remote_host[i][len(grpc_prefix):]
else:
job_def.tasks[i] = remote_host[i]
server_def = ServerDef(cluster=cluster_def,
job_name="localhost",
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)
def testServerDefChanged(self):
"""Update server def, and run ops on new cluster."""
context.set_server_def(server_def=get_server_def(
ALT_JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % ALT_JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
# Set the server def back to JOB_NAME
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
def testServerDefChanged(self):
"""Update server def, and run ops on new cluster."""
context.set_server_def(
server_def=get_server_def(
ALT_JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % ALT_JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
# Set the server def back to JOB_NAME
context.set_server_def(
server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME):
x1 = array_ops.ones([2, 2])
y = math_ops.matmul(x1, x1)
np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy())
def setUp(self):
# Start the local server.
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
super(RemoteExecutionTest, self).setUp()
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
# Start the local server.
local_port = pywrap_tensorflow.TF_PickUnusedPortOrDie()
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
# Start the local server.
context.set_server_def(
server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
super(RemoteReplicateTest, self).setUp()
# Start the local server.
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
context.set_server_def(server_def=_get_server_def(
JOB_NAME,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
super(DynamicClusterTest, self).setUp()
os.environ["TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE"] = str(False)
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
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.compat.v1.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)
def connect_to_cluster(cluster_spec_or_resolver,
job_name="localhost",
task_index=0,
protocol=None):
"""Connects to the given cluster.
Will make devices on the cluster available to use. Note that calling this more
than once will work, but will invalidate any tensor handles on the old remote
devices.
If the given local job name is not present in the cluster specification, it
will be automatically added, using an unused port on the localhost.
Args:
cluster_spec_or_resolver: A `ClusterSpec` or `ClusterResolver` describing
the cluster.
job_name: The name of the local job.
task_index: The local task index.
protocol: The communication protocol, such as `"grpc"`. If unspecified, will
use the default from `python/platform/remote_utils.py`.
"""
protocol = protocol or remote_utils.get_default_communication_protocol()
if isinstance(cluster_spec_or_resolver, server_lib.ClusterSpec):
cluster_spec = cluster_spec_or_resolver
elif isinstance(cluster_spec_or_resolver,
cluster_resolver.ClusterResolver):
cluster_spec = cluster_spec_or_resolver.cluster_spec()
else:
raise ValueError(
"`cluster_spec_or_resolver` must be a `ClusterSpec` or a "
"`ClusterResolver`.")
cluster_def = cluster_spec.as_cluster_def()
# Automatically add local job, if not part of the cluster spec.
if job_name not in cluster_spec.jobs:
local_port = pywrap_tensorflow.TF_PickUnusedPortOrDie()
job_def = cluster_def.job.add()
job_def.name = job_name
# TODO(fishx): Update this to make sure remote worker has valid ip address
# to connect with local.
job_def.tasks[0] = "localhost:{}".format(local_port)
server_def = ServerDef(cluster=cluster_def,
job_name=job_name,
task_index=task_index,
protocol=protocol)
# 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)
def __init__(self, methodName="runTest"): # pylint: disable=invalid-name
super(RemoteExecutionTest, self).__init__(methodName)
self._cached_server1 = server_lib.Server.create_local_server()
self._cached_server2 = server_lib.Server.create_local_server()
os.environ["TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC"] = "1"
self._cached_server1_target = self._cached_server1.target[len("grpc://"):]
self._cached_server2_target = self._cached_server2.target[len("grpc://"):]
# Start the local server.
context.set_server_def(
server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def __init__(self, methodName="runTest"): # pylint: disable=invalid-name
super(RemoteExecutionTest, self).__init__(methodName)
self._cached_server1 = server_lib.Server.create_local_server()
self._cached_server2 = server_lib.Server.create_local_server()
os.environ["TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC"] = "1"
self._cached_server1_target = self._cached_server1.target[len("grpc://"
):]
self._cached_server2_target = self._cached_server2.target[len("grpc://"
):]
# Start the local server.
context.set_server_def(server_def=get_server_def(
JOB_NAME,
local_server_port=0,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def setUp(self):
super(EagerClusterReplicateTest, self).setUp()
if context.context().use_tfrt:
self.skipTest(
"b/171412104: This test requires distributed support.")
# TODO(b/171412104): Move create server to __init__ once tfrt support it.
self._cached_server1 = server_lib.Server.create_local_server()
self._cached_server2 = server_lib.Server.create_local_server()
self._cached_server1_target = self._cached_server1.target[len("grpc://"
):]
self._cached_server2_target = self._cached_server2.target[len("grpc://"
):]
# Start the local server.
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
context.set_server_def(server_def=_get_server_def(
self._job_name,
local_server_port=local_port,
remote_server_addresses=[
self._cached_server1_target, self._cached_server2_target
],
task_index=0))
def connect_to_cluster(cluster_spec_or_resolver,
job_name="localhost",
task_index=0,
protocol=None,
make_master_device_default=True):
"""Connects to the given cluster.
Will make devices on the cluster available to use. Note that calling this more
than once will work, but will invalidate any tensor handles on the old remote
devices.
If the given local job name is not present in the cluster specification, it
will be automatically added, using an unused port on the localhost.
Args:
cluster_spec_or_resolver: A `ClusterSpec` or `ClusterResolver` describing
the cluster.
job_name: The name of the local job.
task_index: The local task index.
protocol: The communication protocol, such as `"grpc"`. If unspecified, will
use the default from `python/platform/remote_utils.py`.
make_master_device_default: If True and a cluster resolver is passed, will
automatically enter the master task device scope, which indicates the
master becomes the default device to run ops. It won't do anything if
a cluster spec is passed. Will throw an error if the caller is currently
already in some device scope.
"""
protocol = protocol or remote_utils.get_default_communication_protocol()
if isinstance(cluster_spec_or_resolver, server_lib.ClusterSpec):
cluster_spec = cluster_spec_or_resolver
elif isinstance(cluster_spec_or_resolver,
cluster_resolver.ClusterResolver):
cluster_spec = cluster_spec_or_resolver.cluster_spec()
else:
raise ValueError(
"`cluster_spec_or_resolver` must be a `ClusterSpec` or a "
"`ClusterResolver`.")
cluster_def = cluster_spec.as_cluster_def()
# Automatically add local job, if not part of the cluster spec.
if job_name not in cluster_spec.jobs:
local_port = pywrap_tensorflow.TF_PickUnusedPortOrDie()
job_def = cluster_def.job.add()
job_def.name = job_name
# TODO(fishx): Update this to make sure remote worker has valid ip address
# to connect with local.
job_def.tasks[0] = "localhost:{}".format(local_port)
server_def = ServerDef(cluster=cluster_def,
job_name=job_name,
task_index=task_index,
protocol=protocol)
# 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)
if make_master_device_default and isinstance(
cluster_spec_or_resolver, cluster_resolver.ClusterResolver
) and cluster_spec_or_resolver.master():
master = cluster_spec_or_resolver.master()
master_job_name = None
master_task_id = None
for job_name in cluster_spec.jobs:
for task_id in cluster_spec.task_indices(job_name):
task_address = cluster_spec.task_address(job_name, task_id)
if master in task_address or task_address in master:
master_job_name = job_name
master_task_id = task_id
break
if not master_job_name:
raise ValueError(
"`make_master_device_default` is set to True but cannot find "
"master %s in the cluster" % master)
master_device = "/job:{}/replica:0/task:{}".format(
master_job_name, master_task_id)
if not _device_stack_is_empty():
raise ValueError(
"`connect_to_cluster` should not be called inside "
"an existing device scope")
logging.info("Entering into master device scope: %s", master_device)
# TODO(b/138389076): Think of the entering device scope behavior in the
# failure recovery case when dealing with preemptions.
ops.device(master_device).__enter__()
def connect_to_cluster(cluster_spec_or_resolver,
job_name="localhost",
task_index=0,
protocol=None,
make_master_device_default=True):
"""Connects to the given cluster.
Will make devices on the cluster available to use. Note that calling this more
than once will work, but will invalidate any tensor handles on the old remote
devices.
If the given local job name is not present in the cluster specification, it
will be automatically added, using an unused port on the localhost.
Args:
cluster_spec_or_resolver: A `ClusterSpec` or `ClusterResolver` describing
the cluster.
job_name: The name of the local job.
task_index: The local task index.
protocol: The communication protocol, such as `"grpc"`. If unspecified, will
use the default from `python/platform/remote_utils.py`.
make_master_device_default: If True and a cluster resolver is passed, will
automatically enter the master task device scope, which indicates the
master becomes the default device to run ops. It won't do anything if
a cluster spec is passed. Will throw an error if the caller is currently
already in some device scope.
"""
if not context.executing_eagerly():
raise ValueError(
"`tf.config.experimental_connect_to_cluster` can only be called in "
"eager mode.")
protocol = protocol or remote_utils.get_default_communication_protocol()
if isinstance(cluster_spec_or_resolver, server_lib.ClusterSpec):
cluster_spec = cluster_spec_or_resolver
elif isinstance(cluster_spec_or_resolver,
cluster_resolver.ClusterResolver):
if cluster_spec_or_resolver.master() in _LOCAL_MASTERS:
# Do nothing if the master is local.
return
cluster_spec = cluster_spec_or_resolver.cluster_spec()
else:
raise ValueError(
"`cluster_spec_or_resolver` must be a `ClusterSpec` or a "
"`ClusterResolver`.")
cluster_def = copy.deepcopy(cluster_spec.as_cluster_def())
# Automatically add local job, if not part of the cluster spec.
if job_name not in cluster_spec.jobs:
local_port = pywrap_tensorflow.TF_PickUnusedPortOrDie()
job_def = cluster_def.job.add()
job_def.name = job_name
# TODO(fishx): Update this to make sure remote worker has valid ip address
# to connect with local.
job_def.tasks[0] = "localhost:{}".format(local_port)
server_def = ServerDef(cluster=cluster_def,
job_name=job_name,
task_index=task_index,
protocol=protocol,
default_session_config=context.context().config)
if context.get_server_def() is None:
context.set_server_def(server_def)
else:
context.update_server_def(server_def)
if make_master_device_default and isinstance(
cluster_spec_or_resolver, cluster_resolver.ClusterResolver
) and cluster_spec_or_resolver.master():
master = cluster_spec_or_resolver.master()
master_job_name = None
master_task_id = None
for job_name in cluster_spec.jobs:
for task_id in cluster_spec.task_indices(job_name):
task_address = cluster_spec.task_address(job_name, task_id)
if master in task_address or task_address in master:
master_job_name = job_name
master_task_id = task_id
break
if not master_job_name:
raise ValueError(
"`make_master_device_default` is set to True but cannot find "
"master %s in the cluster" % master)
master_device = "/job:{}/replica:0/task:{}".format(
master_job_name, master_task_id)
master_device = device_util.canonicalize(master_device)
current_device = device_util.current()
if current_device:
current_device = device_util.canonicalize(current_device)
if current_device and current_device != master_device:
raise ValueError(
"`connect_to_cluster` is called inside existing device "
"scope %s, which is different from the master device "
"scope %s to enter. This is not allowed." %
(current_device, master_device))
# TODO(b/138389076): Think of the entering device scope behavior in the
# failure recovery case when dealing with preemptions.
if not current_device:
logging.info("Entering into master device scope: %s",
master_device)
ops.device(master_device).__enter__()
def connect_to_cluster(cluster_spec_or_resolver,
job_name="localhost",
task_index=0,
protocol=None,
make_master_device_default=True,
cluster_device_filters=None):
"""Connects to the given cluster.
Will make devices on the cluster available to use. Note that calling this more
than once will work, but will invalidate any tensor handles on the old remote
devices.
If the given local job name is not present in the cluster specification, it
will be automatically added, using an unused port on the localhost.
Device filters can be specified to isolate groups of remote tasks to avoid
undesired accesses between workers. Workers accessing resources or launching
ops / functions on filtered remote devices will result in errors (unknown
devices). For any remote task, if no device filter is present, all cluster
devices will be visible; if any device filter is specified, it can only
see devices matching at least one filter. Devices on the task itself are
always visible. Device filters can be particially specified.
For example, for a cluster set up for parameter server training, the following
device filters might be specified:
```python
cdf = tf.config.experimental.ClusterDeviceFilters()
# For any worker, only the devices on PS nodes and itself are visible
for i in range(num_workers):
cdf.set_device_filters('worker', i, ['/job:ps'])
# Similarly for any ps, only the devices on workers and itself are visible
for i in range(num_ps):
cdf.set_device_filters('ps', i, ['/job:worker'])
tf.config.experimental_connect_to_cluster(cluster_def,
cluster_device_filters=cdf)
```
Args:
cluster_spec_or_resolver: A `ClusterSpec` or `ClusterResolver` describing
the cluster.
job_name: The name of the local job.
task_index: The local task index.
protocol: The communication protocol, such as `"grpc"`. If unspecified, will
use the default from `python/platform/remote_utils.py`.
make_master_device_default: If True and a cluster resolver is passed, will
automatically enter the master task device scope, which indicates the
master becomes the default device to run ops. It won't do anything if
a cluster spec is passed. Will throw an error if the caller is currently
already in some device scope.
cluster_device_filters: an instance of
`tf.train.experimental/ClusterDeviceFilters` that specify device filters
to the remote tasks in cluster.
"""
if not context.executing_eagerly():
raise ValueError(
"`tf.config.experimental_connect_to_cluster` can only be called in "
"eager mode.")
protocol = protocol or remote_utils.get_default_communication_protocol()
if isinstance(cluster_spec_or_resolver, server_lib.ClusterSpec):
cluster_spec = cluster_spec_or_resolver
elif isinstance(cluster_spec_or_resolver,
cluster_resolver.ClusterResolver):
if cluster_spec_or_resolver.master() in _LOCAL_MASTERS:
# Do nothing if the master is local.
return
cluster_spec = cluster_spec_or_resolver.cluster_spec()
else:
raise ValueError(
"`cluster_spec_or_resolver` must be a `ClusterSpec` or a "
"`ClusterResolver`.")
cluster_def = copy.deepcopy(cluster_spec.as_cluster_def())
if cluster_device_filters:
if isinstance(cluster_device_filters, server_lib.ClusterDeviceFilters):
cluster_device_filters = copy.deepcopy(
cluster_device_filters._as_cluster_device_filters()) # pylint: disable=protected-access
else:
raise ValueError("`cluster_device_filters` must be an instance of "
"`tf.train.experimental.ClusterDeviceFilters`.")
# Automatically add local job, if not part of the cluster spec.
if job_name not in cluster_spec.jobs:
local_port = pywrap_tfe.TF_PickUnusedPortOrDie()
job_def = cluster_def.job.add()
job_def.name = job_name
ipstr = _get_local_ip_address(local_port)
if ipstr:
job_def.tasks[0] = "{}:{}".format(ipstr, local_port)
else:
job_def.tasks[0] = "localhost:{}".format(local_port)
server_def = ServerDef(cluster=cluster_def,
job_name=job_name,
task_index=task_index,
protocol=protocol,
default_session_config=context.context().config,
cluster_device_filters=cluster_device_filters)
if context.get_server_def() is None:
context.set_server_def(server_def)
else:
context.update_server_def(server_def)
if make_master_device_default and isinstance(
cluster_spec_or_resolver, cluster_resolver.ClusterResolver
) and cluster_spec_or_resolver.master():
master = cluster_spec_or_resolver.master()
master_job_name = None
master_task_id = None
for job_name in cluster_spec.jobs:
for task_id in cluster_spec.task_indices(job_name):
task_address = cluster_spec.task_address(job_name, task_id)
if master in task_address or task_address in master:
master_job_name = job_name
master_task_id = task_id
break
if not master_job_name:
raise ValueError(
"`make_master_device_default` is set to True but cannot find "
"master %s in the cluster" % master)
master_device = "/job:{}/replica:0/task:{}".format(
master_job_name, master_task_id)
master_device = device_util.canonicalize(master_device)
current_device = device_util.current()
if current_device:
current_device = device_util.canonicalize(current_device)
if current_device and current_device != master_device:
raise ValueError(
"`connect_to_cluster` is called inside existing device "
"scope %s, which is different from the master device "
"scope %s to enter. This is not allowed." %
(current_device, master_device))
# TODO(b/138389076): Think of the entering device scope behavior in the
# failure recovery case when dealing with preemptions.
if not current_device:
logging.info("Entering into master device scope: %s",
master_device)
ops.device(master_device).__enter__()
import tensorflow as tf
from tensorflow.core.protobuf.tensorflow_server_pb2 import ServerDef
from tensorflow.python.eager import context
from tensorflow.python.training.server_lib import ClusterSpec
cluster_def = ClusterSpec({'worker': ['127.0.0.1:15293']}).as_cluster_def()
# 15293 is just some random available port
server_def = ServerDef(cluster=cluster_def,
job_name='worker',
task_index=0,
protocol='grpc')
v = tf.Variable(3)
print(v.device)
# > /job:localhost/replica:0/task:0/device:CPU:0
context.set_server_def(server_def)
####################################
print(v.device)
# > Segmentation fault (core dumped)
####################################
