def testZeroItemsInClusterSpecMasterRead(self):
os.environ['TF_CONFIG'] = """
{}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('', cluster_resolver.master())
def _all_devices():
devices = []
tfconfig = TFConfigClusterResolver()
if tfconfig.cluster_spec().as_dict():
devices = _cluster_spec_to_device_list(tfconfig.cluster_spec(),
context.num_gpus())
return devices if devices else all_local_devices()
def get_num_workers():
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec().as_dict()
if cluster_spec:
task_type = cluster_resolver.task_type
return int(multi_worker_util.worker_count(cluster_spec, task_type))
return 1
def testOneItemInClusterSpecMasterRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"worker": ["worker0:2222"]
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('', cluster_resolver.master())
def maybe_shard_dataset(dataset):
"""Shard the dataset if running in multi-node environment."""
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec().as_dict()
if cluster_spec:
dataset = dataset.shard(
multi_worker_util.worker_count(cluster_spec,
cluster_resolver.task_type),
multi_worker_util.id_in_cluster(
cluster_spec, cluster_resolver.task_type, cluster_resolver.task_id))
return dataset
def __init__(self, container_strategy, num_gpus_per_worker):
# Use TFConfigClusterResolver to parse TF_CONFIG. We don't want to change
# the constructor's interface to allow customized cluster resolver. Use
# SimpleClusterResolver to override num_accelerators.
tfconfig = TFConfigClusterResolver()
cluster_resolver = SimpleClusterResolver(
cluster_spec=tfconfig.cluster_spec(),
task_type=tfconfig.task_type,
task_id=tfconfig.task_id,
num_accelerators=num_gpus_per_worker)
super(ParameterServerExtended, self).__init__(
container_strategy, cluster_resolver=cluster_resolver)
def __init__(self, container_strategy, num_gpus_per_worker):
# Use TFConfigClusterResolver to parse TF_CONFIG. We don't want to change
# the constructor's interface to allow customized cluster resolver. Use
# SimpleClusterResolver to override num_accelerators.
tfconfig = TFConfigClusterResolver()
cluster_resolver = SimpleClusterResolver(
cluster_spec=tfconfig.cluster_spec(),
task_type=tfconfig.task_type,
task_id=tfconfig.task_id,
num_accelerators={'GPU': num_gpus_per_worker})
super(ParameterServerExtended,
self).__init__(container_strategy,
cluster_resolver=cluster_resolver)
def batch_and_maybe_shard_dataset(dataset, global_batch_size):
"""Shard the dataset if running in multi-node environment."""
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec().as_dict()
if cluster_spec:
task_type = cluster_resolver.task_type
task_id = cluster_resolver.task_id
num_workers = int(multi_worker_util.worker_count(cluster_spec, task_type))
id_in_cluster = int(
multi_worker_util.id_in_cluster(cluster_spec, task_type, task_id))
dataset = dataset.shard(num_workers, id_in_cluster)
return dataset.batch(global_batch_size)
def __init__(self, container_strategy, num_gpus_per_worker, communication):
# Use TFConfigClusterResolver to parse TF_CONFIG. We don't want to change
# the constructor's interface to allow customized cluster resolver. Use
# SimpleClusterResolver to override num_accelerators.
tfconfig = TFConfigClusterResolver()
cluster_resolver = SimpleClusterResolver(
cluster_spec=tfconfig.cluster_spec(),
task_type=tfconfig.task_type,
task_id=tfconfig.task_id,
num_accelerators={"GPU": num_gpus_per_worker})
super(CollectiveAllReduceExtended,
self).__init__(container_strategy,
communication=communication,
cluster_resolver=cluster_resolver)
def batch_and_maybe_shard_dataset(dataset, global_batch_size):
"""Shard the dataset if running in multi-node environment."""
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec().as_dict()
if cluster_spec:
task_type = cluster_resolver.task_type
task_id = cluster_resolver.task_id
num_workers = int(
multi_worker_util.worker_count(cluster_spec, task_type))
id_in_cluster = int(
multi_worker_util.id_in_cluster(cluster_spec, task_type, task_id))
dataset = dataset.shard(num_workers, id_in_cluster)
return dataset.batch(global_batch_size)
def testParameterOverrides(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": "ps",
"index": 1
}
}
"""
cluster_resolver = TFConfigClusterResolver(task_type='ps',
task_index=0,
num_accelerators=8)
self.assertEqual('grpc://ps0:2222', cluster_resolver.master())
self.assertEqual('ps', cluster_resolver.task_type)
self.assertEqual(0, cluster_resolver.task_index)
self.assertEqual(8, cluster_resolver.num_accelerators())
cluster_resolver.task_type = 'worker'
cluster_resolver.task_index = 1
cluster_resolver.rpc_layer = 'test'
self.assertEqual('test://worker1:2222', cluster_resolver.master())
self.assertEqual('worker', cluster_resolver.task_type)
self.assertEqual(1, cluster_resolver.task_index)
self.assertEqual('test', cluster_resolver.rpc_layer)
def __init__(self, cluster_resolver=None):
"""Initializes this strategy.
Args:
cluster_resolver: Optional
`tf.distribute.cluster_resolver.ClusterResolver` object. Defaults to a
`tf.distribute.cluster_resolver.TFConfigClusterResolver`.
"""
if cluster_resolver is None:
cluster_resolver = TFConfigClusterResolver()
if not cluster_resolver.cluster_spec():
raise ValueError("Cluster spec must be non-empty in `cluster_resolver`.")
extended = ParameterServerStrategyExtended(
self, cluster_resolver=cluster_resolver)
super(ParameterServerStrategy, self).__init__(extended)
def __init__(self, cluster_resolver=None):
"""Initializes this strategy.
Args:
cluster_resolver: Optional
`tf.distribute.cluster_resolver.ClusterResolver` object. Defaults to a
`tf.distribute.cluster_resolver.TFConfigClusterResolver`.
"""
if cluster_resolver is None:
cluster_resolver = TFConfigClusterResolver()
if not cluster_resolver.cluster_spec():
raise ValueError("Cluster spec must be non-empty in `cluster_resolver`.")
extended = ParameterServerStrategyExtended(
self, cluster_resolver=cluster_resolver)
super(ParameterServerStrategy, self).__init__(extended)
def __init__(self, container_strategy, devices=None, cross_device_ops=None):
super(MirroredExtended, self).__init__(container_strategy)
if context.executing_eagerly():
if devices and not _is_device_list_single_worker(devices):
raise RuntimeError("In-graph multi-worker training with "
"`MirroredStrategy` is not supported in eager mode.")
else:
if TFConfigClusterResolver().cluster_spec().as_dict():
# if you are executing in eager mode, only the single machine code
# path is supported.
logging.info("Initializing local devices since in-graph multi-worker "
"training with `MirroredStrategy` is not supported in "
"eager mode. TF_CONFIG will be ignored when "
"when initializing `MirroredStrategy`.")
devices = devices or all_local_devices()
else:
devices = devices or all_devices()
assert devices, ("Got an empty `devices` list and unable to recognize "
"any local devices.")
self._cross_device_ops = cross_device_ops
self._communication_options = collective_util.Options()
self._initialize_strategy(devices)
# TODO(b/128995245): Enable last partial batch support in graph mode.
if ops.executing_eagerly_outside_functions():
self.experimental_enable_get_next_as_optional = True
# Flag to turn on VariablePolicy.
self._use_var_policy = False
def testSpecifiedTaskTypeAndIndexMasterRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('worker1:2222', cluster_resolver.master('worker', 1))
def testAutomaticMasterRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('ps0:2222', cluster_resolver.master())
def testSpecifiedTaskTypeAndIndexMasterRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('worker1:2222', cluster_resolver.master('worker', 1))
def testAutomaticMasterRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('ps0:2222', cluster_resolver.master())
def fn(functions_scheduled_event, test_finished_event):
# TODO(b/170664373): This is needed for TF2 parameter server training in
# OSS. Remove this when resolved.
os.environ["GRPC_FAIL_FAST"] = "use_caller"
cluster_resolver = TFConfigClusterResolver()
if cluster_resolver.task_type != "chief":
utils.start_server(cluster_resolver, "grpc")
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
cluster_resolver)
ps_coordinator = coordinator_lib.ClusterCoordinator(strategy)
with strategy.scope():
v = variables.Variable(initial_value=0, dtype=dtypes.int32)
@def_function.function
def worker_fn():
# An ever-running function.
for _ in math_ops.range(100000):
v.assign_add(1)
# Keep the two workers occupied.
ps_coordinator.schedule(worker_fn)
ps_coordinator.schedule(worker_fn)
# Now the main process can terminate.
functions_scheduled_event.set()
# Verified that join and schedule indeed raise UnavailableError.
try:
if test_join:
ps_coordinator.join()
if test_schedule:
while ps_coordinator.cluster._closure_queue._error is None:
time.sleep(1)
ps_coordinator.schedule(worker_fn)
except errors.UnavailableError:
# The following verifies that after PS fails, continue executing
# functions on workers should fail and indicate it's PS failure.
for worker_id in range(3):
with ops.device(
"/job:worker/replica:0/task:{}".format(worker_id)):
try:
# Executing a function after PS fails should result in a PS
# failure.
worker_fn()
except Exception as e: # pylint: disable=broad-except
if coordinator_lib._is_ps_failure(e):
if worker_id < 2:
continue
logging.info(
"_test_translate_ps_failure_error ends properly."
)
# Now we can safely exit the test.
test_finished_event.set()
return
raise RuntimeError(
"Executing a function after PS fails, should "
"result in a PS failure.")
raise RuntimeError("UnavailableError supposed to be raised.")
def _from_local_devices(
cls,
devices,
communication=cross_device_ops_lib.CollectiveCommunication.AUTO):
"""A convenience method to create an object with a list of devices."""
obj = cls(communication)
obj.extended._initialize_local(TFConfigClusterResolver(), devices=devices) # pylint: disable=protected-access
return obj
def testParameterOverrides(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": "ps",
"index": 1
}
}
"""
cluster_resolver = TFConfigClusterResolver(task_type='ps', task_index=0,
num_accelerators_per_worker=8)
self.assertEqual('grpc://ps0:2222', cluster_resolver.master())
self.assertEqual('ps', cluster_resolver.task_type)
self.assertEqual(0, cluster_resolver.task_index)
self.assertEqual(8, cluster_resolver.num_accelerators_per_worker())
cluster_resolver.task_type = 'worker'
cluster_resolver.task_index = 1
cluster_resolver.rpc_layer = 'test'
self.assertEqual('test://worker1:2222', cluster_resolver.master())
self.assertEqual('worker', cluster_resolver.task_type)
self.assertEqual(1, cluster_resolver.task_index)
self.assertEqual('test', cluster_resolver.rpc_layer)
def __init__(self,
container_strategy,
cluster_resolver=TFConfigClusterResolver()):
super(ParameterServerStrategyExtended, self).__init__(container_strategy)
self._initialize_strategy(cluster_resolver)
# We typically don't need to do all-reduce in this strategy.
self._cross_device_ops = (
cross_device_ops_lib.ReductionToOneDevice(reduce_to_device=_LOCAL_CPU))
def __init__(self,
container_strategy,
cluster_resolver=TFConfigClusterResolver()):
distribute_lib.DistributionStrategyExtended.__init__(
self, container_strategy)
self._cross_device_ops = None
self._initialize_strategy(cluster_resolver)
assert isinstance(self._get_cross_device_ops(),
cross_device_ops_lib.CollectiveAllReduce)
def __init__(self, container_strategy, communication, cluster_resolver):
cluster_resolver = cluster_resolver or TFConfigClusterResolver()
distribute_lib.StrategyExtendedV1.__init__(self, container_strategy)
assert isinstance(communication,
cross_device_ops_lib.CollectiveCommunication)
self._communication = communication
self._initialize_strategy(cluster_resolver)
assert isinstance(self._get_cross_device_ops(),
cross_device_ops_lib.CollectiveAllReduce)
def __init__(self,
container_strategy,
num_gpus_per_worker,
communication):
# Use TFConfigClusterResolver to parse TF_CONFIG. We don't want to change
# the constructor's interface to allow customized cluster resolver. Use
# SimpleClusterResolver to override num_accelerators.
tfconfig = TFConfigClusterResolver()
cluster_resolver = SimpleClusterResolver(
cluster_spec=tfconfig.cluster_spec(),
task_type=tfconfig.task_type,
task_id=tfconfig.task_id,
num_accelerators={"GPU": num_gpus_per_worker},
rpc_layer=tfconfig.rpc_layer)
super(CollectiveAllReduceExtended, self).__init__(
container_strategy,
communication=communication,
cluster_resolver=cluster_resolver)
def proc_func(functions_scheduled_event, test_finished_event):
cluster_resolver = TFConfigClusterResolver()
if cluster_resolver.task_type != "chief":
utils.start_server(cluster_resolver, "grpc")
ps_client = parameter_server_client.ParameterServerClient(
cluster_resolver)
with ps_client._strategy.scope():
v = variables.Variable(initial_value=0, dtype=dtypes.int32)
@def_function.function
def worker_fn():
# An ever-running function.
for _ in math_ops.range(100000):
v.assign_add(1)
# Keep the two workers occupied.
ps_client.schedule(worker_fn)
ps_client.schedule(worker_fn)
# Now the main process can terminate.
functions_scheduled_event.set()
# Verified that join and schedule indeed raise
# ParameterServerFailureError.
try:
if test_join:
ps_client.join()
if test_schedule:
while ps_client.cluster._closure_queue._error is None:
time.sleep(1)
ps_client.schedule(worker_fn)
except client.ParameterServerFailureError:
# The following verifies that after PS fails, continue executing
# functions on workers should fail and indicate it's PS failure.
for worker_id in range(3):
with ops.device(
"/job:worker/replica:0/task:{}".format(worker_id)):
try:
# Executing a function after PS fails should result in a PS
# failure.
worker_fn()
except Exception as e: # pylint: disable=broad-except
if client._is_ps_failure(e):
if worker_id < 2:
continue
logging.info(
"_test_translate_ps_failure_error ends properly."
)
# Now we can safely exit the test.
test_finished_event.set()
return
raise RuntimeError(
"Executing a function after PS fails, should "
"result in a PS failure.")
raise RuntimeError(
"ParameterServerFailureError supposed to be raised.")
def __init__(self, container_strategy, cluster_resolver,
communication_options):
self._cluster_resolver = cluster_resolver or TFConfigClusterResolver()
distribute_lib.StrategyExtendedV1.__init__(self, container_strategy)
self._communication_options = communication_options
self._initialize_strategy(self._cluster_resolver)
self._cfer_fn_cache = weakref.WeakKeyDictionary()
self.experimental_enable_get_next_as_optional = True
assert isinstance(self._cross_device_ops,
cross_device_ops_lib.CollectiveAllReduce)
def __init__(self, cluster_resolver=None):
"""Initializes this strategy with an optional `cluster_resolver`.
Args:
cluster_resolver: Optional
`tf.distribute.cluster_resolver.ClusterResolver` object. Defaults to a
`tf.distribute.cluster_resolver.TFConfigClusterResolver`.
"""
if cluster_resolver is None:
cluster_resolver = TFConfigClusterResolver()
if not cluster_resolver.cluster_spec():
raise ValueError("Cluster spec must be non-empty in `cluster_resolver`.")
extended = ParameterServerStrategyExtended(
self, cluster_resolver=cluster_resolver)
super(ParameterServerStrategy, self).__init__(extended)
distribute_lib.distribution_strategy_gauge.get_cell("V2").set(
"ParameterServerStrategy")
distribute_lib.distribution_strategy_replica_gauge.get_cell("num_ps").set(
len(self.extended.parameter_devices))
def __init__(self, container_strategy, communication, cluster_resolver):
self._cluster_resolver = cluster_resolver or TFConfigClusterResolver()
distribute_lib.StrategyExtendedV1.__init__(self, container_strategy)
assert isinstance(communication,
cross_device_ops_lib.CollectiveCommunication)
self._communication = communication
self._initialize_strategy(self._cluster_resolver)
self._cfer_fn_cache = weakref.WeakKeyDictionary()
assert isinstance(self._cross_device_ops,
cross_device_ops_lib.CollectiveAllReduce)
def __init__(self, cluster_resolver=None):
"""Initializes this strategy."""
# The `cluster_resolver` must be set so that
# `ParameterServerStrategyExtended` will keep num_gpus for `configure`
# method.
if cluster_resolver is None:
cluster_resolver = TFConfigClusterResolver()
extended = parameter_server_strategy.ParameterServerStrategyExtended(
self, cluster_resolver=cluster_resolver)
super(ParameterServerStrategy, self).__init__(extended)
def testNumAcceleratorsFilterTasksByEnvVar(self, mock_list_devices,
mock_eager_list_devices):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"worker1": ["w10:2222"],
"worker2": ["w21:2222", "w22:2222", "w23:2222", "w24:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": "worker1",
"index": "0"
}
}
"""
devices = [
LogicalDevice('/job:worker1/task:0/device:TPU:0', 'TPU'),
LogicalDevice('/job:worker1/task:0/device:TPU:1', 'TPU'),
LogicalDevice('/job:worker1/task:0/device:GPU:0', 'GPU'),
LogicalDevice('/job:worker1/task:0/device:GPU:1', 'GPU'),
LogicalDevice('/job:worker2/task:1/device:TPU:2', 'TPU'),
LogicalDevice('/job:worker2/task:2/device:TPU:3', 'TPU'),
LogicalDevice('/job:worker2/task:3/device:GPU:2', 'GPU'),
LogicalDevice('/job:worker2/task:4/device:GPU:3', 'GPU'),
]
device_list = [
session._DeviceAttributes(d.name, d.device_type, 1024, 0)
for d in devices
]
mock_eager_list_devices.return_value = devices
mock_list_devices.return_value = device_list
resolver = TFConfigClusterResolver()
# By default we read from TF_CONFIG
self.assertEqual(resolver.num_accelerators(), {'TPU': 2, 'GPU': 2})
# Override still works when we want it to
self.assertEqual(
resolver.num_accelerators(task_type='worker2', task_id=3),
{'GPU': 1})
def task_function(start_events, finish_events):
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec()
task_type = cluster_resolver.task_type
task_id = cluster_resolver.task_id
rpc_layer = cluster_resolver.rpc_layer
# TODO(yuefengz): support GPU clusters.
server_config = config_pb2.ConfigProto()
server_config.device_count['GPU'] = 0
if collective_leader:
server_config.experimental.collective_group_leader = collective_leader
server_config.experimental.collective_nccl = False
logging.info(
'Enabling collective ops with cluster_spec = %r, task_type = %r, '
'task_id = %r, rpc_layer = %r, collective_leader = %s',
cluster_spec, task_type, task_id, rpc_layer,
collective_leader)
else:
logging.info(
'Starting server with cluster_spec = %r, task_type = %r, '
'task_id = %r, rpc_layer = %r', cluster_spec, task_type,
task_id, rpc_layer)
server_lib.Server(cluster_spec,
job_name=task_type,
protocol=rpc_layer,
task_index=task_id,
config=server_config,
start=True)
start_event = start_events[task_type][task_id]
start_event.set()
finish_event = finish_events[task_type][task_id]
finish_event.wait()
os._exit(0) # pylint: disable=protected-access
def testTaskIndexOverride(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"worker": ["worker0:2222", "worker1:2222"]
},
"task": {
"type": "worker",
"index": "0"
}
}
"""
cluster_resolver = TFConfigClusterResolver(task_id=1)
self.assertEqual(1, cluster_resolver.task_id)
def testNormalClusterSpecRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
expected_proto = """
job { name: 'ps' tasks { key: 0 value: 'ps0:2222' }
tasks { key: 1 value: 'ps1:2222' } }
job { name: 'worker' tasks { key: 0 value: 'worker0:2222' }
tasks { key: 1 value: 'worker1:2222' }
tasks { key: 2 value: 'worker2:2222' } }
"""
actual_cluster_spec = cluster_resolver.cluster_spec()
self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
def testNormalClusterSpecRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
expected_proto = """
job { name: 'ps' tasks { key: 0 value: 'ps0:2222' }
tasks { key: 1 value: 'ps1:2222' } }
job { name: 'worker' tasks { key: 0 value: 'worker0:2222' }
tasks { key: 1 value: 'worker1:2222' }
tasks { key: 2 value: 'worker2:2222' } }
"""
actual_cluster_spec = cluster_resolver.cluster_spec()
self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
def __init__(self, cluster_resolver=None):
"""Initializes this strategy with an optional `cluster_resolver`.
Args:
cluster_resolver: Optional
`tf.distribute.cluster_resolver.ClusterResolver` object. Defaults to a
`tf.distribute.cluster_resolver.TFConfigClusterResolver`.
"""
if cluster_resolver is None:
cluster_resolver = TFConfigClusterResolver()
super(ParameterServerStrategyV1, self).__init__(
ParameterServerStrategyExtended(
self, cluster_resolver=cluster_resolver))
distribute_lib.distribution_strategy_gauge.get_cell("V1").set(
"ParameterServerStrategy")
def task_function(start_events, finish_events):
cluster_resolver = TFConfigClusterResolver()
cluster_spec = cluster_resolver.cluster_spec()
task_type = cluster_resolver.task_type
task_id = cluster_resolver.task_id
rpc_layer = cluster_resolver.rpc_layer
logging.info(
'Starting server with cluster_spec = %r, task_type = %r, '
'task_id = %r, rpc_layer = %r', cluster_spec, task_type, task_id,
rpc_layer)
# TODO(yuefengz): support GPU clusters.
server_config = config_pb2.ConfigProto()
server_config.device_count['GPU'] = 0
# Set the environment variable to prevent hanging upon job failure and
# restart. Note that it defaults to 'use_caller' at Google, but defaults
# to False in OSS.
os.environ['GRPC_FAIL_FAST'] = 'use_caller'
server_lib.Server(
cluster_spec,
job_name=task_type,
protocol=rpc_layer,
task_index=task_id,
config=server_config,
start=True)
start_event = start_events[task_type][task_id]
start_event.set()
finish_event = finish_events[task_type][task_id]
finish_event.wait()
os._exit(0) # pylint: disable=protected-access
def testTaskTypeCastToString(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"123456": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": 123456,
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('123456', cluster_resolver.task_type)
def testTaskIndexCastToInteger(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": "ps",
"index": "1"
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual(1, cluster_resolver.task_id)
def __init__(self, container_strategy, cluster_resolver,
communication_options):
if not isinstance(communication_options, collective_util.Options):
raise ValueError("communication_options must be an instance of "
"tf.distribute.experimental.CommunicationOptions")
self._cluster_resolver = cluster_resolver or TFConfigClusterResolver()
if not isinstance(self._cluster_resolver, ClusterResolver):
raise ValueError("cluster_resolver must be an instance of "
"tf.distribute.cluster_resolver.ClusterResolver")
distribute_lib.StrategyExtendedV1.__init__(self, container_strategy)
self._communication_options = communication_options
self._collective_key_base = container_strategy._collective_key_base # pylint: disable=protected-access
self._initialize_strategy(self._cluster_resolver)
self._cfer_fn_cache = weakref.WeakKeyDictionary()
self.experimental_enable_get_next_as_optional = True
assert isinstance(self._cross_device_ops,
cross_device_ops_lib.CollectiveAllReduce)
def testTaskTypeIndexRpcRead(self):
os.environ['TF_CONFIG'] = """
{
"cluster": {
"ps": ["ps0:2222", "ps1:2222"],
"worker": ["worker0:2222", "worker1:2222", "worker2:2222"]
},
"rpc_layer": "grpc",
"task": {
"type": "ps",
"index": 0
}
}
"""
cluster_resolver = TFConfigClusterResolver()
self.assertEqual('ps', cluster_resolver.task_type)
self.assertEqual(0, cluster_resolver.task_id)
self.assertEqual('grpc', cluster_resolver.rpc_layer)
def fn(first_fetch_occurred_event, worker_terminated_event):
os.environ["GRPC_FAIL_FAST"] = "use_caller"
cluster_resolver = TFConfigClusterResolver()
if cluster_resolver.task_type != "chief":
utils.start_server(cluster_resolver, "grpc")
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
cluster_resolver)
ps_coordinator = coordinator_lib.ClusterCoordinator(strategy)
with strategy.scope():
v = variables.Variable(initial_value=0, dtype=dtypes.int32)
@def_function.function
def worker_fn():
return v + 1, v - 1
remote_value = ps_coordinator.schedule(worker_fn)
logging.info("result (1st fetch): %r", remote_value.fetch())
first_fetch_occurred_event.set()
worker_terminated_event.wait()
logging.info("result (2nd fetch): %r", remote_value.fetch())
def fn(functions_scheduled_event):
# TODO(b/170664373): This is needed for TF2 parameter server training in
# OSS. Remove this when resolved.
os.environ["GRPC_FAIL_FAST"] = "use_caller"
cluster_resolver = TFConfigClusterResolver()
if cluster_resolver.task_type != "chief":
utils.start_server(cluster_resolver, "grpc")
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
cluster_resolver)
ps_client = coordinator_lib.ClusterCoordinator(strategy)
with strategy.scope():
v = variables.Variable(initial_value=1)
@def_function.function
def worker_fn(input_tensor):
def replica_fn(input_tensor):
return input_tensor + v
run_result = strategy.run(replica_fn,
args=(input_tensor, ))
check_ops.assert_equal_v2(run_result, 4)
return run_result
for i in range(5000):
if i % 500 == 0:
logging.info("Scheduling function-{}...".format(i))
result = ps_client.schedule(worker_fn,
args=(constant_op.constant(3), ))
functions_scheduled_event.set()
logging.info("Joining...")
ps_client.join()
logging.info("Finished joining.")
if result.fetch() != 4:
raise AssertionError(
"Unexpected RemoteValue result: {}".format(result.fetch()))
logging.info("testStrategyRun succeeded")
