def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
master_channel = build_channel(args.master_addr)
ps_channels = []
if args.ps_addrs:
ps_addrs = args.ps_addrs.split(",")
for addr in ps_addrs:
# addr is in the form as "ps-pod-name.namespace.svc:port"
channel = build_channel(addr)
# Wait the channel is ready by a Future object.
grpc.channel_ready_future(channel).result()
logger.info("grpc channel %s to connect pod %s is ready" %
(addr, addr.split(".")[0]))
ps_channels.append(channel)
worker = Worker(args, channel=master_channel, ps_channels=ps_channels)
worker.run()
def main():
args = parse_worker_args()
channel = grpc.insecure_channel(
args.master_addr,
options=[
("grpc.max_send_message_length", GRPC.MAX_SEND_MESSAGE_LENGTH),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
logger = log_util.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
worker = Worker(
args.worker_id,
args.job_type,
args.minibatch_size,
args.model_zoo,
channel=channel,
embedding_service_endpoint=eval(args.embedding_service_endpoint),
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_def=args.model_def,
model_params=args.model_params,
get_model_steps=args.get_model_steps,
)
worker.run()
def main():
args = parse_worker_args()
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
channel = grpc.insecure_channel(
args.master_addr,
options=[
("grpc.max_send_message_length", GRPC.MAX_SEND_MESSAGE_LENGTH),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
# TODO, create PS channels here
ps_addrs = args.ps_addrs.split(",")
# Just print ps_addrs out to avoid flake8 failure
# This print can be removed once we initialize ps_channels
# by using ps_addrs
print("Parameter server addresses are %s" % ps_addrs)
ps_channels = None
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
worker = Worker(args, channel=channel, ps_channels=ps_channels)
worker.run()
def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
master_channel = build_channel(args.master_addr)
ps_channels = []
if args.ps_addrs:
# TODO: use ps_addrs from master directly after ps service is working.
# Get ps pod ip for ps grpc connection for now.
ps_addrs = args.ps_addrs.split(",")
config.load_incluster_config()
api = client.CoreV1Api()
for addr in ps_addrs:
# addr is in the form as "ps-pod-name.namespace.svc:port"
addr_splitted = addr.split(".")
while True:
pod = api.read_namespaced_pod(
namespace=addr_splitted[1], name=addr_splitted[0]
)
if pod.status.pod_ip:
break
# If ps pod is not ready yet, sleep 2 seconds and try again.
time.sleep(2)
addr = pod.status.pod_ip + ":" + addr.split(":")[-1]
channel = grpc.insecure_channel(
addr,
options=[
(
"grpc.max_send_message_length",
GRPC.MAX_SEND_MESSAGE_LENGTH,
),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
# Wait the channel is ready by a Future object.
grpc.channel_ready_future(channel).result()
logger.info(
"grpc channel %s to connect pod %s is ready"
% (addr, pod.metadata.name)
)
ps_channels.append(channel)
worker = Worker(args, channel=master_channel, ps_channels=ps_channels)
worker.run()
def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
master_channel = grpc.insecure_channel(
args.master_addr,
options=[
("grpc.max_send_message_length", GRPC.MAX_SEND_MESSAGE_LENGTH),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
ps_channels = []
if args.ps_addrs:
# TODO: use ps_addrs from master directly after ps service is working.
# Get ps pod ip for ps grpc connection for now.
ps_addrs = args.ps_addrs.split(",")
from kubernetes import client, config
config.load_incluster_config()
api = client.CoreV1Api()
for addr in ps_addrs:
# addr is in the form as "ps-pod-name.namespace.svc:port"
addr_splitted = addr.split(".")
pod = api.read_namespaced_pod(namespace=addr_splitted[1],
name=addr_splitted[0])
addr = pod.status.pod_ip + ":" + addr.split(":")[-1]
channel = grpc.insecure_channel(
addr,
options=[
(
"grpc.max_send_message_length",
GRPC.MAX_SEND_MESSAGE_LENGTH,
),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
ps_channels.append(channel)
worker = Worker(args, channel=master_channel, ps_channels=ps_channels)
worker.run()
def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
master_channel = build_channel(args.master_addr)
ps_channels = []
if args.ps_addrs:
ps_addrs = args.ps_addrs.split(",")
for addr in ps_addrs:
# addr is in the form as "ps-pod-name.namespace.svc:port"
channel = build_channel(addr)
succeeded = False
for i in range(CONNECT_PS_MAX_RETRIES):
try:
grpc.channel_ready_future(channel).result(
timeout=CONNECT_PS_TIMEOUT)
logger.info("grpc channel %s to connect pod %s is ready" %
(addr, addr.split(".")[0]))
ps_channels.append(channel)
succeeded = True
break
except grpc.FutureTimeoutError:
logger.warning("Failed to connect pod %s with %d retry" %
(addr.split(".")[0], i))
if not succeeded:
raise TimeoutError(
"Time out to connect pod %s with 3 retries" %
addr.split(".")[0])
if args.distribution_strategy == DistributionStrategy.ALLREDUCE:
logger.info("Wait for %s seconds for FTLib consensus service to "
"detect the worker pod" %
str(_ALLREDUCE_STRATEGY_WARM_UP_SECS))
time.sleep(_ALLREDUCE_STRATEGY_WARM_UP_SECS)
worker = Worker(
args,
channel=master_channel,
ps_channels=ps_channels,
set_parallelism=True,
)
worker.run()
def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
logger.info("Starting worker %d", args.worker_id)
if args.master_addr is None:
raise ValueError("master_addr is missing for worker")
master_client = MasterClient(build_channel(args.master_addr),
args.worker_id)
ps_client = None
if (args.distribution_strategy == DistributionStrategy.PARAMETER_SERVER
and args.ps_addrs):
ps_channels = []
ps_addrs = args.ps_addrs.split(",")
for addr in ps_addrs:
# addr is in the form as "ps-pod-name.namespace.svc:port"
channel = build_channel(addr)
succeeded = False
for i in range(CONNECT_PS_MAX_RETRIES):
try:
grpc.channel_ready_future(channel).result(
timeout=CONNECT_PS_TIMEOUT)
logger.info("grpc channel %s to connect pod %s is ready" %
(addr, addr.split(".")[0]))
ps_channels.append(channel)
succeeded = True
break
except grpc.FutureTimeoutError:
logger.warning("Failed to connect pod %s with %d retry" %
(addr.split(".")[0], i))
if not succeeded:
raise TimeoutError(
"Time out to connect pod %s with 3 retries" %
addr.split(".")[0])
ps_client = PSClient(ps_channels)
worker = Worker(
args,
master_client=master_client,
ps_client=ps_client,
set_parallelism=True,
)
worker.run()
def main():
args = parse_worker_args()
logger = log_utils.get_logger(__name__)
master_addr = args.master_addr
worker_id = int(args.worker_id)
logger.info("Starting worker %d", worker_id)
master_client = MasterClient(build_channel(master_addr), worker_id)
logger.info("Building PS connection....")
ps_client = (build_ps_client(args.ps_addrs, logger)
if args.distribution_strategy
== DistributionStrategy.PARAMETER_SERVER else None)
logger.info("Have builded PS.")
worker = Worker(
args,
master_client=master_client,
ps_client=ps_client,
set_parallelism=True,
)
worker.run()
def distributed_train_and_evaluate(
feature_shape,
model_zoo_path,
model_def,
model_params="",
eval_metrics_fn="eval_metrics_fn",
loss="loss",
training=True,
dataset_name=DatasetName.IMAGE_DEFAULT,
callback_classes=[],
use_async=False,
get_model_steps=1,
ps_channels=None,
pservers=None,
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
):
"""Runs distributed training and evaluation with a local master. Grpc
calls are mocked by local master call.
Args:
feature_shape: The shape of model input.
model_zoo_path: The directory that contains user-defined model files
or a specific model file.
model_def: The import path to the model definition function/class in
the model zoo, e.g. "cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that will
be used to instantiate the model, e.g. "param1=1,param2=2".
eval_metrics_fn: The name of the evaluation metrics function defined
in the model file.
loss: The name of the loss function defined in the model file.
training: True for job type `TRAIN_WITH_EVALUATION`, False for
job type `EVALUATION`.
dataset_name: A dataset name from `DatasetName`.
callback_classes: A List of callbacks that will be called at given
stages of the training procedure.
use_async: A bool. True if using asynchronous updates.
get_model_steps: Worker will perform `get_model` from the parameter
server every this many steps.
ps_channels: A channel list to all parameter server pods.
pservers: A list of parameter server pods.
distribution_strategy: The distribution startegy used by workers, e.g.
DistributionStrategy.PARAMETER_SERVER or
DistributionStrategy.AllreduceStrategy.
Returns:
An integer indicating the model version after the distributed training
and evaluation.
"""
job_type = (JobType.TRAINING_WITH_EVALUATION
if training else JobType.EVALUATION_ONLY)
evaluation_steps = 1 if job_type == JobType.TRAINING_WITH_EVALUATION else 0
batch_size = 8 if dataset_name == DatasetName.IMAGENET else 16
pservers = pservers or []
ps_channels = ps_channels or []
model_module = load_module(get_module_file_path(model_zoo_path,
model_def)).__dict__
for channel in ps_channels:
grpc.channel_ready_future(channel).result()
worker_arguments = [
"--worker_id",
"1",
"--job_type",
job_type,
"--minibatch_size",
batch_size,
"--model_zoo",
model_zoo_path,
"--model_def",
model_def,
"--model_params",
model_params,
"--loss",
loss,
"--get_model_steps",
get_model_steps,
"--distribution_strategy",
distribution_strategy,
]
args = parse_worker_args(worker_arguments)
worker = Worker(args, ps_channels=ps_channels)
if dataset_name in [DatasetName.IMAGENET, DatasetName.FRAPPE]:
record_num = batch_size
else:
record_num = 128
shards = {
create_recordio_file(record_num, dataset_name, feature_shape): (
0,
record_num,
)
}
if training:
training_shards = shards
evaluation_shards = shards
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
if training:
evaluation_service = EvaluationService(
None,
task_d,
0,
0,
evaluation_steps,
False,
model_module[eval_metrics_fn],
)
else:
evaluation_service = EvaluationService(
None,
task_d,
0,
0,
evaluation_steps,
True,
model_module[eval_metrics_fn],
)
task_d.set_evaluation_service(evaluation_service)
master = MasterServicer(
batch_size,
task_d,
evaluation_service=evaluation_service,
)
callbacks = [
callback_class(master, worker) for callback_class in callback_classes
]
in_process_master = InProcessMaster(master, callbacks)
worker._stub = in_process_master
for pservicer in pservers:
pservicer._master_stub = in_process_master
worker.run()
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = 1
task = master.get_task(req, None)
# No more task.
if task.shard_name:
raise RuntimeError(
"There are some tasks unfinished after worker exits.")
return master._version
def distributed_train_and_evaluate(
feature_shape,
model_zoo_path,
model_def,
model_params="",
eval_metrics_fn="eval_metrics_fn",
training=True,
dataset_name=DatasetName.IMAGE_DEFAULT,
callback_classes=[],
use_async=False,
get_model_steps=1,
):
"""Runs distributed training and evaluation with a local master. Grpc
calls are mocked by local master call.
Args:
feature_shape: The shape of model input.
model_zoo_path: The directory that contains user-defined model files
or a specific model file.
model_def: The import path to the model definition function/class in
the model zoo, e.g. "cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that will
be used to instantiate the model, e.g. "param1=1,param2=2".
training: True for job type `TRAIN_WITH_EVALUATION`, False for
job type `EVALUATION`.
dataset_name: A dataset name from `DatasetName`.
callback_classes: A List of callbacks that will be called at given
stages of the training procedure.
use_async: A python bool. True if using asynchronous updates.
get_model_steps: Worker will perform `get_model` from the parameter
server every this many steps.
Returns:
An integer indicating the model version after the distributed training
and evaluation.
"""
job_type = (JobType.TRAINING_WITH_EVALUATION
if training else JobType.EVALUATION_ONLY)
batch_size = 8 if dataset_name == DatasetName.IMAGENET else 16
arguments = [
"--worker_id",
"1",
"--job_type",
job_type,
"--minibatch_size",
batch_size,
"--model_zoo",
model_zoo_path,
"--model_def",
model_def,
"--model_params",
model_params,
"--get_model_steps",
get_model_steps,
]
args = parse_worker_args(arguments)
worker = Worker(args)
if dataset_name in [DatasetName.IMAGENET, DatasetName.FRAPPE]:
record_num = batch_size
else:
record_num = 128
shards = {
create_recordio_file(record_num, dataset_name, feature_shape): (
0,
record_num,
)
}
if training:
training_shards = shards
evaluation_shards = shards
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
model_module = load_module(get_module_file_path(model_zoo_path,
model_def)).__dict__
checkpoint_service = CheckpointService("", 0, 0, True)
if training:
evaluation_service = EvaluationService(
checkpoint_service,
None,
task_d,
0,
0,
1,
False,
model_module[eval_metrics_fn],
)
else:
evaluation_service = EvaluationService(
checkpoint_service,
None,
task_d,
0,
0,
0,
True,
model_module[eval_metrics_fn],
)
task_d.set_evaluation_service(evaluation_service)
grads_to_wait = 1 if use_async else 2
master = MasterServicer(
grads_to_wait,
batch_size,
worker._opt_fn(),
task_d,
init_var=[],
checkpoint_filename_for_init="",
checkpoint_service=checkpoint_service,
evaluation_service=evaluation_service,
use_async=use_async,
)
callbacks = [
callback_class(master, worker) for callback_class in callback_classes
]
worker._stub = InProcessMaster(master, callbacks)
for var in worker._model.trainable_variables:
master.set_model_var(var.name, var.numpy())
worker.run()
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = 1
task = master.GetTask(req, None)
# No more task.
if task.shard_name:
raise RuntimeError(
"There are some tasks unfinished after worker exits.")
return master._version
def distributed_train_and_evaluate(
self,
feature_shape,
model_def,
model_params="",
training=True,
dataset="",
):
"""
Run distributed training and evaluation with a local master.
grpc calls are mocked by local master call.
"""
job_type = (JobType.TRAINING_ONLY
if training else JobType.EVALUATION_ONLY)
batch_size = 16
worker = Worker(
1,
job_type,
batch_size,
_model_zoo_path,
model_def=model_def,
model_params=model_params,
channel=None,
)
if dataset == "imagenet":
batch_size = 8
shards = {create_imagenet_recordio_file(8, feature_shape): (0, 8)}
elif dataset == "frappe":
shards = {
create_frappe_recordio_file(16, feature_shape, 5383): (0, 16)
}
else:
shards = {create_recordio_file(128, feature_shape): (0, 128)}
if training:
training_shards = shards
evaluation_shards = shards
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
# Initialize checkpoint service
checkpoint_service = CheckpointService("", 0, 0, True)
if training:
evaluation_service = EvaluationService(checkpoint_service, None,
task_d, 0, 0, 1, False)
else:
evaluation_service = EvaluationService(checkpoint_service, None,
task_d, 0, 0, 0, True)
task_d.set_evaluation_service(evaluation_service)
# The master service
master = MasterServicer(
2,
batch_size,
worker._opt_fn(),
task_d,
init_var=[],
checkpoint_filename_for_init="",
checkpoint_service=checkpoint_service,
evaluation_service=evaluation_service,
)
worker._stub = InProcessMaster(master)
for var in worker._model.trainable_variables:
master.set_model_var(var.name, var.numpy())
worker.run()
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = 1
task = master.GetTask(req, None)
# No more task.
self.assertTrue(not task.shard_name)
def distributed_train_and_evaluate(self, training=True):
"""
Run distributed training and evaluation with a local master.
grpc calls are mocked by local master call.
"""
class _Master(InProcessMaster):
def ReportGradient(self, req):
if 2 < self._m._version < 80:
# For testing of retrain when gradient not accepted.
# Increase master version to reject the gradient.
self._m._version += 1
return self._m.ReportGradient(req, None)
def ReportEvaluationMetrics(self, req):
if 2 < self._m._version < 80:
# Testing of evaluation retries. Increase the master
# version so the evaluation metrics will not be accepted.
self._m._version += 1
return self._m.ReportEvaluationMetrics(req, None)
job_type = (JobType.TRAINING_ONLY
if training else JobType.EVALUATION_ONLY)
batch_size = 16
worker = Worker(
1,
job_type,
batch_size,
_model_zoo_path,
model_def="test_module.custom_model",
channel=None,
)
shards = {create_recordio_file(128): 128}
if training:
training_shards = shards
evaluation_shards = {}
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
if not training:
evaluation_service = EvaluationService(None, None, task_d, 0, 0, 0,
True)
task_d.set_evaluation_service(evaluation_service)
else:
evaluation_service = None
master = MasterServicer(
2,
batch_size,
worker._opt_fn(),
task_d,
init_var=[],
checkpoint_filename_for_init="",
checkpoint_service=None,
evaluation_service=evaluation_service,
)
worker._stub = _Master(master)
for var in worker._model.trainable_variables:
master.set_model_var(var.name, var.numpy())
worker.run()
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = 1
task = master.GetTask(req, None)
# No more task.
self.assertTrue(not task.shard_name)
def testMaxCheckpointVersions(self):
with tempfile.TemporaryDirectory() as tempdir:
chkp_dir = os.path.join(tempdir, "testMaxCheckpointVersions")
os.makedirs(chkp_dir)
# Save checkpoints every 2 steps, and keep 5 checkpoints at most
checkpointer = CheckpointService(chkp_dir, 2, 5, False)
self.assertTrue(checkpointer.is_enabled())
batch_size = 2
# Launch the training
arguments = [
"--worker_id",
1,
"--job_type",
JobType.TRAINING_ONLY,
"--minibatch_size",
batch_size,
"--model_zoo",
_model_zoo_path,
"--model_def",
"test_module.custom_model",
]
args = parse_worker_args(arguments)
worker = Worker(args)
filename = create_recordio_file(128, DatasetName.TEST_MODULE, 1)
task_d = _TaskDispatcher({filename: (0, 128)}, {}, {},
records_per_task=64,
num_epochs=1)
master = MasterServicer(
2,
batch_size,
worker._opt_fn(),
task_d,
init_var=worker._model.trainable_variables,
checkpoint_filename_for_init="",
checkpoint_service=checkpointer,
evaluation_service=None,
)
worker._stub = InProcessMaster(master)
worker.run()
# We should have 5 checkpoints when the training finishes
checkpoint_files = sorted(os.listdir(checkpointer._directory))
self.assertEqual(
checkpoint_files,
[
"model_v24.chkpt",
"model_v26.chkpt",
"model_v28.chkpt",
"model_v30.chkpt",
"model_v32.chkpt",
],
)
# Latest version should be 32
self.assertEqual(32, checkpointer.get_latest_checkpoint_version())
# Check all checkpoints
for version in [24, 26, 28, 30, 32]:
model = checkpointer.get_checkpoint_model(version)
self.assertEqual(version, model.version)
# Checkpoint not found
self.assertRaisesRegex(
RuntimeError,
"Failed to read model checkpoint from file",
checkpointer.get_checkpoint_model,
100,
)
def distributed_train_and_evaluate(
feature_shape,
model_zoo_path,
model_def,
model_params="",
eval_metrics_fn="eval_metrics_fn",
loss="loss",
training=True,
dataset_name=DatasetName.IMAGE_DEFAULT,
use_async=False,
get_model_steps=1,
ps_channels=None,
pservers=None,
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
):
"""Runs distributed training and evaluation with a local master. Grpc
calls are mocked by local master call.
Args:
feature_shape: The shape of model input.
model_zoo_path: The directory that contains user-defined model files
or a specific model file.
model_def: The import path to the model definition function/class in
the model zoo, e.g. "cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that will
be used to instantiate the model, e.g. "param1=1,param2=2".
eval_metrics_fn: The name of the evaluation metrics function defined
in the model file.
loss: The name of the loss function defined in the model file.
training: True for job type `TRAIN_WITH_EVALUATION`, False for
job type `EVALUATION`.
dataset_name: A dataset name from `DatasetName`.
use_async: A bool. True if using asynchronous updates.
get_model_steps: Worker will perform `get_model` from the parameter
server every this many steps.
ps_channels: A channel list to all parameter server pods.
pservers: A list of parameter server pods.
distribution_strategy: The distribution startegy used by workers, e.g.
DistributionStrategy.PARAMETER_SERVER or
DistributionStrategy.AllreduceStrategy.
Returns:
An integer indicating the model version after the distributed training
and evaluation.
"""
job_type = (JobType.TRAINING_WITH_EVALUATION
if training else JobType.EVALUATION_ONLY)
evaluation_steps = 1 if job_type == JobType.TRAINING_WITH_EVALUATION else 0
batch_size = 8 if dataset_name == DatasetName.IMAGENET else 16
pservers = pservers or []
ps_channels = ps_channels or []
model_module = load_module(get_module_file_path(model_zoo_path,
model_def)).__dict__
worker_arguments = [
"--worker_id",
"1",
"--job_type",
job_type,
"--minibatch_size",
batch_size,
"--model_zoo",
model_zoo_path,
"--model_def",
model_def,
"--model_params",
model_params,
"--loss",
loss,
"--get_model_steps",
get_model_steps,
"--distribution_strategy",
distribution_strategy,
]
args = parse_worker_args(worker_arguments)
if dataset_name in [DatasetName.IMAGENET, DatasetName.FRAPPE]:
record_num = batch_size
else:
record_num = 128
shards = {
create_recordio_file(record_num, dataset_name, feature_shape): (
0,
record_num,
)
}
if training:
training_shards = shards
evaluation_shards = shards
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
if training:
evaluation_service = EvaluationService(
None,
task_d,
0,
0,
evaluation_steps,
False,
model_module[eval_metrics_fn],
)
else:
evaluation_service = EvaluationService(
None,
task_d,
0,
0,
evaluation_steps,
True,
model_module[eval_metrics_fn],
)
task_d.set_evaluation_service(evaluation_service)
master = Mock(
task_d=task_d,
instance_manager=None,
distribution_strategy=None,
)
def master_creator():
return MasterServicer(
batch_size,
evaluation_service=evaluation_service,
master=master,
)
svc, port = _server(master_creator)
mc = MasterClient(build_channel("localhost:%d" % port), 1)
worker = Worker(args, master_client=mc, ps_client=PSClient(ps_channels))
for pservicer in pservers:
# FIXME(yancey1989): decouple pserver and master client
pservicer._master_stub = mc
worker.run()
task = mc.get_task()
# stop the master servicer
svc.stop(0)
# No more task.
if task.shard_name:
raise RuntimeError(
"There are some tasks unfinished after worker exits.")
return task.model_version
def distributed_train_and_evaluate(
self,
training=True,
callback_classes=[],
use_async=False,
grads_to_wait=2,
get_model_steps=1,
):
"""
Run distributed training and evaluation with a local master.
grpc calls are mocked by local master call.
"""
if use_async and grads_to_wait > 1:
raise ValueError(
"grads_to_wait should be 1 when using asynchronous SGD."
)
job_type = (
JobType.TRAINING_ONLY if training else JobType.EVALUATION_ONLY
)
batch_size = 16
worker = Worker(
1,
job_type,
batch_size,
_model_zoo_path,
model_def="test_module.custom_model",
channel=None,
get_model_steps=get_model_steps,
)
shards = {create_recordio_file(128): (0, 128)}
if training:
training_shards = shards
evaluation_shards = {}
else:
training_shards = {}
evaluation_shards = shards
task_d = _TaskDispatcher(
training_shards,
evaluation_shards,
{},
records_per_task=64,
num_epochs=1,
)
if not training:
evaluation_service = EvaluationService(
None, None, task_d, 0, 0, 0, True
)
task_d.set_evaluation_service(evaluation_service)
else:
evaluation_service = None
master = MasterServicer(
grads_to_wait,
batch_size,
worker._opt_fn(),
task_d,
init_var=[],
checkpoint_filename_for_init="",
checkpoint_service=None,
evaluation_service=evaluation_service,
use_async=use_async,
)
callbacks = [
callback_class(master, worker, self)
for callback_class in callback_classes
]
worker._stub = InProcessMaster(master, callbacks)
for var in worker._model.trainable_variables:
master.set_model_var(var.name, var.numpy())
worker.run()
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = 1
task = master.GetTask(req, None)
# No more task.
self.assertTrue(not task.shard_name)
