def _run_mnist_training_job(sagemaker_session, instance_type, instance_count,
chainer_version, py_version):
script_path = (os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
if instance_type == 1 else os.path.join(
DATA_DIR, "chainer_mnist", "distributed_mnist.py"))
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
framework_version=chainer_version,
py_version=py_version,
instance_count=instance_count,
instance_type=instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={"epochs": 1},
# test output_path without trailing slash
output_path="s3://{}".format(sagemaker_session.default_bucket()),
)
train_input = "file://" + os.path.join(data_path, "train")
test_input = "file://" + os.path.join(data_path, "test")
job_name = unique_name_from_base("test-chainer-training")
chainer.fit({"train": train_input, "test": test_input}, job_name=job_name)
return chainer
def test_training_with_additional_hyperparameters(sagemaker_session):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
sagemaker_session=sagemaker_session,
hyperparameters={'epochs': 1},
use_mpi=True,
num_processes=2,
process_slots_per_host=2,
additional_mpi_options="-x NCCL_DEBUG=INFO")
train_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
chainer.fit({'train': train_input, 'test': test_input})
return chainer.latest_training_job.name
def _run_mnist_training_job(sagemaker_session,
instance_type,
instance_count,
chainer_full_version,
wait=True):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py') if instance_type == 1 else \
os.path.join(DATA_DIR, 'chainer_mnist', 'distributed_mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path,
role='SageMakerRole',
framework_version=chainer_full_version,
train_instance_count=instance_count,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={'epochs': 1})
train_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
chainer.fit({'train': train_input, 'test': test_input}, wait=wait)
return chainer.latest_training_job.name
def test_training_with_additional_hyperparameters(sagemaker_local_session,
chainer_latest_version,
chainer_latest_py_version):
script_path = os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
instance_count=1,
instance_type="local",
framework_version=chainer_latest_version,
py_version=chainer_latest_py_version,
sagemaker_session=sagemaker_local_session,
hyperparameters={"epochs": 1},
use_mpi=True,
num_processes=2,
process_slots_per_host=2,
additional_mpi_options="-x NCCL_DEBUG=INFO",
)
train_input = "file://" + os.path.join(data_path, "train")
test_input = "file://" + os.path.join(data_path, "test")
chainer.fit({"train": train_input, "test": test_input})
def test_training_with_additional_hyperparameters(sagemaker_session,
chainer_full_version):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
framework_version=chainer_full_version,
py_version=PYTHON_VERSION,
sagemaker_session=sagemaker_session,
hyperparameters={'epochs': 1},
use_mpi=True,
num_processes=2,
process_slots_per_host=2,
additional_mpi_options="-x NCCL_DEBUG=INFO")
train_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
job_name = unique_name_from_base('test-chainer-training')
chainer.fit({
'train': train_input,
'test': test_input
},
job_name=job_name)
return chainer.latest_training_job.name
def _run_mnist_training_job(sagemaker_session,
instance_type,
instance_count,
chainer_full_version,
wait=True):
script_path = (os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
if instance_type == 1 else os.path.join(
DATA_DIR, "chainer_mnist", "distributed_mnist.py"))
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
framework_version=chainer_full_version,
py_version=PYTHON_VERSION,
train_instance_count=instance_count,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={"epochs": 1},
)
train_input = "file://" + os.path.join(data_path, "train")
test_input = "file://" + os.path.join(data_path, "test")
job_name = unique_name_from_base("test-chainer-training")
chainer.fit({
"train": train_input,
"test": test_input
},
wait=wait,
job_name=job_name)
return chainer
def main(parser=argparse.ArgumentParser()):
import logging
logging.basicConfig(level=logging.WARN)
parser.add_argument('--profile', type=str)
parser.add_argument('--local-mode', action='store_true')
parser.add_argument('--instance-type',
type=str,
default=DEFAULT_INSTANCE_TYPE)
parser.add_argument('--region', type=str, default=DEFAULT_REGION)
parser.add_argument('--arn', type=str, default=None)
parser.add_argument('--max-runtime',
type=int,
default=DEFAULT_RUNTIME,
help='seconds')
args = parser.parse_args()
boto_session = Session(profile_name=args.profile, region_name=args.region)
sagemaker_session = sagemaker.Session(boto_session=boto_session)
role = args.arn if args.arn is not None else sagemaker.get_execution_role(
sagemaker_session)
hyperparameters = {
'gpu':
0 if args.instance_type.startswith('ml.p') else -1,
'mjcf':
'env/ant_simple.xml',
'action-dim':
8,
'obs-dim':
28,
'skip-step':
10,
'algorithm':
'TRPO',
'foot-list':
'right_back_foot left_back_foot front_right_foot front_left_foot'
}
chainer_estimator = Chainer(
entry_point='train.py',
source_dir='../',
role=role,
image_name=IMAGE,
framework_version='5.0.0',
sagemaker_session=LocalSession(boto_session)
if args.local_mode else sagemaker_session,
train_instance_count=1,
train_instance_type='local' if args.local_mode else args.instance_type,
hyperparameters=hyperparameters,
base_job_name='roboschool-TRPO-skipstep10-continue-1',
train_max_run=args.max_runtime)
chainer_estimator.fit(wait=args.local_mode)
def test_failed_training_job(sagemaker_session, chainer_full_version):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'failure_script.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path, role='SageMakerRole',
framework_version=chainer_full_version,
train_instance_count=1, train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session)
train_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
with pytest.raises(ValueError):
chainer.fit(train_input)
def test_failed_training_job(sagemaker_session, chainer_full_version):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'failure_script.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path, role='SageMakerRole',
framework_version=chainer_full_version,
train_instance_count=1, train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session)
train_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
with pytest.raises(ValueError):
chainer.fit(train_input)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_async_fit(sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(
sagemaker_timestamp())
with timeout(minutes=5):
training_job_name = _run_mnist_training_job(
sagemaker_session,
"ml.c4.xlarge",
1,
chainer_full_version=CHAINER_VERSION,
wait=False)
print("Waiting to re-attach to the training job: %s" %
training_job_name)
time.sleep(20)
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=35):
print("Re-attaching now to: %s" % training_job_name)
estimator = Chainer.attach(training_job_name=training_job_name,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
"ml.c4.xlarge",
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_failed_training_job(sagemaker_session, chainer_full_version):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist',
'failure_script.py')
chainer = Chainer(entry_point=script_path,
role='SageMakerRole',
framework_version=chainer_full_version,
py_version=PYTHON_VERSION,
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session)
with pytest.raises(ValueError) as e:
chainer.fit()
assert 'This failure is expected' in str(e.value)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = unique_name_from_base('test-chainer-attach-deploy')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
'ml.m4.xlarge',
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(sagemaker_session, chainer_latest_version,
chainer_latest_py_version, cpu_instance_type):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
framework_version=chainer_latest_version,
py_version=chainer_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={"epochs": 1},
)
train_input = sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/chainer_mnist/train")
test_input = sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/chainer_mnist/test")
job_name = unique_name_from_base("test-chainer-training")
chainer.fit({
"train": train_input,
"test": test_input
},
wait=False,
job_name=job_name)
endpoint_name = unique_name_from_base("test-chainer-attach-deploy")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer.latest_training_job.name,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
cpu_instance_type,
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def main(parser=argparse.ArgumentParser()):
import logging
logging.basicConfig(level=logging.WARN)
parser.add_argument('--profile', type=str, default='default')
parser.add_argument('--local-mode', action='store_true')
parser.add_argument('--instance-type',
type=str,
default=DEFAULT_INSTANCE_TYPE)
parser.add_argument('--region', type=str, default=DEFAULT_REGION)
parser.add_argument('--arn', type=str, default=None)
parser.add_argument('--max-runtime',
type=int,
default=DEFAULT_RUNTIME,
help='seconds')
args = parser.parse_args()
boto_session = Session(profile_name=args.profile, region_name=args.region)
sagemaker_session = LocalSession(
boto_session) if args.local_mode else sagemaker.Session(boto_session)
role = args.arn if args.arn is not None else sagemaker.get_execution_role(
sagemaker_session)
gpu = 0 if args.instance_type.startswith(
'ml.p') and not args.local_mode else -1
hyperparameters = {
'gpu': gpu,
}
chainer_estimator = Chainer(
entry_point='train.py',
source_dir='./',
role=role,
image_name=IMAGE,
framework_version='5.0.0',
sagemaker_session=sagemaker_session,
train_instance_count=1,
train_instance_type='local' if args.local_mode else args.instance_type,
hyperparameters=hyperparameters,
base_job_name='chainer-sagemaker-sample',
train_max_run=args.max_runtime)
chainer_estimator.fit(wait=args.local_mode)
def _run_mnist_training_job(sagemaker_session, instance_type, instance_count,
chainer_full_version, wait=True):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py') if instance_type == 1 else \
os.path.join(DATA_DIR, 'chainer_mnist', 'distributed_mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path, role='SageMakerRole',
framework_version=chainer_full_version,
train_instance_count=instance_count, train_instance_type=instance_type,
sagemaker_session=sagemaker_session, hyperparameters={'epochs': 1})
train_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
chainer.fit({'train': train_input, 'test': test_input}, wait=wait)
return chainer.latest_training_job.name
def test_training_with_additional_hyperparameters(sagemaker_session, chainer_full_version):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path, role='SageMakerRole',
train_instance_count=1, train_instance_type="ml.c4.xlarge",
framework_version=chainer_full_version,
sagemaker_session=sagemaker_session, hyperparameters={'epochs': 1},
use_mpi=True,
num_processes=2,
process_slots_per_host=2,
additional_mpi_options="-x NCCL_DEBUG=INFO")
train_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
chainer.fit({'train': train_input, 'test': test_input})
return chainer.latest_training_job.name
def _run_mnist_training_job(sagemaker_session,
instance_type,
instance_count,
chainer_full_version,
wait=True):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = (os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
if instance_type == 1 else os.path.join(
DATA_DIR, "chainer_mnist", "distributed_mnist.py"))
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
framework_version=chainer_full_version,
py_version=PYTHON_VERSION,
train_instance_count=instance_count,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={"epochs": 1},
)
train_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/chainer_mnist/train")
test_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/chainer_mnist/test")
job_name = unique_name_from_base("test-chainer-training")
chainer.fit({
"train": train_input,
"test": test_input
},
wait=wait,
job_name=job_name)
return chainer.latest_training_job.name
def _run_mnist_training_job(sagemaker_session,
instance_type,
instance_count,
chainer_full_version,
wait=True):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py') if instance_type == 1 else \
os.path.join(DATA_DIR, 'chainer_mnist', 'distributed_mnist.py')
data_path = os.path.join(DATA_DIR, 'chainer_mnist')
chainer = Chainer(entry_point=script_path,
role='SageMakerRole',
framework_version=chainer_full_version,
py_version=PYTHON_VERSION,
train_instance_count=instance_count,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={'epochs': 1})
train_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/chainer_mnist/train')
test_input = chainer.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/chainer_mnist/test')
job_name = unique_name_from_base('test-chainer-training')
chainer.fit({
'train': train_input,
'test': test_input
},
wait=wait,
job_name=job_name)
return chainer.latest_training_job.name
def test_async_fit(sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout(minutes=5):
training_job_name = _run_mnist_training_job(sagemaker_session, "ml.c4.xlarge", 1,
chainer_full_version=CHAINER_VERSION, wait=False)
print("Waiting to re-attach to the training job: %s" % training_job_name)
time.sleep(20)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
print("Re-attaching now to: %s" % training_job_name)
estimator = Chainer.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, "ml.c4.xlarge", endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def exec_training(session, client, job_name, setting, pytorch,
max_parallel_jobs):
sagemaker_session = sagemaker.Session(boto_session=session,
sagemaker_client=client)
conf = yaml.load(open(setting))
# input data
inputs = conf['inputs']
if 'upload_data' in conf and isinstance(conf['upload_data'], list):
for d in conf['upload_data']:
s3_dir = sagemaker_session.upload_data(path=d['path'],
key_prefix=os.path.join(
job_name,
d['key_prefix']))
inputs[d['name']] = s3_dir
estimator_args = conf['estimator']
estimator_args['sagemaker_session'] = sagemaker_session
hyperparameters = estimator_args.pop('hyperparameters')
fixed, targets = {}, {}
for k, v in hyperparameters.items():
if isinstance(v, dict):
targets[k] = v
else:
fixed[k] = v
estimator_args['hyperparameters'] = fixed
if pytorch:
estimator = PyTorch(**estimator_args)
else:
estimator = Chainer(**estimator_args)
if len(targets) == 0:
estimator.fit(inputs, job_name=job_name)
else:
if 'tuner' in conf:
tuner_args = conf['tuner']
hyperparameter_ranges = {}
for k, v in targets.items():
hyperparameter_ranges[k] = hp_type[v['type'].lower()](
v['range'])
else: # use default values
tuner_args = {
'objective_metric_name': 'metric_name',
'metric_definitions': [{
'Name': 'metric_name',
'Regex': 'ignore'
}],
'strategy': 'Random',
'objective_type': 'Maximize',
'early_stopping_type': 'Off'
}
max_jobs = 1
hyperparameter_ranges = {}
for k, v in targets.items():
if v['type'].lower() != 'categorical':
raise ValueError(
'the default tuner only supports Categorigal params.')
max_jobs *= len(v['range'])
hyperparameter_ranges[k] = hp_type[v['type'].lower()](
v['range'])
tuner_args['max_jobs'] = max_jobs
tuner_args['estimator'] = estimator
tuner_args['hyperparameter_ranges'] = hyperparameter_ranges
tuner_args['max_parallel_jobs'] = max_parallel_jobs
tuner_args['base_tuning_job_name'] = job_name
tuner_args['warm_start_config'] = None # not supported yet.
tuner = HyperparameterTuner(**tuner_args)
tuner.fit(inputs, job_name=job_name)
entry_point="entry_point.py",
),
RLEstimator(
entry_point="cartpole.py",
toolkit=RLToolkit.RAY,
framework=RLFramework.TENSORFLOW,
toolkit_version="0.8.5",
role=sagemaker.get_execution_role(),
instance_type=INSTANCE_TYPE,
instance_count=1,
),
Chainer(
role=sagemaker.get_execution_role(),
entry_point="entry_point.py",
use_mpi=True,
num_processes=4,
framework_version="5.0.0",
instance_type=INSTANCE_TYPE,
instance_count=1,
py_version="py3",
),
],
)
def test_training_step_with_framework_estimator(estimator, pipeline_session,
training_input,
hyperparameters):
estimator.source_dir = DUMMY_S3_SOURCE_DIR
estimator.set_hyperparameters(**hyperparameters)
estimator.volume_kms_key = "volume-kms-key"
estimator.output_kms_key = "output-kms-key"
estimator.dependencies = ["dep-1", "dep-2"]
def exec_training(
session, client, job_name, setting, pytorch, max_parallel_jobs, is_spot
):
sagemaker_session = sagemaker.Session(
boto_session=session, sagemaker_client=client
)
conf = yaml.load(open(setting))
# input data
inputs = conf["inputs"]
if "upload_data" in conf and isinstance(conf["upload_data"], list):
for d in conf["upload_data"]:
s3_dir = sagemaker_session.upload_data(
path=d["path"],
key_prefix=os.path.join(job_name, d["key_prefix"]),
)
inputs[d["name"]] = s3_dir
estimator_args = conf["estimator"]
estimator_args["sagemaker_session"] = sagemaker_session
hyperparameters = estimator_args.pop("hyperparameters")
fixed, targets = {}, {}
for k, v in hyperparameters.items():
if isinstance(v, dict):
targets[k] = v
else:
fixed[k] = v
estimator_args["hyperparameters"] = fixed
if is_spot:
estimator_args["train_use_spot_instances"] = True
if "checkpoint_s3_uri" not in estimator_args:
bucket_name = sagemaker_session.default_bucket()
uri = os.path.join("s3://", bucket_name, job_name, "checkpoints")
estimator_args["checkpoint_s3_uri"] = uri
if pytorch:
estimator = PyTorch(**estimator_args)
else:
estimator = Chainer(**estimator_args)
if len(targets) == 0:
estimator.fit(inputs, wait=False, job_name=job_name)
else:
if "tuner" in conf:
tuner_args = conf["tuner"]
hyperparameter_ranges = {}
for k, v in targets.items():
hyperparameter_ranges[k] = hp_type[v["type"].lower()](
v["range"]
)
else: # use default values
tuner_args = {
"objective_metric_name": "metric_name",
"metric_definitions": [
{"Name": "metric_name", "Regex": "ignore"}
],
"strategy": "Random",
"objective_type": "Maximize",
"early_stopping_type": "Off",
}
max_jobs = 1
hyperparameter_ranges = {}
for k, v in targets.items():
if v["type"].lower() != "categorical":
raise ValueError(
"the default tuner only supports Categorigal params."
)
max_jobs *= len(v["range"])
hyperparameter_ranges[k] = hp_type[v["type"].lower()](
v["range"]
)
tuner_args["max_jobs"] = max_jobs
tuner_args["estimator"] = estimator
tuner_args["hyperparameter_ranges"] = hyperparameter_ranges
tuner_args["max_parallel_jobs"] = max_parallel_jobs
tuner_args["base_tuning_job_name"] = job_name
tuner_args["warm_start_config"] = None # not supported yet.
tuner = HyperparameterTuner(**tuner_args)
tuner.fit(inputs, job_name=job_name)
