def test_marketplace_model(sagemaker_session, cpu_instance_type):
region = sagemaker_session.boto_region_name
account = REGION_ACCOUNT_MAP[region]
model_package_arn = MODEL_PACKAGE_ARN % (region, account)
def predict_wrapper(endpoint, session):
return sagemaker.RealTimePredictor(
endpoint, session, serializer=sagemaker.predictor.csv_serializer)
model = ModelPackage(
role="SageMakerRole",
model_package_arn=model_package_arn,
sagemaker_session=sagemaker_session,
predictor_cls=predict_wrapper,
)
endpoint_name = "test-marketplace-model-endpoint{}".format(
sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=20):
predictor = model.deploy(1,
cpu_instance_type,
endpoint_name=endpoint_name)
data_path = os.path.join(DATA_DIR, "marketplace", "training")
shape = pandas.read_csv(os.path.join(data_path, "iris.csv"),
header=None)
a = [50 * i for i in range(3)]
b = [40 + i for i in range(10)]
indices = [i + j for i, j in itertools.product(a, b)]
test_data = shape.iloc[indices[:-1]]
test_x = test_data.iloc[:, 1:]
print(predictor.predict(test_x.values).decode("utf-8"))
def test_marketplace_model(sagemaker_session):
def predict_wrapper(endpoint, session):
return sagemaker.RealTimePredictor(
endpoint, session, serializer=sagemaker.predictor.csv_serializer)
model = ModelPackage(
role='SageMakerRole',
model_package_arn=(MODEL_PACKAGE_ARN %
sagemaker_session.boto_region_name),
sagemaker_session=sagemaker_session,
predictor_cls=predict_wrapper)
endpoint_name = 'test-marketplace-model-endpoint{}'.format(
sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=20):
predictor = model.deploy(1,
'ml.m4.xlarge',
endpoint_name=endpoint_name)
data_path = os.path.join(DATA_DIR, 'marketplace', 'training')
shape = pandas.read_csv(os.path.join(data_path, 'iris.csv'),
header=None)
a = [50 * i for i in range(3)]
b = [40 + i for i in range(10)]
indices = [i + j for i, j in itertools.product(a, b)]
test_data = shape.iloc[indices[:-1]]
test_x = test_data.iloc[:, 1:]
print(predictor.predict(test_x.values).decode('utf-8'))
EndpointName=args.endpoint_name,
EndpointConfigName=ep_config_name
)
create_config('Y')
except ClientError as error:
# endpoint does not exist
if "Could not find endpoint" in error.response['Error']['Message']:
model_package_approved = get_approved_package(args.model_package_group_name)
model_package_arn = model_package_approved["ModelPackageArn"]
model = ModelPackage(role=args.role,
model_package_arn=model_package_arn,
sagemaker_session=sagemaker_session)
try:
model.deploy(initial_instance_count=args.initial_instance_count,
instance_type=args.endpoint_instance_type,
endpoint_name=args.endpoint_name)
create_config('Y')
except ClientError as error:
print(error.response['Error']['Message'])
create_config('N')
error_message = error.response["Error"]["Message"]
LOGGER.error("{}".format(stacktrace))
raise Exception(error_message)
else:
print(error.response['Error']['Message'])
create_config('N')
error_message = error.response["Error"]["Message"]
LOGGER.error("{}".format(stacktrace))
raise Exception(error_message)
