def test_marketplace_estimator(sagemaker_session):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, 'marketplace', 'training')
region = sagemaker_session.boto_region_name
account = REGION_ACCOUNT_MAP[region]
algorithm_arn = ALGORITHM_ARN % (region, account)
algo = AlgorithmEstimator(algorithm_arn=algorithm_arn,
role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session)
train_input = algo.sagemaker_session.upload_data(
path=data_path, key_prefix='integ-test-data/marketplace/train')
algo.fit({'training': train_input})
endpoint_name = 'test-marketplace-estimator{}'.format(
sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=20):
predictor = algo.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
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_estimator(sagemaker_session, cpu_instance_type):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, "marketplace", "training")
region = sagemaker_session.boto_region_name
account = REGION_ACCOUNT_MAP[region]
algorithm_arn = ALGORITHM_ARN.format(partition=_aws_partition(region),
region=region,
account=account)
algo = AlgorithmEstimator(
algorithm_arn=algorithm_arn,
role="SageMakerRole",
train_instance_count=1,
train_instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
)
train_input = algo.sagemaker_session.upload_data(
path=data_path, key_prefix="integ-test-data/marketplace/train")
algo.fit({"training": train_input})
endpoint_name = "test-marketplace-estimator{}".format(
sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=20):
predictor = algo.deploy(1,
cpu_instance_type,
endpoint_name=endpoint_name)
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"))
