def test_sparkml_model_deploy(sagemaker_session, cpu_instance_type):
# Uploads an MLeap serialized MLeap model to S3 and use that to deploy a SparkML model to perform inference
data_path = os.path.join(DATA_DIR, "sparkml_model")
endpoint_name = "test-sparkml-deploy-{}".format(sagemaker_timestamp())
model_data = sagemaker_session.upload_data(
path=os.path.join(data_path, "mleap_model.tar.gz"),
key_prefix="integ-test-data/sparkml/model",
)
schema = json.dumps(
{
"input": [
{"name": "Pclass", "type": "float"},
{"name": "Embarked", "type": "string"},
{"name": "Age", "type": "float"},
{"name": "Fare", "type": "float"},
{"name": "SibSp", "type": "float"},
{"name": "Sex", "type": "string"},
],
"output": {"name": "features", "struct": "vector", "type": "double"},
}
)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = SparkMLModel(
model_data=model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session,
env={"SAGEMAKER_SPARKML_SCHEMA": schema},
)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
valid_data = "1.0,C,38.0,71.5,1.0,female"
assert predictor.predict(valid_data) == "1.0,0.0,38.0,1.0,71.5,0.0,1.0"
invalid_data = "1.0,28.0,C,38.0,71.5,1.0"
assert predictor.predict(invalid_data) is None
from pathlib import Path
import boto3
import sagemaker
from cfn_tools import load_yaml
from sagemaker.sparkml.model import SparkMLModel
account = boto3.client('sts').get_caller_identity()['Account']
with open(Path(__file__).parents[2]/'setup/stack.yaml') as f:
env = load_yaml(f)
file_name = "model.tar.gz"
bucket_name = env['Parameters']['PreBucket']['Default']
role = f'arn:aws:iam::{account}:role/sagemaker-role'
endpoint_name = env['Mappings']['TaskMap']['predict']['name']
model_name = "model"
instance_type = "ml.t2.medium"
if __name__ == "__main__":
sparkml_model = SparkMLModel(model_data=f"s3://{bucket_name}/{file_name}",
role=role,
sagemaker_session=sagemaker.Session(
boto3.session.Session()),
name=model_name)
sparkml_model.deploy(initial_instance_count=1,
instance_type=instance_type,
endpoint_name=endpoint_name)
