1787f783e2f9fc4f2144bd4b4f90281a2bb018b5,tests/integ/test_inference_pipeline.py,,test_inference_pipeline_model_deploy,#Any#,33

Before Change

    xgb_model_data = sagemaker_session.upload_data(
        path=os.path.join(xgboost_data_path, "xgb_model.tar.gz"),
        key_prefix="integ-test-data/xgboost/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):
        sparkml_model = SparkMLModel(model_data=sparkml_model_data,
                                     env={"SAGEMAKER_SPARKML_SCHEMA": schema},

After Change

                                      serializer=json_serializer, content_type=CONTENT_TYPE_CSV,
                                      accept=CONTENT_TYPE_CSV)

        with open(VALID_DATA_PATH, "r") as f:
            valid_data = f.read()
            assert predictor.predict(valid_data) == "0.714013934135"

        with open(INVALID_DATA_PATH, "r") as f:
            invalid_data = f.read()
            assert (predictor.predict(invalid_data) is None)

Instances