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fix region error in test #534

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Merged
merged 2 commits into from
Dec 8, 2018
Merged

fix region error in test #534

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b42d5ee
fix region error in test
Dec 7, 2018
842e7f9
Merge branch 'master' into master
jesterhazy Dec 7, 2018
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24 changes: 13 additions & 11 deletions tests/integ/test_inference_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,30 +16,30 @@
import os

import pytest
from tests.integ import DATA_DIR
from tests.integ.timeout import timeout_and_delete_endpoint_by_name

from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker.content_types import CONTENT_TYPE_CSV
from sagemaker.model import Model
from sagemaker.pipeline import PipelineModel
from sagemaker.predictor import RealTimePredictor, json_serializer
from sagemaker.session import Session
from sagemaker.sparkml.model import SparkMLModel
from sagemaker.utils import sagemaker_timestamp
from tests.integ import DATA_DIR
from tests.integ.timeout import timeout_and_delete_endpoint_by_name


@pytest.mark.continuous_testing
@pytest.mark.regional_testing
def test_inference_pipeline_model_deploy(sagemaker_session):
# Creates a Pipeline model comprising of SparkML (serialized by MLeap) and XGBoost and deploys to one endpoint
sparkml_data_path = os.path.join(DATA_DIR, 'sparkml_model')
xgboost_data_path = os.path.join(DATA_DIR, 'xgboost_model')
endpoint_name = 'test-inference-pipeline-deploy-{}'.format(sagemaker_timestamp())
sparkml_model_data = sagemaker_session.upload_data(path=os.path.join(sparkml_data_path, 'mleap_model.tar.gz'),
key_prefix='integ-test-data/sparkml/model')
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')
sparkml_model_data = sagemaker_session.upload_data(
path=os.path.join(sparkml_data_path, 'mleap_model.tar.gz'),
key_prefix='integ-test-data/sparkml/model')
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": [
{
Expand Down Expand Up @@ -74,10 +74,12 @@ def test_inference_pipeline_model_deploy(sagemaker_session):
}
})
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
sparkml_model = SparkMLModel(model_data=sparkml_model_data, env={'SAGEMAKER_SPARKML_SCHEMA': schema},
sparkml_model = SparkMLModel(model_data=sparkml_model_data,
env={'SAGEMAKER_SPARKML_SCHEMA': schema},
sagemaker_session=sagemaker_session)
xgb_image = get_image_uri(Session().boto_region_name, 'xgboost')
xgb_model = Model(model_data=xgb_model_data, image=xgb_image, sagemaker_session=sagemaker_session)
xgb_image = get_image_uri(sagemaker_session.boto_region_name, 'xgboost')
xgb_model = Model(model_data=xgb_model_data, image=xgb_image,
sagemaker_session=sagemaker_session)
model = PipelineModel(models=[sparkml_model, xgb_model], role='SageMakerRole',
sagemaker_session=sagemaker_session, name=endpoint_name)
model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
Expand Down