def test_deploy_update_endpoint(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
endpoint_name = "endpoint-name"
sparkml_model = SparkMLModel(model_data=MODEL_DATA_2,
role=ROLE,
sagemaker_session=sagemaker_session)
model = PipelineModel(models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session)
model.deploy(
instance_type=INSTANCE_TYPE,
initial_instance_count=1,
endpoint_name=endpoint_name,
update_endpoint=True,
)
sagemaker_session.create_endpoint_config.assert_called_with(
name=model.name,
model_name=model.name,
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
tags=None,
)
config_name = sagemaker_session.create_endpoint_config(
name=model.name,
model_name=model.name,
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
)
sagemaker_session.update_endpoint.assert_called_with(
endpoint_name, config_name)
sagemaker_session.create_endpoint.assert_not_called()
def test_deploy_tags(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(model_data=MODEL_DATA_2,
role=ROLE,
sagemaker_session=sagemaker_session)
model = PipelineModel(models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session)
tags = [{"ModelName": "TestModel"}]
model.deploy(instance_type=INSTANCE_TYPE,
initial_instance_count=1,
tags=tags)
sagemaker_session.endpoint_from_production_variants.assert_called_with(
name="mi-1-2017-10-10-14-14-15",
production_variants=[{
"InitialVariantWeight": 1,
"ModelName": "mi-1-2017-10-10-14-14-15",
"InstanceType": INSTANCE_TYPE,
"InitialInstanceCount": 1,
"VariantName": "AllTraffic",
}],
tags=tags,
wait=True,
kms_key=None,
data_capture_config_dict=None,
)
def test_inference_pipeline_model_deploy_with_update_endpoint(
sagemaker_session, cpu_instance_type, alternative_cpu_instance_type
):
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",
)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
sparkml_model = SparkMLModel(
model_data=sparkml_model_data,
env={"SAGEMAKER_SPARKML_SCHEMA": SCHEMA},
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,
)
model.deploy(1, alternative_cpu_instance_type, endpoint_name=endpoint_name)
old_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name
)
old_config_name = old_endpoint["EndpointConfigName"]
model.deploy(1, cpu_instance_type, update_endpoint=True, endpoint_name=endpoint_name)
# Wait for endpoint to finish updating
# Endpoint update takes ~7min. 40 retries * 30s sleeps = 20min timeout
for _ in retries(40, "Waiting for 'InService' endpoint status", seconds_to_sleep=30):
new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name
)
if new_endpoint["EndpointStatus"] == "InService":
break
new_config_name = new_endpoint["EndpointConfigName"]
new_config = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=new_config_name
)
assert old_config_name != new_config_name
assert new_config["ProductionVariants"][0]["InstanceType"] == cpu_instance_type
assert new_config["ProductionVariants"][0]["InitialInstanceCount"] == 1
model.delete_model()
with pytest.raises(Exception) as exception:
sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
assert "Could not find model" in str(exception.value)
def test_delete_model(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
pipeline_model = PipelineModel(
[framework_model], role=ROLE, sagemaker_session=sagemaker_session
)
pipeline_model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
pipeline_model.delete_model()
sagemaker_session.delete_model.assert_called_with(pipeline_model.name)
def test_inference_pipeline_model_deploy(sagemaker_session, cpu_instance_type):
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",
)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
sparkml_model = SparkMLModel(
model_data=sparkml_model_data,
env={"SAGEMAKER_SPARKML_SCHEMA": SCHEMA},
sagemaker_session=sagemaker_session,
)
xgb_image = image_uris.retrieve("xgboost",
sagemaker_session.boto_region_name,
version="1",
image_scope="inference")
xgb_model = Model(model_data=xgb_model_data,
image_uri=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, cpu_instance_type, endpoint_name=endpoint_name)
predictor = Predictor(
endpoint_name=endpoint_name,
sagemaker_session=sagemaker_session,
serializer=JSONSerializer,
content_type="text/csv",
accept="text/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
model.delete_model()
with pytest.raises(Exception) as exception:
sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
assert "Could not find model" in str(exception.value)
def inference_pipeline_ep(role, sess, spark_model_uri, region, bucket,
pipeline_model_name, endpoint_name, **context):
timestamp_prefix = Variable.get("timestamp")
# sm = boto3.client('sagemaker', region_name=region)
s3client = boto3.client('s3', region_name=region)
s3_sparkml_data_uri = spark_model_uri
# Using S3 calls for listing model artifcats
s3_xgb_objects = s3client.list_objects_v2(
Bucket=bucket, StartAfter='sagemaker/spark-preprocess/model/xgboost/')
obj_list = s3_xgb_objects['Contents']
obj_list.sort(key=lambda x: x['LastModified'], reverse=False)
xgboost_model_latest = obj_list[-1]['Key']
s3_xgboost_model_uri = 's3://' + bucket + '/' + xgboost_model_latest
# AirFlow XCOM feature
# s3_xgboost_model_uri = context['task_instance'].xcom_pull(
# task_ids='xgboost_model_training')['Training']['ModelArtifacts']['S3ModelArtifacts']
xgb_container = get_image_uri(sess.region_name,
'xgboost',
repo_version='0.90-1')
schema_json = schema_utils.abalone_schema()
sparkml_model = SparkMLModel(
model_data=s3_sparkml_data_uri,
role=role,
sagemaker_session=sagemaker.session.Session(sess),
env={'SAGEMAKER_SPARKML_SCHEMA': schema_json})
xgb_model = Model(model_data=s3_xgboost_model_uri,
role=role,
sagemaker_session=sagemaker.session.Session(sess),
image=xgb_container)
pipeline_model_name = pipeline_model_name
sm_model = PipelineModel(name=pipeline_model_name,
role=role,
sagemaker_session=sagemaker.session.Session(sess),
models=[sparkml_model, xgb_model])
endpoint_name = endpoint_name
sm_model.deploy(initial_instance_count=1,
instance_type='ml.c4.xlarge',
endpoint_name=endpoint_name)
def test_inference_pipeline_model_deploy_and_update_endpoint(
sagemaker_session, cpu_instance_type, alternative_cpu_instance_type):
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",
)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
sparkml_model = SparkMLModel(
model_data=sparkml_model_data,
env={"SAGEMAKER_SPARKML_SCHEMA": SCHEMA},
sagemaker_session=sagemaker_session,
)
xgb_image = image_uris.retrieve("xgboost",
sagemaker_session.boto_region_name,
version="1",
image_scope="inference")
xgb_model = Model(model_data=xgb_model_data,
image_uri=xgb_image,
sagemaker_session=sagemaker_session)
model = PipelineModel(
models=[sparkml_model, xgb_model],
role="SageMakerRole",
predictor_cls=Predictor,
sagemaker_session=sagemaker_session,
)
predictor = model.deploy(1,
alternative_cpu_instance_type,
endpoint_name=endpoint_name)
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)
old_config_name = endpoint_desc["EndpointConfigName"]
predictor.update_endpoint(initial_instance_count=1,
instance_type=cpu_instance_type)
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)
new_config_name = endpoint_desc["EndpointConfigName"]
new_config = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=new_config_name)
assert old_config_name != new_config_name
assert new_config["ProductionVariants"][0][
"InstanceType"] == cpu_instance_type
assert new_config["ProductionVariants"][0]["InitialInstanceCount"] == 1
model.delete_model()
with pytest.raises(Exception) as exception:
sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
assert "Could not find model" in str(exception.value)
def test_deploy(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(model_data=MODEL_DATA_2,
role=ROLE,
sagemaker_session=sagemaker_session)
model = PipelineModel(models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session)
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
sagemaker_session.endpoint_from_production_variants.assert_called_with(
'mi-1-2017-10-10-14-14-15', [{
'InitialVariantWeight': 1,
'ModelName': 'mi-1-2017-10-10-14-14-15',
'InstanceType': INSTANCE_TYPE,
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'
}], None)
def test_inference_pipeline_model_deploy(sagemaker_session):
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')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
sparkml_model = SparkMLModel(model_data=sparkml_model_data,
env={'SAGEMAKER_SPARKML_SCHEMA': SCHEMA},
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)
predictor = RealTimePredictor(endpoint=endpoint_name,
sagemaker_session=sagemaker_session,
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)
model.delete_model()
with pytest.raises(Exception) as exception:
sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
assert 'Could not find model' in str(exception.value)
def test_network_isolation(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(model_data=MODEL_DATA_2,
role=ROLE,
sagemaker_session=sagemaker_session)
model = PipelineModel(
models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session,
enable_network_isolation=True,
)
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
sagemaker_session.create_model.assert_called_with(
model.name,
ROLE,
[
{
"Image": "mi-1",
"Environment": {
"SAGEMAKER_PROGRAM": "blah.py",
"SAGEMAKER_SUBMIT_DIRECTORY":
"s3://mybucket/mi-1-2017-10-10-14-14-15/sourcedir.tar.gz",
"SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
"SAGEMAKER_REGION": "us-west-2",
},
"ModelDataUrl": "s3://bucket/model_1.tar.gz",
},
{
"Image":
"246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-sparkml-serving:2.2",
"Environment": {},
"ModelDataUrl": "s3://bucket/model_2.tar.gz",
},
],
vpc_config=None,
enable_network_isolation=True,
)
def test_inference_pipeline_model_deploy(sagemaker_session):
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')
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},
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)
predictor = RealTimePredictor(endpoint=endpoint_name, sagemaker_session=sagemaker_session,
serializer=json_serializer, content_type=CONTENT_TYPE_CSV,
accept=CONTENT_TYPE_CSV)
valid_data = '1.0,C,38.0,71.5,1.0,female'
assert predictor.predict(valid_data) == "0.714013934135"
invalid_data = "1.0,28.0,C,38.0,71.5,1.0"
assert (predictor.predict(invalid_data) is None)
model.delete_model()
with pytest.raises(Exception) as exception:
sagemaker_session.sagemaker_client.describe_model(ModelName=model.name)
assert 'Could not find model' in str(exception.value)
preproc_model_url = args.preproc_model
sm_role = get_sm_execution_role(False, region)
preproc_model = SKLearnModel(model_data=preproc_model_url,
source_dir=os.path.abspath(
os.path.dirname(__file__)),
role=sm_role,
entry_point='infer_preproc.py',
sagemaker_session=sess)
ll_image = get_image_uri(region, 'linear-learner')
ll_model = Model(model_data=model_url,
image=ll_image,
role=sm_role,
sagemaker_session=sess)
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
model_name = 'inference-pipeline-' + timestamp_prefix
endpoint_name = 'inference-pipeline-ep-' + timestamp_prefix
sm_model = PipelineModel(name=model_name,
role=sm_role,
models=[preproc_model, ll_model],
sagemaker_session=sess)
print('Deploying SM EndPoint')
sm_model.deploy(initial_instance_count=1,
instance_type='ml.c4.xlarge',
endpoint_name=endpoint_name)
print(f'SageMaker deployed to endpoint - {endpoint_name}')
