def test_candidate_estimator_rerun_with_optional_args(sagemaker_session,
cpu_instance_type):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
candidate = candidates[1]
candidate_estimator = CandidateEstimator(candidate, sagemaker_session)
inputs = sagemaker_session.upload_data(path=TEST_DATA,
key_prefix=PREFIX + "/input")
endpoint_name = unique_name_from_base(
"sagemaker-auto-ml-rerun-candidate-test")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
candidate_estimator.fit(inputs, encrypt_inter_container_traffic=True)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=cpu_instance_type,
candidate=candidate,
endpoint_name=endpoint_name,
)
endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)["EndpointStatus"]
assert endpoint_status == "InService"
sagemaker_session.sagemaker_client.delete_endpoint(
EndpointName=endpoint_name)
def test_deploy(sagemaker_session, candidate_mock):
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
auto_ml.best_candidate = Mock(name="best_candidate",
return_value=CANDIDATE_DICT)
auto_ml._deploy_inference_pipeline = Mock("_deploy_inference_pipeline",
return_value=None)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
sagemaker_session=sagemaker_session,
)
auto_ml._deploy_inference_pipeline.assert_called_once()
auto_ml._deploy_inference_pipeline.assert_called_with(
candidate_mock.containers,
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
name=None,
sagemaker_session=sagemaker_session,
endpoint_name=None,
tags=None,
wait=True,
update_endpoint=False,
vpc_config=None,
enable_network_isolation=False,
model_kms_key=None,
predictor_cls=None,
)
def test_candidate_estimator_default_rerun_and_deploy(sagemaker_session):
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
candidate = candidates[1]
candidate_estimator = CandidateEstimator(candidate, sagemaker_session)
inputs = sagemaker_session.upload_data(path=TEST_DATA,
key_prefix=PREFIX + "/input")
endpoint_name = unique_name_from_base(
"sagemaker-auto-ml-rerun-candidate-test")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
candidate_estimator.fit(inputs)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=HOSTING_INSTANCE_TYPE,
candidate=candidate,
endpoint_name=endpoint_name,
)
endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)["EndpointStatus"]
assert endpoint_status == "InService"
sagemaker_session.sagemaker_client.delete_endpoint(
EndpointName=endpoint_name)
def test_deploy_optional_args(candidate_estimator, sagemaker_session,
candidate_mock):
candidate_estimator.return_value = candidate_mock
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
mock_pipeline = Mock(name="pipeline_model")
mock_pipeline.deploy = Mock(name="model_deploy")
auto_ml.best_candidate = Mock(name="best_candidate",
return_value=CANDIDATE_DICT)
auto_ml.create_model = Mock(name="create_model",
return_value=mock_pipeline)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
candidate=CANDIDATE_DICT,
sagemaker_session=sagemaker_session,
name=JOB_NAME,
endpoint_name=JOB_NAME,
tags=TAGS,
wait=False,
update_endpoint=True,
vpc_config=VPC_CONFIG,
enable_network_isolation=True,
model_kms_key=OUTPUT_KMS_KEY,
predictor_cls=RealTimePredictor,
inference_response_keys=None,
)
auto_ml.create_model.assert_called_once()
auto_ml.create_model.assert_called_with(
name=JOB_NAME,
sagemaker_session=sagemaker_session,
candidate=CANDIDATE_DICT,
inference_response_keys=None,
vpc_config=VPC_CONFIG,
enable_network_isolation=True,
model_kms_key=OUTPUT_KMS_KEY,
predictor_cls=RealTimePredictor,
)
mock_pipeline.deploy.assert_called_once()
mock_pipeline.deploy.assert_called_with(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
endpoint_name=JOB_NAME,
tags=TAGS,
wait=False,
update_endpoint=True,
)
def test_deploy(sagemaker_session, candidate_mock):
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
mock_pipeline = Mock(name="pipeline_model")
mock_pipeline.deploy = Mock(name="model_deploy")
auto_ml.best_candidate = Mock(name="best_candidate", return_value=CANDIDATE_DICT)
auto_ml.create_model = Mock(name="create_model", return_value=mock_pipeline)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
sagemaker_session=sagemaker_session,
)
auto_ml.create_model.assert_called_once()
mock_pipeline.deploy.assert_called_once()
def test_deploy_best_candidate(sagemaker_session):
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
best_candidate = auto_ml.best_candidate(job_name=AUTO_ML_JOB_NAME)
endpoint_name = unique_name_from_base(
"sagemaker-auto-ml-best-candidate-test")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.deploy(
candidate=best_candidate,
initial_instance_count=INSTANCE_COUNT,
instance_type=HOSTING_INSTANCE_TYPE,
endpoint_name=endpoint_name,
)
endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)["EndpointStatus"]
assert endpoint_status == "InService"
sagemaker_session.sagemaker_client.delete_endpoint(
EndpointName=endpoint_name)
