def test_candidate_estimator_default_rerun_and_deploy(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)
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_candidate_estimator_rerun_with_optional_args(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=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_candidate_estimator_fit(sagemaker_session):
candidate_estimator = CandidateEstimator(
CANDIDATE_DICT, sagemaker_session=sagemaker_session)
candidate_estimator._check_all_job_finished = Mock(
name="_check_all_job_finished", return_value=True)
inputs = DEFAULT_S3_INPUT_DATA
candidate_estimator.fit(inputs)
sagemaker_session.train.assert_called()
sagemaker_session.transform.assert_called()
def test_candidate_estimator_get_steps(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)
steps = candidate_estimator.get_steps()
assert len(steps) == 3
def test_candidate_estimator_get_steps(sagemaker_session):
candidate_estimator = CandidateEstimator(
CANDIDATE_DICT, sagemaker_session=sagemaker_session)
steps = candidate_estimator.get_steps()
assert len(steps) == 3
