def test_list_candidates_default(sagemaker_session):
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
auto_ml.current_job_name = "current_job_name"
auto_ml.list_candidates()
sagemaker_session.list_candidates.assert_called_once()
sagemaker_session.list_candidates.assert_called_with(job_name=auto_ml.current_job_name)
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_list_candidates(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)
assert len(candidates) == 3
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_list_candidates_with_optional_args(sagemaker_session):
auto_ml = AutoML(role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session)
auto_ml.list_candidates(
job_name=JOB_NAME,
status_equals="Completed",
candidate_name="candidate-name",
candidate_arn="candidate-arn",
sort_order="Ascending",
sort_by="Status",
max_results=99,
)
sagemaker_session.list_candidates.assert_called_once()
_, args = sagemaker_session.list_candidates.call_args
assert args == {
"job_name": JOB_NAME,
"status_equals": "Completed",
"candidate_name": "candidate-name",
"candidate_arn": "candidate-arn",
"sort_order": "Ascending",
"sort_by": "Status",
"max_results": 99,
}