def test_create_tuning_kmeans_identical_dataset_algorithm_tuner(sagemaker_session,
kmeans_train_set,
kmeans_estimator,
hyperparameter_ranges):
"""Tests Identical dataset and algorithm use case with one parent and child job launched with
.create_identical_dataset_and_algorithm_tuner() """
parent_tuning_job_name = name_from_base("km-iden2-parent", max_length=32, short=True)
child_tuning_job_name = name_from_base("km-iden2-child", max_length=32, short=True)
parent_tuner = _tune(kmeans_estimator, kmeans_train_set, job_name=parent_tuning_job_name,
hyperparameter_ranges=hyperparameter_ranges, max_parallel_jobs=1, max_jobs=1)
child_tuner = create_identical_dataset_and_algorithm_tuner(parent=parent_tuner.latest_tuning_job.name,
sagemaker_session=sagemaker_session)
_tune(kmeans_estimator, kmeans_train_set, job_name=child_tuning_job_name, tuner=child_tuner, max_parallel_jobs=1,
max_jobs=1)
child_warm_start_config_response = WarmStartConfig.from_job_desc(
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=child_tuning_job_name)["WarmStartConfig"])
assert child_warm_start_config_response.type == child_tuner.warm_start_config.type
assert child_warm_start_config_response.parents == child_tuner.warm_start_config.parents
def test_create_identical_dataset_and_algorithm_tuner(sagemaker_session, additional_parents):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = create_identical_dataset_and_algorithm_tuner(parent=JOB_NAME,
additional_parents=additional_parents,
sagemaker_session=sagemaker_session)
assert tuner.warm_start_config.type == WarmStartTypes.IDENTICAL_DATA_AND_ALGORITHM
if additional_parents:
additional_parents.add(JOB_NAME)
assert tuner.warm_start_config.parents == additional_parents
else:
assert tuner.warm_start_config.parents == {JOB_NAME}
