def test_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
.identical_dataset_and_algorithm_tuner()"""
parent_tuning_job_name = unique_name_from_base("km-iden1-parent",
max_length=32)
child_tuning_job_name = unique_name_from_base("km-iden1-child",
max_length=32)
parent_tuner = _tune(
kmeans_estimator,
kmeans_train_set,
job_name=parent_tuning_job_name,
hyperparameter_ranges=hyperparameter_ranges,
)
child_tuner = parent_tuner.identical_dataset_and_algorithm_tuner()
_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_tuning_kmeans_identical_dataset_algorithm_tuner_raw(
sagemaker_session, kmeans_train_set, kmeans_estimator,
hyperparameter_ranges):
parent_tuning_job_name = unique_name_from_base("kmeans-identical",
max_length=32)
child_tuning_job_name = unique_name_from_base("c-kmeans-identical",
max_length=32)
_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 = _tune(
kmeans_estimator,
kmeans_train_set,
job_name=child_tuning_job_name,
hyperparameter_ranges=hyperparameter_ranges,
warm_start_config=WarmStartConfig(
warm_start_type=WarmStartTypes.IDENTICAL_DATA_AND_ALGORITHM,
parents=[parent_tuning_job_name],
),
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_transfer_learning_tuner(sagemaker_session,
kmeans_train_set,
kmeans_estimator,
hyperparameter_ranges):
"""Tests Transfer learning use case with two parents and child job launched with
create_transfer_learning_tuner() """
parent_tuning_job_name_1 = name_from_base("km-tran2-parent1", max_length=32, short=True)
parent_tuning_job_name_2 = name_from_base("km-tran2-parent2", max_length=32, short=True)
child_tuning_job_name = name_from_base("km-tran2-child", max_length=32, short=True)
parent_tuner_1 = _tune(kmeans_estimator, kmeans_train_set, job_name=parent_tuning_job_name_1,
hyperparameter_ranges=hyperparameter_ranges, max_parallel_jobs=1, max_jobs=1)
parent_tuner_2 = _tune(kmeans_estimator, kmeans_train_set, job_name=parent_tuning_job_name_2,
hyperparameter_ranges=hyperparameter_ranges, max_parallel_jobs=1, max_jobs=1)
child_tuner = create_transfer_learning_tuner(parent=parent_tuner_1.latest_tuning_job.name,
sagemaker_session=sagemaker_session,
estimator=kmeans_estimator,
additional_parents={parent_tuner_2.latest_tuning_job.name})
_tune(kmeans_estimator, kmeans_train_set, job_name=child_tuning_job_name, tuner=child_tuner)
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_prepare_warm_start_config_cls(warm_start_config_req):
warm_start_config = WarmStartConfig.from_job_desc(warm_start_config_req)
assert warm_start_config.type == WarmStartTypes(
warm_start_config_req["WarmStartType"]), "Warm start type initialization failed."
for p in warm_start_config_req["ParentHyperParameterTuningJobs"]:
assert p['HyperParameterTuningJobName'] in warm_start_config.parents, \
"Warm start parents config initialization failed."
def test_transfer_learning_tuner(sagemaker_session,
kmeans_train_set,
kmeans_estimator,
hyperparameter_ranges):
"""Tests Transfer learning use case with one parent and child job launched with
.transfer_learning_tuner() """
parent_tuning_job_name = name_from_base("km-tran1-parent", max_length=32, short=True)
child_tuning_job_name = name_from_base("km-tran1-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_jobs=1, max_parallel_jobs=1)
child_tuner = parent_tuner.transfer_learning_tuner()
_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_prepare_warm_start_config_cls_negative(warm_start_config_req):
warm_start_config = WarmStartConfig.from_job_desc(warm_start_config_req)
assert warm_start_config is None, "Warm start config should be None for invalid type/parents"