def test_attach_tuning_job_with_estimator_from_kwarg(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session,
estimator_cls='sagemaker.estimator.Estimator')
assert isinstance(tuner.estimator, Estimator)
def test_attach_tuning_job_with_estimator_from_kwarg(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session,
estimator_cls='sagemaker.estimator.Estimator')
assert isinstance(tuner.estimator, Estimator)
def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert tuner.latest_tuning_job.name == JOB_NAME
assert tuner.objective_metric_name == OBJECTIVE_METRIC_NAME
assert tuner.max_jobs == 1
assert tuner.max_parallel_jobs == 1
assert tuner.metric_definitions == METRIC_DEFINITIONS
assert tuner.strategy == "Bayesian"
assert tuner.objective_type == "Minimize"
assert tuner.early_stopping_type == "Off"
assert isinstance(tuner.estimator, PCA)
assert tuner.estimator.role == ROLE
assert tuner.estimator.train_instance_count == 1
assert tuner.estimator.train_max_run == 24 * 60 * 60
assert tuner.estimator.input_mode == "File"
assert tuner.estimator.output_path == BUCKET_NAME
assert tuner.estimator.output_kms_key == ""
assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "10"
def test_attach_tuning_pytorch(sagemaker_session):
mnist_dir = os.path.join(DATA_DIR, "pytorch_mnist")
mnist_script = os.path.join(mnist_dir, "mnist.py")
estimator = PyTorch(
entry_point=mnist_script,
role="SageMakerRole",
train_instance_count=1,
py_version=PYTHON_VERSION,
train_instance_type="ml.c4.xlarge",
sagemaker_session=sagemaker_session,
)
with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
objective_metric_name = "evaluation-accuracy"
metric_definitions = [{
"Name": "evaluation-accuracy",
"Regex": r"Overall test accuracy: (\d+)"
}]
hyperparameter_ranges = {"batch-size": IntegerParameter(50, 100)}
tuner = HyperparameterTuner(
estimator,
objective_metric_name,
hyperparameter_ranges,
metric_definitions,
max_jobs=2,
max_parallel_jobs=2,
early_stopping_type="Auto",
)
training_data = estimator.sagemaker_session.upload_data(
path=os.path.join(mnist_dir, "training"),
key_prefix="integ-test-data/pytorch_mnist/training",
)
tuning_job_name = unique_name_from_base("pytorch", max_length=32)
tuner.fit({"training": training_data}, job_name=tuning_job_name)
print("Started hyperparameter tuning job with name:" + tuning_job_name)
time.sleep(15)
tuner.wait()
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session)
assert attached_tuner.early_stopping_type == "Auto"
best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job,
sagemaker_session):
predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
predictor.predict(data)
batch_size = 100
data = np.random.rand(batch_size, 1, 28, 28).astype(np.float32)
output = predictor.predict(data)
assert output.shape == (batch_size, 10)
def test_attach_tuning_job_with_estimator_from_image(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
job_details['TrainingJobDefinition']['AlgorithmSpecification']['TrainingImage'] = '1111.amazonaws.com/pca:1'
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert isinstance(tuner.estimator, PCA)
def test_attach_tuning_job_with_estimator_from_image(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
job_details['TrainingJobDefinition']['AlgorithmSpecification']['TrainingImage'] = '1111.amazonaws.com/pca:1'
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert isinstance(tuner.estimator, PCA)
def test_attach_with_no_specified_estimator(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
del job_details['TrainingJobDefinition']['StaticHyperParameters']['sagemaker_estimator_module']
del job_details['TrainingJobDefinition']['StaticHyperParameters']['sagemaker_estimator_class_name']
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert isinstance(tuner.estimator, Estimator)
def test_attach_with_no_specified_estimator(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
del job_details['TrainingJobDefinition']['StaticHyperParameters']['sagemaker_estimator_module']
del job_details['TrainingJobDefinition']['StaticHyperParameters']['sagemaker_estimator_class_name']
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert isinstance(tuner.estimator, Estimator)
def test_identical_dataset_and_algorithm_tuner(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
parent_tuner = tuner.identical_dataset_and_algorithm_tuner(additional_parents={"p1", "p2"})
assert parent_tuner.warm_start_config.type == WarmStartTypes.IDENTICAL_DATA_AND_ALGORITHM
assert parent_tuner.warm_start_config.parents == {tuner.latest_tuning_job.name, "p1", "p2"}
def test_attach_tuning_pytorch(sagemaker_session):
mnist_dir = os.path.join(DATA_DIR, 'pytorch_mnist')
mnist_script = os.path.join(mnist_dir, 'mnist.py')
estimator = PyTorch(entry_point=mnist_script,
role='SageMakerRole',
train_instance_count=1,
py_version=PYTHON_VERSION,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session)
with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
objective_metric_name = 'evaluation-accuracy'
metric_definitions = [{
'Name': 'evaluation-accuracy',
'Regex': r'Overall test accuracy: (\d+)'
}]
hyperparameter_ranges = {'batch-size': IntegerParameter(50, 100)}
tuner = HyperparameterTuner(estimator,
objective_metric_name,
hyperparameter_ranges,
metric_definitions,
max_jobs=2,
max_parallel_jobs=2,
early_stopping_type='Auto')
training_data = estimator.sagemaker_session.upload_data(
path=os.path.join(mnist_dir, 'training'),
key_prefix='integ-test-data/pytorch_mnist/training')
tuning_job_name = unique_name_from_base('pytorch', max_length=32)
tuner.fit({'training': training_data}, job_name=tuning_job_name)
print('Started hyperparameter tuning job with name:' + tuning_job_name)
time.sleep(15)
tuner.wait()
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session)
assert attached_tuner.early_stopping_type == 'Auto'
best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job,
sagemaker_session):
predictor = attached_tuner.deploy(1, 'ml.c4.xlarge')
data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
predictor.predict(data)
batch_size = 100
data = np.random.rand(batch_size, 1, 28, 28).astype(np.float32)
output = predictor.predict(data)
assert output.shape == (batch_size, 10)
def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
job_details['HyperParameterTuningJobConfig']['TrainingJobEarlyStoppingType'] = 'Auto'
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert tuner.latest_tuning_job.name == JOB_NAME
assert tuner.early_stopping_type == 'Auto'
assert isinstance(tuner.estimator, PCA)
def test_transfer_learning_tuner(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
parent_tuner = tuner.transfer_learning_tuner(additional_parents={"p1", "p2"})
assert parent_tuner.warm_start_config.type == WarmStartTypes.TRANSFER_LEARNING
assert parent_tuner.warm_start_config.parents == {tuner.latest_tuning_job.name, "p1", "p2"}
assert parent_tuner.estimator == tuner.estimator
def test_attach_with_warm_start_config(sagemaker_session):
warm_start_config = WarmStartConfig(warm_start_type=WarmStartTypes.TRANSFER_LEARNING, parents={"p1", "p2"})
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
job_details["WarmStartConfig"] = warm_start_config.to_input_req()
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(name='describe_tuning_job',
return_value=job_details)
tuner = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert tuner.warm_start_config.type == warm_start_config.type
assert tuner.warm_start_config.parents == warm_start_config.parents
def _attach_tuner(sagemaker_session, tuning_job_name):
print("Attaching hyperparameter tuning job {} to a new tuner instance".
format(tuning_job_name))
return HyperparameterTuner.attach(
tuning_job_name,
sagemaker_session=sagemaker_session,
estimator_cls={
ESTIMATOR_FM: "sagemaker.estimator.Estimator",
ESTIMATOR_KNN: "sagemaker.estimator.Estimator",
},
)
def test_attach_tuning_pytorch(sagemaker_session):
mnist_dir = os.path.join(DATA_DIR, 'pytorch_mnist')
mnist_script = os.path.join(mnist_dir, 'mnist.py')
estimator = PyTorch(entry_point=mnist_script, role='SageMakerRole', train_instance_count=1,
train_instance_type='ml.c4.xlarge', sagemaker_session=sagemaker_session)
with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
objective_metric_name = 'evaluation-accuracy'
metric_definitions = [{'Name': 'evaluation-accuracy', 'Regex': 'Overall test accuracy: (\d+)'}]
hyperparameter_ranges = {'batch-size': IntegerParameter(50, 100)}
tuner = HyperparameterTuner(estimator, objective_metric_name, hyperparameter_ranges, metric_definitions,
max_jobs=2, max_parallel_jobs=2)
training_data = estimator.sagemaker_session.upload_data(path=os.path.join(mnist_dir, 'training'),
key_prefix='integ-test-data/pytorch_mnist/training')
tuner.fit({'training': training_data})
tuning_job_name = tuner.latest_tuning_job.name
print('Started hyperparameter tuning job with name:' + tuning_job_name)
time.sleep(15)
tuner.wait()
attached_tuner = HyperparameterTuner.attach(tuning_job_name, sagemaker_session=sagemaker_session)
best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = attached_tuner.deploy(1, 'ml.c4.xlarge')
data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
predictor.predict(data)
batch_size = 100
data = np.random.rand(batch_size, 1, 28, 28).astype(np.float32)
output = predictor.predict(data)
assert output.shape == (batch_size, 10)
def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session):
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 = HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session)
assert tuner.latest_tuning_job.name == JOB_NAME
assert tuner.objective_metric_name == OBJECTIVE_METRIC_NAME
assert tuner.max_jobs == 1
assert tuner.max_parallel_jobs == 1
assert tuner.metric_definitions == METRIC_DEFINTIONS
assert tuner.strategy == 'Bayesian'
assert tuner.objective_type == 'Minimize'
assert isinstance(tuner.estimator, PCA)
assert tuner.estimator.role == ROLE
assert tuner.estimator.train_instance_count == 1
assert tuner.estimator.train_max_run == 24 * 60 * 60
assert tuner.estimator.input_mode == 'File'
assert tuner.estimator.output_path == BUCKET_NAME
assert tuner.estimator.output_kms_key == ''
assert '_tuning_objective_metric' not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()['num_components'] == '1'
def test_attach_tuning_job_with_job_details(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session, job_details=job_details)
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job.assert_not_called
def test_attach_tuning_job_with_job_details(sagemaker_session):
job_details = copy.deepcopy(TUNING_JOB_DETAILS)
HyperparameterTuner.attach(JOB_NAME, sagemaker_session=sagemaker_session, job_details=job_details)
sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job.assert_not_called
def test_tuning_lda(sagemaker_session, cpu_instance_type):
with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "lda")
data_filename = "nips-train_1.pbr"
with open(os.path.join(data_path, data_filename), "rb") as f:
all_records = read_records(f)
# all records must be same
feature_num = int(
all_records[0].features["values"].float32_tensor.shape[0])
lda = LDA(
role="SageMakerRole",
instance_type=cpu_instance_type,
num_topics=10,
sagemaker_session=sagemaker_session,
)
record_set = prepare_record_set_from_local_files(
data_path, lda.data_location, len(all_records), feature_num,
sagemaker_session)
test_record_set = prepare_record_set_from_local_files(
data_path, lda.data_location, len(all_records), feature_num,
sagemaker_session)
test_record_set.channel = "test"
# specify which hp you want to optimize over
hyperparameter_ranges = {
"alpha0": ContinuousParameter(1, 10),
"num_topics": IntegerParameter(1, 2),
}
objective_metric_name = "test:pwll"
tuner = HyperparameterTuner(
estimator=lda,
objective_metric_name=objective_metric_name,
hyperparameter_ranges=hyperparameter_ranges,
objective_type="Maximize",
max_jobs=2,
max_parallel_jobs=2,
early_stopping_type="Auto",
)
tuning_job_name = unique_name_from_base("test-lda", max_length=32)
print("Started hyperparameter tuning job with name:" + tuning_job_name)
tuner.fit([record_set, test_record_set],
mini_batch_size=1,
job_name=tuning_job_name)
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session)
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1
best_training_job = attached_tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job,
sagemaker_session):
predictor = tuner.deploy(1, cpu_instance_type)
predict_input = np.random.rand(1, feature_num)
result = predictor.predict(predict_input)
assert len(result) == 1
for record in result:
assert record.label["topic_mixture"] is not None
