def test_stop_tuning_job(sagemaker_session):
feature_num = 14
train_input = np.random.rand(1000, feature_num)
rcf = RandomCutForest(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge',
num_trees=50, num_samples_per_tree=20, sagemaker_session=sagemaker_session,
base_job_name='test-randomcutforest')
records = rcf.record_set(train_input)
records.distribution = 'FullyReplicated'
test_records = rcf.record_set(train_input, channel='test')
test_records.distribution = 'FullyReplicated'
hyperparameter_ranges = {'num_trees': IntegerParameter(50, 100),
'num_samples_per_tree': IntegerParameter(1, 2)}
objective_metric_name = 'test:f1'
tuner = HyperparameterTuner(estimator=rcf, objective_metric_name=objective_metric_name,
hyperparameter_ranges=hyperparameter_ranges, objective_type='Maximize', max_jobs=2,
max_parallel_jobs=2)
tuner.fit([records, test_records])
time.sleep(15)
latest_tuning_job_name = tuner.latest_tuning_job.name
print('Attempting to stop {}'.format(latest_tuning_job_name))
tuner.stop_tuning_job()
desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client\
.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=latest_tuning_job_name)
assert desc['HyperParameterTuningJobStatus'] == 'Stopping'
def test_stop_tuning_job(sagemaker_session):
feature_num = 14
train_input = np.random.rand(1000, feature_num)
rcf = RandomCutForest(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge',
num_trees=50, num_samples_per_tree=20, sagemaker_session=sagemaker_session,
base_job_name='test-randomcutforest')
records = rcf.record_set(train_input)
records.distribution = 'FullyReplicated'
test_records = rcf.record_set(train_input, channel='test')
test_records.distribution = 'FullyReplicated'
hyperparameter_ranges = {'num_trees': IntegerParameter(50, 100),
'num_samples_per_tree': IntegerParameter(1, 2)}
objective_metric_name = 'test:f1'
tuner = HyperparameterTuner(estimator=rcf, objective_metric_name=objective_metric_name,
hyperparameter_ranges=hyperparameter_ranges, objective_type='Maximize', max_jobs=2,
max_parallel_jobs=2)
tuner.fit([records, test_records])
time.sleep(15)
latest_tuning_job_name = tuner.latest_tuning_job.name
print('Attempting to stop {}'.format(latest_tuning_job_name))
tuner.stop_tuning_job()
desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client\
.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName=latest_tuning_job_name)
assert desc['HyperParameterTuningJobStatus'] == 'Stopping'
def test_stop_tuning_job(sagemaker_session, cpu_instance_type):
feature_num = 14
train_input = np.random.rand(1000, feature_num)
rcf = RandomCutForest(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_trees=50,
num_samples_per_tree=20,
sagemaker_session=sagemaker_session,
)
records = rcf.record_set(train_input)
records.distribution = "FullyReplicated"
test_records = rcf.record_set(train_input, channel="test")
test_records.distribution = "FullyReplicated"
hyperparameter_ranges = {
"num_trees": IntegerParameter(50, 100),
"num_samples_per_tree": IntegerParameter(1, 2),
}
objective_metric_name = "test:f1"
tuner = HyperparameterTuner(
estimator=rcf,
objective_metric_name=objective_metric_name,
hyperparameter_ranges=hyperparameter_ranges,
objective_type="Maximize",
max_jobs=2,
max_parallel_jobs=2,
)
tuning_job_name = unique_name_from_base("test-randomcutforest",
max_length=32)
tuner.fit([records, test_records], tuning_job_name, wait=False)
time.sleep(15)
latest_tuning_job_name = tuner.latest_tuning_job.name
print("Attempting to stop {}".format(latest_tuning_job_name))
tuner.stop_tuning_job()
desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name)
assert desc["HyperParameterTuningJobStatus"] == "Stopping"
