def test_tf(sagemaker_session):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
estimator = TensorFlow(entry_point=script_path,
role='SageMakerRole',
training_steps=1,
evaluation_steps=1,
hyperparameters={'input_tensor_name': 'inputs'},
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session,
base_job_name='test-tf')
inputs = estimator.sagemaker_session.upload_data(
path=DATA_PATH, key_prefix='integ-test-data/tf_iris')
estimator.fit(inputs)
print('job succeeded: {}'.format(estimator.latest_training_job.name))
with timeout_and_delete_endpoint(estimator=estimator, minutes=20):
json_predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.c4.xlarge')
result = json_predictor.predict([6.4, 3.2, 4.5, 1.5])
print('predict result: {}'.format(result))
def test_cifar(sagemaker_session):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'cifar_10', 'source')
dataset_path = os.path.join(DATA_DIR, 'cifar_10', 'data')
estimator = TensorFlow(entry_point='resnet_cifar_10.py',
source_dir=script_path,
role='SageMakerRole',
training_steps=20,
evaluation_steps=5,
train_instance_count=2,
train_instance_type='ml.p2.xlarge',
sagemaker_session=sagemaker_session,
base_job_name='test-cifar')
inputs = estimator.sagemaker_session.upload_data(
path=dataset_path, key_prefix='data/cifar10')
estimator.fit(inputs)
print('job succeeded: {}'.format(estimator.latest_training_job.name))
with timeout_and_delete_endpoint(estimator=estimator, minutes=20):
predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.p2.xlarge')
predictor.serializer = PickleSerializer()
predictor.content_type = PICKLE_CONTENT_TYPE
data = np.random.randn(32, 32, 3)
predict_response = predictor.predict(data)
assert len(
predict_response['outputs']['probabilities']['floatVal']) == 10
def test_tf_async(sagemaker_session, tf_full_version):
training_job_name = ""
with timeout(minutes=5):
script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
estimator = TensorFlow(entry_point=script_path,
role='SageMakerRole',
framework_version=tf_full_version,
training_steps=1,
evaluation_steps=1,
hyperparameters={'input_tensor_name': 'inputs'},
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session,
base_job_name='test-tf')
inputs = estimator.sagemaker_session.upload_data(
path=DATA_PATH, key_prefix='integ-test-data/tf_iris')
estimator.fit(inputs, wait=False)
training_job_name = estimator.latest_training_job.name
time.sleep(20)
with timeout_and_delete_endpoint(estimator=estimator, minutes=35):
estimator = TensorFlow.attach(training_job_name=training_job_name,
sagemaker_session=sagemaker_session)
json_predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.c4.xlarge')
result = json_predictor.predict([6.4, 3.2, 4.5, 1.5])
print('predict result: {}'.format(result))