def test_mxnet_local_mode(sagemaker_local_session, mxnet_full_version):
script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
mx = MXNet(entry_point=script_path,
role='SageMakerRole',
py_version=PYTHON_VERSION,
train_instance_count=1,
train_instance_type='local',
sagemaker_session=sagemaker_local_session,
framework_version=mxnet_full_version)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/mxnet_mnist/train')
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/mxnet_mnist/test')
mx.fit({'train': train_input, 'test': test_input})
endpoint_name = mx.latest_training_job.name
with local_mode_utils.lock():
try:
predictor = mx.deploy(1, 'local', endpoint_name=endpoint_name)
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
mx.delete_endpoint()
def test_tf_local_data_local_script():
with timeout(minutes=5):
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='local',
base_job_name='test-tf',
sagemaker_session=LocalNoS3Session())
inputs = 'file://' + DATA_PATH
estimator.fit(inputs)
print('job succeeded: {}'.format(estimator.latest_training_job.name))
endpoint_name = estimator.latest_training_job.name
with local_mode_utils.lock():
try:
json_predictor = estimator.deploy(initial_instance_count=1,
instance_type='local',
endpoint_name=endpoint_name)
features = [6.4, 3.2, 4.5, 1.5]
dict_result = json_predictor.predict({'inputs': features})
print('predict result: {}'.format(dict_result))
list_result = json_predictor.predict(features)
print('predict result: {}'.format(list_result))
assert dict_result == list_result
finally:
estimator.delete_endpoint()
def test_source_dirs(tmpdir, sagemaker_local_session):
source_dir = os.path.join(DATA_DIR, 'pytorch_source_dirs')
lib = os.path.join(str(tmpdir), 'alexa.py')
with open(lib, 'w') as f:
f.write('def question(to_anything): return 42')
estimator = PyTorch(entry_point='train.py',
role='SageMakerRole',
source_dir=source_dir,
dependencies=[lib],
py_version=PYTHON_VERSION,
train_instance_count=1,
train_instance_type='local',
sagemaker_session=sagemaker_local_session)
estimator.fit()
with local_mode_utils.lock():
try:
predictor = estimator.deploy(initial_instance_count=1,
instance_type='local')
predict_response = predictor.predict([7])
assert predict_response == [49]
finally:
estimator.delete_endpoint()
def test_local_mode_serving_from_local_model(tmpdir, sagemaker_local_session,
mxnet_model):
predictor = None
with local_mode_utils.lock():
try:
path = 'file://%s' % (str(tmpdir))
model = mxnet_model(path)
model.sagemaker_session = sagemaker_local_session
predictor = model.deploy(initial_instance_count=1,
instance_type='local')
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
if predictor:
predictor.delete_endpoint()
def test_local_mode_serving_from_s3_model(sagemaker_local_session, mxnet_model,
mxnet_full_version):
path = 's3://%s' % sagemaker_local_session.default_bucket()
s3_model = mxnet_model(path)
s3_model.sagemaker_session = sagemaker_local_session
predictor = None
with local_mode_utils.lock():
try:
predictor = s3_model.deploy(initial_instance_count=1,
instance_type='local')
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
if predictor:
predictor.delete_endpoint()
def test_local_transform_mxnet(sagemaker_local_session, tmpdir,
mxnet_full_version):
data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
script_path = os.path.join(data_path, 'mnist.py')
mx = MXNet(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
framework_version=mxnet_full_version,
sagemaker_session=sagemaker_local_session)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/mxnet_mnist/train')
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/mxnet_mnist/test')
with timeout(minutes=15):
mx.fit({'train': train_input, 'test': test_input})
transform_input_path = os.path.join(data_path, 'transform')
transform_input_key_prefix = 'integ-test-data/mxnet_mnist/transform'
transform_input = mx.sagemaker_session.upload_data(
path=transform_input_path, key_prefix=transform_input_key_prefix)
output_path = 'file://%s' % (str(tmpdir))
transformer = mx.transformer(1,
'local',
assemble_with='Line',
max_payload=1,
strategy='SingleRecord',
output_path=output_path)
with local_mode_utils.lock():
transformer.transform(transform_input,
content_type='text/csv',
split_type='Line')
transformer.wait()
assert os.path.exists(os.path.join(str(tmpdir), 'data.csv.out'))
def test_mxnet_local_data_local_script():
data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
script_path = os.path.join(data_path, 'mnist_framework_mode.py')
mx = MXNet(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type='local',
sagemaker_session=LocalNoS3Session())
train_input = 'file://' + os.path.join(data_path, 'train')
test_input = 'file://' + os.path.join(data_path, 'test')
mx.fit({'train': train_input, 'test': test_input})
endpoint_name = mx.latest_training_job.name
with local_mode_utils.lock():
try:
predictor = mx.deploy(1, 'local', endpoint_name=endpoint_name)
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
mx.delete_endpoint()
