def test_mnist_training_and_serving(docker_image, sagemaker_local_session,
local_instance_type, framework_version,
tmpdir):
mx = MXNet(entry_point=SCRIPT_PATH,
role='SageMakerRole',
train_instance_count=1,
train_instance_type=local_instance_type,
sagemaker_session=sagemaker_local_session,
image_name=docker_image,
framework_version=framework_version,
output_path='file://{}'.format(tmpdir))
_train_and_assert_success(mx, str(tmpdir))
with local_mode_utils.lock():
try:
model = mx.create_model(
model_server_workers=NUM_MODEL_SERVER_WORKERS)
predictor = _csv_predictor(model, local_instance_type)
data = numpy.zeros(shape=(1, 1, 28, 28))
prediction = predictor.predict(data)
finally:
mx.delete_endpoint()
# Check that there is a probability for each possible class in the prediction
prediction_values = prediction.decode('utf-8').split(',')
assert len(prediction_values) == 10
def predictor(docker_image, sagemaker_local_session, local_instance_type):
default_handler_path = os.path.join(RESOURCE_PATH, 'default_handlers')
m = MXNetModel('file://{}'.format(os.path.join(default_handler_path, 'model')), 'SageMakerRole',
os.path.join(default_handler_path, 'code', 'empty_module.py'),
image=docker_image, sagemaker_session=sagemaker_local_session,
model_server_workers=NUM_MODEL_SERVER_WORKERS)
with local_mode_utils.lock():
try:
predictor = m.deploy(1, local_instance_type)
yield predictor
finally:
sagemaker_local_session.delete_endpoint(m.endpoint_name)
def predictor(docker_image, sagemaker_local_session, local_instance_type):
model = MXNetModel('file://{}'.format(MODEL_PATH),
'SageMakerRole',
SCRIPT_PATH,
image=docker_image,
sagemaker_session=sagemaker_local_session)
with local_mode_utils.lock():
try:
predictor = model.deploy(1, local_instance_type)
yield predictor
finally:
predictor.delete_endpoint()
def test_hosting(docker_image, sagemaker_local_session, local_instance_type):
model = MXNetModel('file://{}'.format(MODEL_PATH),
'SageMakerRole',
SCRIPT_PATH,
image=docker_image,
sagemaker_session=sagemaker_local_session)
input = json.dumps({'some': 'json'})
with local_mode_utils.lock():
try:
predictor = model.deploy(1, local_instance_type)
output = predictor.predict(input)
assert input == output
finally:
sagemaker_local_session.delete_endpoint(model.endpoint_name)
def test_hosting(docker_image, sagemaker_local_session, local_instance_type):
hosting_resource_path = os.path.join(RESOURCE_PATH, 'dummy_hosting')
m = MXNetModel('file://{}'.format(os.path.join(hosting_resource_path, 'code')), 'SageMakerRole',
os.path.join(hosting_resource_path, 'code', 'dummy_hosting_module.py'),
image=docker_image, sagemaker_session=sagemaker_local_session,
model_server_workers=NUM_MODEL_SERVER_WORKERS)
input = json.dumps({'some': 'json'})
with local_mode_utils.lock():
try:
predictor = m.deploy(1, local_instance_type)
output = predictor.predict(input)
assert input == output
finally:
sagemaker_local_session.delete_endpoint(m.endpoint_name)
def test_gluon_hosting(docker_image, sagemaker_local_session, local_instance_type):
model = MXNetModel('file://{}'.format(MODEL_PATH),
'SageMakerRole',
SCRIPT_PATH,
image=docker_image,
sagemaker_session=sagemaker_local_session)
with open(os.path.join(RESOURCE_PATH, 'mnist', 'images', '04.json'), 'r') as f:
input = json.load(f)
with local_mode_utils.lock():
try:
predictor = model.deploy(1, local_instance_type)
output = predictor.predict(input)
assert [4.0] == output
finally:
predictor.delete_endpoint()
def test_onnx_import(docker_image, sagemaker_local_session, local_instance_type):
model_path = 'file://{}'.format(os.path.join(ONNX_PATH, 'onnx_model'))
m = MXNetModel(model_path, 'SageMakerRole', SCRIPT_PATH, image=docker_image,
sagemaker_session=sagemaker_local_session,
model_server_workers=NUM_MODEL_SERVER_WORKERS)
input = numpy.zeros(shape=(1, 1, 28, 28))
with local_mode_utils.lock():
try:
predictor = m.deploy(1, local_instance_type)
output = predictor.predict(input)
finally:
sagemaker_local_session.delete_endpoint(m.endpoint_name)
# Check that there is a probability for each possible class in the prediction
assert len(output[0]) == 10
def test_onnx_import(docker_image, sagemaker_local_session,
local_instance_type):
model = MXNetModel('file://{}'.format(MODEL_PATH),
'SageMakerRole',
SCRIPT_PATH,
image=docker_image,
sagemaker_session=sagemaker_local_session)
input = numpy.zeros(shape=(1, 1, 28, 28))
with local_mode_utils.lock():
try:
predictor = model.deploy(1, local_instance_type)
output = predictor.predict(input)
finally:
predictor.delete_endpoint()
# Check that there is a probability for each possible class in the prediction
assert len(output[0]) == 10
def test_hosting(docker_image, sagemaker_local_session, local_instance_type):
model = MXNetModel('file://{}'.format(MODEL_PATH),
'SageMakerRole',
SCRIPT_PATH,
image=docker_image,
sagemaker_session=sagemaker_local_session)
with local_mode_utils.lock():
try:
predictor = model.deploy(1, local_instance_type)
predictor.serializer = None
predictor.deserializer = StringDeserializer()
predictor.accept = None
predictor.content_type = None
input = 'some data'
output = predictor.predict(input)
assert input == output
finally:
predictor.delete_endpoint()
def test_gluon_hosting(docker_image, sagemaker_local_session,
local_instance_type):
gluon_path = os.path.join(RESOURCE_PATH, 'gluon_hosting')
m = MXNetModel('file://{}'.format(os.path.join(gluon_path, 'model')),
'SageMakerRole',
os.path.join(gluon_path, 'code', 'gluon.py'),
image=docker_image,
sagemaker_session=sagemaker_local_session,
model_server_workers=NUM_MODEL_SERVER_WORKERS)
with open(os.path.join(RESOURCE_PATH, 'mnist_images', '04.json'),
'r') as f:
input = json.load(f)
with local_mode_utils.lock():
try:
predictor = m.deploy(1, local_instance_type)
output = predictor.predict(input)
assert [4.0] == output
finally:
sagemaker_local_session.delete_endpoint(m.endpoint_name)
def test_default_model_fn(docker_image, sagemaker_local_session,
local_instance_type):
default_handler_path = os.path.join(RESOURCE_PATH, 'default_handlers')
m = MXNetModel('file://{}'.format(
os.path.join(default_handler_path, 'model')),
'SageMakerRole',
os.path.join(default_handler_path, 'code',
'empty_module.py'),
image=docker_image,
sagemaker_session=sagemaker_local_session,
model_server_workers=NUM_MODEL_SERVER_WORKERS)
input = [[1, 2]]
with local_mode_utils.lock():
try:
predictor = m.deploy(1, local_instance_type)
output = predictor.predict(input)
assert [[4.9999918937683105]] == output
finally:
sagemaker_local_session.delete_endpoint(m.endpoint_name)