def test_mxnet_local_data_local_script():
local_mode_lock_fd = open(LOCK_PATH, 'w')
local_mode_lock = local_mode_lock_fd.fileno()
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',
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
try:
# Since Local Mode uses the same port for serving, we need a lock in order
# to allow concurrent test execution. The serving test is really fast so it still
# makes sense to allow this behavior.
fcntl.lockf(local_mode_lock, fcntl.LOCK_EX)
predictor = mx.deploy(1, 'local', endpoint_name=endpoint_name)
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
mx.delete_endpoint()
time.sleep(5)
fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)
def test_mxnet_local_data_local_script():
local_mode_lock_fd = open(LOCK_PATH, 'w')
local_mode_lock = local_mode_lock_fd.fileno()
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',
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
try:
# Since Local Mode uses the same port for serving, we need a lock in order
# to allow concurrent test execution. The serving test is really fast so it still
# makes sense to allow this behavior.
fcntl.lockf(local_mode_lock, fcntl.LOCK_EX)
predictor = mx.deploy(1, 'local', endpoint_name=endpoint_name)
data = numpy.zeros(shape=(1, 1, 28, 28))
predictor.predict(data)
finally:
mx.delete_endpoint()
time.sleep(5)
fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)
def test_mxnet_local_data_local_script(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="local",
framework_version=mxnet_full_version,
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 lock.lock(LOCK_PATH):
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_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 lock.lock(LOCK_PATH):
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_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_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 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()
