def test_cartpole(docker_image, sagemaker_local_session, processor, tmpdir):
source_dir = os.path.join(RESOURCE_PATH, 'coach_cartpole')
dependencies = [os.path.join(RESOURCE_PATH, 'sagemaker_rl')]
cartpole = 'train_coach.py'
instance_type = 'local' if processor == 'cpu' else 'local_gpu'
estimator = RLEstimator(entry_point=cartpole,
source_dir=source_dir,
role='SageMakerRole',
instance_count=1,
instance_type=instance_type,
sagemaker_session=sagemaker_local_session,
image_uri=docker_image,
output_path='file://{}'.format(tmpdir),
dependencies=dependencies,
hyperparameters={
"save_model": 1,
"RLCOACH_PRESET": "preset_cartpole_clippedppo",
"rl.agent_params.algorithm.discount": 0.9,
"rl.evaluation_steps:EnvironmentEpisodes": 1,
})
estimator.fit()
local_mode_utils.assert_output_files_exist(str(tmpdir), 'output',
['success'])
assert os.path.exists(os.path.join(str(tmpdir),
'model.tar.gz')), 'model file not found'
def test_linear_regression(docker_image, sagemaker_local_session,
local_instance_type, framework_version, tmpdir):
lr_path = os.path.join(RESOURCE_PATH, 'linear_regression')
mx = MXNet(entry_point=os.path.join(lr_path, 'linear_regression.py'),
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))
data_path = os.path.join(lr_path, 'data')
s3_prefix = 'integ-test-data/mxnet-linear-regression'
train_input = sagemaker_local_session.upload_data(path=os.path.join(
data_path, 'training'),
key_prefix=s3_prefix)
eval_input = sagemaker_local_session.upload_data(path=os.path.join(
data_path, 'evaluation'),
key_prefix=s3_prefix)
mx.fit({'training': train_input, 'evaluation': eval_input})
for directory, files in MODEL_SUCCESS_FILES.items():
local_mode_utils.assert_output_files_exist(str(tmpdir), directory,
files)
def test_onnx_export(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))
input_path = 'file://{}'.format(os.path.join(ONNX_PATH, 'mxnet_module'))
mx.fit({'train': input_path})
local_mode_utils.assert_output_files_exist(str(tmpdir), 'model', ['model.onnx'])
def test_keras_training(docker_image, sagemaker_local_session, local_instance_type,
framework_version, tmpdir):
keras_path = os.path.join(RESOURCE_PATH, 'keras')
script_path = os.path.join(keras_path, 'keras_mnist.py')
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 = 'file://{}'.format(os.path.join(keras_path, 'data'))
mx.fit({'train': train})
for directory, files in MODEL_SUCCESS_FILES.items():
local_mode_utils.assert_output_files_exist(str(tmpdir), directory, files)
def test_vw_cb_explore(local_instance_type, sagemaker_local_session, docker_image,
tmpdir, training_data_bandits, role):
source_path = os.path.join(RESOURCE_PATH, 'vw')
estimator = RLEstimator(entry_point="train_cb_explore.py",
source_dir=source_path,
role=role,
train_instance_count=1,
hyperparameters={"num_arms": 7},
train_instance_type=local_instance_type,
sagemaker_session=sagemaker_local_session,
output_path='file://{}'.format(tmpdir),
image_name=docker_image)
estimator.fit(inputs=training_data_bandits)
local_mode_utils.assert_output_files_exist(str(tmpdir), 'output', ['success'])
local_mode_utils.assert_output_files_exist(str(tmpdir), 'model', ['vw.model', 'vw.metadata'])
assert os.path.exists(os.path.join(str(tmpdir), 'model.tar.gz')), 'model file not found'
def test_gym(local_instance_type, sagemaker_local_session, docker_image,
tmpdir, framework):
source_path = os.path.join(RESOURCE_PATH, 'gym')
gym_script = 'launcher.sh' if framework == 'tensorflow' else 'gym_envs.py'
estimator = RLEstimator(entry_point=gym_script,
source_dir=source_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type=local_instance_type,
sagemaker_session=sagemaker_local_session,
output_path='file://{}'.format(tmpdir),
image_name=docker_image)
estimator.fit()
local_mode_utils.assert_output_files_exist(str(tmpdir), 'output',
['success'])
assert os.path.exists(os.path.join(str(tmpdir),
'model.tar.gz')), 'model file not found'
def test_ray_tf(local_instance_type, sagemaker_local_session, docker_image,
tmpdir):
source_dir = os.path.join(RESOURCE_PATH, 'ray_cartpole')
cartpole = 'train_ray.py'
estimator = RLEstimator(entry_point=cartpole,
source_dir=source_dir,
role='SageMakerRole',
train_instance_count=1,
train_instance_type=local_instance_type,
sagemaker_session=sagemaker_local_session,
output_path='file://{}'.format(tmpdir),
image_name=docker_image)
estimator.fit()
local_mode_utils.assert_output_files_exist(str(tmpdir), 'output',
['success'])
assert os.path.exists(os.path.join(str(tmpdir),
'model.tar.gz')), 'model file not found'
def _train_and_assert_success(estimator, output_path):
estimator.fit({'train': TRAIN_INPUT, 'test': TEST_INPUT})
for directory, files in MODEL_SUCCESS_FILES.items():
local_mode_utils.assert_output_files_exist(output_path, directory, files)