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_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))
try:
with timeout(minutes=15):
estimator.deploy(initial_instance_count=1,
instance_type='ml.c4.xlarge')
finally:
try:
estimator.delete_endpoint()
except Exception:
pass
def test_tf(sagemaker_session):
with timeout(minutes=15):
script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
data_path = os.path.join(DATA_DIR, 'iris', 'data')
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))
try:
with timeout(minutes=15):
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))
finally:
try:
estimator.delete_endpoint()
except Exception:
pass
def test_tf_local_mode(tf_full_version, sagemaker_local_session):
local_mode_lock_fd = open(LOCK_PATH, 'w')
local_mode_lock = local_mode_lock_fd.fileno()
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='local',
base_job_name='test-tf',
sagemaker_session=sagemaker_local_session)
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))
endpoint_name = estimator.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)
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()
time.sleep(5)
fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)
def test_tf_local_mode(tf_full_version, sagemaker_local_session):
local_mode_lock_fd = open(LOCK_PATH, 'w')
local_mode_lock = local_mode_lock_fd.fileno()
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='local',
base_job_name='test-tf',
sagemaker_session=sagemaker_local_session)
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))
endpoint_name = estimator.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)
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()
time.sleep(5)
fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)
def test_tf_local_mode(sagemaker_local_session):
with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
estimator = TensorFlow(
entry_point=script_path,
role="SageMakerRole",
framework_version="1.12",
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=sagemaker_local_session,
)
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))
endpoint_name = estimator.latest_training_job.name
with lock.lock(LOCK_PATH):
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()
