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()
# endpoint tests all use the same port, so we use this lock to prevent concurrent execution
with lock.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_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)
try:
estimator.fit()
predictor = estimator.deploy(initial_instance_count=1,
instance_type='local')
predict_response = predictor.predict([7])
assert predict_response == [49]
finally:
estimator.delete_endpoint()
