def test_github(sagemaker_local_session, pytorch_inference_latest_version,
pytorch_inference_latest_py_version):
script_path = "mnist.py"
git_config = {"repo": GIT_REPO, "branch": BRANCH, "commit": COMMIT}
pytorch = PyTorch(
entry_point=script_path,
role="SageMakerRole",
source_dir="pytorch",
framework_version=pytorch_inference_latest_version,
py_version=pytorch_inference_latest_py_version,
instance_count=1,
instance_type="local",
sagemaker_session=sagemaker_local_session,
git_config=git_config,
)
data_path = os.path.join(DATA_DIR, "pytorch_mnist")
pytorch.fit({"training": "file://" + os.path.join(data_path, "training")})
with lock.lock(LOCK_PATH):
try:
predictor = pytorch.deploy(initial_instance_count=1,
instance_type="local")
data = numpy.zeros(shape=(1, 1, 28, 28)).astype(numpy.float32)
result = predictor.predict(data)
assert 10 == len(
result[0]) # check that there is a probability for each label
finally:
predictor.delete_endpoint()
def test_github(sagemaker_local_session):
script_path = "mnist.py"
data_path = os.path.join(DATA_DIR, "pytorch_mnist")
git_config = {"repo": GIT_REPO, "branch": BRANCH, "commit": COMMIT}
pytorch = PyTorch(
entry_point=script_path,
role="SageMakerRole",
source_dir="pytorch",
framework_version=PYTORCH_VERSION,
py_version=PYTHON_VERSION,
train_instance_count=1,
train_instance_type="local",
sagemaker_session=sagemaker_local_session,
git_config=git_config,
)
pytorch.fit({
"training":
"file://" + os.path.join(data_path, "training", MNIST_FOLDER_NAME)
})
with lock.lock(LOCK_PATH):
try:
predictor = pytorch.deploy(initial_instance_count=1,
instance_type="local")
data = numpy.zeros(shape=(1, 1, 28, 28)).astype(numpy.float32)
result = predictor.predict(data)
assert result is not None
finally:
predictor.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,
)
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()
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")
# TODO: fails on newer versions of pytorch in call to np.load(BytesIO(stream.read()))
# "ValueError: Cannot load file containing pickled data when allow_pickle=False"
estimator = PyTorch(
entry_point="train.py",
role="SageMakerRole",
source_dir=source_dir,
dependencies=[lib],
framework_version=
"0.4", # hard-code to last known good pytorch for now (see TODO above)
py_version="py3",
instance_count=1,
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:
predictor.delete_endpoint()
def test_fit_deploy(sagemaker_local_session, pytorch_full_version):
pytorch = PyTorch(
entry_point=MNIST_SCRIPT,
role="SageMakerRole",
framework_version=pytorch_full_version,
py_version="py3",
train_instance_count=1,
train_instance_type="local",
sagemaker_session=sagemaker_local_session,
)
pytorch.fit({"training": "file://" + os.path.join(MNIST_DIR, "training")})
predictor = pytorch.deploy(1, "local")
try:
batch_size = 100
data = numpy.random.rand(batch_size, 1, 28, 28).astype(numpy.float32)
output = predictor.predict(data)
assert output.shape == (batch_size, 10)
finally:
predictor.delete_endpoint()
