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()