def test_tfs_model(region, boto_session, sagemaker_client, sagemaker_runtime_client, model_name, tfs_model, image_uri, instance_type, accelerator_type): input_data = {'instances': [1.0, 2.0, 5.0]} util.create_and_invoke_endpoint(region, boto_session, sagemaker_client, sagemaker_runtime_client, model_name, tfs_model, image_uri, instance_type, accelerator_type, input_data)
def test_invoke_endpoint(boto_session, sagemaker_client, sagemaker_runtime_client, model_name, model_data, image_uri, instance_type, accelerator_type, input_data): util.create_and_invoke_endpoint(boto_session, sagemaker_client, sagemaker_runtime_client, model_name, model_data, image_uri, instance_type, accelerator_type, input_data)
def test_python_model_with_lib(region, boto_session, sagemaker_client, sagemaker_runtime_client, model_name, python_model_with_lib, image_uri, instance_type, accelerator_type): if 'p3' in instance_type: pytest.skip('skip for p3 instance') # the python service needs to transform this to get a valid prediction input_data = {'x': [1.0, 2.0, 5.0]} output_data = util.create_and_invoke_endpoint(region, boto_session, sagemaker_client, sagemaker_runtime_client, model_name, python_model_with_lib, image_uri, instance_type, accelerator_type, input_data) # python service adds this to tfs response assert output_data['python'] is True assert output_data['dummy_module'] == '0.1'
def test_python_model_with_lib(boto_session, sagemaker_client, sagemaker_runtime_client, model_name, python_model_with_lib, image_uri, instance_type, accelerator_type): if "p3" in instance_type: pytest.skip("skip for p3 instance") # the python service needs to transform this to get a valid prediction input_data = {"x": [1.0, 2.0, 5.0]} output_data = util.create_and_invoke_endpoint( boto_session, sagemaker_client, sagemaker_runtime_client, model_name, python_model_with_lib, image_uri, instance_type, accelerator_type, input_data) # python service adds this to tfs response assert output_data["python"] is True assert output_data["dummy_module"] == "0.1"