example_sagemaker

sagemaker-tensorflow-containers with OpenVINO Model Server for inference execution

Overview

Sagemaker-tensorflow-containers is a component which exposes REST API interface for inference execution including pre and post processing. It was designed for deploying TensorFlow framework in Amazon but it could be used also as a standalone local docker container.

In the backend, sagemaker-tensorflow-containers is using TensorFlow Serving component for inference execution over gRPC interface. While this works correctly it is not performing in the optimal manner on the CPU. Based on our tests with various AI graph topologies, employing OpenVINO and its Inference Engine can speed up the inference processing over 3 times on the same HW!

The solution presented in this example is swapping TensorFlow Serving with OpenVINO Model Server inside the Sagemaker container.

Because the gRPC API is compatible between those 2 component the replacement is pretty transparent. There is just needed a patch in the code altering the service startup command and instead of TensorFlow saved models format there should be used intermediate representation format, which can be converted by OpenVINO model optimizer.

Building the docker image

The first step in the building process should applying the git patch 0001-patch-sagemaker-for-OpenVINO-model-server.patch on the original repository https://github.com/aws/sagemaker-tensorflow-container (tested with a commit 1456108c6c025a6b81cf0de462522c8517b3005e). It is updating the command for staring the model server from TensorFlow Serving to OpenVINO Model Server.

git apply 0001-patch-sagemaker-for-OpenVINO-model-server.patch

The next step should be creating SageMaker TensorFlow Container Python package according to the official documentation:

# Create the SageMaker TensorFlow Container Python package.
cd sagemaker-tensorflow-containers
python setup.py sdist

#. Copy your Python package to “example_sagemaker” folder with the Dockerfile.
cp dist/sagemaker_tensorflow_container-*.tar.gz [path]/example_sagemeker/

It is also required to download OpenVINO installer package from https://software.intel.com/en-us/openvino-toolkit/choose-download The Linux or Linux FPGA versions can be used. It is recommended to download online installer as it results in a smaller docker image size. Place the installer in the same folder like the Dockerfile and Sagemaker python package.

Eventually build the docker image with a command:

docker build --build-arg HTTP_PROXY="$http_proxy" --build-arg HTTPS_PROXY="$http_proxy" -f Dockerfile -t sagemaker-ie_serving .

With this step docker image is build and ready to be used.

Starting the sagemaker container

In the example below sagemaker docker container starts serving models from folder /tmp/test_models/saved_models on port 8080:

docker run --rm -d -v /tmp/test_models/saved_models/:/opt/ml/:ro -p 8080:8080 --env-file=env.txt -t sagemaker-ie_serving serve

With that the predictions calls can be executed in a way similar to:

serialized_output = requests.post("http://localhost:8080/invocations",
                                          MessageToJson(tensor_proto),
                                          headers={'Content-type': 'application/json'}).content

or

serialized_output = requests.post("http://localhost:8080/invocations",
                                      data=request.SerializeToString(),
                                      headers={
                                          'Content-type': 'application/octet-stream',
                                          'Accept': 'application/octet-stream'
                                      }).content