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Deploying the BMW-Anonymization-Api with docker compose

In this section, docker compose will build and run a network of containers including the Anonymization API alongside multiple inference APIs.

In the following section, we encapsulate the BMW-IntelOpenVINO-Inference-API and the BMW-IntelOpenVINO-Segmentation-API with our anonymization API.

These two inference APIs contain example models optimzed via OpenVINO. Other OpenVINO models in Intermediate Representation(IR) format, converted via the Intel® OpenVINO™ toolkit v2021.1, can be deployed with our APIs. Currently, OpenVINO supports conversion for DL-based models trained via several Machine Learning frameworks including Caffe, Tensorflow etc. Please refer to the OpenVINO documentation for further details on converting your Model.

Build and Run the network

In this section, docker compose will build and run a network of containers including the Anonymization API alongside the OpenVINO inference APIs for detection and segmentation. The instructions are provided below.

To run the APIs together, clone the BMW-Anonymization-API, the BMW-IntelOpenVINO-Inference-API and the BMW-IntelOpenVINO-Segmentation-API into the same directory.

The folder structure should be similar to as shown below:

│──BMW-Anonymization-API
  │──docker 
  |──jsonFiles  
  │──...
  |──docker-compose.yml  
  │──Readme.md  
│──BMW-IntelOpenVINO-Segmentation-API 
  │──docker 
  |──...
  │──docs  
  │──Readme.md
│──BMW-IntelOpenVINO-Detection-Inference-API
  │──docker 
  |──...
  │──docs  
  │──Readme.md

In the BMW-Anonymization API replace the ./BMW-Anonymization-API/jsonFiles/url_configuration.json with the provided ./url_for_openvino_compose/url_configuration.json.

Three services are configured in the docker-compose.yml file in this repository: the BMW-Anonymization-API, the BMW-IntelOpenVINO-Inference-API and the BMW-IntelOpenVINO-Segmentation-API. You can modify the build context to specify the base directories of the APIs (ensure the correct path is also given for the mounted volumes). You can also modify the host ports you wish to use for the APIs.

After you configure your docker-compose.yml file, you can run the following command in the anonymization API directory:

Build the images

To build the images, run the following command in this directory:

docker-compose build

Run the network

To run the network, use the following command in this directory:

docker-compose up

Stop the running containers

To stop the network, run the following command in this directory:

docker-compose down

Restart the network

To restart the network, run the following command in this directory:

docker-compose restart

API Endpoints

To see all available endpoints, open your favorite browser and navigate to:

http://<machine_IP>:<docker_host_port>/docs

If you use the standard configuration of the docker-compose.yml the folllowing endpoints are available:

API Endpoint
BMW-Anonymization-API http://localhost:8070/docs
RCV-IntelOpenVINO-Detection-API http://localhost:8081/docs
BMW-IntelOpenVINO-Segmentation-API http://localhost:8090/docs

Please refer to the Endpoints Summary section in the initial readme

Using other inference APIs

Other inference APIs can also be configured within the docker-compose.yml such as our tensorflow CPU detection API and semantic segmentation CPU/GPU) If you wish to deploy other inference APIs, please make sure to the docker-compose.yml accordingly:

  • Modify the context in order to specify the base directory of each API
  • Modify the dockerfile entry to match the path of the dockerfile in the API directory
  • Modify the ports and choose the ones you wish to use for each API
  • In case you are setting up a GPU-based inference API, do not forget to set the runtime entry as "nvidia"

We provided a sample docker-compose file ./BMW-Anonymization-API/docker-compose_tf_gluoncv.yml