improving-container-security

A Data Science service with its security improved as per the "Improving the security of Data Science containers" Medium post.

Parts of the service

The service has three parts:

iris_trainer - This container trains a random forest regressor in scikit-learn using the Iris Dataset. After the model is trained, it is written to a Pickle file (model.pkl) in the directory represented by the MODEL_DIR environment variable. The image of this container is defined in the Dockerfile.model file.
iris_predictor - This container loads a random forest regressor of scikit-learn as saved in a model.pkl file of the directory represented by the MODEL_DIR environment variable. The loaded model is then exposed via Flask on port 5000 to accept a POST with an array of two numbers in the JSON body, i.e. { [ 1, 2 ] }. The response to the POST request is as JSON object with a value field and a number as its value, i.e. { "value" = 1 }. The Flask endpoint is simply /, thus it is accessed via http://localhost:5000/. The image of this container is defined in the Dockerfile.model file.
iris_frontend - This container runs an AngularJS web application with Nginx on port 4200. The UI has a simple form that POST's two input numbers to an endpoint that is assumed to listen on http://localhost:5000/. The response of the POST is then displayed by accessing its value field. Therefore, this container ideally connects to iris_predictor. Note that the AngularJS application is built with a builder image using Node.js, which can be seen in the Dockerfile.frontend file.

To run the service:

Open a terminal and change to the directory that contains the docker-compose.yml file (in the root of this repo).
Execute docker-compose build to create the Docker images.
Execute docker-compose up -d to create containers and start the service. This will first run the iris_trainer that will train the model if it does not exist. Then, the iris_predictor will run to load the model that should exist in the attached volume. Finally, the iris_frontend will run making the web UI available on http://localhost:4200/.