This system implements an approach for automated feedback suggestions of programming exercises based on prior manual feedback. It is integrated into the Themis App, as well as the LMS Artemis.
Themis-ML is built using a microservice architecture. For now, it has two components:
- Load Balancer: Provides service in the form of a HTTP REST API to communicate with the Themis App.
- Feedback Suggestion: Component for generating automated feedback suggestions based on Machine Learning models.
In local deployment, the server can be accessed via localhost.
Using the docker-compose.yml file included in the root directory of the repository is the easiest way to start the system.
The execution of
docker-compose up -d
will automatically build and start all components. (The -d parameter will run containers in the background).
By default, a traefik-container will manage API-Endpoints and expose them on port 80 (default HTTP-port).
Traefik provides a dashboard to monitor the status of the underlying components on port 8080.
In the development process the whole system or a single component (container) can be re-built and started using e.g.
docker-compose up -d --build
or
docker-compose up -d --build feedback-suggestion
To stop the whole system or a single container (as seen in the command above) use either
docker-compose stop
to stop all running containers or
docker-compose down
to stop and remove all containers.
In production deployment, the server can be accessed via ios2223cit.ase.cit.tum.de.
Using the docker-compose.production.yml file included in the root directory of the repository is the easiest way to start the system.
The execution of
docker compose -f docker-compose.production.yml up -d
will automatically build and start all components.
All the other commands above have to be adapted accordingly for production.