AIoD - RAIL (Research and Innovation AI Lab)

RAIL service is a tool that allows AI practitioners to explore and use AI assets directly in AIoD. RAIL is developed within the AI4Europe project as one of the core services of the AI on Demand platform.

More extensively, RAIL:

• Is a web application
• … that enables AI practitioners
• … to work with AIoD AI Assets (explore, search, compare, …)
• … and create experiments that are reproducible and reusable
• … that are executable directly in the AIoD platform supported by its infrastructure
• … and that make use of AIoD AI Assets.

IMPORTANT: RAIL is a service built on top of AIoD API and relies heavily on its contents and functionalities.

Deployment

This repository consists of two main components - frontend and backend applications, served by nginx configured as a reverse proxy (for more details see nginx/default.conf). Each of these components can be built and deployed individually (see the corresponding sub-folders frontend and backend). However, it is recommended to deploy RAIL as a whole using Docker Compose from the root directory.

Running command

docker compose up -d

starts the backend REST API application (FastAPI) together with its underlying database (MongoDB) and the frontend web application (Angular).

Note: Some additional changes to file docker-compose.yml might be needed in order to change the default ports, etc.

Dependencies

In order for RAIL to work properly, you may need to deploy additional AIoD services or other misc services RAIL depends on, especially when you wish to build the entire AIoD ecosystem from scratch. Unless you utilize and point RAIL to already existing official AIoD services, you need to deploy the following dependencies first for RAIL to work:

• AIoD Metadata Catalogue for accessing AIoD assets
  • Keycloak auth service is a part of this service
• AIoD MyLibrary for accessing your bookmarked AIoD assets
• AIoD Enhanced Interaction for performing Enhanced Search and Chatbot functionality
• REANA for executing experiments

If you decide to utilize official AIoD services, you need to contact AIoD team to create credentials for most of these services. More specifically you would need the following credentials for your own RAIL instance to work with official AIoD platform:

• AIoD Keycloak credentials (new clients setup for RAIL frontend and backend)
• REANA credentials

Configuration

• Backend - It is mandatory to configure environment variables in file backend/.env. See the section "Deployment" in the corresponding README file for more details.
• Frontend - It is mandatory to configure frontend in frontend/src/app/environments/environment.production.ts granted you use the prod value for PROFILE Docker argument (that you can change in docker-compose.yml). See the section "Deployment" in the corresponding README file for more details.

Deployment checklist

• Unless you use official AIoD services, deploy additional dependencies and services RAIL relies on specified above
• Configure backend and frontend components accordingly. For more information, check their respective README files
• Perform any additional changes to the main docker-compose.yml file you deem necessary for your setup
• Execute the command: docker compose up -d --build

Development and debugging

For development purposes, we recommend running RAIL's development docker containers (docker compose) and then attach to the applications running inside. This helps to ensure that all developers develop in the same environment.

To start development and debugging, follow these steps:

1. Create development .env-dev file in the Backend root folder. It must define all variables the .env file defines.
2. Start the images either by running run-dev-docker-env.sh or by running the following command from the root of the project:

   • docker compose -f docker-compose.yml -f docker-compose-dev.yml up -d --build

3. Attach to the application.
   • For Visual Studio Code, there are a .vscode/launch.json configuration file in the root directory. In the Run and Debug menu, you should see:
     • 

This gif shows how to start development containers and attach a debugger in Visual Studio Code:

Under the hood:

• Frontend starts the development server (ng serve ...).
• Backend utilizes the debugpy utility (debugpy ... -m uvicorn ...). The debug server listens on 0.0.0.0:5678.

Notes

When you install a new package (Python lib or an NPM package), you need to restart the dev containers.

Current setup doesn't support Frontend hot reload (--watch). You need to refresh your browser after every change.

For better development experience, we recommend installing the local dev environments and libraries. Depending on your IDE, this will allow for features like autocomplete.

• Frontend
  • Install node and npm package manager.
  • Install all the dependencies: npm ci
• Backend
  • [optional] Create a Python virtual environment, conda environment or equivalent.
  • Install all the dependencies: pip install --no-cache-dir --upgrade -r requirements.txt

Contact

For any questions please contact the maintainers:

• Martin Tamajka (martin.tamajka@kinit.sk)
• Andrej Ridzik (andrej.ridzik@kinit.sk)