T-FORS

Traveling Ionospheric Disturbances Forecasting System (funded by the European Community, Horizon Europe)

Table of Contents

• What is it?

• How can I run it?

  • Run locally 💻

  • Spin up a container 🐋

• How can I help?

What is it?

We aim at the development of a machine-learning-based algorithm to forecast Large Scale Traveling Ionospheric Disturbances. The work is carried out within the "T-FORS - Traveling Ionospheric Disturbances Forecasting System" project.

How can I run it?

This project can be run either locally or within a Docker container (recommended). Below are the instructions for both modes.

Run locally 💻

• First, you need to clone the repo and install dependencies via Poetry with poetry install

• Activate a virtual environment

  • There are several virtual environments:

    • one in ./src (back-end)
    • one in ./app (front-end)
    • one in ./jupyters (REPL environment for prototyping)

  • cd into the directory of interest and activate the corresponding environment via poetry shell

• To launch a web server and execute jupyter notebooks, (on Windows) you can run the scripts/run-jupyter.ps1 script; otherwise, execute the poetry run jupyter notebook command

• To start a MLflow tracking server, (on Windows) you can run the scripts/run-mlflow-ui.ps1 script; the tracking UI can be accessed locally by navigating to http://localhost:5000/

• To launch the Streamlit web app, execute the streamlit run ./app/0_🏠_Home.py command

Spin up a container 🐋

• Clone the repo and ensure that the Docker daemon is running

• Build a Docker image

  • There are several Dockerfiles:

    • ./src/Dockerfile (back-end, exposing port 8000)
    • ./app/Dockerfile (front-end, exposing port 8501)
    • ./jupyters/Dockerfile (REPL environment for prototyping, exposing port 8890)

  • cd into the directory of interest and build the corresponding image via docker build -t <image-name> .

• Run a container and expose it on a preferred port (for example, 8080) via docker run --rm -p 8080:8501 <image-name> – or run it using the host's network via docker run --rm --network host <image-name>

• To orchestrate the services, you can use Docker Compose

  • There are several services:
    • api (back-end, exposing port 8000)
    • web-app (front-end, exposing port 8501)
    • jupyter (REPL environment for prototyping, exposing port 8890)

• Run a specific service via docker compose up --build <service-name> (omit the --build flag if the image is already built) from the project root directory

How can I help?

Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature_amazing_feature)
3. Commit your Changes (git commit -m 'Add some amazing stuff')
4. Push to the Branch (git push origin feature_amazing_feature)
5. Open a Pull Request

An (hopefully) up-to-date list of things to do can be found here.