A modern React + Node.js web app that tracks & visualizes the energy consumption and carbon footprint of machine learning training runs in real‑time.
Machine‑learning models are becoming ever more powerful—and energy‑hungry. AI Model Efficiency Dashboard gives data scientists, MLOps engineers, and sustainability officers a single pane of glass to:
- Monitor energy usage & carbon emissions in real time as training runs progress.
- Store every run’s metrics in a persistent MongoDB database.
- Visualize trends with interactive charts, heat‑maps, and KPI widgets.
- React instantly to anomalies via WebSocket‑powered live updates.
Note: This repository currently holds the concept & design for the dashboard. Development is open‑source and we welcome contributors to bring the idea to life!
| ✅ | Feature |
|---|---|
| 🌱 | Real‑time energy & carbon tracking – Stream per‑step consumption data via WebSockets. |
| 📊 | Dynamic visualizations – Line charts, bar graphs, and gauge widgets powered by Chart.js / D3. |
| 🗄️ | MongoDB persistence – Every training run is archived for historical analysis. |
| 👤 | User authentication & role‑based access – Secure view for admins, engineers, and auditors. |
| ⚙️ | Configurable thresholds & alerts – Get notified when a run exceeds a carbon budget. |
| 📱 | Responsive UI – Works on desktop, tablet, and mobile browsers. |
| 📦 | Modular architecture – Front‑end (React) & back‑end (Node/Express) are decoupled for easy scaling. |
(Add more features as the project evolves!)
⚠️ There are currently no runnable code artifacts.
This repository is a project blueprint. When the first implementation is ready, the installation steps will be added here.
If you’d like to start building the dashboard now, you can clone the repo and set up a typical React + Node environment:
git clone https://github.com/your-username/ai-model-efficiency-dashboard.git
cd ai-model-efficiency-dashboard
# Front‑end
cd client && npm install && npm start
# Back‑end
cd ../server && npm install && npm run devReplace the placeholder commands with the actual scripts once they exist.
No usable application is available yet.
When the dashboard is live, typical usage will look like:
- Connect your ML training pipeline to the WebSocket endpoint (
ws://<server>/run-stream). - Push JSON payloads containing
timestamp,energy_kWh,co2_kg, and any custom metrics. - Open the web UI (
http://localhost:3000) and watch the live charts update. - Explore historical runs via the “Archives” tab, filter by model, dataset, or date range.
More detailed usage docs will be added after the first MVP release.
We love community contributions! Follow these steps to get started:
-
Fork the repository.
-
Create a branch for your feature or bug‑fix:
git checkout -b feat/awesome-feature
-
Write clean, documented code and adhere to the existing folder structure (
client/for React,server/for Node). -
Add tests where applicable (Jest for front‑end, Mocha/Chai for back‑end).
-
Run the linter (
npm run lint) and ensure all tests pass. -
Commit with a clear message and push to your fork.
-
Open a Pull Request against the
mainbranch, describing the change and linking any relevant issue.
Please read our CODE_OF_CONDUCT.md (to be added) and follow the community guidelines.
- Set up local MongoDB instance (or use Atlas).
- Implement WebSocket server (
socket.ioor native WS). - Create React UI skeleton with routing (
react-router). - Add chart components (Chart.js, Recharts, or D3).
- Write unit & integration tests.
Feel free to open an issue to discuss ideas before diving in!
This project is licensed under the GNU General Public License v3.0. See the full license text in the LICENSE file.
Copyright (C) 2025 THE PURPLE MOVEMENT
The Purple Movement
- Website:purple-movement
- Email:
- X: @PurpleMovement
If you have questions, suggestions, or just want to say hi, drop us a line!
Placeholder images – replace with real screenshots once the UI is built.
| Overview | Real‑time Chart | Settings |
|---|---|---|
https://github.com/The-Purple-Movement/ai-model-efficiency-dashboard
Coming soon – stay tuned!
Happy coding! 🚀