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README.md

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+# Stellars JupyterHub for Data Science Platform
+
+**Multi-user JupyterHub 4 with Miniforge, Data Science stack, and NativeAuthenticator.**
+
+This platform is built to support multiple data scientists on a shared environment with isolated sessions. Powered by JupyterHub, it ensures secure, user-specific access via the `NativeAuthenticator` plugin. It includes a full data science stack with GPU support (optional), and integrates seamlessly into modern Docker-based workflows.
+
+This deployment provides access to a centralized JupyterHub instance for managing user sessions. Optional integrations such as TensorBoard, MLFlow, or Optuna can be added manually via service extensions.
+
+## References
+
+This project spawns user environments using docker image: `stellars/stellars-jupyterlab-ds`  
+
+Visit the project page for stellars-jupyterlab-ds: https://github.com/stellarshenson/stellars-jupyterlab-ds
+
+## Quickstart
+
+### Docker Compose
+1. Download `compose.yml` and `config/jupyterhub_config.py` config file
+2. Run: `docker compose up --no-build`
+3. Open `https://localhost/jupyterhub` in your browser 
+4. Add `admin` user through self-sign-in (user will be authorised automatically)
+5. Log in as `admin`
+
+### Start Scripts
+- `start.sh` or `start.bat` – standard startup for the environment
+- `scripts/build.sh` alternatively `make build` – builds required Docker containers
+
+### Authentication
+This stack uses [NativeAuthenticator](https://github.com/jupyterhub/nativeauthenticator) for user management. Admins can whitelist users or allow self-registration. Passwords are stored securely.
+
+
+## Deployment Notes
+
+- Ensure `config/jupyterhub_config.py` is correctly set for your environment (e.g., TLS, admin list).
+- Optional volume mounts and configuration can be modified in `compose.yml` for shared storage.
+
+
+
+