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40 lines
1.9 KiB
Markdown
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40 lines
1.9 KiB
Markdown
Executable File
# Stellars JupyterHub for Data Science Platform
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**Multi-user JupyterHub 4 with Miniforge, Data Science stack, and NativeAuthenticator.**
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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.
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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.
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## References
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This project spawns user environments using docker image: `stellars/stellars-jupyterlab-ds`
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Visit the project page for stellars-jupyterlab-ds: https://github.com/stellarshenson/stellars-jupyterlab-ds
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## Quickstart
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### Docker Compose
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1. Download `compose.yml` and `config/jupyterhub_config.py` config file
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2. Run: `docker compose up --no-build`
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3. Open `https://localhost/jupyterhub` in your browser
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4. Add `admin` user through self-sign-in (user will be authorised automatically)
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5. Log in as `admin`
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### Start Scripts
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- `start.sh` or `start.bat` – standard startup for the environment
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- `scripts/build.sh` alternatively `make build` – builds required Docker containers
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### Authentication
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This stack uses [NativeAuthenticator](https://github.com/jupyterhub/nativeauthenticator) for user management. Admins can whitelist users or allow self-registration. Passwords are stored securely.
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## Deployment Notes
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- Ensure `config/jupyterhub_config.py` is correctly set for your environment (e.g., TLS, admin list).
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- Optional volume mounts and configuration can be modified in `jupyterhub_config.py` for shared storage.
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