2.8 KiB
JupyterHub
Multi-user platform for interactive computing:
- Collaborative Jupyter notebook environment
- Integrated with Keycloak for OIDC authentication
- Persistent storage for user workspaces
- Support for multiple kernels and environments
- Vault integration for secure secrets management
See JupyterHub Documentation for detailed setup and configuration.
Installation
just jupyterhub::install
Access
Access JupyterHub at https://jupyter.yourdomain.com and authenticate via Keycloak.
buunstack Package & SecretStore
JupyterHub includes the buunstack Python package, which provides seamless integration with HashiCorp Vault for secure secrets management in your notebooks.
Key Features
- 🔒 Secure Secrets Management: Store and retrieve secrets securely using HashiCorp Vault
- 🚀 Pre-acquired Authentication: Uses Vault tokens created automatically at notebook spawn
- 📱 Simple API: Easy-to-use interface similar to Google Colab's
userdata.get() - 🔄 Automatic Token Renewal: Built-in token refresh for long-running sessions
Quick Example
from buunstack import SecretStore
# Initialize with pre-acquired Vault token (automatic)
secrets = SecretStore()
# Store secrets
secrets.put('api-keys',
openai_key='sk-your-key-here',
github_token='ghp_your-token',
database_url='postgresql://user:pass@host:5432/db'
)
# Retrieve secrets
api_keys = secrets.get('api-keys')
openai_key = api_keys['openai_key']
# Or get a specific field directly
openai_key = secrets.get('api-keys', field='openai_key')
Learn More
For detailed documentation, usage examples, and API reference, see:
📖 buunstack Package Documentation
Custom Container Images
JupyterHub uses custom container images with pre-installed data science tools and integrations:
datastack-notebook (CPU)
Standard notebook image based on jupyter/pytorch-notebook:
- PyTorch: Deep learning framework
- PySpark: Apache Spark integration for big data processing
- ClickHouse Client: Direct database access
- Python 3.12: Latest Python runtime
datastack-cuda-notebook (GPU)
GPU-enabled notebook image based on jupyter/pytorch-notebook:cuda12:
- CUDA 12: GPU acceleration support
- PyTorch with GPU: Hardware-accelerated deep learning
- PySpark: Apache Spark integration
- ClickHouse Client: Direct database access
- Python 3.12: Latest Python runtime
Both images are based on the official Jupyter Docker Stacks and include all standard data science libraries (NumPy, pandas, scikit-learn, matplotlib, etc.).