Data Science Pack
The Data Science Pack is a Helm chart that deploys JupyterHub
with jhub-apps on Kubernetes. It wraps
the upstream Zero to JupyterHub chart, adding
Nebari’s custom images, per-group shared storage, and integration with the
Nebari Operator via the
NebariApp CRD.
What it does
Section titled “What it does”| Capability | Description |
|---|---|
| JupyterHub | Multi-user notebook server with Nebari’s custom images |
| jhub-apps | Deploy and share data science applications (Streamlit, Panel, custom commands) alongside notebooks |
| NebariApp integration | Registers routing, Keycloak OAuth, and a landing-page card via the Nebari Operator |
| Shared storage | Per-group directories (/shared/<group>) mounted into every user pod |
| Nebi integration | Ships the nebi environment-manager binary into JupyterLab pods via an init container |
| RBAC bootstrap | One-shot Keycloak Job that wires group-membership claims and shared-mount roles |
| Dummy authenticator | Any username/password works for local development; OAuth/Keycloak configurable for production |
Guides
Section titled “Guides”- Quick Start — install the chart and access JupyterHub.
- Architecture — how the proxy, hub, jhub-apps, and user pods fit together.
- Shared Storage — per-group directories, StorageClass requirements, and the transitional NFS mode.
Reference
Section titled “Reference”- Configuration — the top-level
values.yamlsections. - NebariApp Integration — the CRD fields this chart sets and why.
Source, issues, and the full values.yaml live in the
data-science-pack repository.