# Stellars JupyterHub for Data Science Platform
[](https://kolomolo.com)
[](https://www.paypal.com/donate/?hosted_button_id=B4KPBJDLLXTSA)
Multi-user JupyterHub 4 deployment platform with data science stack, GPU support, and NativeAuthenticator. The platform spawns isolated JupyterLab environments per user using DockerSpawner, backed by the [stellars/stellars-jupyterlab-ds](https://hub.docker.com/r/stellars/stellars-jupyterlab-ds) image (from [stellars-jupyterlab-ds](https://github.com/stellarshenson/stellars-jupyterlab-ds) project).
## Features
- **GPU Auto-Detection**: Automatic NVIDIA CUDA GPU detection and configuration for spawned user containers
- **Notification Broadcast**: Admin broadcast to all active servers via `/hub/notifications`. Supports six notification types, 140-character limit. Requires [jupyterlab_notifications_extension](https://github.com/stellarshenson/jupyterlab_notifications_extension)
- **User Self-Service**: Users can restart their JupyterLab containers and selectively reset persistent volumes (home/workspace/cache) without admin intervention
- **Privileged Access Control**: Group-based docker.sock access for trusted users enabling container orchestration from within JupyterLab
- **Isolated Environments**: Each user gets dedicated JupyterLab container with persistent volumes via DockerSpawner
- **Native Authentication**: Built-in user management with NativeAuthenticator supporting self-registration and admin approval
- **Shared Storage**: Optional CIFS/NAS mount support for shared datasets across all users
- **Production Ready**: Traefik reverse proxy with TLS termination, automatic container updates via Watchtower
## User Interface
**User Control Panel**
User control panel with server restart and volume management options.
**Volume Management**
Access volume management when server is stopped.
**Volume Selection**
Select individual volumes to reset - home directory, workspace files, or cache data.
**Admin Notification Broadcast**
Admin panel for broadcasting notifications to all active JupyterLab servers.
## Architecture
```mermaid
graph TB
User[User Browser] -->|HTTPS| Traefik[Traefik Proxy
TLS Termination]
Traefik --> Hub[JupyterHub
Port 8000]
Hub -->|Authenticates| Auth[NativeAuthenticator]
Hub -->|Spawns via| Spawner[DockerSpawner]
Spawner -->|Creates| Lab1[JupyterLab
alice]
Spawner -->|Creates| Lab2[JupyterLab
bob]
Spawner -->|Creates| Lab3[JupyterLab
charlie]
Lab1 --> Vol1[Volumes
home/workspace/cache]
Lab2 --> Vol2[Volumes
home/workspace/cache]
Lab3 --> Vol3[Volumes
home/workspace/cache]
Lab1 --> Shared[Shared Storage
CIFS/NAS]
Lab2 --> Shared
Lab3 --> Shared
style Hub stroke:#f59e0b,stroke-width:3px
style Traefik stroke:#0284c7,stroke-width:3px
style Auth stroke:#10b981,stroke-width:3px
style Spawner stroke:#a855f7,stroke-width:3px
style Lab1 stroke:#3b82f6,stroke-width:2px
style Lab2 stroke:#3b82f6,stroke-width:2px
style Lab3 stroke:#3b82f6,stroke-width:2px
style Shared stroke:#ef4444,stroke-width:2px
```
Users access JupyterHub through Traefik reverse proxy with TLS termination. After authentication via NativeAuthenticator, JupyterHub spawns isolated JupyterLab containers per user using DockerSpawner. Each user gets dedicated persistent volumes for home directory, workspace files, and cache data, with optional shared storage for collaborative datasets.
## Configuration Flow
```mermaid
graph TB
subgraph ENV["Environment Variables (compose.yml)"]
ADMIN[JUPYTERHUB_ADMIN
Admin username]
BASEURL[JUPYTERHUB_BASE_URL
URL prefix]
IMG[DOCKER_NOTEBOOK_IMAGE
User container image]
NET[DOCKER_NETWORK_NAME
Container network]
SSL[ENABLE_JUPYTERHUB_SSL
0=off, 1=on]
GPU[ENABLE_GPU_SUPPORT
0=off, 1=on, 2=auto]
TFLOG[TF_CPP_MIN_LOG_LEVEL
TensorFlow verbosity]
NVIMG[NVIDIA_AUTODETECT_IMAGE
CUDA test image]
subgraph SVCEN["ENABLE_SERVICE_*
Passed to Lab as env"]
direction LR
MLF[ENABLE_SERVICE_MLFLOW]
GLN[ENABLE_SERVICE_GLANCES]
TNS[ENABLE_SERVICE_TENSORBOARD]
SVC_MORE[...]
end
end
subgraph CONFIG["jupyterhub_config.py"]
AUTH[NativeAuthenticator
open_signup=False, enable_signup=True]
SPAWN[DockerSpawner
spawner_class, remove=True]
NBDIR[DOCKER_NOTEBOOK_DIR
/home/lab/workspace]
VOLS[DOCKER_SPAWNER_VOLUMES
home/workspace/cache/shared]
VOLDESC[VOLUME_DESCRIPTIONS
Optional UI labels]
GROUPS[BUILTIN_GROUPS
docker-privileged]
HOOK[pre_spawn_hook
Group check + docker.sock]
HANDLERS[extra_handlers
ManageVolumes, RestartServer, Notifications]
TEMPLATES[template_paths
Custom + Native + Default]
end
subgraph RUNTIME["Spawned User Container"]
LAB[JupyterLab Server
Port 8888]
SERVICES[Services: MLflow, Glances, TensorBoard
Controlled by ENABLE_SERVICE_* env]
GPUACCESS[GPU Access
device_requests if enabled]
end
ADMIN --> AUTH
BASEURL --> CONFIG
IMG --> SPAWN
NET --> SPAWN
SSL --> CONFIG
TFLOG --> |Passed as env| LAB
NVIMG --> |Used for auto-detect| CONFIG
GPU --> |Auto-detect via nvidia-smi| SPAWN
GPU --> |Passed as env| LAB
GPU --> |device_requests| GPUACCESS
SVCEN --> |Passed as env| LAB
SVCEN --> |Controls startup| SERVICES
AUTH --> |Validates| SPAWN
SPAWN --> |Creates| LAB
NBDIR --> SPAWN
VOLS --> |Mounts| LAB
VOLDESC --> HANDLERS
GROUPS --> HOOK
HOOK --> |Conditionally mounts
docker.sock| LAB
HANDLERS --> |API endpoints| LAB
TEMPLATES --> |Custom UI| LAB
style ENV stroke:#f59e0b,stroke-width:3px
style SVCEN stroke:#a855f7,stroke-width:2px
style CONFIG stroke:#10b981,stroke-width:3px
style RUNTIME stroke:#3b82f6,stroke-width:3px
style HOOK stroke:#ef4444,stroke-width:2px
style HANDLERS stroke:#ef4444,stroke-width:2px
```
Environment variables defined in `compose.yml` are consumed by `config/jupyterhub_config.py` to configure authentication, spawner behavior, and GPU detection. The configuration defines `DOCKER_SPAWNER_VOLUMES` for persistent storage, `VOLUME_DESCRIPTIONS` for optional UI labels, and `BUILTIN_GROUPS` for protected group management. When spawning user containers, these settings control which services are enabled (MLflow, Glances, TensorBoard), whether GPU access is granted via `device_requests`, and what volumes are mounted. The pre-spawn hook checks user group membership against `BUILTIN_GROUPS` to conditionally mount docker.sock for privileged users.
## GPU Auto-Detection
```mermaid
graph LR
START[ENABLE_GPU_SUPPORT=2] --> CHECK{Check value}
CHECK -->|0| DISABLED[GPU Disabled]
CHECK -->|1| ENABLED[GPU Enabled]
CHECK -->|2| DETECT[Auto-detect]
DETECT --> SPAWN[Spawn test container
nvidia/cuda:12.9.1-base]
SPAWN --> RUN[Execute nvidia-smi
with runtime=nvidia]
RUN --> SUCCESS{Success?}
SUCCESS -->|Yes| SET_ON[Set ENABLE_GPU_SUPPORT=1
Set NVIDIA_DETECTED=1]
SUCCESS -->|No| SET_OFF[Set ENABLE_GPU_SUPPORT=0
Set NVIDIA_DETECTED=0]
SET_ON --> CLEANUP1[Remove test container
jupyterhub_nvidia_autodetect]
SET_OFF --> CLEANUP2[Remove test container
jupyterhub_nvidia_autodetect]
CLEANUP1 --> APPLY_ON[Apply device_requests
to spawned containers]
CLEANUP2 --> APPLY_OFF[No GPU access
for spawned containers]
ENABLED --> APPLY_ON
DISABLED --> APPLY_OFF
style START stroke:#f59e0b,stroke-width:3px
style DETECT stroke:#a855f7,stroke-width:3px
style SET_ON stroke:#10b981,stroke-width:2px
style SET_OFF stroke:#ef4444,stroke-width:2px
style APPLY_ON stroke:#10b981,stroke-width:3px
style APPLY_OFF stroke:#6b7280,stroke-width:2px
```
When `ENABLE_GPU_SUPPORT=2` (auto-detect mode), JupyterHub spawns a temporary CUDA container running `nvidia-smi` with `runtime=nvidia`. If the command succeeds, GPU support is enabled and `device_requests` are added to spawned user containers. If it fails, GPU support is disabled. The test container is always removed after detection. Manual override is possible by setting `ENABLE_GPU_SUPPORT=1` (force enable) or `ENABLE_GPU_SUPPORT=0` (force disable).
## User Self-Service Workflow
```mermaid
graph LR
HOME[Home Page] --> RUNNING{Server State}
RUNNING -->|Running| RESTART[Restart Server
container.restart]
RUNNING -->|Stopped| START[Start Server
spawner.start]
RUNNING -->|Stopped| VOLUMES[Manage Volumes
Select + Delete]
RESTART --> |Docker API| REFRESH1[Page Refresh]
VOLUMES --> |Docker API| DELETE[volume.remove]
DELETE --> REFRESH2[Page Refresh]
style HOME stroke:#0284c7,stroke-width:3px
style RESTART stroke:#10b981,stroke-width:2px
style VOLUMES stroke:#ef4444,stroke-width:2px
style START stroke:#a855f7,stroke-width:2px
```
Users manage their servers through the home page. Running servers can be restarted via Docker API without recreation. Stopped servers can be started normally or have volumes selectively deleted through a modal interface presenting checkboxes for home, workspace, and cache volumes with optional descriptions from configuration.
## Volume Architecture
```mermaid
graph TB
subgraph HOST["Docker Host"]
VOLHOME1["jupyterlab-user1_home
Docker Volume"]
VOLWORK1["jupyterlab-user1_workspace
Docker Volume"]
VOLCACHE1["jupyterlab-user1_cache
Docker Volume"]
VOLHOME2["jupyterlab-user2_home
Docker Volume"]
VOLWORK2["jupyterlab-user2_workspace
Docker Volume"]
VOLCACHE2["jupyterlab-user2_cache
Docker Volume"]
VOLSHARED["jupyterhub_shared
Docker Volume - Shared"]
end
VOLHOME1 -.->|Mount| M1HOME
VOLWORK1 -.->|Mount| M1WORK
VOLCACHE1 -.->|Mount| M1CACHE
VOLHOME2 -.->|Mount| M2HOME
VOLWORK2 -.->|Mount| M2WORK
VOLCACHE2 -.->|Mount| M2CACHE
VOLSHARED --> MSHARED
subgraph CONTAINER1["User Container: user1"]
M1HOME["/home"]
M1WORK["/home/lab/workspace"]
M1CACHE["/home/lab/.cache"]
end
subgraph CONTAINER2["User Container: user2"]
M2HOME["/home"]
M2WORK["/home/lab/workspace"]
M2CACHE["/home/lab/.cache"]
end
MSHARED["/mnt/shared
Shared across all users"]
MSHARED ----> CONTAINER1
MSHARED ----> CONTAINER2
style HOST stroke:#f59e0b,stroke-width:3px
style CONTAINER1 stroke:#3b82f6,stroke-width:3px
style CONTAINER2 stroke:#3b82f6,stroke-width:3px
style VOLSHARED stroke:#10b981,stroke-width:3px
style MSHARED stroke:#10b981,stroke-width:3px
```
Each user receives four persistent volumes. Three user-specific volumes store home directory files, workspace projects, and cache data. The shared volume provides collaborative storage accessible across all user environments. Volume names follow the pattern `jupyterlab-{username}_` for per-user isolation. The shared volume can be configured as CIFS mount for NAS integration.
Users can selectively reset their personal volumes (home, workspace, cache) at any time through the Manage Volumes feature when their server is stopped. The shared volume cannot be reset by individual users as it contains collaborative data accessible to all users.
## References
This project spawns user environments using docker image: [stellars/stellars-jupyterlab-ds](https://hub.docker.com/r/stellars/stellars-jupyterlab-ds)
Visit the project page for stellars-jupyterlab-ds: https://github.com/stellarshenson/stellars-jupyterlab-ds
## Requirements
**Docker Socket Access Required**: This JupyterHub implementation requires read-write access to the Docker socket (`/var/run/docker.sock`) mounted into the JupyterHub container. This is essential for:
- **DockerSpawner**: Spawning and managing isolated JupyterLab containers for each user
- **Volume Management**: Allowing users to reset their persistent volumes (home/workspace/cache)
- **Container Control**: Enabling server restart functionality from the user control panel
- **Privileged Access**: Supporting optional docker.sock access for trusted users within their JupyterLab environments
The `compose.yml` file includes this mount by default:
```yaml
volumes:
- /var/run/docker.sock:/var/run/docker.sock:rw
```
> [!WARNING]
> The JupyterHub container has full access to the Docker daemon. Only trusted administrators should have access to JupyterHub configuration.
## 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 `jupyterhub_config.py` for shared storage.
## Customisation
You should customise the deployment by creating a `compose_override.yml` file.
#### Custom configuration file
Example below introduces custom config file `jupyterhub_config_override.py` to use for your deployment:
```yaml
services:
jupyterhub:
volumes:
- ./config/jupyterhub_config_override.py:/srv/jupyterhub/jupyterhub_config.py:ro # config file (read only)
```
#### Enable GPU
No changes required in the configuration if you allow NVidia autodetection to be performed.
Otherwise change the `ENABLE_GPU_SUPPORT = 1`
Changes in your `compose_override.yml`:
```yaml
services:
jupyterhub:
environment:
- ENABLE_GPU_SUPPORT=1 # enable NVIDIA GPU, values: 0 - disabled, 1 - enabled, 2 - auto-detect
```
#### Enable shared CIFS mount
Changes in your `compose_override.yml`:
```yaml
jupyterhub:
volumes:
- ./config/jupyterhub_config_override.py:/srv/jupyterhub/jupyterhub_config.py:ro # config file (read only)
- jupyterhub_shared_nas:/mnt/shared # cifs share
volumes:
# remote drive for large datasets
jupyterhub_shared_nas:
driver: local
name: jupyterhub_shared_nas
driver_opts:
type: cifs
device: //nas_ip_or_dns_name/data
o: username=xxxx,password=yyyy,uid=1000,gid=1000
```
in the config file you will refer to this volume by its name `jupyterhub_shared_nas`:
```python
# User mounts in the spawned container
c.DockerSpawner.volumes = {
"jupyterlab-{username}_home": "/home",
"jupyterlab-{username}_workspace": DOCKER_NOTEBOOK_DIR,
"jupyterlab-{username}_cache": "/home/lab/.cache",
"jupyterhub_shared_nas": "/mnt/shared"
}
```
#### Grant Docker Socket Access to Privileged Users
> [!WARNING]
> Docker socket access grants effective root-level control over the Docker host. Only grant this permission to trusted users.
The platform supports granting specific users read-write access to `/var/run/docker.sock` within their JupyterLab containers via the `docker-privileged` group. This enables container orchestration, Docker builds, and Docker Compose operations from within user environments.
**How to Grant Access**:
1. Log in as admin and navigate to Admin Panel (`https://localhost/jupyterhub/hub/admin`)
2. Click "Groups" in the navigation
3. Click on the `docker-privileged` group (automatically created at startup)
4. Add users who need docker.sock access to this group
5. Users must restart their server (Stop My Server -> Start My Server) for changes to take effect
**Technical Details**:
The `docker-privileged` group is a built-in protected group that cannot be permanently deleted. It is automatically created at JupyterHub startup and recreated before every container spawn if missing. A pre-spawn hook (`config/jupyterhub_config.py::pre_spawn_hook`) checks user group membership before spawning containers. Users in the `docker-privileged` group will have `/var/run/docker.sock` mounted with read-write permissions in their JupyterLab environment.
**Use Cases**:
- Building custom Docker images from within JupyterLab
- Running Docker Compose stacks for local development
- Container orchestration and management tasks
- Advanced DevOps workflows requiring Docker API access