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