OBIJupyterHub - the DNA Metabarcoding Learning Server

Intended use

This project packages the MetabarcodingSchool training lab into one reproducible bundle. You get Python, R, and Bash kernels, a Quarto-built course website, and preconfigured admin/student accounts, so onboarding a class is a single command instead of a day of setup. Everything runs locally on a single machine, student work persists between sessions, and ./start-jupyterhub.sh takes care of building images, rendering the site, preparing volumes, and bringing JupyterHub up at http://localhost:8888. Defaults (accounts, passwords, volumes) live in the repo so instructors can tweak them quickly.

Prerequisites (with quick checks)

You need Docker, Docker Compose, Quarto, and Python 3 available on the machine that will host the lab.

  • macOS: install OrbStack (recommended) or Docker Desktop; both ship Docker Engine and Compose.
  • Linux: install Docker Engine and the Compose plugin from your distribution (e.g., sudo apt install docker.io docker-compose-plugin) or from Docker’s official packages.
  • Windows: install Docker Desktop with the WSL2 backend enabled.
  • Quarto CLI: get installers from https://quarto.org/docs/get-started/.
  • Python 3: any recent version is fine (only the standard library is used).

Verify from a terminal; if a command is missing, install it before moving on:

docker --version
docker compose version    # or: docker-compose --version
quarto --version
python3 --version

How the startup script works

./start-jupyterhub.sh is the single entry point. It builds the Docker images, renders the course website, prepares the volume folders, and starts the stack. Internally it:

  • creates the jupyterhub_volumes/ tree (caddy, course, shared, users, web…)
  • builds jupyterhub-student and jupyterhub-hub images
  • renders the Quarto site from web_src/, generates PDF galleries and pages.json, and copies everything into jupyterhub_volumes/web/
  • runs docker-compose up -d --remove-orphans

You can tailor what it does with a few flags:

  • --no-build (or --offline): skip Docker image builds and reuse existing images (useful when offline).
  • --force-rebuild: rebuild images without cache.
  • --stop-server: stop the stack and remove student containers, then exit.
  • --update-lectures: rebuild the course website only (no Docker stop/start).

Installation and first run

  1. Clone the project:
git clone https://forge.metabarcoding.org/MetabarcodingSchool/OBIJupyterHub.git
cd OBIJupyterHub
  1. (Optional) glance at the structure you’ll populate:
OBIJupyterHub
├── start-jupyterhub.sh                  - single entry point (build + render + start)
├── obijupyterhub                        - Docker images and stack definitions
│   ├── docker-compose.yml
│   ├── Dockerfile
│   ├── Dockerfile.hub
│   └── jupyterhub_config.py
├── jupyterhub_volumes                   - data persisted on the host
│   ├── course                           - read-only for students (notebooks, data, bin, R packages)
│   ├── shared                           - shared read/write space for everyone
│   ├── users                            - per-user persistent data
│   └── web                              - rendered course website
└── web_src                              - Quarto sources for the course website
  1. Prepare course materials (optional before first run):
  • Put notebooks, datasets, scripts, binaries, or PDFs for students under jupyterhub_volumes/course/. They will appear read-only at /home/jovyan/work/course/.
  • For collaborative work, drop files in jupyterhub_volumes/shared/ (read/write for all at /home/jovyan/work/shared/).
  • Edit or add Quarto sources in web_src/ to update the course website; the script will render them.
  1. Start everything (build + render + launch):
./start-jupyterhub.sh

  1. Access JupyterHub in a browser at http://localhost:8888.

  2. Stop the stack when you’re done (run from obijupyterhub/):

docker-compose down

Operating the stack (one command, a few options)

  • Start or rebuild: ./start-jupyterhub.sh (rebuilds images, regenerates the website, starts the stack).
  • Start without rebuilding images (offline): ./start-jupyterhub.sh --no-build
  • Force rebuild without cache: ./start-jupyterhub.sh --force-rebuild
  • Stop only: ./start-jupyterhub.sh --stop-server
  • Rebuild website only (no Docker stop/start): ./start-jupyterhub.sh --update-lectures
  • Access at http://localhost:8888 (students: any username / password metabar2025; admin: admin / admin2025).
  • Check logs from obijupyterhub/ with docker-compose logs -f jupyterhub.
  • Stop with docker-compose down (from obijupyterhub/). Rerun ./start-jupyterhub.sh to start again or after config changes.

Managing shared data

Each student lands in /home/jovyan/work/ with three key areas: their own files, a shared space, and a read-only course space. Everything under work/ is persisted on the host in jupyterhub_volumes.

work/                             # Personal workspace root (persistent)
├── [student files]               # Their own files and notebooks
├── R_packages/                   # Personal R packages (writable by student)
├── shared/                       # Shared workspace (read/write, shared with all)
└── course/                       # Course files (read-only, managed by admin)
    ├── R_packages/               # Shared R packages (read-only, installed by prof)
    ├── bin/                      # Shared executables (in PATH)
    └── [course materials]        # Your course files

R looks for packages in this order: personal work/R_packages/, then shared work/course/R_packages/, then system libraries. Because everything lives under work/, student files survive restarts.

User Accounts

Defaults are defined in obijupyterhub/docker-compose.yml: admin (admin / admin2025) with write access to course/, and students (any username, password metabar2025) with read-only access to course/. Adjust JUPYTERHUB_ADMIN_PASSWORD and JUPYTERHUB_PASSWORD there, then rerun ./start-jupyterhub.sh.

Installing R Packages (Admin Only)

From the host, install shared R packages into course/R_packages/:

# Install packages
tools/install_packages.sh reshape2 plotly knitr

Students can install their own packages into their personal work/R_packages/:

# Install in personal library (each student has their own)
install.packages('mypackage')  # Will install in work/R_packages/

Using R Packages (Students)

Students simply load packages normally:

library(reshape2)  # R checks: 1) work/R_packages/ 2) work/course/R_packages/ 3) system
library(plotly)

R automatically searches in this order:

  1. Personal packages: /home/jovyan/work/R_packages/ (R_LIBS_USER)
  2. Prof packages: /home/jovyan/work/course/R_packages/ (R_LIBS_SITE)
  3. System packages

List Available Packages

# List all available packages (personal + course + system)
installed.packages()[,"Package"]

# Check personal packages
list.files("/home/jovyan/work/R_packages")

# Check course packages (installed by prof)
list.files("/home/jovyan/work/course/R_packages")

Deposit or retrieve course and student files

On the host, place course files in jupyterhub_volumes/course/ (they appear read-only to students), shared files in jupyterhub_volumes/shared/, and collect student work from jupyterhub_volumes/users/.

User Management

Option 1: Predefined User List

In jupyterhub_config.py, uncomment and modify:

c.Authenticator.allowed_users = {'student1', 'student2', 'student3'}

Option 2: Allow Everyone (for testing)

By default, the configuration allows any user:

c.Authenticator.allow_all = True

⚠️ Warning: DummyAuthenticator is ONLY for local testing!

Kernel Verification

Once logged in, create a new notebook and verify you have access to:

  • Python 3 (default kernel)
  • R (R kernel)
  • Bash (bash kernel)

Customization for Your Labs

Add Additional R Packages

Modify the Dockerfile (before USER ${NB_UID}):

RUN R -e "install.packages(c('your_package'), repos='http://cran.rstudio.com/')"

Then rerun ./start-jupyterhub.sh to rebuild and restart.

Add Python Packages

Add to the Dockerfile (before USER ${NB_UID}):

RUN pip install numpy pandas matplotlib seaborn

Then rerun ./start-jupyterhub.sh to rebuild and restart.

Change Port (if 8000 is occupied)

Modify in docker-compose.yml:

ports:
  - "8001:8000"  # Accessible on localhost:8001

Advantages of This Approach

✅ Everything in Docker: No need to install Python/JupyterHub on your computer
✅ Portable: Easy to deploy on another server
✅ Isolated: No pollution of your system environment
✅ Easy to Clean: A simple docker-compose down is enough
✅ Reproducible: Students will have exactly the same environment

Troubleshooting

  • Docker daemon unavailable: make sure OrbStack/Docker Desktop/daemon is running; verify /var/run/docker.sock exists.
  • Student containers do not start: check docker-compose logs jupyterhub and confirm the images exist with docker images | grep jupyterhub-student.
  • Port conflict: change the published port in docker-compose.yml.

I want to start from scratch:

pushd obijupyterhub
docker-compose down -v
docker rmi jupyterhub-hub jupyterhub-student
popd

# Then rebuild everything
./start-jupyterhub.sh