# JupyterHub Configuration with OrbStack on Mac (all in Docker)

## 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](https://orbstack.dev/) (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:

```bash
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`

## Installation and first run

1) Clone the project:

```bash
git clone https://forge.metabarcoding.org/MetabarcodingSchool/OBIJupyterHub.git
cd OBIJupyterHub
```

2) (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
```

3) 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.

4) Start everything (build + render + launch):

```bash
./start-jupyterhub.sh
```

5) Access JupyterHub in a browser at `http://localhost:8888`.

6) Stop the stack when you’re done (run from `obijupyterhub/`):

```bash
docker-compose down
```

### Operating the stack (with one command)

- Start or rebuild at any time with `./start-jupyterhub.sh` from the project root. It rebuilds images, regenerates the website, and starts the stack.
- 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/`:

``` bash
# Install packages
tools/install_packages.sh reshape2 plotly knitr
```

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

```r
# 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:

``` r
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) 
1. Prof packages: `/home/jovyan/work/course/R_packages/` (R_LIBS_SITE) 
1. System packages

### List Available Packages

``` r
# 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:

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

### Option 2: Allow Everyone (for testing)

By default, the configuration allows any user:

``` python
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}`):

``` dockerfile
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}`):

``` dockerfile
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`:

``` yaml
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**:

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

# Then rebuild everything
./start-jupyterhub.sh
```