OBIJupyterHub/Readme.md

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

## Prerequisites

You must have docker running on your computer

-   On MacOS, [OrbStack](https://orbstack.dev/ "A Docker implementation optimised for MacOS") is recommanded

## Installation Steps

### 1. Create Directory Structure

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

Enter into the `OBIJupyterHub` directory

```bash
cd OBIJupyterHub
```

#### File Structure

Your `OBIJupyterHub` directory should contain:

```         
OBIJupyterHub
├── start-jupyterhub.sh                  - The script used to setup and start the server
├── obijupyterhub                        - The files describing the docker images and the stack
│   ├── Caddyfile
│   ├── docker-compose.yml
│   ├── Dockerfile
│   ├── Dockerfile.hub
│   ├── jupyterhub_config.py
│   ├── sftpgo_config.json
│   └── start-notebook.sh
├── jupyterhub_volumes                   - The directory containing the docker volumes
│   ├── caddy
│   ├── course                           - Read only volume mounted on every student container
│   │   ├── bin
│   │   └── R_packages
│   ├── jupyterhub
│   ├── shared                           - Read write volume shared in every student container
│   ├── users
│   └── web
│       ├── img
│       │   └── welcome_metabar.webp
│       ├── index.html
│       └── pages
├── Readme.md                            - This documentation
├── tools
│   ├── generate_pages_json.py
│   └── install_packages.sh
└─── web_src                             - The quarto document sources used to build the web site
    ├── _output
    ├── _quarto.yml
    ├── 00_home.qmd
    ├── lectures
    │   └── computers
    │       └── regex
    │           ├── lecture_regex.qmd
    │           ├── slides_regex.qmd
    │           └── slides.css
    └── scripts
         └── copy-to-web.sh
```

### 2. Start JupyterHub

From the terminal, in the `OBIJupyterHub` directory, run the following command:

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

### 3. Access JupyterHub

Open your browser and go to: **http://localhost:8888**

You can log in as a student with any username and password: `metabar2025`

## Useful Commands

### View JupyterHub logs

``` bash
cd obijupyterhub
docker-compose logs -f jupyterhub
```

### View all containers (hub + students)

``` bash
docker ps | grep jupyterhub
```

### Stop JupyterHub

``` bash
cd obijupyterhub
docker-compose down
```

### Restart JupyterHub (after config modification)

``` bash
cd obijupyterhub
docker-compose restart jupyterhub
```

### View logs for a specific student

``` bash
docker logs jupyter-<username>
```

Replace `<username>` by the actual username of the student.

### Clean up after lab

``` bash
# Stop and remove all containers
cd obijupyterhub
docker-compose down

# Remove student containers
docker ps -a | grep jupyter- | awk '{print $1}' | xargs docker rm -f

# Remove volumes (WARNING: deletes student data)
docker volume ls | grep jupyterhub-user | awk '{print $2}' | xargs docker volume rm

# Clean everything (containers + volumes + network)
docker-compose down -v
docker ps -a | grep jupyter- | awk '{print $1}' | xargs docker rm -f
docker volume prune -f
```

## Managing Shared Data

### Directory Structure for Each Student

Each student will see this directory structure in their JupyterLab (everything under `work/` is persistent):

```         
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 Package Priority:** 

1. R checks `work/R_packages/` first (personal, writable) 
1. Then `work/course/R_packages/` (shared, read-only, installed by prof) 
1. Then system libraries

**Important:** Everything is under `work/`, so all student files are automatically saved in their persistent volume.

### User Accounts

**Admin Account:** 

- Username: `admin` 
- Password: `admin2025` (change in docker-compose.yml: `JUPYTERHUB_ADMIN_PASSWORD`) 
- Can write to `course/` directory

**Student Accounts:** 

- Username: any name 
- Password: `metabar2025` (change in docker-compose.yml: `JUPYTERHUB_PASSWORD`) 
- Read-only access to `course/` directory

### Installing R Packages (Admin Only)

**From your Mac (recommended):**

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

This script: - Installs packages in the `course/R_packages/` directory - All students can use them (read-only) - No need to rebuild the image

**Students can also install their own packages:**

Students can install packages in 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 Files for Course

To put files in the `course/` directory (accessible read-only):

``` bash
# Create a temporary directory
mkdir -p ~/jupyterhub-tp/course-files

# Copy your files into it
cp my_notebooks.ipynb ~/jupyterhub-tp/course-files/
cp my_data.csv ~/jupyterhub-tp/course-files/

# Copy into Docker volume
docker run --rm \
  -v jupyterhub-course:/target \
  -v ~/jupyterhub-tp/course-files:/source \
  alpine sh -c "cp -r /source/* /target/"
```

### Retrieve Student Work

``` bash
# List user volumes
docker volume ls | grep 'obijupyterhub_user-'

# Copy files from a specific student
docker run --rm \
  -v obijupyterhub_user-alice:/source \
  -v ~/submissions:/target \
  alpine sh -c "cp -r /source/* /target/alice/"

# Copy all shared work
docker run --rm \
  -v obijupyterhub_shared:/source \
  -v ~/submissions/shared:/target \
  alpine sh -c "cp -r /source/* /target/"
```

## 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 restart the server (it rebuilds the images if needed):

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

### Add Python Packages

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

``` dockerfile
RUN pip install numpy pandas matplotlib seaborn
```

### Distribute Files to Students

Create a `files_lab/` directory and add to the `Dockerfile`:

``` dockerfile
COPY files_lab/ /home/${NB_USER}/lab/
RUN chown -R ${NB_UID}:${NB_GID} /home/${NB_USER}/lab
```

### 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

**Error "Cannot connect to Docker daemon"**: 

- Check that OrbStack is running 
- Verify the socket exists: `ls -la /var/run/docker.sock`

**Student containers don't start**: 

- Check logs: `docker-compose logs jupyterhub` 
- Verify student image exists: `docker images | grep jupyterhub-student`

**Port 8000 already in use**: 

- Change port in `docker-compose.yml`


**I want to start from scratch**:

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

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