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
```

#### File Structure

Your `~/OBIJupyterHub` directory should contain:

```         
~/OBIJupyterHub/
├── Dockerfile                  # Image for students (already created)
├── Dockerfile.hub              # Image for JupyterHub (new)
├── jupyterhub_config.py        # Configuration
├── docker-compose.yml          # Orchestration
└── start-jupyterhub.sh         # Startup script
```

### 2. Start JupyterHub

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

### 3. Access JupyterHub

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

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

## Useful Commands

### View JupyterHub logs

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

### View all containers (hub + students)

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

### Stop JupyterHub

``` bash
docker-compose down
```

### Restart JupyterHub (after config modification)

``` bash
docker-compose restart jupyterhub
```

### Rebuild after Dockerfile modification

``` bash
# For student image
docker build -t jupyterhub-student:latest -f Dockerfile .
docker-compose restart jupyterhub

# For hub image
docker-compose up -d --build
```

### View logs for a specific student

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

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

### Clean up after lab

``` bash
# Stop and remove all containers
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) 2. Then `work/course/R_packages/` (shared, read-only, installed by prof) 3. 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
chmod +x install-r-packages-admin.sh

# Install packages
./install-r-packages-admin.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

**From admin notebook:**

Login as `admin` and create an R notebook:

``` r
# Install packages in course/R_packages (admin only, available to all students)
course_lib <- "/home/jovyan/work/course/R_packages"
dir.create(course_lib, recursive = TRUE, showWarnings = FALSE)

install.packages(c('reshape2', 'plotly', 'knitr'), 
                 lib = course_lib,
                 repos = 'http://cran.rstudio.com/')
```

Note: Admin account has write access to the course directory.

**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(c('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) 2. Prof packages: `/home/jovyan/work/course/R_packages/` (R_LIBS_SITE) 3. 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/"
```

### Access Shared Files Between Students

Students can collaborate via the `shared/` directory:

``` python
# In a notebook, to read a shared file
import pandas as pd
df = pd.read_csv('/home/jovyan/work/shared/group_data.csv')

# To write a shared file
df.to_csv('/home/jovyan/work/shared/alice_results.csv')
```

### Retrieve Student Work

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

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

# Copy all shared work
docker run --rm \
  -v jupyterhub-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 rebuild:

``` bash
docker build -t jupyterhub-student:latest -f Dockerfile .
docker-compose restart jupyterhub
```

### 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`

**After config modification, changes are not applied**:

``` bash
docker-compose restart jupyterhub
```

**I want to start from scratch**:

``` bash
docker-compose down -v
docker rmi jupyterhub-hub jupyterhub-student
# Then rebuild everything
./start-jupyterhub.sh
```