Artificial Intelligence Homelab Documentation: A Bash‑First Playbook

If you’ve ever told yourself “I’ll remember how I set that up,” you already know the punchline: six months later, you don’t. AI stacks evolve fast, drivers shift, dependencies break, and that one critical flag you flipped at 2 a.m. is gone. Good homelab documentation turns fragile, one‑off tinkering into a repeatable, scalable system you can trust.

This guide shows you how to build living documentation for your AI homelab using Bash‑friendly tools. You’ll capture hardware/software inventory, publish clean docs, version everything, and back it up—so you can reproduce results and onboard collaborators without the headaches.

Why document an AI homelab?

Reproducibility: Training, inference, and benchmarking depend on exact library/GPU/driver combos.
Velocity: Future‑you (and teammates) move faster with a known baseline and proven runbooks.
Recovery: When a disk dies or an upgrade regresses, docs + backups save you days.
Shareability: Clear docs let you invite others to replicate your setup—or deploy it on a second node.

Below are five actionable steps—including cross‑distro install commands—to stand up a reliable, Bash-first documentation stack.

1) Generate a one‑command system inventory

Start with an automated snapshot of your hardware and OS. Save it into your docs so every change is traceable.

Install tools:

Debian/Ubuntu (apt):

sudo apt update && sudo apt install -y lshw pciutils usbutils

Fedora/RHEL/CentOS Stream (dnf):

sudo dnf install -y lshw pciutils usbutils

openSUSE (zypper):

sudo zypper install -y lshw pciutils usbutils

Create a simple inventory script:

mkdir -p scripts docs
cat > scripts/inventory.sh << 'EOF'
#!/usr/bin/env bash
set -euo pipefail
OUT="docs/hardware.md"
TS="$(date -Is)"

command -v lshw >/dev/null 2>&1 || { echo "lshw not found"; exit 1; }

{
  echo "# Hardware & OS Inventory"
  echo
  echo "_Generated: ${TS}_"
  echo
  echo "## CPU"
  lscpu || true
  echo
  echo "## Memory"
  free -h || true
  echo
  echo "## Block Devices"
  lsblk -o NAME,MODEL,SIZE,TYPE,MOUNTPOINT || true
  echo
  echo "## PCI Devices (Top 20)"
  lspci -nn | head -n 20 || true
  echo
  echo "## USB Devices (Top 20)"
  lsusb | head -n 20 || true
  echo
  echo "## Network (IPs)"
  ip -brief addr || true
  echo
  echo "## Storage Usage"
  df -hT | sort -k6 || true
  echo
  echo "## lshw (short)"
  sudo lshw -short || true
  echo
  if command -v nvidia-smi >/dev/null 2>&1; then
    echo "## NVIDIA GPU"
    nvidia-smi -q | sed -n '1,80p'
  elif command -v rocm-smi >/dev/null 2>&1; then
    echo "## AMD ROCm GPU"
    rocm-smi
  else
    echo "## GPU"
    echo "No nvidia-smi or rocm-smi found."
  fi
} > "${OUT}"

echo "Wrote ${OUT}"
EOF
chmod +x scripts/inventory.sh

Run it any time your system changes:

sudo scripts/inventory.sh

Commit the output to your repo (see step 3) to track deltas over time.

2) Turn notes into a searchable site with MkDocs Material

MkDocs + Material theme keeps your docs clean, fast, and Markdown‑native.

Install Python and pipx:

Debian/Ubuntu (apt):

sudo apt update && sudo apt install -y python3 python3-venv pipx
pipx ensurepath
exec $SHELL -l

Fedora/RHEL/CentOS Stream (dnf):

sudo dnf install -y python3 python3-pip pipx
pipx ensurepath
exec $SHELL -l

openSUSE (zypper):

sudo zypper install -y python3 python3-pip pipx
pipx ensurepath
exec $SHELL -l

Install MkDocs Material and helpful plugins:

pipx install mkdocs-material
pipx inject mkdocs-material mkdocs-mermaid2-plugin mkdocs-git-revision-date-localized-plugin

Scaffold your docs:

mkdir -p ai-homelab-docs/docs
cd ai-homelab-docs

Create a minimal mkdocs.yml:

cat > mkdocs.yml << 'EOF'
site_name: AI Homelab
theme:
  name: material
  features:
    - navigation.indexes
    - content.code.copy
plugins:
  - search
  - git-revision-date-localized:
      type: timeago
  - mermaid2
markdown_extensions:
  - admonition
  - codehilite
  - toc:
      permalink: true
nav:
  - Home: index.md
  - Hardware: hardware.md
  - Services:
      - Jupyter: services/jupyter.md
      - MLflow: services/mlflow.md
  - Experiments:
      - Template: experiments/template.md
EOF

Seed a few pages:

mkdir -p docs/services docs/experiments
cat > docs/index.md << 'EOF'
# AI Homelab

Welcome! This site documents hardware, services, and experiments so they’re reproducible.
EOF

cat > docs/experiments/template.md << 'EOF'
# Experiment Template

- Date: 2026-01-01

- Commit: <git sha>

- Dataset: <name/version>

- Objective: <what you tested>

- Hardware: <GPU/CPU/RAM>

- Environment: <container tag or conda/pip freeze link>

- Command:
  ```
  python train.py --config configs/exp.yaml
  ```

- Results: <metrics/plots>

- Notes: <what to try next>
EOF

Preview locally:

mkdocs serve

Open the printed localhost URL and iterate quickly.

Tip: Use Mermaid diagrams inline in Markdown:

```mermaid
flowchart LR
  Data -->|ETL| Features -->|train| Model -->|serve| Inference


---

## 3) Version everything with Git (and auto‑preview changes)

Install Git:

- Debian/Ubuntu (apt):
  ```
  sudo apt update && sudo apt install -y git
  ```

- Fedora/RHEL/CentOS Stream (dnf):
  ```
  sudo dnf install -y git
  ```

- openSUSE (zypper):
  ```
  sudo zypper install -y git
  ```

Initialize and commit:

git init cat > .gitignore << 'EOF' site/ pycache/ *.log .DS_Store .env EOF git add . git commit -m "docs: bootstrap ai homelab site"


Optional: publish to GitHub/GitLab/Gitea. For GitHub Pages, add a CI workflow:

mkdir -p .github/workflows cat > .github/workflows/gh-pages.yml << 'EOF' name: Deploy MkDocs on: push: branches: [ main ] jobs: build-deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.x' - run: pip install mkdocs-material mkdocs-mermaid2-plugin mkdocs-git-revision-date-localized-plugin - run: mkdocs build - uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: ./site EOF


Push, enable Pages, and your docs will auto‑publish on every commit.

---

## 4) Document services as code with Compose (Docker or Podman)

Capture your Jupyter/MLflow stack as a single, documented file.

Install Docker (or Podman):

- Docker:
  - Debian/Ubuntu (apt):
    ```
    sudo apt update && sudo apt install -y docker.io docker-compose-plugin
    sudo systemctl enable --now docker
    ```
  - Fedora/RHEL/CentOS Stream (dnf):
    ```
    # On Fedora, Docker is provided by moby-engine
    sudo dnf install -y moby-engine docker-compose-plugin
    sudo systemctl enable --now docker
    ```
  - openSUSE (zypper):
    ```
    sudo zypper install -y docker docker-compose
    sudo systemctl enable --now docker
    ```

- Podman (rootless alternative):
  - Debian/Ubuntu (apt):
    ```
    sudo apt update && sudo apt install -y podman podman-compose
    ```
  - Fedora/RHEL/CentOS Stream (dnf):
    ```
    sudo dnf install -y podman podman-compose
    ```
  - openSUSE (zypper):
    ```
    sudo zypper install -y podman podman-compose
    ```

Create a compose file:

cat > docker-compose.yml << 'EOF' services: jupyter: image: jupyter/minimal-notebook:lab-4.2.5 container_name: jupyter ports: - "127.0.0.1:8888:8888" volumes: - ./work/notebooks:/home/jovyan/work environment: - JUPYTER_ENABLE_LAB=yes restart: unless-stopped

mlflow: image: ghcr.io/mlflow/mlflow:latest container_name: mlflow command: mlflow server --backend-store-uri sqlite:///mlflow.db --host 0.0.0.0 --port 5000 --default-artifact-root /mlruns ports: - "127.0.0.1:5000:5000" volumes: - ./work/mlruns:/mlruns working_dir: /mlruns restart: unless-stopped EOF


Start services:

- Docker:
  ```
  docker compose up -d
  ```

- Podman:
  ```
  podman-compose up -d
  ```

Document your services in `docs/services/jupyter.md` and `docs/services/mlflow.md`, including:

- Image tags and update cadence

- Ports, volumes, and data locations

- Exact commands to rebuild/restart

- Links to relevant experiments and notebooks

Example snippet for Jupyter page:

Jupyter Service

Image: jupyter/minimal-notebook:lab-4.2.5
Ports: 127.0.0.1:8888
Data: ./work/notebooks

Start:

docker compose up -d jupyter

Update:

docker compose pull jupyter && docker compose up -d jupyter


---

## 5) Back it up (and prove you can restore)

Use restic for simple, encrypted backups.

Install restic:

- Debian/Ubuntu (apt):
  ```
  sudo apt update && sudo apt install -y restic
  ```

- Fedora/RHEL/CentOS Stream (dnf):
  ```
  sudo dnf install -y restic
  ```

- openSUSE (zypper):
  ```
  sudo zypper install -y restic
  ```

Initialize a local repo (you can swap this for S3/MinIO later):

export RESTIC_PASSWORD='choose-a-strong-password' sudo mkdir -p /backups/ai-homelab-docs && sudo chown "$USER":"$USER" /backups/ai-homelab-docs restic init -r /backups/ai-homelab-docs


Back up docs and config:

restic backup -r /backups/ai-homelab-docs docs mkdocs.yml docker-compose.yml scripts


Retention and prune:

restic forget -r /backups/ai-homelab-docs --keep-daily 7 --keep-weekly 4 --keep-monthly 6 --prune


Restore test (always verify!):

restic snapshots -r /backups/ai-homelab-docs restic restore -r /backups/ai-homelab-docs latest --target /tmp/restore-test


Automate via cron:

crontab -e

Add:

0 2 * * * RESTIC_PASSWORD='choose-a-strong-password' restic backup -r /backups/ai-homelab-docs $HOME/ai-homelab-docs >/tmp/restic.log 2>&1 ```

Real‑world mini‑example: “What did I change?”

Day 1: Run sudo scripts/inventory.sh, commit. Add docker-compose.yml for Jupyter/MLflow, commit.
Day 10: Update NVIDIA driver; run inventory again. git diff docs/hardware.md shows driver version bump and new kernel.
Day 30: An experiment regresses. Check docs/experiments/2026-01-30-runA.md for exact image tags and command. Recreate environment, reproduce bug in minutes, not days.

Conclusion and next steps

You now have:

A one‑command inventory report
A clean, searchable docs site
Versioned, composable services
Encrypted backups with restore verification

Next steps:

Add a “Runbook” section for common ops (driver upgrades, kernel pinning, GPU passthrough).
Track experiments with a consistent template and link to MLflow runs.
Add diagrams (Mermaid) for network/storage topology.
Publish your site internally and invite collaborators to contribute via PRs.

Your homelab is only as durable as its documentation. Start today—run the inventory script, scaffold the MkDocs site, and commit your first version. Future‑you will thank you.