HomeLab Monitor

One page for your whole home lab & AI rig — GPU truth (any vendor), tokens/sec, power cost by the hour, uptime, training runs, containers, disks. No agents, no separate metrics stack, no cloud.

Your home lab grew into a couple of machines, a Pi, and a GPU that's mysteriously always busy — and lately it's running models too. HomeLab Monitor gives you one self-hosted page that answers the real questions: what's that GPU actually doing, which model is holding it, what's it costing you to run, which container is eating RAM, what's filling your disks, and is anything down — across every box over SSH: Linux, a Pi, even Windows. Readable from your phone over the VPN.

Get started

# Grab the compose file and go. No GPU required — the GPU panels just light up when one's present.
curl -fsSLO https://raw.githubusercontent.com/SikamikanikoBG/homelab-monitor/main/docker-compose.yml
docker compose up -d

Open http://<your-host>:9800 and you're done. Full options (from source, GPU toolkit, Windows/WSL2) → Install docs.

🆕 What's new — every release is written up in full, with the reasoning behind it: latest release · changelog. The dashboard also shows you the notes once, in-app, after it updates itself.

What you get

One page, every box, the questions you actually have. The classics are all here — and a whole AI cockpit builds on top of them.

Your GPU, demystified — and the same tab on every box. A card pinned at "100% util" can still be throttling, memory-bandwidth-bound, or quietly drooping its clocks. The GPU tab decodes nvidia-smi's throttle reasons, and shows memory-bandwidth util, core/mem clocks, power-vs-limit, p-state — and fan speed — for every machine in the fleet, not just the one running the container. Multi-GPU boxes get one panel per card on a shared scale per metric, so a taller temperature line really is a hotter card; thermal-throttle windows are shaded right on the sparkline. You can see which card a service is sitting on (a 3×3090 box shows a model's 63 GB split 22.5 / 22.1 / 18.8 across the cards, not one pooled number), what each service cost in energy, and get alerted when a card throttles, overheats or loses a fan — sustained, per card, with per-host thresholds, because a box running a deliberately lowered power limit is supposed to sit at its cap. And it's no longer NVIDIA-only: AMD GPUs are read on Linux straight from the kernel's amdgpu interface (no ROCm), and AMD and Intel GPUs on Windows hosts — so your card shows up with its name, utilisation and VRAM, no vendor tools required. Anything a driver won't report says so, instead of drawing a confident zero.

What it costs — down to the process. Power becomes money: per machine, then per component (GPU measured via nvidia-smi, CPU/DRAM via RAPL), then per process, container or model — click any row to see what it drew and what it cost over any window. Day & night tariffs (Economy 7, Heures Creuses, …), or just pick your country for a sensible estimate. Every watt is measured or a baseline you set; wall power is never guessed. And a busy-hours heatmap turns months of samples into one picture of when your lab costs you money — a 7×24 day-of-week × hour grid that shows which hour of the week is priciest at a glance.

Your training runs, priced. Push a run from Jupyter, Colab or Kaggle with a one-file client (or mirror it from MLflow), and it comes back with the loss curve and the real GPU energy it burned, on the same timeline. Create, name, expire and revoke API keys yourself.

"Will it fit?" — measured, not guessed. The Benchmark Lab loads each of your local ollama models and sweeps a ladder of context sizes on your actual cards, recording generation & prompt tokens/sec, load time, how much spilled from VRAM into system RAM, and the largest context that still fits fully in VRAM — the cap worth setting. Pick which GPU(s) to test (via a throwaway pinned ollama container — your main one is never touched), overlay stored runs to compare cards, and every run comes back with the energy it burned and what it cost. Results are stored: benchmark once, re-run only when something changes.

And the rest of the lab, the way it always was:

• Containers, honestly — health plus RAM and VRAM in separate columns (real resident RAM, not page cache), and click one to tail its logs in a side drawer.
• systemd services — local or remote, your own units highlighted, failures first.
• WizTree-style disk treemaps — on any box in the fleet. Click into the folders filling a disk on the hub or on any Linux host you've added; a remote is scanned over the same SSH connection everything else uses, so there's still nothing to install on it. Plus network I/O with per-container top talkers and a mini-htop for who's eating CPU and RAM.
• It moves like a live dashboard. Utilisation, RAM, temperature and power update every couple of seconds over a push stream instead of a fixed poll — and it does that while making fewer requests than before, because the expensive history query is fetched only as often as its own chart buckets can change, and a tab you're not looking at stops costing anything at all. Sampling and storage cadence are untouched, so history stays exactly as dense (and costs stay exactly as accurate) as they were.
• Multi-machine over SSH — paste one key per box; Linux, a Pi, even Windows. No agents, no installs. The GPU tab works per host too: a remote multi-GPU rig shows every card's VRAM, utilisation, power and temperature, and the processes holding the memory.
• Uptime monitoring, in the box — watch any HTTP endpoint or TCP port (your services, a NAS, a remote site) straight from the container: heartbeat strip, 24h/7d uptime %, latency, and smart per-check alerts — anti-flap confirm, recovery with downtime, and an optional slow-response warning. No extra uptime service to self-host — it's already in the box.
• Push alerts — Discord, ntfy.sh and Telegram, edge-triggered so they don't spam.

Full tab-by-tab tour → Features.

Multi-machine, in two sentences

Open the Hosts tab, paste the hub's auto-generated SSH key onto each remote, and the hub starts polling it — no agents, just SSH + Python 3 (PowerShell on Windows). The hub pipes a small self-contained probe over SSH; nothing persists on the remote. The same connection is what lets you open a remote's GPU cockpit and scan its disks from the hub — still with nothing installed on the far end.

Onboarding, Windows setup, and the security model → Multi-machine docs.

Configuration

Set these under environment: in docker-compose.yml (all optional):