spark-control

A browser-based control panel for a dual-DGX-Spark vLLM cluster. Designed to run as a StartOS 0.4 package on a Start9 server on the same LAN as the Sparks.

What it does

• Shows which LLM is currently loaded on the cluster (:8888/v1/models).
• Click to swap to a different model — stops the current one, launches the new one, streams logs to the UI until Application startup complete. appears.
• Surfaces health for Parakeet (STT, :8000) and Magpie (TTS, :9000) on Spark 2.

Architecture

[Browser/phone] ──► [StartOS reverse proxy] ──► [spark-control container]
                                                       │  (SSH over LAN)
                                                       ▼
                                                  [Spark 1] ──► launch-cluster.sh
                                                       │
                                                       ▼
                                                  [Spark 2]

Two layers in this repo:

• image/ — a self-contained FastAPI app + static UI. Runs anywhere with uvicorn and an SSH client. Useful for development.
• package/ — a thin StartOS 0.4 wrapper that packages the image, exposes the UI on the LAN, and gives the user actions to configure SSH access to the Sparks.

Quick start (local dev, no StartOS yet)

cd image
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
export SPARK1_HOST=<spark-1-ip>
export SPARK1_USER=<spark-user>
export SPARK2_HOST=<spark-2-ip>
export SPARK2_USER=<spark-user>
export SSH_KEY_PATH="$HOME/Library/Application Support/NVIDIA/Sync/config/nvsync.key"
uvicorn app.server:app --host 0.0.0.0 --port 9999 --reload

Open http://localhost:9999.

Note: use the IP <spark-1-ip> for Spark 1, not <spark-1-host>.local. mDNS resolves to IPv6 first and httpx hangs on it because vLLM only binds IPv4.

Build the StartOS package

cd package
npm i        # one-time
make x86     # produces spark-control_x86_64.s9pk (~55 MB)

Requires start-cli, Node ≥ 22, Docker. The build runs tsc + ncc for the TS bundle, then docker build on image/Dockerfile, then start-cli s9pk pack to produce the .s9pk.

To sideload onto your Start9: make install (needs host: set in ~/.startos/config.yaml), or upload the .s9pk via the Start9 web UI's sideload feature.

Post-install setup (one-time per Start9 install)

1. Open the Spark Control service → Actions → Show Public Key → copy the line.
2. SSH to each Spark and append the line to ~/.ssh/authorized_keys for the <spark-user> user.
3. Actions → Configure Sparks → enter <spark-1-ip> / <spark-user> for Spark 1 and <spark-2-ip> / <spark-user> for Spark 2.
4. Start the service. Open the Web UI — current model + health should show within ~5 s.

Repo layout

• image/ — Docker image source (FastAPI app + models.yaml)
• package/ — StartOS 0.4 package source
• runbook.md — operating notes
• known-issues.md — known quirks and workarounds
• LICENSE — MIT

Service discovery API

Other services on your LAN can hit GET /api/endpoints to learn where the current model lives without hardcoding Spark IPs. Stable JSON shape:

{
  "vllm":    { "ready": true,  "base_url": "http://<spark-1-ip>:8888/v1", "model": "RedHatAI/Qwen3.6-35B-A3B-NVFP4", "openai_compat": true },
  "parakeet":{ "ready": true,  "base_url": "http://<spark-2-ip>:8000",   "kind": "stt", "model": "nvidia/parakeet-tdt-0.6b-v3" },
  "magpie":  { "ready": false, "base_url": "http://<spark-2-ip>:9000",   "kind": "tts" }
}

base_url is filled in whenever Configure Sparks has been completed (even if the underlying service isn't currently up). Pair the URL with ready: true to safely route traffic.

Status

v0.2.3 — installed and verified on a Start9 server. Five bundled LLMs in the catalog (qwen3-vl, gemma4, qwen36, qwen3-235b-fp8, qwen2.5-72b), plus any custom models added through the UI.

What v0.2 added on top of v0.1

• Service discovery API (/api/endpoints) for other LAN services
• Magpie crash fix documented (chown the model-cache volume to uid 1000)
• Always-on services panel with Start/Stop/Restart for Parakeet + Magpie, plus per-service host configuration in Configure Sparks (so Parakeet/Magpie can live on Spark 1, Spark 2, or anywhere)
• Model download from the dashboard — paste an HF repo, pick solo or cluster, watch percent progress with bytes/rate/ETA. After completion, an "Add to catalog" dialog appears pre-filled.
• spark-vllm-docker update check — banner shows "N commits behind upstream"; Apply Update runs git pull && ./build-and-copy.sh -c over SSH with a streamed log
• Per-model Advanced settings — knobs for max context, GPU memory %, and three optimization toggles (fastsafetensors, prefix caching, FP8 KV cache). Persisted to /data/models-overrides.yaml so they survive package updates. Bundled and custom models alike.

v0.3+ roadmap (loose): richer dashboard (SSH/GPU/tokens-per-sec), Open WebUI deep-link integration, optional auth, multi-cluster.