bvandeusen
0a618db10c
THE root cause of "the Status section doesn't update" (chased across several
rounds; the backend was always healthy). `#capn` (the max-concurrency number)
was nested inside `#conchint`:
<div id=conchint>… · max <b id=capn>8</b></div>
and applyStatus() ran, every call: `capn.textContent=CAP` AND
`conchint.textContent = '…max '+CAP`. Setting conchint.textContent replaces
ALL of conchint's children — destroying the <b id=capn> node. So:
call 1: capn exists → tiles update → conchint.textContent DELETES capn
call 2+: `capn.textContent` → "capn is not defined" (ReferenceError) →
applyStatus throws on its FIRST line → aborts before any tile →
frozen.
This is exactly the observed "ticks a couple times then freezes", and why
/gpu + /logs (which never touch capn) kept updating fine.
The capn write was redundant anyway — conchint.textContent already renders
the max. Remove the nested <b id=capn> element and the capn.textContent line;
the hint still shows "· max N". VERSION → .10.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
FabledCurator GPU agent
A desktop-GPU worker that embeds characters (CCIP) + figure crops for FabledCurator. It talks to FC only over HTTP — it leases jobs, fetches image pixels, runs the models on your GPU, and posts results back. Your FC database and Redis stay private; the agent never touches them.
You run it when you want a burst and stop it to reclaim the card.
0. Host prerequisite — NVIDIA Container Toolkit
Docker needs the toolkit to hand the GPU to a container (else: "could not select device driver nvidia with capabilities gpu"). On Arch/CachyOS:
sudo pacman -S nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
# verify:
docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi
1. Get a token
In FC: Settings → Tagging → GPU agent → Generate token (or Rotate). Copy it.
2. Pull (CI publishes it alongside the web/ml images)
docker pull git.fabledsword.com/bvandeusen/fabledcurator-agent:latest
Local build for development instead:
docker build -t fc-gpu-agent agent/
3. Run (on the machine with the GPU)
docker run --rm --gpus all -p 8770:8770 \
-e FC_URL=http://curator.traefik.internal \
-e FC_TOKEN=<paste-the-token> \
-v fc-agent-models:/models \
git.fabledsword.com/bvandeusen/fabledcurator-agent:latest
Then open http://localhost:8770 — the control page. Click Start to begin
draining the queue; Pause/Stop to yield the GPU. The -v fc-agent-models
volume caches the downloaded ONNX models so restarts are fast.
Kick off a backfill from FC (GPU agent card → Queue character embedding), then watch the queue counts on the control page (or FC's card) drain.
Config (env)
| var | default | meaning |
|---|---|---|
FC_URL |
http://localhost:8000 |
FC base URL |
FC_TOKEN |
— | the bearer token (required) |
AGENT_ID |
desktop-agent |
identifies this agent's leases |
BATCH_SIZE |
4 |
jobs leased per round (still processed one at a time) |
CCIP_MODEL |
imgutils default | CCIP model name |
DETECTOR_LEVEL |
m |
person-detector size: n < s < m < x |
POLL_IDLE_SECONDS |
10 |
wait between empty leases |
⚠️ Verify on first run
This part can't be CI-tested (no GPU/models in CI), so confirm against your
installed dghs-imgutils (pip show dghs-imgutils) — see fc_agent/models.py:
imgutils.detect.detect_person(image, level=...)returns[((x0,y0,x1,y1), label, score), ...].imgutils.metrics.ccip_extract_feature(image, model=...)returns a vector (768-d for caformer). If you want the F1-0.94 variant, setCCIP_MODEL=ccip-caformer_b36-24(verify the exact string in imgutils).
If FC's matcher under/over-fires, tune the cosine threshold in
backend/app/services/ml/ccip.py (DEFAULT_SIM_THRESHOLD) and use
GET /api/ccip/overview + /api/ccip/images/<id> to spot-check.
CPU fallback
Swap onnxruntime-gpu → onnxruntime in requirements.txt and drop --gpus all
to grind it slowly on the server instead. Same agent, no card.