The new per-job workload (3 detectors + several SigLIP embeds) is far more
GPU-bound than the old I/O-bound CCIP pass, so the right worker count shifted and
is hard to guess. Add an Auto mode (default ON) that finds it:
- _control_loop samples jobs/sec + GPU util/VRAM every ~6s and hill-climbs the
target: grow while throughput keeps improving and VRAM stays under budget,
revert a step that doesn't help, back off under memory pressure (VRAM >= 90%),
then settle and periodically re-probe (the GPU/IO balance shifts over a run).
- A manual concurrency set is an override → leaves Auto; an "Auto" toggle in the
control UI re-enables it. status() reports `auto`; the dial reflects the
auto-chosen count (read-only) while Auto is on.
- AUTO_SCALE env (default on) + compose doc. Agent py-compiled (outside CI).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
For redeploying curator while away with nobody to restart the agent:
- _process now distinguishes a TRANSPORT error (curator down/redeploying, 5xx,
401/403/408/409/429, or our lease reclaimed mid-flight) from a genuine job
fault. On a transport error it hands the job back (best effort) and signals
the loop to back off — instead of calling fail(), which would burn the job's
server-side attempt budget (MAX_ATTEMPTS=3) and permanently error good jobs
across a redeploy. Job-specific 4xx (404 image gone) still fail so they don't
re-lease forever.
- lease loop retries with capped exponential backoff (poll_idle → 60s) and
resets on the first successful lease, so a long outage is gentle and recovery
is automatic within ≤60s of curator returning. Sleeps are interruptible so
Stop / pool-shrink stays responsive.
- AUTO_START env (default on in compose) resumes the worker on container start,
so a host reboot / crash-restart (restart: unless-stopped) self-heals with
nobody at the desktop.
- control UI shows a "waited out" counter + an "curator unreachable, holding
work" banner so the recovering state reads as recovery, not failure.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
At 8 workers the GPU sat at ~5% util / <5GB VRAM — the pipeline is I/O-bound
(downloading + decoding images over HTTP), so the GPU starves until many workers
overlap that I/O. Raise MAX_CONCURRENCY 8→32 and make the UI worker control a
number input (reaching 32 by ±1 was tedious); the cap is reported via /status so
the UI clamps to it. Also size the shared requests pool (pool_maxsize=64) — the
default 10 would have throttled 32 workers + spammed "connection pool is full".
Verified by running; watch GPU util/VRAM climb as you dial up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Control UI gains what the operator asked for:
- GPU load (nvidia-smi): util %, VRAM used/total + bar, temp — so you can see how
hard the card is working while you're at the desktop.
- Worker count is now a live − / + control (POST /concurrency), not just an env:
the worker is a pool of independent slots (shared model, so slots add concurrent
inference, not N× VRAM). Dial up for speed, down to free the card. Replaces
pause/resume with Start/Stop + the worker dial.
- Graceful release on stop / pool-shrink: a slot hands its still-leased jobs back
via client.release() so they're re-picked immediately (pairs with the server
recovery sweep).
Not CI-tested (agent/ outside CI) — verified by running.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
The last piece: a Dockerised desktop-GPU worker that talks to FC ONLY over HTTP
(lease → fetch pixels → detect figures + CCIP-embed → submit), so Redis/Postgres
stay private. New top-level agent/ (outside CI scope — verified by running it):
- fc_agent/worker.py: the lease/compute/submit loop, concurrency 1, start/pause/
stop (stop frees the card; unprocessed leases expire + re-queue).
- fc_agent/models.py: imgutils wrappers — detect_person (figures) + CCIP embed.
The two API seams to verify against the installed dghs-imgutils (flagged).
- fc_agent/media.py: stills + video frame sampling (ffmpeg) at FC's cadence →
per-frame instances (the bag).
- fc_agent/crops.py: vendored crop primitive. client.py: the FC HTTP client.
- fc_agent/app.py: FastAPI localhost control UI (start/pause/stop + progress +
queue depth). Dockerfile (CUDA + onnxruntime-gpu + ffmpeg) + requirements +
README (token → build → run --gpus all → Start; CPU-fallback path).
This completes the CCIP pipeline end to end: agent produces region CCIP vectors →
RegionService stores → matcher suggests characters → rail. Verified by running on
the desktop (not CI). README calls out the imgutils API + model-string checks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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