Bump the GPU-agent base image from 12.4.1-cudnn-runtime-ubuntu22.04 (Python 3.10,
CUDA 12.4, early-2024) to 12.9.2-cudnn-runtime-ubuntu24.04:
- Ubuntu 24.04 LTS → Python 3.12 — one modern runtime, no more 3.10.
- CUDA 12.9 + cuDNN 9 — current within the CUDA-12 / cuDNN-9 line that the
default onnxruntime-gpu wheel AND torch cu124 are built against. NOT CUDA 13:
ONNX Runtime's CUDA-13 support is still nascent (separate wheels + open
"Unsupported CUDA version: 13" reports), and torch bundles cu124 anyway. The
GPU (Ampere/Ada, 12 GB) is fine on either — this is a library-alignment call,
not a hardware limit.
- PIP_BREAK_SYSTEM_PACKAGES=1: 24.04 marks system Python externally-managed
(PEP 668); a single-purpose container owns its environment, so global installs
are fine and simplest.
- agent/ruff.toml pinned to py312 (was py310) so CI lints against the real
runtime; from __future__ import annotations stays (PEP 649 lazy annotations
are 3.14, so self-refs still evaluate on 3.12).
CI builds the image but has no GPU — validate on the desktop after pull that it
starts and loads CUDAExecutionProvider (not CPU fallback).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray,
lactation) that the whole-image SigLIP vector washes out: the GPU agent now
embeds figure crops with SigLIP too, stored as kind='concept' regions, and the
suggestion rail scores each image as a BAG (whole-image + every concept crop),
taking each head's MAX over the bag. The whole-image vector is always in the
bag, so this can never score lower than before.
Model-agnostic by construction: the server ANNOUNCES the embedding model
(HF name + version) in the lease, so the agent loads whatever the heads were
trained in and stays in lock-step — a model swap is a server setting + a
re-embed migration, never an agent change.
- agent: model-agnostic CropEmbedder (torch/transformers get_image_features,
fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits
figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the
back-catalogue backfill never churns figure/CCIP regions; torch cu124 +
transformers in the image.
- server: lease announces embed_model_name/embed_version; score_image is
max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill
'siglip' gates on a missing concept region (drains the back-catalogue,
retries failures, no double-enqueue); daily siglip-backfill beat; UI button;
/api/ccip/overview reports images_with_concept_siglip.
- v1 scope: suggestion rail only — auto-apply stays whole-image (conservative;
heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
onnxruntime-gpu needs cuDNN 9; the plain cuda:12.4.1-runtime image lacks it
(libcudnn.so.9 missing → CUDAExecutionProvider falls back to CPU). Switch to
the -cudnn-runtime variant which bundles cuDNN 9.
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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