Agent:
- The image ran PyPI's CUDA-13 torch 2.14 and onnxruntime-gpu 1.30 on a
CUDA 12.9 cudnn-runtime base. requirements.txt had silently replaced the
Dockerfile's torch 2.6+cu124, because ultralytics pulls torchvision, which
pulls its own torch. That left ~3 GB of base libraries and a ~3 GB torch
nothing loaded: 10 GB compressed.
- Now: an nvidia/cuda 13.0.3 `base` image, with torch and torchvision
installed together from cu130. CUDA and cuDNN come from the nvidia-* pip
packages; onnxruntime-gpu declares its [cuda,cudnn] extras.
- fc_agent/accel.py preloads those libraries for onnxruntime. It then logs,
and reports in /status, whether torch and the ONNX CUDA provider actually
got the GPU, since both fall back to the CPU silently.
Web image:
- Drop opencv-python-headless and onnxruntime, plus the opencv-only apt libs.
Both have been listed since the scaffold and nothing in backend/ imports
them.
- torch/torchvision move to 2.14/0.29, and the unexplained caps are lifted
(rule 154).
Redis: 8-alpine in both compose files and both CI service containers. That
gives an AGPLv3 licence option, where 7.4 was RSAL/SSPL only. The client
moves to >=8.1.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR
ac70f2a removed the `ml` image but CI went red on six tests:
`tests/test_artifact_identity.py` parametrises over its own
`ARTIFACTS = ("web", "ml", "agent", "extension")`, and every case now hits
the dispatch guard that same commit added.
My miss, and a specific one. I grepped for `fabledcurator-ml` and `ML_PATHS`
and called the survey done — but the artifact is also named as a bare `"ml"`,
which neither pattern finds. Rule 90 (grep pinned tests when changing a
shared symbol) was surfaced to me while I was making the change and I ran a
narrower sweep than it asks for. Lesson #4275 names the shape exactly: an
absence claim is only as good as the search behind it, and a grep that
matched nothing looks identical to a grep that asked the wrong question.
The re-run was done by value, not by name: every occurrence of a bare `ml` in
the repo, then filtering. That distinguishes the two things the token means —
the celery LANE `ml` and the `backend/app/services/ml` package both stay and
account for nearly every hit; only the IMAGE name went. Worth stating in the
test, since the next person to grep will hit the same ambiguity.
Also swept the prose the first pass left describing the old pipeline: "Four
artifacts" (README, ci-requirements), "leaves web and ml alone" (×5 in
build.yml, plus both docs), "CI publishes it alongside the web/ml images"
(agent/README). The run 4896 build-time measurements keep their `ml` number —
that was measured when ml was a real build — with a note saying the name has
since gone.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR
Build + push fabledcurator-agent alongside web/ml (own CUDA + onnxruntime-gpu
image, context=agent/, same tag cadence: main → :main/:latest/:c-<sha>, tag →
:<version>). So the operator PULLS + runs it on the GPU machine instead of
building locally. README switched to docker pull.
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
bvandeusenandClaude Opus 5.5