bvandeusen
c6f38b0dac
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
28 lines
1.3 KiB
Docker
28 lines
1.3 KiB
Docker
# FabledCurator GPU agent — runs on the desktop with the GPU.
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# CUDA + cuDNN runtime so onnxruntime-gpu can use the card (it needs cuDNN 9 —
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# the plain -runtime image lacks it: "libcudnn.so.9: cannot open shared object
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# file"); ffmpeg for video frames.
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FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends python3 python3-pip ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# torch from the CUDA-12.4 wheel index (matches the base image); its wheels
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# bundle their own CUDA + cuDNN and coexist with onnxruntime-gpu. Installed
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# first + separately so the GPU build of torch is deterministic and layer-cached.
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RUN pip3 install --no-cache-dir torch==2.6.0 --index-url https://download.pytorch.org/whl/cu124
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COPY requirements.txt .
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RUN pip3 install --no-cache-dir -r requirements.txt
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COPY fc_agent ./fc_agent
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# imgutils ONNX models + the transformers SigLIP weights both cache here; mount
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# a volume to persist them across restarts (the SigLIP download is ~3.5 GB once).
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ENV HF_HOME=/models
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EXPOSE 8770
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# The control UI; the worker is started from it (or POST /start).
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CMD ["uvicorn", "fc_agent.app:app", "--host", "0.0.0.0", "--port", "8770"]
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