Paul Nothaft
74a8f9bf24
Three Trivy cleanups off the code-scanning tab. ml/Dockerfile — install no runtime apt packages at all. Neither libgl1 nor libglib2.0-0 is needed: opencv-python-headless 4.14 bundles what it needs and `ldd .../cv2/cv2*.so` resolves fully on a bare slim base. The old comment claimed the headless wheel still links libGL, which was true of much older wheels. libgl1 was dragging in 36 transitive packages (mesa, LLVM, X11) for a service that never opens a display. Measured with `trivy image` on locally built variants: before: 165 findings — 88 low / 49 med / 19 high / 6 crit without libgl1: 133 findings — 58 low / 48 med / 19 high / 5 crit without either: 123 findings — 57 low / 46 med / 13 high / 4 crit 42 findings gone, image 1.05GB -> 774MB. Not one of the 165 had an upstream fix available, so not installing the packages is the only lever there is. docker-build.yml — set ignore-unfixed on all four Trivy steps. All 123 remaining ML findings are unfixed base-OS CVEs; Debian has them resolved in sid and pending backport to trixie, and apt-get upgrade -y behind CACHEBUST picks each one up automatically. Reporting them buries anything actionable, and suppressing them is the precondition for ever setting exit-code: 1. backend — deepmerge-ts <8.0.0 has a stack-exhaustion advisory (CVE-2026-40345, high) reached via mailparser -> html-to-text, which pins ^7.1.5 so npm cannot get there alone. Not reachable in our code: html-to-text only feeds deepmerge-ts its options object (html-to-text.mjs:1468, :1442), never parsed email content. npm audit goes 3 high -> 0. The lockfile also picks up the version field release-please had left at 3.103.1-beta.0, plus some "peer": true metadata npm 11.6 recomputes. Co-authored-by: Paul Nothaft <paul@MacStudio-von-Paul.local>
139 lines
6.4 KiB
Docker
139 lines
6.4 KiB
Docker
# picpeak-ml — optional face-detection sidecar (#1074).
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#
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# Debian slim rather than Alpine: onnxruntime publishes manylinux wheels for
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# x86_64 and aarch64 but nothing for musl, so Alpine would mean compiling ORT
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# from source on both legs of the multi-arch build. The slim base costs ~40MB
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# over Alpine and saves an hour of CI per build.
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# ---------------------------------------------------------------------------
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# Stage 1 — fetch and verify model weights
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# ---------------------------------------------------------------------------
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# Weights are baked in, never downloaded at runtime: airgapped installs must
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# work, and a model that changes under a running deployment would silently
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# invalidate every stored embedding.
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#
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# Both artifacts are pinned by URL *and* SHA-256. The checksum is the point —
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# an immutable-looking URL that starts serving different bytes must fail the
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# build rather than quietly reshape the embedding space.
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FROM python:3.12-slim AS models
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ARG YUNET_URL=https://media.githubusercontent.com/media/opencv/opencv_zoo/f12e12798e8314f7c074a6656816c048dcc95b7a/models/face_detection_yunet/face_detection_yunet_2023mar.onnx
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ARG YUNET_SHA256=8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4
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# FaceNet-512 as ONNX. deepface distributes this model as Keras .h5 only, so
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# the ONNX is produced once by `tools/convert_facenet.py` and published as a
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# release asset — converting inside this build would drag TensorFlow (~600MB)
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# through both architecture legs to produce a file that is identical either
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# way.
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#
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# Defaults to the canonical published artifact so `docker build ml/` and
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# `docker compose --profile faces up` both work with no arguments. Override
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# both together to use a different embedder. Blanking either one still fails
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# the build loudly (below) rather than silently producing an image with no
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# embedder — the checksum is what makes the URL safe to trust, so a URL
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# without one is never acceptable.
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ARG FACENET_ONNX_URL=https://github.com/PicPeak/picpeak/releases/download/ml-models-v1/facenet512.onnx
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ARG FACENET_ONNX_SHA256=a1c06dcb79dc17a42af01d5bcbce4822caa148b9c24bf7eb8b8e556b4fd0d5db
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends curl ca-certificates \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /models
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RUN curl -fsSL -o face_detection_yunet_2023mar.onnx "${YUNET_URL}" \
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&& echo "${YUNET_SHA256} face_detection_yunet_2023mar.onnx" | sha256sum -c -
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RUN if [ -z "${FACENET_ONNX_URL}" ] || [ -z "${FACENET_ONNX_SHA256}" ]; then \
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echo "ERROR: FACENET_ONNX_URL and FACENET_ONNX_SHA256 build args are required." >&2; \
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echo " Produce the artifact with ml/tools/convert_facenet.py, publish it," >&2; \
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echo " then pass both args. See ml/README.md." >&2; \
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exit 1; \
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fi \
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&& curl -fsSL -o facenet512.onnx "${FACENET_ONNX_URL}" \
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&& echo "${FACENET_ONNX_SHA256} facenet512.onnx" | sha256sum -c -
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# ---------------------------------------------------------------------------
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# Stage 2 — runtime
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# ---------------------------------------------------------------------------
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FROM python:3.12-slim
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ARG BUILD_DATE
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ARG VCS_REF
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ARG VERSION
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LABEL org.opencontainers.image.source="https://github.com/PicPeak/picpeak"
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LABEL org.opencontainers.image.description="PicPeak ML sidecar — face detection and embedding"
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LABEL org.opencontainers.image.licenses="MIT"
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# Busts the apt layer each CI run so the image picks up current Debian
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# security updates instead of reusing a stale cached upgrade layer — same
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# reasoning as backend/Dockerfile.
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ARG CACHEBUST=1
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RUN echo "cachebust=${CACHEBUST}" \
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&& apt-get update \
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&& apt-get upgrade -y \
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&& rm -rf /var/lib/apt/lists/*
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# No runtime apt packages at all — deliberately.
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#
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# opencv-python-headless 4.14 needs neither libgl1 nor libglib2.0-0. The
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# wheel bundles what it needs; `ldd .../cv2/cv2*.so` resolves fully on a
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# bare python:3.12-slim. Both were installed here on the assumption that
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# the headless build still links libGL, which was true of much older
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# wheels and is not true of this one.
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#
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# What they cost: libgl1 alone pulls 36 transitive packages (mesa, LLVM,
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# X11) into a service that never opens a display, and libglib2.0-0t64
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# carries a critical plus six highs. Together they accounted for 42 of
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# this image's Trivy findings — none of which have an upstream fix, so
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# not installing them is the only lever that exists.
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#
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# Before re-adding either, confirm it is actually needed: build without
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# it and run `ml/tests` plus a real FaceDetectorYN.detect() call, since
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# the API tests stub the pipeline and will pass either way.
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WORKDIR /app
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COPY requirements.txt .
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# pip itself is ~10MB of an image that never installs anything at runtime.
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#
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# The `|| true` is scoped to the uninstall ONLY. Written as
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# `pip install && pip uninstall || true` the shell parses it as
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# `(install && uninstall) || true`, so a failed requirements install still
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# produces a green layer — and CI would publish an ML image with no FastAPI,
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# no uvicorn and no onnxruntime that fails at container start instead of at
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# build time.
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RUN pip install --no-cache-dir -r requirements.txt \
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&& { pip uninstall -y pip setuptools 2>/dev/null || true; }
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# Non-root. Nothing in this container writes anything — no volumes, no
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# database, no model download — so the whole filesystem can stay read-only to
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# the service account.
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#
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# Created BEFORE the copies so ownership can be set by COPY --chown. A
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# `chown -R` afterwards would rewrite every copied file into a fresh layer,
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# duplicating the 90MB model and adding ~94MB to the image for nothing.
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RUN useradd --system --uid 1001 --create-home picpeak
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COPY --from=models --chown=picpeak:picpeak /models /models
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COPY --chown=picpeak:picpeak app ./app
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USER picpeak
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ENV FACE_MODEL_DIR=/models \
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PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1
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EXPOSE 8000
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# Mirrors the backend's healthcheck shape. /health is unauthenticated so this
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# needs no secret; it reports liveness only, because a failed model load
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# aborts startup and the container never serves at all.
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HEALTHCHECK --interval=30s --timeout=5s --start-period=40s --retries=3 \
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CMD python -c "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=4).status == 200 else 1)"
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# Single worker on purpose: the models are loaded per process, so a second
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# worker doubles RSS for a service the backend calls at concurrency 1.
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "1"]
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