flanandClaude Sonnet 4.6
ee585d4bae
Introduces a VARIANT=gpu|cpu build arg to the Dockerfile. The cpu variant uses ubuntu:22.04 (no CUDA base), installs torch+cpu and onnxruntime (no GPU deps) via a separate pyproject-cpu.toml / uv-cpu.lock, and is published as :cpu (dev-cpu on the dev branch) via a new build-cpu CI job. Saves ~2 GB over the default GPU image. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
90 lines
3.9 KiB
Docker
90 lines
3.9 KiB
Docker
# ── Base images ───────────────────────────────────────────────────────────────
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# amd64 + gpu: NVIDIA CUDA 13.3 + cuDNN (GPU acceleration when available)
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# amd64 + cpu: Ubuntu 22.04 (CPU-only, ~2 GB smaller image)
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# arm64: Ubuntu 24.04 (CPU-only; no CUDA wheels on ARM)
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ARG VARIANT=gpu
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FROM --platform=$BUILDPLATFORM nvidia/cuda:13.3.0-cudnn-runtime-ubuntu22.04 AS base-amd64-gpu
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FROM ubuntu:22.04 AS base-amd64-cpu
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FROM ubuntu:24.04 AS base-arm64-gpu
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FROM ubuntu:24.04 AS base-arm64-cpu
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# ── Build stage ───────────────────────────────────────────────────────────────
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ARG TARGETARCH
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FROM base-${TARGETARCH}-${VARIANT} AS build
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ARG VARIANT=gpu
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ENV DEBIAN_FRONTEND=noninteractive
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# Both Ubuntu 22.04 and 24.04 get Python 3.13 from the deadsnakes PPA.
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# GNUPGHOME is isolated so gpg never contacts an agent socket under QEMU.
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates curl gnupg software-properties-common \
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&& GNUPGHOME=$(mktemp -d) add-apt-repository ppa:deadsnakes/ppa -y \
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&& apt-get update \
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&& apt-get install -y --no-install-recommends \
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python3.13 python3.13-venv python3.13-dev \
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libgl1 libglib2.0-0 libxext6 g++ \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.13 /usr/bin/python3
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RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
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&& cp /root/.local/bin/uv /usr/local/bin/uv
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WORKDIR /app
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# For cpu variant, swap in the CPU-only pyproject and lockfile before syncing.
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COPY pyproject.toml uv.lock pyproject-cpu.toml uv-cpu.lock ./
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RUN if [ "$VARIANT" = "cpu" ]; then \
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cp pyproject-cpu.toml pyproject.toml && \
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cp uv-cpu.lock uv.lock; \
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fi && \
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uv sync --frozen --no-dev \
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&& uv cache clean
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COPY winnow/ winnow/
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COPY entrypoint.sh scheduler.py ./
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RUN chmod +x /app/entrypoint.sh
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# ── Runtime stage ─────────────────────────────────────────────────────────────
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# Starts fresh from the base image — excludes build tools (g++,
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# python3.13-dev, gnupg, software-properties-common) not needed at runtime.
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FROM base-${TARGETARCH}-${VARIANT} AS runtime
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ARG VARIANT=gpu
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates curl gnupg software-properties-common tini \
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&& GNUPGHOME=$(mktemp -d) add-apt-repository ppa:deadsnakes/ppa -y \
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&& apt-get update \
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&& apt-get install -y --no-install-recommends \
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python3.13 python3.13-venv \
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libgl1 libglib2.0-0 libxext6 \
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&& apt-get purge -y --auto-remove curl gnupg software-properties-common \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.13 /usr/bin/python3
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# Copy app (with .venv) and uv from build stage
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COPY --from=build /app /app
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COPY --from=build /usr/local/bin/uv /usr/local/bin/uv
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# Expose CUDA/cuDNN libraries from pip packages so onnxruntime-gpu can find
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# libcublasLt.so.12 and libcudnn.so.9 at runtime (amd64-gpu only).
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# On cpu builds these paths don't exist; non-existent entries are ignored.
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ENV LD_LIBRARY_PATH="/app/.venv/lib/python3.13/site-packages/nvidia/cudnn/lib:/app/.venv/lib/python3.13/site-packages/nvidia/cuda_runtime/lib:${LD_LIBRARY_PATH}"
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RUN groupadd -g 568 apps && useradd -u 568 -g apps -m -s /bin/bash appuser \
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&& mkdir -p /models/.insightface /models/huggingface \
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&& chown -R appuser:apps /app /models
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WORKDIR /app
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USER appuser
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ENV HF_HOME=/models/huggingface INSIGHTFACE_HOME=/models/.insightface
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HEALTHCHECK CMD test -f /app/entrypoint.sh || exit 1
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ENTRYPOINT ["tini", "--", "/app/entrypoint.sh"]
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