a13c361dee
- Dockerfiles use monorepo root as build context - common/ and core/ installed as local packages (no git+https) - Client tarball built from local client/ dir - Unified CI with change detection: common/core -> rebuild both - Per-repo CI workflows removed Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
104 lines
3.3 KiB
Docker
104 lines
3.3 KiB
Docker
# GPU-enabled worker image
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# Multi-stage build: use devel image for compiling, runtime for final image
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# Stage 1: Build decord with CUDA
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FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04 AS builder
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3.11 \
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python3.11-venv \
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python3.11-dev \
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python3-pip \
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git \
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cmake \
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build-essential \
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pkg-config \
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libavcodec-dev \
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libavformat-dev \
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libavutil-dev \
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libavdevice-dev \
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libavfilter-dev \
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libswresample-dev \
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libswscale-dev \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.11 /usr/bin/python3 \
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&& ln -sf /usr/bin/python3 /usr/bin/python
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# Download Video Codec SDK headers for NVDEC/NVCUVID
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RUN git clone https://github.com/FFmpeg/nv-codec-headers.git /tmp/nv-codec-headers && \
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cd /tmp/nv-codec-headers && make install && rm -rf /tmp/nv-codec-headers
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# Create stub for libnvcuvid (real library comes from driver at runtime)
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RUN echo 'void* __nvcuvid_stub__;' | gcc -shared -x c - -o /usr/local/cuda/lib64/libnvcuvid.so
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# Build decord with CUDA support
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RUN git clone --recursive https://github.com/dmlc/decord /tmp/decord && \
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cd /tmp/decord && \
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mkdir build && cd build && \
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cmake .. -DUSE_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
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-DCMAKE_CUDA_ARCHITECTURES="70;75;80;86;89;90" && \
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make -j$(nproc) && \
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cd ../python && pip install --target=/decord-install .
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# Stage 2: Runtime image
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FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
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WORKDIR /app
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# Install Python 3.11 and system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3.11 \
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python3.11-venv \
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python3-pip \
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git \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.11 /usr/bin/python3 \
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&& ln -sf /usr/bin/python3 /usr/bin/python
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# Upgrade pip
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RUN python3 -m pip install --upgrade pip
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# Install CPU dependencies first
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COPY l1/requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Install GPU-specific dependencies (CuPy for CUDA 12.x)
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RUN pip install --no-cache-dir cupy-cuda12x
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# Install PyNvVideoCodec for zero-copy GPU encoding
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RUN pip install --no-cache-dir PyNvVideoCodec
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# Copy decord from builder stage
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COPY --from=builder /decord-install /usr/local/lib/python3.11/dist-packages/
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COPY --from=builder /tmp/decord/build/libdecord.so /usr/local/lib/
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RUN ldconfig
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# Install monorepo shared packages
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COPY common/ /tmp/common/
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COPY core/ /tmp/core/
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RUN pip install --no-cache-dir /tmp/common/ /tmp/core/ && rm -rf /tmp/common /tmp/core
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# Clone effects repo (before COPY so it gets cached)
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RUN git clone https://git.rose-ash.com/art-dag/effects.git /app/artdag-effects
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# Copy application (this invalidates cache for any code change)
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COPY l1/ .
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# Create cache directory
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RUN mkdir -p /data/cache
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONDONTWRITEBYTECODE=1
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ENV EFFECTS_PATH=/app/artdag-effects
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ENV PYTHONPATH=/app
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# GPU persistence enabled - frames stay on GPU throughout pipeline
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ENV STREAMING_GPU_PERSIST=1
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# Preload libnvcuvid for decord NVDEC GPU decode
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ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libnvcuvid.so
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# Use cluster's public IPFS gateway for HLS segment URLs
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ENV IPFS_GATEWAY_URL=https://celery-artdag.rose-ash.com/ipfs
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# Default command runs celery worker
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CMD ["celery", "-A", "celery_app", "worker", "--loglevel=info", "-E", "-Q", "gpu,celery"]
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