86830019ad
- Add IPFSHLSOutput class that uploads segments to IPFS as they're created - Update streaming task to use IPFS HLS output for distributed streaming - Add /ipfs-stream endpoint to get IPFS playlist URL - Update /stream endpoint to redirect to IPFS when available - Add GPU persistence mode (STREAMING_GPU_PERSIST=1) to keep frames on GPU - Add hardware video decoding (NVDEC) support for faster video processing - Add GPU-accelerated primitive libraries: blending_gpu, color_ops_gpu, geometry_gpu - Add streaming_gpu module with GPUFrame class for tracking CPU/GPU data location - Add Dockerfile.gpu for building GPU-enabled worker image Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
45 lines
1.1 KiB
Docker
45 lines
1.1 KiB
Docker
# GPU-enabled worker image
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# Based on NVIDIA CUDA with Python for CuPy support
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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 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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# Copy application
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COPY . .
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# Clone effects repo
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RUN git clone https://git.rose-ash.com/art-dag/effects.git /app/artdag-effects
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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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# 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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