43 lines
1.4 KiB
Docker
43 lines
1.4 KiB
Docker
## GPU-ready base image with CUDA 12 + cuDNN 9 runtime
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# If you don't have an NVIDIA GPU or the NVIDIA Container Toolkit, this image still runs on CPU.
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# For smaller CPU-only images, you can switch back to python:3.11-slim.
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# Use a widely available CUDA + cuDNN runtime tag. If you prefer newer CUDA,
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# adjust this to a tag that exists on Docker Hub.
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FROM nvidia/cuda:12.3.2-cudnn8-runtime-ubuntu22.04
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# Keep python fast/quiet and pip lean
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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PIP_NO_CACHE_DIR=1 \
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LANG=C.UTF-8 \
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LC_ALL=C.UTF-8 \
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# sensible defaults (can be overridden by .env)
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WHISPER_MODEL=large-v3 \
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WHISPER_PRECISION=int8
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# System deps: ffmpeg for media, curl for healthcheck, jq for scripts, poppler-utils for PDFs
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3 python3-pip python3-venv \
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ffmpeg \
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curl \
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jq \
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poppler-utils \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# Upgrade pip toolchain then install Python deps
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COPY requirements.txt .
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RUN python3 -m pip install --upgrade pip setuptools wheel \
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&& pip3 install --no-cache-dir -r requirements.txt \
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&& pip3 check || true
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# App code
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COPY app.py worker.py scanner.py ./
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RUN pip3 install --no-cache-dir gunicorn==22.0.0
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# Healthcheck against the app's /health endpoint
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EXPOSE 8080
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CMD ["gunicorn", "-b", "0.0.0.0:8080", "app:app", "--workers", "2", "--threads", "4"]
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