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92 lines
3.1 KiB
Docker
92 lines
3.1 KiB
Docker
# GPU-enabled variant of the Transcribe server image (NVIDIA / CUDA).
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#
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# This Dockerfile is the same as Dockerfile.transcribe, but:
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# - The base image is nvidia/cuda (so the CUDA runtime libs are present).
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# - Node.js 24 is installed on top of the CUDA base image.
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# - The CUDA build of llama-mtmd-cli is copied from the official llama.cpp
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# CUDA image (ghcr.io/ggml-org/llama.cpp:full-cuda-b5449). We use a
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# multi-stage COPY here because llama.cpp does not publish a prebuilt
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# Linux/CUDA zip on its release page — only Windows CUDA zips and the
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# official Docker images.
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#
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# To run with GPU:
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# docker run --gpus all -e HTR_CLI_GPU_LAYERS=9999 \
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# --rm --env-file .env-transcribe -p 4567:4567 \
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# -v ./data:/data joplin/transcribe:gpu-latest
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#
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# Requires the NVIDIA Container Toolkit on the host.
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# Apple Silicon / Metal is not supported through Docker (Docker Desktop on
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# macOS cannot expose the GPU to containers) — see packages/transcribe/README.md
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# for the native-run instructions.
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# Stage 1: pull the CUDA-built llama.cpp binaries from the official image.
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FROM ghcr.io/ggml-org/llama.cpp:full-cuda-b5449 AS llama-cuda
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# Stage 2: our runtime image.
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FROM nvidia/cuda:13.1.1-cudnn-runtime-ubuntu22.04
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RUN apt-get update \
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&& apt-get install -y \
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ca-certificates curl wget \
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python3 tini gnupg \
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&& curl -fsSL https://deb.nodesource.com/setup_24.x | bash - \
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&& apt-get install -y nodejs \
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&& rm -rf /var/lib/apt/lists/*
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ENV NODE_ENV=production
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RUN corepack enable
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# Copy the CUDA-built llama-mtmd-cli binary and its runtime shared libraries
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# from the official llama.cpp CUDA image. The /app directory of that image
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# contains the built binaries and their .so dependencies.
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RUN mkdir -p /opt/llama/build/bin
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COPY --from=llama-cuda /app/llama-mtmd-cli /opt/llama/build/bin/llama-mtmd-cli
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COPY --from=llama-cuda /app/*.so /opt/llama/build/bin/
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RUN chmod +x /opt/llama/build/bin/llama-mtmd-cli
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# Create non-root user for security
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RUN groupadd -r transcribe && useradd -r -g transcribe -m transcribe
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WORKDIR /app
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COPY .yarn/releases ./.yarn/releases
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COPY .yarn/patches ./.yarn/patches
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COPY package.json .
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COPY .yarnrc.yml .
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COPY yarn.lock .
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COPY gulpfile.js .
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COPY tsconfig.json .
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COPY packages/lib ./packages/lib
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COPY packages/utils ./packages/utils
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COPY packages/tools ./packages/tools
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COPY packages/renderer ./packages/renderer
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COPY packages/htmlpack ./packages/htmlpack
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COPY packages/transcribe ./packages/transcribe
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# We don't want to build onenote-converter since it is not used by the server
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RUN sed --in-place '/onenote-converter/d' ./packages/lib/package.json
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RUN BUILD_SEQUENCIAL=1 yarn install --inline-builds \
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&& yarn cache clean \
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&& rm -rf .yarn/berry
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# Create data directory and set permissions
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RUN mkdir -p /data/images \
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&& chown -R transcribe:transcribe /data
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WORKDIR /app/packages/transcribe
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# Switch to non-root user
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USER transcribe
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# Set environment variables
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ENV HTR_CLI_BINARY_PATH=/opt/llama/build/bin/llama-mtmd-cli
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ENV LD_LIBRARY_PATH=/opt/llama/build/bin
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ENV DATA_DIR=/data
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ENV QUEUE_DRIVER=sqlite
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# Start the Node.js application
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ENTRYPOINT ["/usr/bin/tini", "--"]
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CMD ["yarn", "start"]
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