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docker : update CUDA images (ggerganov#9213)
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@@ -1,35 +1,37 @@ | ||
ARG UBUNTU_VERSION=22.04 | ||
# This needs to generally match the container host's environment. | ||
ARG CUDA_VERSION=11.7.1 | ||
ARG CUDA_VERSION=12.6.0 | ||
# Target the CUDA build image | ||
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} | ||
# Target the CUDA runtime image | ||
ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} | ||
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FROM ${BASE_CUDA_DEV_CONTAINER} AS build | ||
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# Unless otherwise specified, we make a fat build. | ||
ARG CUDA_DOCKER_ARCH=all | ||
# CUDA architecture to build for (defaults to all supported archs) | ||
ARG CUDA_DOCKER_ARCH=default | ||
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RUN apt-get update && \ | ||
apt-get install -y build-essential git | ||
apt-get install -y build-essential git cmake | ||
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WORKDIR /app | ||
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COPY . . | ||
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# Set nvcc architecture | ||
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} | ||
# Enable CUDA | ||
ENV GGML_CUDA=1 | ||
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RUN make -j$(nproc) llama-cli | ||
# Use the default CUDA archs if not specified | ||
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ | ||
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ | ||
fi && \ | ||
cmake -B build -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ | ||
cmake --build build --config Release --target llama-cli -j$(nproc) | ||
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FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime | ||
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RUN apt-get update && \ | ||
apt-get install -y libgomp1 | ||
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COPY --from=build /app/llama-cli /llama-cli | ||
COPY --from=build /app/build/ggml/src/libggml.so /libggml.so | ||
COPY --from=build /app/build/src/libllama.so /libllama.so | ||
COPY --from=build /app/build/bin/llama-cli /llama-cli | ||
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ENTRYPOINT [ "/llama-cli" ] |
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