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Match the presubmit test machine setup in the Dockerfile.

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PiperOrigin-RevId: 212309247
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A. Unique TensorFlower 2018-09-10 12:08:32 -07:00 committed by TensorFlower Gardener
parent 96b77a647b
commit 656b3e9c84
2 changed files with 83 additions and 43 deletions
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83
tensorflow/tools/ci_build/Dockerfile.rbe.cuda9.0-cudnn7-ubuntu14.04 Normal file
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# To push a new version, run:
# $ docker build -f Dockerfile.rbe.cuda9.0-cudnn7-ubuntu14.04 \
# --tag "gcr.io/asci-toolchain/nosla-cuda9.0-cudnn7-ubuntu14.04" .
# $ docker push gcr.io/asci-toolchain/nosla-cuda9.0-cudnn7-ubuntu14.04
#
# TODO(klimek): Include clang in this image so we can also target clang
# builds.
FROM ubuntu:14.04
LABEL maintainer="Manuel Klimek <klimek@google.com>"
RUN apt-get update && apt-get install -y --no-install-recommends ca-certificates apt-transport-https gnupg-curl && \
rm -rf /var/lib/apt/lists/* && \
NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && \
apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +2 > cudasign.pub && \
echo "$NVIDIA_GPGKEY_SUM cudasign.pub" | sha256sum -c --strict - && rm cudasign.pub && \
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list
ENV CUDA_VERSION 9.0.176
ENV CUDA_PKG_VERSION 9-0=$CUDA_VERSION-1
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
ENV NVIDIA_REQUIRE_CUDA "cuda>=9.0"
ENV NCCL_VERSION 2.2.13
ENV CUDNN_VERSION 7.2.1.38
# TODO(b/110903506): /usr/loca/cuda/lib64/stubs should not be needed in
# LD_LIBRARY_PATH. The stubs/libcuda.so is not meant to used at runtime. The
# correct way to pass the path to bfd-ld is to pass
# -Wl,-rpath-link=/usr/local/cuda/lib64/stubs to all binaries transitively
# depending on libcuda. Optimally, builds targeting cuda would do that
# internally.
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/stubs
LABEL com.nvidia.volumes.needed="nvidia_driver"
LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"
RUN apt-get update && apt-get install -y --no-install-recommends \
cuda-cudart-$CUDA_PKG_VERSION \
cuda-libraries-$CUDA_PKG_VERSION \
cuda-cublas-9-0=9.0.176.4-1 \
libnccl2=$NCCL_VERSION-1+cuda9.0 \
cuda-libraries-dev-$CUDA_PKG_VERSION \
cuda-nvml-dev-$CUDA_PKG_VERSION \
cuda-minimal-build-$CUDA_PKG_VERSION \
cuda-command-line-tools-$CUDA_PKG_VERSION \
cuda-core-9-0=9.0.176.3-1 \
cuda-cublas-dev-9-0=9.0.176.4-1 \
libnccl-dev=$NCCL_VERSION-1+cuda9.0 \
libcudnn7-dev=$CUDNN_VERSION-1+cuda9.0 \
libcudnn7=$CUDNN_VERSION-1+cuda9.0 && \
ln -s cuda-9.0 /usr/local/cuda && \
apt-mark hold libnccl2 && \
apt-mark hold libcudnn7 libcudnn7-dev && \
rm -rf /var/lib/apt/lists/*
RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
# TODO(b/110903506): Provide a link to the SONAME of libcuda.so.
# https://github.com/NVIDIA/nvidia-docker/issues/775
RUN ln -s libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
# TODO(klimek): Once the TODO in tensorflow's configure.py to correctly find
# libnccl is resolved, delete this block.
RUN ln -s /usr/lib/x86_64-linux-gnu/libnccl.so /usr/lib/libnccl.so \
&& ln -s /usr/lib/x86_64-linux-gnu/libnccl.so /usr/lib/libnccl.so.2
# Copy and run the install scripts.
COPY install/*.sh /install/
ARG DEBIAN_FRONTEND=noninteractive
RUN /install/install_bootstrap_deb_packages.sh
RUN add-apt-repository -y ppa:openjdk-r/ppa && \
add-apt-repository -y ppa:george-edison55/cmake-3.x
RUN /install/install_deb_packages.sh
RUN /install/install_pip_packages.sh
RUN /install/install_golang.sh

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tensorflow/tools/ci_build/Dockerfile.rbe.gcc.gpu
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# To push a new version, run:
# $ docker build -f Dockerfile.rbe.gcc.gpu \
# --tag "gcr.io/asci-toolchain/nosla-nvidia-gcc" .
# $ docker push gcr.io/asci-toolchain/nosla-nvidia-gcc
FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
LABEL maintainer="Manuel Klimek <klimek@google.com>"
# TODO(b/110903506): Fix the nvidia docker image by providing a link to the
# SONAME of libcuda.so. Alternatively, consider using gold or lld which do not
# run into the same problem - that will only work once the tensorflow build does
# not link to libcuda from generators anymore.
# https://github.com/NVIDIA/nvidia-docker/issues/775
RUN ln -s libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
# TODO(klimek): Once the TODO in tensorflow's configure.py to correctly find
# libnccl is resolved, delete this block.
RUN ln -s /usr/lib/x86_64-linux-gnu/libnccl.so /usr/lib/libnccl.so \
&& ln -s /usr/lib/x86_64-linux-gnu/libnccl.so /usr/lib/libnccl.so.2
# TODO(b/110903506): Fix tensorflow to not require the use of LD_LIBRARY_PATH.
# The stubs/libcuda.so is not meant to used at runtime. The correct way to
# pass the path to bfd-ld is to pass -Wl,-rpath-link=/usr/local/cuda/lib64/stubs
# to all binaries transitively depending on libcuda. Optimally the tensorflow
# build would do that internally.
ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs
# Copy and run the install scripts.
COPY install/*.sh /install/
ARG DEBIAN_FRONTEND=noninteractive
RUN /install/install_bootstrap_deb_packages.sh
RUN add-apt-repository -y ppa:openjdk-r/ppa && \
add-apt-repository -y ppa:george-edison55/cmake-3.x
RUN /install/install_deb_packages.sh
RUN /install/install_pip_packages.sh
RUN /install/install_golang.sh
# Install nccl2.
RUN apt-get update && apt-get install -y \
libnccl2 \
libnccl-dev \
&& rm -rf /var/lib/apt-lists/*