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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/*
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