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# FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 AS nvidia
FROM continuumio/anaconda3:2019.03
# Avoid interactive configuration prompts/dialogs during apt-get.
ENV DEBIAN_FRONTEND=noninteractive
# This is necessary to for apt to access HTTPS sources
# RUN apt-get update && \
# apt-get install apt-transport-https
# Cuda support
# COPY --from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/
# COPY --from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/
# COPY --from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg
# Ensure the cuda libraries are compatible with the GPU image.
# TODO(b/120050292): Use templating to keep in sync.
# ENV CUDA_VERSION=10.0.130
# ENV CUDA_PKG_VERSION=10-0=$CUDA_VERSION-1
# LABEL com.nvidia.volumes.needed="nvidia_driver"
# LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
# TODO(rosbo): Is this needed?
# ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
# The stub is useful to us both for built-time linking and run-time linking, on CPU-only systems.
# When intended to be used with actual GPUs, make sure to (besides providing access to the host
# CUDA user libraries, either manually or through the use of nvidia-docker) exclude them. One
# convenient way to do so is to obscure its contents by a bind mount:
# docker run .... -v /non-existing-directory:/usr/local/cuda/lib64/stubs:ro ...
ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs"
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV NVIDIA_REQUIRE_CUDA="cuda>=10.1"
# RUN apt-get update && apt-get install -y --no-install-recommends \
# cuda-cupti-$CUDA_PKG_VERSION \
# cuda-cudart-$CUDA_PKG_VERSION \
# cuda-cudart-dev-$CUDA_PKG_VERSION \
# cuda-libraries-$CUDA_PKG_VERSION \
# 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 \
# libcudnn7=7.5.0.56-1+cuda10.0 \
# libcudnn7-dev=7.5.0.56-1+cuda10.0 \
# libnccl2=2.4.2-1+cuda10.0 \
# libnccl-dev=2.4.2-1+cuda10.0 && \
# ln -s /usr/local/cuda-10.0 /usr/local/cuda && \
# ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
RUN conda install -y cudatoolkit=10.1.243 cudnn=7.6.5 && \
conda install -y install nccl=2.5.7.1 -c conda-forge && \
ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1
# Work to upgrade to Python 3.7 can be found on this branch: https://github.com/Kaggle/docker-python/blob/upgrade-py37/Dockerfile
RUN conda install -y python=3.6.6 && pip install --upgrade pip && pip install numpy
# See _TF_(MIN|MAX)_BAZEL_VERSION at https://github.com/tensorflow/tensorflow/blob/master/configure.py.
ENV BAZEL_VERSION=0.29.1
RUN apt-get install -y gnupg zip openjdk-8-jdk && \
apt-get install -y --no-install-recommends \
bash-completion \
zlib1g-dev && \
wget --no-verbose "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/bazel_${BAZEL_VERSION}-linux-x86_64.deb" && \
dpkg -i bazel_*.deb && \
rm bazel_*.deb
# Fetch tensorflow & install dependencies.
RUN cd /usr/local/src && \
git clone https://github.com/tensorflow/tensorflow && \
cd tensorflow && \
git checkout tags/v2.1.0 && \
pip install keras_applications --no-deps && \
pip install keras_preprocessing --no-deps
# Create a tensorflow wheel for CPU
RUN cd /usr/local/src/tensorflow && \
cat /dev/null | ./configure && \
bazel build --config=opt --config=v2 //tensorflow/tools/pip_package:build_pip_package && \
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_cpu && \
bazel clean
# Create a tensorflow wheel for GPU/cuda
ENV TF_NEED_CUDA=1
ENV TF_CUDA_VERSION=10.1
# 3.7 is for the K80 and 6.0 is for the P100, 7.5 is for the T4: https://developer.nvidia.com/cuda-gpus
ENV TF_CUDA_COMPUTE_CAPABILITIES=3.7,6.0,7.5
ENV TF_CUDNN_VERSION=7
ENV TF_NCCL_VERSION=2
ENV NCCL_INSTALL_PATH=/usr/
RUN cd /usr/local/src/tensorflow && \
# TF_NCCL_INSTALL_PATH is used for both libnccl.so.2 and libnccl.h. Make sure they are both accessible from the same directory.
ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/ && \
cat /dev/null | ./configure && \
echo "/usr/local/cuda-${TF_CUDA_VERSION}/targets/x86_64-linux/lib/stubs" > /etc/ld.so.conf.d/cuda-stubs.conf && ldconfig && \
bazel build --config=opt \
--config=v2 \
--config=cuda \
--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
//tensorflow/tools/pip_package:build_pip_package && \
rm /etc/ld.so.conf.d/cuda-stubs.conf && ldconfig && \
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_gpu && \
bazel clean
# Print out the built .whl files
RUN ls -R /tmp/tensorflow*