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FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
LABEL maintainer="Gunhan Gulsoy <gunan@google.com>"
# It is possible to override these for releases.
ARG TF_BRANCH=master
ARG BAZEL_VERSION=0.15.0
ARG TF_AVAILABLE_CPUS=32
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
curl \
git \
golang \
libcurl3-dev \
libfreetype6-dev \
libpng12-dev \
libzmq3-dev \
pkg-config \
python-dev \
python-pip \
rsync \
software-properties-common \
unzip \
zip \
zlib1g-dev \
openjdk-8-jdk \
openjdk-8-jre-headless \
wget \
&& \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN pip --no-cache-dir install --upgrade \
pip setuptools
RUN pip --no-cache-dir install \
ipykernel \
jupyter \
keras_applications==1.0.4 \
keras_preprocessing==1.0.2 \
matplotlib \
numpy \
scipy \
sklearn \
pandas \
wheel \
&& \
python -m ipykernel.kernelspec
# Set up our notebook config.
COPY jupyter_notebook_config.py /root/.jupyter/
# Jupyter has issues with being run directly:
# https://github.com/ipython/ipython/issues/7062
# We just add a little wrapper script.
COPY run_jupyter.sh /
# Set up Bazel.
# Running bazel inside a `docker build` command causes trouble, cf:
# https://github.com/bazelbuild/bazel/issues/134
# The easiest solution is to set up a bazelrc file forcing --batch.
RUN echo "startup --batch" >>/etc/bazel.bazelrc
# Similarly, we need to workaround sandboxing issues:
# https://github.com/bazelbuild/bazel/issues/418
RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
>>/etc/bazel.bazelrc
WORKDIR /
RUN mkdir /bazel && \
cd /bazel && \
wget --quiet https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
wget --quiet https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
chmod +x bazel-*.sh && \
./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
# Download and build TensorFlow.
WORKDIR /
RUN git clone https://github.com/tensorflow/tensorflow.git && \
cd tensorflow && \
git checkout ${TF_BRANCH}
WORKDIR /tensorflow
# Configure the build for our CUDA configuration.
ENV CI_BUILD_PYTHON=python \
LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:${LD_LIBRARY_PATH} \
CUDNN_INSTALL_PATH=/usr/lib/x86_64-linux-gnu \
PYTHON_BIN_PATH=/usr/bin/python \
PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \
TF_NEED_CUDA=1 \
TF_CUDA_VERSION=9.0 \
TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2,6.0,6.1,7.0 \
TF_CUDNN_VERSION=7
RUN ./configure
# Build and Install TensorFlow.
RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \
bazel build -c opt \
--config=cuda \
--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
--jobs=${TF_AVAILABLE_CPUS} \
tensorflow/tools/pip_package:build_pip_package && \
mkdir /pip_pkg && \
bazel-bin/tensorflow/tools/pip_package/build_pip_package /pip_pkg && \
pip --no-cache-dir install --upgrade /pip_pkg/tensorflow-*.whl && \
rm -rf /pip_pkg && \
rm -rf /root/.cache
# Clean up pip wheel and Bazel cache when done.
WORKDIR /root
# TensorBoard
EXPOSE 6006
# IPython
EXPOSE 8888
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