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FROM ipython/notebook:latest
MAINTAINER Craig Citro <craigcitro@google.com>
# Set up Bazel.
# Install dependencies for bazel.
RUN apt-get update && apt-get install -y \
pkg-config \
zip \
g++ \
zlib1g-dev \
unzip \
swig \
software-properties-common \
wget
# We need to add a custom PPA to pick up JDK8, since trusty doesn't
# have an openjdk8 backport. openjdk-r is maintained by a reliable contributor:
# Matthias Klose (https://launchpad.net/~doko). It will do until
# we either update the base image beyond 14.04 or openjdk-8 is
# finally backported to trusty; see e.g.
# https://bugs.launchpad.net/trusty-backports/+bug/1368094
RUN add-apt-repository -y ppa:openjdk-r/ppa && \
apt-get update && \
apt-get install -y openjdk-8-jdk openjdk-8-jre-headless
# Set up CUDA variables and symlinks
COPY cuda /usr/local/cuda
ENV CUDA_PATH /usr/local/cuda
ENV LD_LIBRARY_PATH /usr/local/cuda/lib64
RUN ln -s libcuda.so.1 /usr/lib/x86_64-linux-gnu/libcuda.so
# 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" >>/root/.bazelrc
# Similarly, we need to workaround sandboxing issues:
# https://github.com/bazelbuild/bazel/issues/418
RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
>>/root/.bazelrc
ENV BAZELRC /root/.bazelrc
# Install the most recent bazel release.
ENV BAZEL_VERSION 0.1.1
WORKDIR /
RUN mkdir /bazel && \
cd /bazel && \
wget https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
wget -O /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE.txt
chmod +x bazel-*.sh && \
./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
cd / && \
rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
# Download and build TensorFlow.
WORKDIR /tensorflow
# Pick up some TF dependencies
RUN apt-get update && \
apt-get install -y python-numpy && \
apt-get install -y libfreetype6-dev
# We can't clone the TF git repo yet, because of permissions issues.
# RUN git clone https://tensorflow.googlesource.com/
# Instead, we manually copy it in:
COPY tensorflow /tensorflow
# Set up the CUDA tensorflow directories
RUN rm -rf /tensorflow/third_party/gpus/cuda/lib64
RUN rm -rf /tensorflow/third_party/gpus/cuda/bin
RUN rm -rf /tensorflow/third_party/gpus/cuda/include
RUN rm -rf /tensorflow/third_party/gpus/cuda/nvvm
RUN ln -s /usr/local/cuda/lib64 /tensorflow/third_party/gpus/cuda/
RUN ln -s /usr/local/cuda/bin /tensorflow/third_party/gpus/cuda/
RUN ln -s /usr/local/cuda/include /tensorflow/third_party/gpus/cuda/
RUN ln -s /usr/local/cuda/nvvm /tensorflow/third_party/gpus/cuda/
# Now we build
RUN bazel clean && \
bazel build -c opt --config=cuda tensorflow/tools/docker:simple_console
ENV PYTHONPATH=/tensorflow/bazel-bin/tensorflow/tools/docker/simple_console.runfiles/:$PYTHONPATH
# Add any notebooks in this directory.
COPY notebooks /notebooks
# Add variables for the local IPython. This sets a fixed password and
# switches to HTTP (to avoid self-signed certificate warnings in
# Chrome).
ENV PASSWORD=JustForNow
ENV USE_HTTP=1
RUN if [ -f /notebooks/requirements.txt ];\
then pip install -r /notebooks/requirements.txt;\
fi
# Set the workdir so we see notebooks on the IPython landing page.
WORKDIR /notebooks
# Remove CUDA libraries, headers, nvcc. The user will have to
# provide this directly when running docker.
RUN rm -rf /usr/local/cuda
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