Dockerfile.devel-gpu

FROM nvidia/cuda:9.0-base-ubuntu16.04

ENV \
  OS_LOCALE="en_US.UTF-8" \
  LANG=${OS_LOCALE} \
  LANGUAGE=en_US:en \
  LC_ALL=${OS_LOCALE}

RUN \
 apt-get update \
 && apt-get -y install curl locales \
 && apt-get clean \
 && rm -rf /var/lib/apt/lists/*

RUN \
  locale-gen ${OS_LOCALE} \
  && localedef -i en_US -f UTF-8 en_us.UTF-8

RUN \
 apt-get update \
 && apt-get install -y wget bzip2

# Install Anaconda with Python3
RUN wget -q https://repo.continuum.io/miniconda/Miniconda3-4.3.31-Linux-x86_64.sh -O /tmp/miniconda.sh  && \
    echo '7fe70b214bee1143e3e3f0467b71453c */tmp/miniconda.sh' | md5sum -c - && \
    bash /tmp/miniconda.sh -f -b -p /opt/conda && \
    /opt/conda/bin/conda install --yes python=3.6 sqlalchemy tornado jinja2 traitlets requests pip && \
    /opt/conda/bin/pip install --upgrade pip && \
    rm /tmp/miniconda.sh

ENV \
  PATH=/opt/conda/bin:$PATH

RUN \
  conda update -n base conda

RUN \
  pip install --upgrade pip

RUN apt-get update && apt-get install -y --no-install-recommends \
        build-essential \
        cuda-command-line-tools-9-0 \
        cuda-cublas-dev-9-0 \
        cuda-cudart-dev-9-0 \
        cuda-cufft-dev-9-0 \
        cuda-curand-dev-9-0 \
        cuda-cusolver-dev-9-0 \
        cuda-cusparse-dev-9-0 \
        curl \
        git \
        libcudnn7=7.0.5.15-1+cuda9.0 \
        libcudnn7-dev=7.0.5.15-1+cuda9.0 \
        libcurl3-dev \
        libfreetype6-dev \
        libpng12-dev \
        libzmq3-dev \
        pkg-config \
        python-dev \
        python3-dev \
        python-numpy \
        python3-numpy \
        python-six \
        python3-six \
        python-wheel \
        python3-wheel \
        python-pip \
        python3-pip \
        rsync \
        software-properties-common \
        swig \
        unzip \
        zip \
        zlib1g-dev \
        wget \
        && \
    rm -rf /var/lib/apt/lists/* && \
    find /usr/local/cuda-9.0/lib64/ -type f -name 'lib*_static.a' -not -name 'libcudart_static.a' -delete && \
    rm /usr/lib/x86_64-linux-gnu/libcudnn_static_v7.a


RUN \
  pip install grpcio

RUN \
  conda install --yes openblas scikit-learn numpy scipy matplotlib pandas seaborn

# Set up Bazel.

# 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 && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# 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 0.8.0

WORKDIR /root

RUN mkdir /bazel && \
    cd /bazel && \
    curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
    curl -fSsL -o /bazel/LICENSE https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
    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 TensorFlow Serving
RUN \
  git clone --recurse-submodules https://github.com/tensorflow/serving && \
  cd serving && \
  git reset --hard c1ec435

WORKDIR /root/serving/

# Override the submoduled version of tensorflow (risky!)
#RUN \
#  rm -rf tensorflow && \
#  git clone --branch=r1.5 https://github.com/tensorflow/tensorflow.git 
#  cd tensorflow && \
#  git reset --hard 3629fc4

# Configure the build for our CUDA configuration.
ENV CC_OPT_FLAGS="-march=native"
ENV LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
ENV TF_NEED_CUDA=1
ENV TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,3.7,5.2,5.3,6.0,6.1,7.0
ENV TF_CUDA_VERSION=9.0
ENV TF_CUDNN_VERSION=7
ENV TF_NEED_JEMALLOC=1
ENV TF_ENABLE_XLA=1
ENV TF_NEED_HDFS=1
ENV TF_NEED_S3=1
ENV TF_NEED_GCP=1
ENV PYTHON_BIN_PATH=/opt/conda/bin/python3.6
ENV PYTHON_LIB_PATH=/opt/conda/lib/python3.6
ENV CI_BUILD_PYTHON=$PYTHON_BIN_PATH

# Fix paths so that CUDNN can be found
# See https://github.com/tensorflow/tensorflow/issues/8264
RUN ls -lah /usr/local/cuda/lib64/*
RUN mkdir /usr/lib/x86_64-linux-gnu/include/ && \
  ln -s /usr/lib/x86_64-linux-gnu/include/cudnn.h /usr/lib/x86_64-linux-gnu/include/cudnn.h && \
  ln -s /usr/include/cudnn.h /usr/local/cuda/include/cudnn.h && \
  ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/local/cuda/lib64/libcudnn.so && \
  ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/local/cuda/lib64/libcudnn.so.7

# Build TensorFlow Serving and Install it in /usr/local/bin
WORKDIR /root/serving

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} \
    CI_TENSORFLOW_SUBMODULE_PATH=tensorflow tensorflow/tensorflow/tools/ci_build/builds/configured GPU \
    bazel build \
    -c opt --config=cuda --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-O3 \
    --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
    --crosstool_top=@local_config_cuda//crosstool:toolchain \
    tensorflow_serving/model_servers:tensorflow_model_server && \
    cp bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server /usr/local/bin/ && \
    rm /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
    bazel clean --expunge

RUN \
  apt-get update \
  && apt-get install -y vim

WORKDIR /root