Dockerfile.example.tensorflow-serving

# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
FROM pai.build.base:hadoop2.7.2-cuda9.0-cudnn7-devel-ubuntu16.04 as base_build

ARG TF_SERVING_VERSION_GIT_BRANCH=master
ARG TF_SERVING_VERSION_GIT_COMMIT=head

LABEL maintainer=gvasudevan@google.com
LABEL tensorflow_serving_github_branchtag=${TF_SERVING_VERSION_GIT_BRANCH}
LABEL tensorflow_serving_github_commit=${TF_SERVING_VERSION_GIT_COMMIT}

ENV NCCL_VERSION=2.2.13
ENV CUDNN_VERSION=7.1.4.18

RUN apt-get update && apt-get install -y --no-install-recommends \
        automake \
        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 \
        libfreetype6-dev \
        libpng12-dev \
        libtool \
        libcudnn7=${CUDNN_VERSION}-1+cuda9.0 \
        libcudnn7-dev=${CUDNN_VERSION}-1+cuda9.0 \
        libcurl3-dev \
        libnccl2=${NCCL_VERSION}-1+cuda9.0 \
        libnccl-dev=${NCCL_VERSION}-1+cuda9.0 \
        libzmq3-dev \
        mlocate \
        openjdk-8-jdk\
        openjdk-8-jre-headless \
        pkg-config \
        python-dev \
        software-properties-common \
        swig \
        unzip \
        wget \
        zip \
        zlib1g-dev \
        && \
    apt-get clean && \
    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 curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
    python get-pip.py && \
    rm get-pip.py

RUN pip --no-cache-dir install \
    grpcio \
    h5py \
    keras_applications==1.0.4 \
    keras_preprocessing==1.0.2 \
    mock \
    numpy==1.14.5

# Set up Bazel
# Need >= 0.15.0 so bazel compiles work with docker bind mounts.
ENV BAZEL_VERSION 0.15.0
WORKDIR /
RUN mkdir /bazel && \
    cd /bazel && \
    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
    curl -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36" -fSsL -o /bazel/LICENSE.txt 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

# Build TensorFlow with the CUDA configuration
ENV CI_BUILD_PYTHON python
ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
ENV TF_NEED_CUDA 1
ENV TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2,6.0,6.1,7.0
ENV TF_CUDA_VERSION=9.0
ENV TF_CUDNN_VERSION=7

# Fix paths so that CUDNN can be found: https://github.com/tensorflow/tensorflow/issues/8264
WORKDIR /
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.${TF_CUDNN_VERSION} /usr/local/cuda/lib64/libcudnn.so.${TF_CUDNN_VERSION}

# NCCL 2.x
ENV TF_NCCL_VERSION=2
ENV NCCL_INSTALL_PATH=/usr/lib/nccl/

# Fix paths so that NCCL can be found
WORKDIR /
RUN mkdir -p ${NCCL_INSTALL_PATH} && \
  mkdir ${NCCL_INSTALL_PATH}include/ && \
  mkdir ${NCCL_INSTALL_PATH}lib/ && \
  ln -s /usr/include/nccl.h ${NCCL_INSTALL_PATH}include/nccl.h && \
  ln -s /usr/lib/x86_64-linux-gnu/libnccl.so ${NCCL_INSTALL_PATH}lib/libnccl.so && \
  ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.${TF_NCCL_VERSION} ${NCCL_INSTALL_PATH}lib/libnccl.so.${TF_NCCL_VERSION}

# Set TMP for nvidia build environment
ENV TMP="/tmp"

# Download TF Serving sources (optionally at specific commit).
WORKDIR /tensorflow-serving
RUN git clone --branch=${TF_SERVING_VERSION_GIT_BRANCH} https://github.com/tensorflow/serving . && \
    git remote add upstream https://github.com/tensorflow/serving.git && \
    if [ "${TF_SERVING_VERSION_GIT_COMMIT}" != "head" ]; then git checkout ${TF_SERVING_VERSION_GIT_COMMIT} ; fi

FROM base_build as binary_build
# Build, and install TensorFlow Serving
ARG TF_SERVING_BUILD_OPTIONS="--copt=-mavx --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0"
ARG TF_SERVING_BAZEL_OPTIONS=""
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 --color=yes --curses=yes --config=cuda \
    ${TF_SERVING_BAZEL_OPTIONS} \
    --verbose_failures \
    --output_filter=DONT_MATCH_ANYTHING \
    ${TF_SERVING_BUILD_OPTIONS} \
    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 build -c opt tensorflow_serving/example:mnist_saved_model

CMD ["/bin/bash"]