最近在做机器学习的服务搭建， 使用的是docker容器内编译， 性能直接提升了30%， 以下是Bazel5.1.1编译
注意
如果你是在墙内， 那你需要参考《Linux Dockerfile build时请求超时的问题解决》以解决编译时的超时问题。
如果你是在墙外， 请注释RUN git config –global https.proxy https://172.17.0.1:10809这一行
编译镜像
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FROM ubuntu:18.04 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}
RUN apt-get update && apt-get install -y --no-install-recommends \
automake \
build-essential \
ca-certificates \
curl \
git \
libcurl3-dev \
libfreetype6-dev \
libpng-dev \
libtool \
libzmq3-dev \
mlocate \
openjdk-8-jdk\
openjdk-8-jre-headless \
pkg-config \
python-dev \
software-properties-common \
swig \
unzip \
wget \
zip \
zlib1g-dev \
python3-distutils \
&& \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install python 3.7.
RUN add-apt-repository ppa:deadsnakes/ppa && \
apt-get update && apt-get install -y \
python3.7 python3.7-dev python3-pip python3.7-venv && \
rm -rf /var/lib/apt/lists/* && \
python3.7 -m pip install pip --upgrade && \
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 0
# Make python3.7 the default python version
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.7 0
RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
python3 get-pip.py && \
rm get-pip.py
RUN pip3 --no-cache-dir install \
future>=0.17.1 \
grpcio \
h5py \
keras_applications>=1.0.8 \
keras_preprocessing>=1.1.0 \
mock \
numpy \
portpicker \
requests \
--ignore-installed setuptools \
--ignore-installed six>=1.12.0
# 需要使用git拉取镜像， 这里指定使用宿主机的代理端口
RUN git config --global https.proxy https://172.17.0.1:10809
# Set up Bazel
ENV BAZEL_VERSION 5.1.1
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 --http1.0 && \
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 --http1.0 && \
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 TF Serving sources (optionally at specific commit).
WORKDIR /tensorflow-serving
RUN curl -sSL --retry 5 https://github.com/tensorflow/serving/tarball/${TF_SERVING_VERSION_GIT_COMMIT} | tar --strip-components=1 -xzf -
FROM base_build as binary_build
# Build, and install TensorFlow Serving
#ARG TF_SERVING_BUILD_OPTIONS="--config=release"
ARG TF_SERVING_BUILD_OPTIONS="--copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.1 --copt=-msse4.2 --local_ram_resources=2048 --local_cpu_resources=2 --worker_max_instances=2"
RUN echo "Building with build options: ${TF_SERVING_BUILD_OPTIONS}"
ARG TF_SERVING_BAZEL_OPTIONS=""
RUN echo "Building with Bazel options: ${TF_SERVING_BAZEL_OPTIONS}"
RUN bazel build --color=yes --curses=yes \
${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/
# Build and install TensorFlow Serving API
RUN bazel build --color=yes --curses=yes \
${TF_SERVING_BAZEL_OPTIONS} \
--verbose_failures \
--output_filter=DONT_MATCH_ANYTHING \
${TF_SERVING_BUILD_OPTIONS} \
tensorflow_serving/tools/pip_package:build_pip_package && \
bazel-bin/tensorflow_serving/tools/pip_package/build_pip_package \
/tmp/pip && \
pip --no-cache-dir install --upgrade \
/tmp/pip/tensorflow_serving_api-*.whl && \
rm -rf /tmp/pip
FROM binary_build as clean_build
# Clean up Bazel cache when done.
RUN bazel clean --expunge --color=yes && \
rm -rf /root/.cache
CMD ["/bin/bash"]
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查看并启动镜像
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docker images |grep hbbtec/tensorflow-serving-devel
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如果编译成功， 并生成镜像， 那么启动镜像
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# 拉取模型
mkdir -p ~/tf_serving_model/clip_text
cd ~/tf_serving_model/
wget https://brandai-upload.oss-cn-hangzhou.aliyuncs.com/clip_text.tar.gz
tar xvzf clip_text.tar.gz -C clip_text
# 启动
docker run --name serving-new \
-dt \
-p 8500:8500 \
-p 8501:8501 \
-v "$HOME/app/tf_serving_model/clip_text:/models/clip_text" \
-e MODEL_BASE_PATH=/models -e MODEL_NAME=clip_text \
-t $USER/tensorflow-serving-devel:latest
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进入容器
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docker exec -it serving-new /bin/bash
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启动服务
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tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=clip_text --model_base_path=/models/clip_text "$@"
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