# 如何编译PaddleServing (简体中文|[English](./COMPILE.md)) ## 编译环境设置 | 组件 | 版本要求 | | :--------------------------: | :----------------------------------------------------------: | | OS | CentOS 7 | | gcc | 4.8.5 and later | | gcc-c++ | 4.8.5 and later | | git | 3.82 and later | | cmake | 3.2.0 and later | | Python | 2.7.2 and later / 3.6 and later | | Go | 1.9.2 and later | | git | 2.17.1 and later | | glibc-static | 2.17 | | openssl-devel | 1.0.2k | | bzip2-devel | 1.0.6 and later | | python-devel / python3-devel | 2.7.5 and later / 3.6.8 and later | | sqlite-devel | 3.7.17 and later | | patchelf | 0.9 | | libXext | 1.3.3 | | libSM | 1.2.2 | | libXrender | 0.9.10 | | python-whl | numpy>=1.12, <=1.16.4
wheel>=0.34.0, <0.35.0
setuptools>=44.1.0
opencv-python==4.2.0.32
google>=2.0.3
protobuf>=3.12.2
grpcio-tools>=1.28.1
grpcio>=1.28.1
func-timeout>=4.3.5
pyyaml>=1.3.0
sentencepiece==0.1.92
flask>=1.1.2
ujson>=2.0.3 | 推荐使用Docker编译,我们已经为您准备好了Paddle Serving编译环境,详见[该文档](DOCKER_IMAGES_CN.md)。 本文档将以Python2为例介绍如何编译Paddle Serving。如果您想用Python3进行编译,只需要调整cmake的Python相关选项即可: - 将`DPYTHON_INCLUDE_DIR`设置为`$PYTHONROOT/include/python3.6m/` - 将`DPYTHON_LIBRARIES`设置为`$PYTHONROOT/lib64/libpython3.6.so` - 将`DPYTHON_EXECUTABLE`设置为`$PYTHONROOT/bin/python3.6` ## 获取代码 ``` python git clone https://github.com/PaddlePaddle/Serving cd Serving && git submodule update --init --recursive ``` ## PYTHONROOT设置 ```shell # 例如python的路径为/usr/bin/python,可以设置PYTHONROOT export PYTHONROOT=/usr/ ``` 我们提供默认Centos7的Python路径为`/usr/bin/python`,如果您要使用我们的Centos6镜像,需要将其设置为`export PYTHONROOT=/usr/local/python2.7/`。 ## 安装Python依赖 ```shell pip install -r python/requirements.txt ``` 如果使用 Python3,请以 `pip3` 替换 `pip`。 ## 编译Server部分 ### 集成CPU版本Paddle Inference Library ``` shell mkdir server-build-cpu && cd server-build-cpu cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DSERVER=ON .. make -j10 ``` 可以执行`make install`把目标产出放在`./output`目录下,cmake阶段需添加`-DCMAKE_INSTALL_PREFIX=./output`选项来指定存放路径。 ### 集成GPU版本Paddle Inference Library ``` shell mkdir server-build-gpu && cd server-build-gpu cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DSERVER=ON -DWITH_GPU=ON .. make -j10 ``` 执行`make install`可以把目标产出放在`./output`目录下。 **注意:** 编译成功后,需要设置`SERVING_BIN`路径,详见后面的[注意事项](https://github.com/PaddlePaddle/Serving/blob/develop/doc/COMPILE_CN.md#注意事项)。 ## 编译Client部分 ``` shell mkdir client-build && cd client-build cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DCLIENT=ON .. make -j10 ``` 执行`make install`可以把目标产出放在`./output`目录下。 ## 编译App部分 ```bash mkdir app-build && cd app-build cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ -DPYTHON_LIBRARIES=$PYTHONROOT/lib/libpython2.7.so -DPYTHON_EXECUTABLE=$PYTHONROOT/bin/python -DCMAKE_INSTALL_PREFIX=./output -DAPP=ON .. make ``` ## 安装wheel包 无论是Client端,Server端还是App部分,编译完成后,安装`python/dist/`下的whl包即可。 ## 注意事项 运行python端Server时,会检查`SERVING_BIN`环境变量,如果想使用自己编译的二进制文件,请将设置该环境变量为对应二进制文件的路径,通常是`export SERVING_BIN=${BUILD_DIR}/core/general-server/serving`。 ## CMake选项说明 | 编译选项 | 说明 | 默认 | | :--------------: | :----------------------------------------: | :--: | | WITH_AVX | Compile Paddle Serving with AVX intrinsics | OFF | | WITH_MKL | Compile Paddle Serving with MKL support | OFF | | WITH_GPU | Compile Paddle Serving with NVIDIA GPU | OFF | | CUDNN_ROOT | Define CuDNN library and header path | | | CLIENT | Compile Paddle Serving Client | OFF | | SERVER | Compile Paddle Serving Server | OFF | | APP | Compile Paddle Serving App package | OFF | | WITH_ELASTIC_CTR | Compile ELASITC-CTR solution | OFF | | PACK | Compile for whl | OFF | ### WITH_GPU选项 Paddle Serving通过PaddlePaddle预测库支持在GPU上做预测。WITH_GPU选项用于检测系统上CUDA/CUDNN等基础库,如检测到合适版本,在编译PaddlePaddle时就会编译出GPU版本的OP Kernel。 在裸机上编译Paddle Serving GPU版本,需要安装这些基础库: - CUDA - CuDNN - NCCL2 这里要注意的是: 1. 编译Serving所在的系统上所安装的CUDA/CUDNN等基础库版本,需要兼容实际的GPU设备。例如,Tesla V100卡至少要CUDA 9.0。如果编译时所用CUDA等基础库版本过低,由于生成的GPU代码和实际硬件设备不兼容,会导致Serving进程无法启动,或出现coredump等严重问题。 2. 运行Paddle Serving的系统上安装与实际GPU设备兼容的CUDA driver,并安装与编译期所用的CUDA/CuDNN等版本兼容的基础库。如运行Paddle Serving的系统上安装的CUDA/CuDNN的版本低于编译时所用版本,可能会导致奇怪的cuda函数调用失败等问题。 以下是PaddlePaddle发布版本所使用的基础库版本匹配关系,供参考: | | CUDA | CuDNN | NCCL2 | | :----: | :-----: | :----------------------: | :----: | | CUDA 8 | 8.0.61 | CuDNN 7.1.2 for CUDA 8.0 | 2.1.4 | | CUDA 9 | 9.0.176 | CuDNN 7.3.1 for CUDA 9.0 | 2.2.12 | ### 如何让Paddle Serving编译系统探测到CuDNN库 从NVIDIA developer官网下载对应版本CuDNN并在本地解压后,在cmake编译命令中增加`-DCUDNN_ROOT`参数,指定CuDNN库所在路径。 ### 如何让Paddle Serving编译系统探测到nccl库 从NVIDIA developer官网下载对应版本nccl2库并解压后,增加如下环境变量 (以nccl2.1.4为例): ```shell export C_INCLUDE_PATH=/path/to/nccl2/cuda8/nccl_2.1.4-1+cuda8.0_x86_64/include:$C_INCLUDE_PATH export CPLUS_INCLUDE_PATH=/path/to/nccl2/cuda8/nccl_2.1.4-1+cuda8.0_x86_64/include:$CPLUS_INCLUDE_PATH export LD_LIBRARY_PATH=/path/to/nccl2/cuda8/nccl_2.1.4-1+cuda8.0_x86_64/lib/:$LD_LIBRARY_PATH ```