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提交 15abbeaa 编写于 作者: T TeslaZhao 提交者： bjjwwang
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Merge pull request #1502 from bjjwwang/compile_doc 

Compile doc， install doc, and suffix 1027->102
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doc/Compile_CN.md
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...@@ -2,7 +2,22 @@ ...@@ -2,7 +2,22 @@
(简体中文|[English](./Compile_EN.md)) (简体中文|[English](./Compile_EN.md))
## 编译环境设置 ## 总体概述
编译Paddle Serving一共分以下几步
- 编译环境准备：根据模型和运行环境的需要，选择最合适的镜像
- 下载代码库：下载Serving代码库，按需要执行初始化操作
- 环境变量准备：根据运行环境的需要，确定Python各个环境变量，如GPU环境还需要确定Cuda，Cudnn，TensorRT等环境变量。
- 正式编译： 编译`paddle-serving-server`, `paddle-serving-client`, `paddle-serving-app`相关whl包
- 安装相关whl包：安装编译出的三个whl包，并设置SERVING_BIN环境变量
此外，针对某些C++二次开发场景，我们也提供了OPENCV的联编方案。
## 编译环境准备
| 组件 | 版本要求 | | 组件 | 版本要求 |
| :--------------------------: | :-------------------------------: | | :--------------------------: | :-------------------------------: |
...@@ -11,7 +26,7 @@ ...@@ -11,7 +26,7 @@
| gcc-c++ | 5.4.0(Cuda 10.1) and 8.2.0 | | gcc-c++ | 5.4.0(Cuda 10.1) and 8.2.0 |
| cmake | 3.2.0 and later | | cmake | 3.2.0 and later |
| Python | 3.6.0 and later | | Python | 3.6.0 and later |
| Go | 1.9.2 and later | | Go | 1.17.2 and later |
| git | 2.17.1 and later | | git | 2.17.1 and later |
| glibc-static | 2.17 | | glibc-static | 2.17 |
| openssl-devel | 1.0.2k | | openssl-devel | 1.0.2k |
...@@ -25,107 +40,149 @@ ...@@ -25,107 +40,149 @@
推荐使用Docker编译，我们已经为您准备好了Paddle Serving编译环境并配置好了上述编译依赖，详见[该文档](Docker_Images_CN.md)。 推荐使用Docker编译，我们已经为您准备好了Paddle Serving编译环境并配置好了上述编译依赖，详见[该文档](Docker_Images_CN.md)。
## 获取代码 我们提供了五个环境的开发镜像，分别是CPU， Cuda10.1+Cudnn7， Cuda10.2+Cudnn7，Cuda10.2+Cudnn8， Cuda11.2+Cudnn8。我们提供了Serving开发镜像涵盖以上环境。与此同时，我们也支持Paddle开发镜像。
``` python 其中Serving镜像名是 **paddlepaddle/serving:${Serving开发镜像Tag}**(如果网络不佳可以访问**registry.baidubce.com/paddlepaddle/serving:${Serving开发镜像Tag}**)， Paddle开发镜像名是 **paddlepaddle/paddle:${Paddle开发镜像Tag}**。为了防止用户对两套镜像出现混淆，我们分别解释一下两套镜像的由来。
git clone https://github.com/PaddlePaddle/Serving
cd Serving && git submodule update --init --recursive Serving开发镜像是Serving套件为了支持各个预测环境提供的用于编译、调试预测服务的镜像，Paddle开发镜像是Paddle在官网发布的用于编译、开发、训练模型使用镜像。为了让Paddle开发者能够在同一个容器内直接使用Serving。对于上个版本就已经使用Serving用户的开发者来说，Serving开发镜像应该不会感到陌生。但对于熟悉Paddle训练框架生态的开发者，目前应该更熟悉已有的Paddle开发镜像。为了适应所有用户的不同习惯，我们对这两套镜像都做了充分的支持。
| 环境 | Serving开发镜像Tag | 操作系统 | Paddle开发镜像Tag | 操作系统 |
| :--------------------------: | :-------------------------------: | :-------------: | :-------------------: | :----------------: |
| CPU | 0.7.0-devel | Ubuntu 16.04 | 2.2.0 | Ubuntu 18.04. |
| Cuda10.1+Cudnn7 | 0.7.0-cuda10.1-cudnn7-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda10.2+Cudnn7 | 0.7.0-cuda10.2-cudnn7-devel | Ubuntu 16.04 | 2.2.0-cuda10.2-cudnn7 | Ubuntu 16.04 |
| Cuda10.2+Cudnn8 | 0.7.0-cuda10.2-cudnn8-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda11.2+Cudnn8 | 0.7.0-cuda11.2-cudnn8-devel | Ubuntu 16.04 | 2.2.0-cuda11.2-cudnn8 | Ubuntu 18.04 |
我们首先要针对自己所需的环境拉取相关镜像。上表**环境**一列下，除了CPU，其余（Cuda**+Cudnn**）都属于GPU环境。
您可以使用Serving开发镜像。
``` ```
docker pull paddlepaddle/serving:${Serving开发镜像Tag}
## PYTHONROOT设置 # 如果是GPU镜像
nvidia-docker run --rm -it paddlepaddle/serving:${Serving开发镜像Tag} bash
```shell # 如果是CPU镜像
# 例如python的路径为/usr/bin/python，可以设置PYTHONROOT docker run --rm -it paddlepaddle/serving:${Serving开发镜像Tag} bash
export PYTHONROOT=/usr
``` ```
如果您使用的是Docker开发镜像，请按照如下，确定好需要编译的Python版本，设置对应的环境变量 也可以使用Paddle开发镜像。
``` ```
#Python3.6 docker pull paddlepaddle/paddle:${Paddle开发镜像Tag}
export PYTHONROOT=/usr/local/
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.6m
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.6m.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.6
#Python3.7
export PYTHONROOT=/usr/local/
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.7m
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.7m.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.7
#Python3.8
export PYTHONROOT=/usr/local/
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.8
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.8.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.8
# 如果是GPU镜像，需要使用nvidia-docker
nvidia-docker run --rm -it paddlepaddle/paddle:${Paddle开发镜像Tag} bash
# 如果是CPU镜像
docker run --rm -it paddlepaddle/paddle:${Paddle开发镜像Tag} bash
``` ```
## 安装Python依赖
```shell ## 下载代码库
pip install -r python/requirements.txt -i https://mirror.baidu.com/pypi/simple **注明： 如果您正在使用Paddle开发镜像，需要在下载代码库后手动运行`bash env_install.sh`(如代码框的第三行所示）**
``` ```
git clone https://github.com/PaddlePaddle/Serving
cd Serving && git submodule update --init --recursive
如果使用其他Python版本，请使用对应版本的`pip`。 # Paddle开发镜像需要运行如下命令，Serving开发镜像不需要运行
bash tools/paddle_env_install.sh
```
## GOPATH 设置 ## 环境变量准备
默认 GOPATH 设置为 `$HOME/go`，您也可以设置为其他值。** 如果是Serving提供的Docker环境，可以不需要设置。** **设置PYTHON环境变量**
```shell
export GOPATH=$HOME/go 如果您使用的是Serving开发镜像，请按照如下，确定好需要编译的Python版本，设置对应的环境变量，一共需要设置三个环境变量，分别是`PYTHON_INCLUDE_DIR`, `PYTHON_LIBRARIES`, `PYTHON_EXECUTABLE`。以下我们以python 3.7为例，介绍如何设置这三个环境变量。
export PATH=$PATH:$GOPATH/bin
1) 设置`PYTHON_INCLUDE_DIR`
搜索Python.h 所在的目录
``` ```
find / -name Python.h
```
通常会有类似于`**/include/python3.7/Python.h`出现，我们只需要取它的文件夹目录就好，比如找到`/usr/include/python3.7/Python.h`，那么我们只需要`export PYTHON_INCLUDE_DIR=/usr/include/python3.7/`就好。
如果没有找到。说明 1）没有安装开发版本的Python，需重新安装 2）权限不足无法查看相关系统目录。
## 获取 Go packages 2) 设置`PYTHON_LIBRARIES`
```shell 搜索 libpython3.7.so
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go get -u github.com/golang/protobuf/protoc-gen-go@v1.4.3
go get -u google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
``` ```
find / -name libpython3.7.so
```
通常会有类似于`**/lib/libpython3.7.so`或者`**/lib/x86_64-linux-gnu/libpython3.7.so`出现，我们只需要取它的文件夹目录就好，比如找到`/usr/local/lib/libpython3.7.so`，那么我们只需要`export PYTHON_LIBRARIES=/usr/local/lib`就好。
如果没有找到，说明 1）静态编译Python，需要重新安装动态编译的Python 2）全县不足无法查看相关系统目录。
3) 设置`PYTHON_EXECUTABLE`
## 编译Server部分 直接查看python3.7路径
```
which python3.7
```
假如结果是`/usr/local/bin/python3.7`，那么直接设置`export PYTHON_EXECUTABLE=/usr/local/bin/python3.7`。
### 集成CPU版本Paddle Inference Library 设置好这三个环境变量至关重要，设置完成后，我们便可以执行下列操作（以下是Paddle Cuda 11.2的开发镜像的PYTHON环境，如果是其他镜像，请更改相应的`PYTHON_INCLUDE_DIR`, `PYTHON_LIBRARIES`, `PYTHON_EXECUTABLE`）。
``` shell
mkdir server-build-cpu && cd server-build-cpu
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR/ \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DSERVER=ON ..
make -j10
``` ```
# 以下三个环境变量是Paddle开发镜像Cuda11.2的环境，如其他镜像可能需要修改
export PYTHON_INCLUDE_DIR=/usr/include/python3.7m/
export PYTHON_LIBRARIES=/usr/lib/x86_64-linux-gnu/libpython3.7m.so
export PYTHON_EXECUTABLE=/usr/bin/python3.7
可以执行`make install`把目标产出放在`./output`目录下，cmake阶段需添加`-DCMAKE_INSTALL_PREFIX=./output`选项来指定存放路径。 export GOPATH=$HOME/go
export PATH=$PATH:$GOPATH/bin
### 集成GPU版本Paddle Inference Library python -m install -r python/requirements.txt
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
go install google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
```
相比CPU环境，GPU环境需要参考以下表格, 如果您是GPU用户需要额外设置`CUDA_PATH`, `CUDNN_LIBRARY`, `CUDA_CUDART_LIBRARY`和`TENSORRT_LIBRARY_PATH`。
**需要说明的是，以下表格对非Docker编译环境作为参考，Docker编译环境已经配置好相关参数，无需在cmake过程指定。** ```
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY="/usr/local/cuda/lib64/"
export TENSORRT_LIBRARY_PATH="/usr/"
```
环境变量的含义如下表所示。
| cmake环境变量 | 含义 | GPU环境注意事项 | Docker环境是否需要 | | cmake环境变量 | 含义 | GPU环境注意事项 | Docker环境是否需要 |
|-----------------------|-------------------------------------|-------------------------------|--------------------| |-----------------------|-------------------------------------|-------------------------------|--------------------|
| CUDA_TOOLKIT_ROOT_DIR | cuda安装路径，通常为/usr/local/cuda | 全部环境都需要 | 否(/usr/local/cuda) | | CUDA_TOOLKIT_ROOT_DIR | cuda安装路径，通常为/usr/local/cuda | 全部GPU环境都需要 | 否(/usr/local/cuda) |
| CUDNN_LIBRARY | libcudnn.so.*所在目录，通常为/usr/local/cuda/lib64/ | 全部环境都需要 | 否(/usr/local/cuda/lib64/) | | CUDNN_LIBRARY | libcudnn.so.*所在目录，通常为/usr/local/cuda/lib64/ | 全部GPU环境都需要 | 否(/usr/local/cuda/lib64/) |
| CUDA_CUDART_LIBRARY | libcudart.so.*所在目录，通常为/usr/local/cuda/lib64/ | 全部环境都需要 | 否(/usr/local/cuda/lib64/) | | CUDA_CUDART_LIBRARY | libcudart.so.*所在目录，通常为/usr/local/cuda/lib64/ | 全部GPU环境都需要 | 否(/usr/local/cuda/lib64/) |
| TENSORRT_ROOT | libnvinfer.so.*所在目录的上一级目录，取决于TensorRT安装目录 | Cuda 9.0/10.0不需要，其他需要 | 否(/usr) | | TENSORRT_ROOT | libnvinfer.so.*所在目录的上一级目录，取决于TensorRT安装目录 | 全部GPU环境都需要 | 否(/usr) |
非Docker环境下，用户可以参考如下执行方式，具体的路径以当时环境为准，代码仅作为参考。TENSORRT_LIBRARY_PATH和TensorRT版本有关，要根据实际情况设置。例如在cuda10.1环境下TensorRT版本是6.0(/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/)，在cuda10.2和cuda11.0环境下TensorRT版本是7.1（/usr/local/TensorRT-7.1.3.4/targets/x86_64-linux-gnu/）。
``` shell
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY="/usr/local/cuda/lib64/"
export TENSORRT_LIBRARY_PATH="/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/"
mkdir server-build-gpu && cd server-build-gpu ## 正式编译
我们一共需要编译三个目标，分别是`paddle-serving-server`, `paddle-serving-client`, `paddle-serving-app`，其中`paddle-serving-server`需要区分CPU或者GPU版本。如果是CPU版本请运行，
### 编译paddle-serving-server
```
mkdir build_server
cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DSERVER=ON \
-DWITH_GPU=OFF ..
make -j20
cd ..
```
如果是GPU版本，请运行，
```
mkdir build_server
cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
...@@ -135,83 +192,76 @@ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ ...@@ -135,83 +192,76 @@ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \ -DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
-DSERVER=ON \ -DSERVER=ON \
-DWITH_GPU=ON .. -DWITH_GPU=ON ..
make -j10 make -j20
``` cd ..
```
执行`make install`可以把目标产出放在`./output`目录下。
### 开启WITH_OPENCV选项编译C++ Server
**注意：** 只有当您需要对Paddle Serving C++部分进行二次开发，且新增的代码依赖于OpenCV库时，您才需要这样做。
编译Serving C++ Server部分，开启WITH_OPENCV选项时，需要已安装的OpenCV库，若尚未安装，可参考本文档后面的说明编译安装OpenCV库。 ### 编译paddle-serving-client 和 paddle-serving-app
以开启WITH_OPENCV选项，编译CPU版本Paddle Inference Library为例，在上述编译命令基础上，加入`DOPENCV_DIR=${OPENCV_DIR}` 和 `DWITH_OPENCV=ON`选项。 接下来，我们继续编译client和app就可以了，这两个包的编译命令在所有平台通用，不区分CPU和GPU的版本。
``` shell
OPENCV_DIR=your_opencv_dir #`your_opencv_dir`为opencv库的安装路径。
mkdir server-build-cpu && cd server-build-cpu
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR/ \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_OPENCV=ON \
-DSERVER=ON ..
make -j10
``` ```
# 编译paddle-serving-client
**注意：** 编译成功后，需要设置`SERVING_BIN`路径，详见后面的[注意事项](#注意事项)。 mkdir build_client
cd build_client
## 编译Client部分
``` shell
mkdir client-build && cd client-build
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DCLIENT=ON .. -DCLIENT=ON ..
make -j10 make -j10
``` cd ..
执行`make install`可以把目标产出放在`./output`目录下。
## 编译App部分
```bash # 编译paddle-serving-app
mkdir app-build && cd app-build mkdir build_app
cd build_app
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DAPP=ON .. -DAPP=ON ..
make make -j10
cd ..
``` ```
## 安装相关whl包
```
pip3.7 install -r build_server/python/dist/*.whl
pip3.7 install -r build_client/python/dist/*.whl
pip3.7 install -r build_app/python/dist/*.whl
export SERVING_BIN=${PWD}/build_server/core/general-server/serving
```
## 注意事项
## 安装wheel包 注意到上一小节的最后一行`export SERVING_BIN`，运行python端Server时，会检查`SERVING_BIN`环境变量，如果想使用自己编译的二进制文件，请将设置该环境变量为对应二进制文件的路径，通常是`export SERVING_BIN=${BUILD_DIR}/core/general-server/serving`。
其中BUILD_DIR为`build_server`的绝对路径。
无论是Client端，Server端还是App部分，编译完成后，安装编译过程临时目录（`server-build-cpu`、`server-build-gpu`、`client-build`、`app-build`）下的`python/dist/` 中的whl包即可。 可以cd build_server路径下，执行`export SERVING_BIN=${PWD}/core/general-server/serving`
例如：cd server-build-cpu/python/dist && pip install -U xxxxx.whl
## 开启WITH_OPENCV选项编译C++ Server
## 注意事项 **注意：** 只有当您需要对Paddle Serving C++部分进行二次开发，且新增的代码依赖于OpenCV库时，您才需要这样做。
运行python端Server时，会检查`SERVING_BIN`环境变量，如果想使用自己编译的二进制文件，请将设置该环境变量为对应二进制文件的路径，通常是`export SERVING_BIN=${BUILD_DIR}/core/general-server/serving`。 编译Serving C++ Server部分，开启WITH_OPENCV选项时，需要已安装的OpenCV库，若尚未安装，可参考本文档后面的说明编译安装OpenCV库。
其中BUILD_DIR为server-build-cpu或server-build-gpu的绝对路径。
可以cd server-build-cpu路径下，执行`export SERVING_BIN=${PWD}/core/general-server/serving`
以开启WITH_OPENCV选项，编译CPU版本Paddle Inference Library为例，在上述编译命令基础上，加入`DOPENCV_DIR=${OPENCV_DIR}` 和 `DWITH_OPENCV=ON`选项。
``` shell
OPENCV_DIR=your_opencv_dir #`your_opencv_dir`为opencv库的安装路径。
mkdir build_server && cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR/ \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_OPENCV=ON \
-DSERVER=ON ..
make -j10
```
**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)。
## 如何验证
请使用 `python/examples` 下的例子进行验证。
## CMake选项说明 ## 附：CMake选项说明
| 编译选项 | 说明 | 默认 | | 编译选项 | 说明 | 默认 |
| :--------------: | :----------------------------------------: | :--: | | :--------------: | :----------------------------------------: | :--: |
...@@ -252,11 +302,11 @@ Paddle Serving通过PaddlePaddle预测库支持在GPU上做预测。WITH_GPU选 ...@@ -252,11 +302,11 @@ Paddle Serving通过PaddlePaddle预测库支持在GPU上做预测。WITH_GPU选
| post102 | 10.2 | CuDNN 8.0.5 | 7.1.3 | | post102 | 10.2 | CuDNN 8.0.5 | 7.1.3 |
| post11 | 11.0 | CuDNN 8.0.4 | 7.1.3 | | post11 | 11.0 | CuDNN 8.0.4 | 7.1.3 |
### 如何让Paddle Serving编译系统探测到CuDNN库 ### 附：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_LIBRARY`参数，指定CuDNN库所在路径。 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_LIBRARY`参数，指定CuDNN库所在路径。
## 编译安装OpenCV库 ## 附：编译安装OpenCV库
**注意：** 只有当您需要在C++代码中引入OpenCV库时，您才需要这样做。 **注意：** 只有当您需要在C++代码中引入OpenCV库时，您才需要这样做。
* 首先需要从OpenCV官网上下载在Linux环境下源码编译的包，以OpenCV3.4.7为例，下载命令如下。 * 首先需要从OpenCV官网上下载在Linux环境下源码编译的包，以OpenCV3.4.7为例，下载命令如下。
......
doc/Compile_EN.md
浏览文件 @ 15abbeaa
...@@ -2,7 +2,19 @@ ...@@ -2,7 +2,19 @@
([简体中文](./Compile_CN.md)|English) ([简体中文](./Compile_CN.md)|English)
## Compilation environment requirements ## Overview
Compiling Paddle Serving is divided into the following steps
- Compilation Environment Preparation: According to the needs of the model and operating environment, select the most suitable image
- Download the Serving Code Repo: Download the Serving code library, and perform initialization operations as needed
- Environment Variable Preparation: According to the needs of the running environment, determine the various environment variables of Python. For example, the GPU environment also needs to determine the environment variables such as Cuda, Cudnn, TensorRT and so on.
- Compilation: Compile `paddle-serving-server`, `paddle-serving-client`, `paddle-serving-app` related whl packages
- Install Related Whl Packages: install the three compiled whl packages, and set the SERVING_BIN environment variable
In addition, for some C++ secondary development scenarios, we also provide OPENCV binding solutions.
## Compilation Environment Requirements
| module | version | | module | version |
| :--------------------------: | :-------------------------------: | | :--------------------------: | :-------------------------------: |
...@@ -11,7 +23,7 @@ ...@@ -11,7 +23,7 @@
| gcc-c++ | 5.4.0(Cuda 10.1) and 8.2.0 | | gcc-c++ | 5.4.0(Cuda 10.1) and 8.2.0 |
| cmake | 3.2.0 and later | | cmake | 3.2.0 and later |
| Python | 3.6.0 and later | | Python | 3.6.0 and later |
| Go | 1.9.2 and later | | Go | 1.17.2 and later |
| git | 2.17.1 and later | | git | 2.17.1 and later |
| glibc-static | 2.17 | | glibc-static | 2.17 |
| openssl-devel | 1.0.2k | | openssl-devel | 1.0.2k |
...@@ -23,251 +35,276 @@ ...@@ -23,251 +35,276 @@
| libSM | 1.2.2 | | libSM | 1.2.2 |
| libXrender | 0.9.10 | | libXrender | 0.9.10 |
It is recommended to use Docker for compilation. We have prepared the Paddle Serving compilation environment for you, see [this document](Docker_Images_EN.md). Docker compilation is recommended. We have prepared the Paddle Serving compilation environment for you and configured the above compilation dependencies. For details, please refer to [this document](DOCKER_IMAGES_CN.md).
## Get Code We provide five environment development images, namely CPU, Cuda10.1+Cudnn7, Cuda10.2+Cudnn7, Cuda10.2+Cudnn8, Cuda11.2+Cudnn8. We provide a Serving development image to cover the above environment. At the same time, we also support Paddle development mirroring.
``` python The Serving image name is **paddlepaddle/serving:${Serving development image Tag}** (If the network is not good, you can visit **registry.baidubce.com/paddlepaddle/serving:${Serving development image Tag}**), The name of the Paddle development image is **paddlepaddle/paddle:${Paddle Development Image Tag}**. In order to prevent users from confusing the two sets of mirroring, we explain the origin of the two sets of mirroring separately.
git clone https://github.com/PaddlePaddle/Serving
cd Serving && git submodule update --init --recursive Serving development mirror is the mirror used to compile and debug prediction services provided by Serving suite in order to support various prediction environments. Paddle development mirror is the mirror used for compilation, development, and training models released by Paddle on the official website. In order to allow Paddle developers to use Serving directly in the same container. For developers who have already used Serving users in the previous version, Serving development image should not be unfamiliar. But for developers who are familiar with the Paddle training framework ecology, they should be more familiar with the existing Paddle development mirrors. In order to adapt to the different habits of all users, we have fully supported both sets of mirrors.
| Environment | Serving Dev Image Tag | OS | Paddle Dev Image Tag | OS |
| :--------------------------: | :-------------------------------: | :-------------: | :-------------------: | :----------------: |
| CPU | 0.7.0-devel | Ubuntu 16.04 | 2.2.0 | Ubuntu 18.04. |
| Cuda10.1+Cudnn7 | 0.7.0-cuda10.1-cudnn7-devel | Ubuntu 16.04 | Nan | Nan |
| Cuda10.2+Cudnn7 | 0.7.0-cuda10.2-cudnn7-devel | Ubuntu 16.04 | 2.2.0-cuda10.2-cudnn7 | Ubuntu 16.04 |
| Cuda10.2+Cudnn8 | 0.7.0-cuda10.2-cudnn8-devel | Ubuntu 16.04 | Nan | Nan |
| Cuda11.2+Cudnn8 | 0.7.0-cuda11.2-cudnn8-devel | Ubuntu 16.04 | 2.2.0-cuda11.2-cudnn8 | Ubuntu 18.04 |
We first need to pull related images for the environment we need. Under the **Environment** column in the above table, except for the CPU, the rest (Cuda**+Cudnn**) belong to the GPU environment.
You can use Serving Dev Images.
``` ```
docker pull paddlepaddle/serving:${Serving Dev Image Tag}
## PYTHONROOT settings # For GPU Image
nvidia-docker run --rm -it paddlepaddle/serving:${Serving Dev Image Tag} bash
```shell # For CPU Image
# For example, the path of python is /usr/bin/python, you can set PYTHONROOT docker run --rm -it paddlepaddle/serving:${Serving Dev Image Tag} bash
export PYTHONROOT=/usr
``` ```
If you are using a Docker development image, please follow the following to determine the Python version to be compiled, and set the corresponding environment variables You can also use Paddle Dev Images.
## Download the Serving Code Repo
**Note: If you are using Paddle to develop the image, you need to manually run `bash env_install.sh` after downloading the code base (as shown in the third line of the code box)**
``` ```
#Python3.6 git clone https://github.com/PaddlePaddle/Serving
export PYTHONROOT=/usr/local/ cd Serving && git submodule update --init --recursive
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.6m
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.6m.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.6
#Python3.7
export PYTHONROOT=/usr/local/
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.7m
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.7m.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.7
#Python3.8
export PYTHONROOT=/usr/local/
export PYTHON_INCLUDE_DIR=$PYTHONROOT/include/python3.8
export PYTHON_LIBRARIES=$PYTHONROOT/lib/libpython3.8.so
export PYTHON_EXECUTABLE=$PYTHONROOT/bin/python3.8
# Paddle development image needs to run the following commands, Serving development image does not need to run
bash tools/paddle_env_install.sh
``` ```
## Install Python dependencies ## Environment Variables Preparation
```shell **Set PYTHON environment variable**
pip install -r python/requirements.txt -i https://mirror.baidu.com/pypi/simple
```
If you use other Python version, please use the right `pip` accordingly. If you are using a Serving development image, please follow the steps below to determine the Python version that needs to be compiled and set the corresponding environment variables. A total of three environment variables need to be set, namely `PYTHON_INCLUDE_DIR`, `PYTHON_LIBRARIES`, `PYTHON_EXECUTABLE`. Below we take python 3.7 as an example to introduce how to set these three environment variables.
## GOPATH Setting 1) Set `PYTHON_INCLUDE_DIR`
The default GOPATH is set to `$HOME/go`, you can also set it to other values. **If it is the Docker environment provided by Serving, you do not need to set up.**
```shell Search the directory where Python.h is located
export GOPATH=$HOME/go
export PATH=$PATH:$GOPATH/bin
``` ```
find / -name Python.h
```
Usually there will be something like `**/include/python3.7/Python.h`, we only need to take its folder directory, for example, find `/usr/include/python3.7/Python.h`, Then we only need `export PYTHON_INCLUDE_DIR=/usr/include/python3.7/`.
If not found. Explanation 1) The development version of Python is not installed and needs to be re-installed. 2) Insufficient permissions cannot view the relevant system directories.
## Get go packages 2) Set `PYTHON_LIBRARIES`
```shell Search for libpython3.7.so
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go get -u github.com/golang/protobuf/protoc-gen-go@v1.4.3
go get -u google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
``` ```
find / -name libpython3.7.so
```
Usually there will be something similar to `**/lib/libpython3.7.so` or `**/lib/x86_64-linux-gnu/libpython3.7.so`, we only need to take its folder directory, For example, find `/usr/local/lib/libpython3.7.so`, then we only need `export PYTHON_LIBRARIES=/usr/local/lib`.
If it is not found, it means 1) Statically compiling Python, you need to reinstall the dynamically compiled Python 2) The county is not enough to view the relevant system catalogs.
3) Set `PYTHON_EXECUTABLE`
## Compile Server View the python3.7 path directly
```
which python3.7
```
If the result is `/usr/local/bin/python3.7`, then directly set `export PYTHON_EXECUTABLE=/usr/local/bin/python3.7`.
### Integrated CPU version paddle inference library It is very important to set these three environment variables. After the settings are completed, we can perform the following operations (the following is the PYTHON environment of the development image of Paddle Cuda 11.2, if it is another image, please change the corresponding `PYTHON_INCLUDE_DIR`, `PYTHON_LIBRARIES` , `PYTHON_EXECUTABLE`).
``` shell
mkdir server-build-cpu && cd server-build-cpu
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DSERVER=ON ..
make -j10
``` ```
# The following three environment variables are the environment of Paddle development mirror Cuda11.2, such as other mirrors may need to be modified
export PYTHON_INCLUDE_DIR=/usr/include/python3.7m/
export PYTHON_LIBRARIES=/usr/lib/x86_64-linux-gnu/libpython3.7m.so
export PYTHON_EXECUTABLE=/usr/bin/python3.7
you can execute `make install` to put targets under directory `./output`, you need to add`-DCMAKE_INSTALL_PREFIX=./output`to specify output path to cmake command shown above. export GOPATH=$HOME/go
export PATH=$PATH:$GOPATH/bin
### Integrated GPU version paddle inference library python -m install -r python/requirements.txt
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
go install google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
```
Compared with CPU environment, GPU environment needs to refer to the following table, If you are a GPU user, you need to set additional `CUDA_PATH`, `CUDNN_LIBRARY`, `CUDA_CUDART_LIBRARY` and `TENSORRT_LIBRARY_PATH`.
**It should be noted that the following table is used as a reference for non-Docker compilation environment. The Docker compilation environment has been configured with relevant parameters and does not need to be specified in cmake process. ** ```
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY="/usr/local/cuda/lib64/"
export TENSORRT_LIBRARY_PATH="/usr/"
```
The meaning of environment variables is shown in the table below.
| cmake environment variable | meaning | GPU environment considerations | whether Docker environment is needed | | cmake environment variable | meaning | GPU environment considerations | whether Docker environment is needed |
|-----------------------|-------------------------------------|-------------------------------|--------------------| |-----------------------|-------------------------------------|-------------------------------|--------------------|
| CUDA_TOOLKIT_ROOT_DIR | cuda installation path, usually /usr/local/cuda | Required for all environments | No (/usr/local/cuda) | | CUDA_TOOLKIT_ROOT_DIR | cuda installation path, usually /usr/local/cuda | Required for all environments | No (/usr/local/cuda) |
| CUDNN_LIBRARY | The directory where libcudnn.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) | | CUDNN_LIBRARY | The directory where libcudnn.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) |
| CUDA_CUDART_LIBRARY | The directory where libcudart.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) | | CUDA_CUDART_LIBRARY | The directory where libcudart.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) |
| TENSORRT_ROOT | The upper level directory of the directory where libnvinfer.so.* is located, depends on the TensorRT installation directory | Cuda 9.0/10.0 does not need, other needs | No (/usr) | | TENSORRT_ROOT | The upper level directory of the directory where libnvinfer.so.* is located, depends on the TensorRT installation directory | Required for all environments | No (/usr) |
If not in Docker environment, users can refer to the following execution methods. The specific path is subject to the current environment, and the code is only for reference.TENSORRT_LIBRARY_PATH is related to the TensorRT version and should be set according to the actual situation。For example, in the cuda10.1 environment, the TensorRT version is 6.0 (/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/)，In the cuda10.2 and cuda11.0 environment, the TensorRT version is 7.1 (/usr/local/TensorRT-7.1.3.4/targets/x86_64-linux-gnu/).
``` shell
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY="/usr/local/cuda/lib64/"
export TENSORRT_LIBRARY_PATH="/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/" ## Compilation
mkdir server-build-gpu && cd server-build-gpu We need to compile three targets in total, namely `paddle-serving-server`, `paddle-serving-client`, and `paddle-serving-app`, among which `paddle-serving-server` needs to distinguish between CPU or GPU version. If it is a CPU version, please run,
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
-DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
-DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
-DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
-DSERVER=ON \
-DWITH_GPU=ON ..
make -j10
```
Execute `make install` to put the target output in the `./output` directory.
### Compile C++ Server under the condition of WITH_OPENCV=ON ### Compile paddle-serving-server
**Note:** Only when you need to redevelop the paddle serving C + + part, and the new code depends on the OpenCV library, you need to do so.
First of all , OpenCV library should be installed, if not, please refer to the `Compile and install OpenCV` section later in this article. ```
mkdir build_server
cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DSERVER=ON \
-DWITH_GPU=OFF ..
make -j20
cd ..
```
In the compile command, add `DOPENCV_DIR=${OPENCV_DIR}` and `DWITH_OPENCV=ON`，for example： If it is the GPU version, please run,
``` shell ```
OPENCV_DIR=your_opencv_dir #`your_opencv_dir` is the installation path of OpenCV library。 mkdir build_server
mkdir server-build-cpu && cd server-build-cpu cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR/ \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DOPENCV_DIR=${OPENCV_DIR} \ -DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
-DWITH_OPENCV=ON \ -DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
-DSERVER=ON .. -DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
make -j10 -DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
-DSERVER=ON \
-DWITH_GPU=ON ..
make -j20
cd ..
``` ```
**Note:** After the compilation is successful, you need to set the `SERVING_BIN` path, see the following [Notes](Compile_EN.md#Notes).
## Compile Client ### Compile paddle-serving-client and paddle-serving-app
``` shell Next, we can continue to compile the client and app. The compilation commands for these two packages are common on all platforms, and do not distinguish between CPU and GPU versions.
mkdir client-build && cd client-build ```
# Compile paddle-serving-client
mkdir build_client
cd build_client
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DCLIENT=ON .. -DCLIENT=ON ..
make -j10 make -j10
``` cd ..
execute `make install` to put targets under directory `./output`
## Compile the App
```bash # Compile paddle-serving-app
mkdir app-build && cd app-build mkdir build_app
cd build_app
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \ cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \ -DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \ -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DAPP=ON .. -DAPP=ON ..
make make -j10
cd ..
``` ```
## Install Related Whl Packages
```
pip3.7 install -r build_server/python/dist/*.whl
pip3.7 install -r build_client/python/dist/*.whl
pip3.7 install -r build_app/python/dist/*.whl
export SERVING_BIN=${PWD}/build_server/core/general-server/serving
```
## Precautions
## Install wheel package Note the last line `export SERVING_BIN` in the previous section. When running the python server, the `SERVING_BIN` environment variable will be checked. If you want to use the binary file compiled by yourself, please set the environment variable to the path of the corresponding binary file. It is `export SERVING_BIN=${BUILD_DIR}/core/general-server/serving`.
Where BUILD_DIR is the absolute path of `build_server`.
Regardless of the client, server or App part, after compiling, install the whl package in `python/dist/` in the temporary directory(`server-build-cpu`, `server-build-gpu`, `client-build`,`app-build`) of the compilation process. You can cd build_server path and execute `export SERVING_BIN=${PWD}/core/general-server/serving`
for example：cd server-build-cpu/python/dist && pip install -U xxxxx.whl
## Enable WITH_OPENCV option to compile C++ Server
## Note **Note:** You only need to do this when you need to do secondary development on the Paddle Serving C++ part and the newly added code depends on the OpenCV library.
When running the python server, it will check the `SERVING_BIN` environment variable. If you want to use your own compiled binary file, set the environment variable to the path of the corresponding binary file, usually`export SERVING_BIN=${BUILD_DIR}/core/general-server/serving`. To compile the Serving C++ Server part, when the WITH_OPENCV option is turned on, the installed OpenCV library is required. If it has not been installed, you can refer to the instructions at the back of this document to compile and install the OpenCV library.
BUILD_DIR is the absolute path of server build CPU or server build GPU。
for example: cd server-build-cpu && export SERVING_BIN=${PWD}/core/general-server/serving
Take the WITH_OPENCV option and compile the CPU version Paddle Inference Library as an example. On the basis of the above compilation command, add the `DOPENCV_DIR=${OPENCV_DIR}` and `DWITH_OPENCV=ON` options.
``` shell
OPENCV_DIR=your_opencv_dir #`your_opencv_dir` is the installation path of the opencv library.
mkdir build_server && cd build_server
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR/ \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_OPENCV=ON \
-DSERVER=ON ..
make -j10
```
**Note:** After the compilation is successful, you need to set the `SERVING_BIN` path, see the following [Notes](https://github.com/PaddlePaddle/Serving/blob/develop/doc/COMPILE_CN.md#Notes) ).
## Verify
Please use the example under `python/examples` to verify.
## CMake Option Description ## Attached: CMake option description
| Compile Options | Description | Default | | Compilation Options | Description | Default |
| :--------------: | :----------------------------------------: | :--: | | :--------------: | :------------------------------- ---------: | :--: |
| WITH_AVX | Compile Paddle Serving with AVX intrinsics | OFF | | WITH_AVX | Compile Paddle Serving with AVX intrinsics | OFF |
| WITH_MKL | Compile Paddle Serving with MKL support | OFF | | WITH_MKL | Compile Paddle Serving with MKL support | OFF |
| WITH_GPU | Compile Paddle Serving with NVIDIA GPU | OFF | | WITH_GPU | Compile Paddle Serving with NVIDIA GPU | OFF |
| WITH_OPENCV | Compile Paddle Serving with OPENCV | OFF | | WITH_TRT | Compile Paddle Serving with TensorRT | OFF |
| CUDNN_LIBRARY | Define CuDNN library and header path | | | WITH_OPENCV | Compile Paddle Serving with OPENCV | OFF |
| CUDA_TOOLKIT_ROOT_DIR | Define CUDA PATH | | | CUDNN_LIBRARY | Define CuDNN library and header path | |
| TENSORRT_ROOT | Define TensorRT PATH | | | CUDA_TOOLKIT_ROOT_DIR | Define CUDA PATH | |
| CLIENT | Compile Paddle Serving Client | OFF | | TENSORRT_ROOT | Define TensorRT PATH | |
| SERVER | Compile Paddle Serving Server | OFF | | CLIENT | Compile Paddle Serving Client | OFF |
| APP | Compile Paddle Serving App package | OFF | | SERVER | Compile Paddle Serving Server | OFF |
| PACK | Compile for whl | OFF | | APP | Compile Paddle Serving App package | OFF |
| PACK | Compile for whl | OFF |
### WITH_GPU Option ### WITH_GPU option
Paddle Serving supports prediction on the GPU through the PaddlePaddle inference library. The WITH_GPU option is used to detect basic libraries such as CUDA/CUDNN on the system. If an appropriate version is detected, the GPU Kernel will be compiled when PaddlePaddle is compiled. Paddle Serving supports prediction on the GPU through the PaddlePaddle prediction library. The WITH_GPU option is used to detect basic libraries such as CUDA/CUDNN on the system. If a suitable version is detected, the GPU version of the OP Kernel will be compiled when the PaddlePaddle is compiled.
To compile the Paddle Serving GPU version on bare metal, you need to install these basic libraries: To compile the Paddle Serving GPU version on bare metal, you need to install these basic libraries:
- CUDA -CUDA
- CuDNN -CuDNN
To compile the TensorRT version, you need to install the TensorRT library. To compile the TensorRT version, you need to install the TensorRT library.
Note here: The things to note here are:
1. The basic library versions such as CUDA/CUDNN installed on the system where Serving is compiled, needs to be compatible with the actual GPU device. For example, the Tesla V100 card requires at least CUDA 9.0. If the version of the basic library such as CUDA used during compilation is too low, the generated GPU code is not compatible with the actual hardware device, which will cause the Serving process to fail to start or serious problems such as coredump.
2. Install the CUDA driver compatible with the actual GPU device on the system running Paddle Serving, and install the basic library compatible with the CUDA/CuDNN version used during compilation. If the version of CUDA/CuDNN installed on the system running Paddle Serving is lower than the version used at compile time, it may cause some cuda function call failures and other problems.
1. Compile the basic library versions such as CUDA/CUDNN installed on the system where Serving is located, and need to be compatible with the actual GPU device. For example, Tesla V100 card requires at least CUDA 9.0. If the version of basic libraries such as CUDA used during compilation is too low, the Serving process cannot be started due to the incompatibility between the generated GPU code and the actual hardware device, or serious problems such as coredump may occur.
2. Install the CUDA driver compatible with the actual GPU device on the system running Paddle Serving, and install the basic library compatible with the CUDA/CuDNN version used during compilation. If the version of CUDA/CuDNN installed on the system running Paddle Serving is lower than the version used during compilation, it may cause strange cuda function call failures and other problems.
The following is the base library version matching relationship used by the PaddlePaddle release version for reference: The following is the matching relationship between PaddleServing mirrored Cuda, Cudnn, and TensorRT for reference:
| | CUDA | CuDNN | TensorRT | | | CUDA | CuDNN | TensorRT |
| :----: | :-----: | :----------: | :----: | | :----: | :-----: | :----------: | :----: |
| post101 | 10.1 | CuDNN 7.6.5 | 6.0.1 | | post101 | 10.1 | CuDNN 7.6.5 | 6.0.1 |
| post102 | 10.2 | CuDNN 8.0.5 | 7.1.3 | | post102 | 10.2 | CuDNN 8.0.5 | 7.1.3 |
| post11 | 11.0 | CuDNN 8.0.4 | 7.1.3 | | post11 | 11.0 | CuDNN 8.0.4 | 7.1.3 |
### How to make the compiler detect the CuDNN library ### Attachment: How to make the Paddle Serving compilation system detect the CuDNN library
Download the corresponding CUDNN version from NVIDIA developer official website and decompressing it, add `-DCUDNN_ROOT` to cmake command, to specify the path of CUDNN. After downloading the corresponding version of CuDNN from the official website of NVIDIA developer and decompressing it locally, add the `-DCUDNN_LIBRARY` parameter to the cmake compilation command and specify the path of the CuDNN library.
## Compile and install OpenCV ## Attachment: Compile and install OpenCV library
**Note:** You need to do this only if you need to import the opencv library into your C + + code. **Note:** You only need to do this when you need to include the OpenCV library in your C++ code.
* First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV3.4.7 as an example, the download command is as follows. * First, you need to download the package compiled from the source code in the Linux environment from the OpenCV official website. Take OpenCV 3.4.7 as an example. The download command is as follows.
``` ```
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz tar -xf 3.4.7.tar.gz
``` ```
Finally, you can see the folder of `opencv-3.4.7/` in the current directory. Finally, you can see the folder `opencv-3.4.7/` in the current directory.
* Compile OpenCV, the OpenCV source path (`root_path`) and installation path (`install_path`) should be set by yourself. Enter the OpenCV source code path and compile it in the following way.
* Compile OpenCV, set the OpenCV source path (`root_path`) and installation path (`install_path`). Enter the OpenCV source code path and compile in the following way.
```shell ```shell
root_path=your_opencv_root_path root_path=your_opencv_root_path
...@@ -299,11 +336,10 @@ make -j ...@@ -299,11 +336,10 @@ make -j
make install make install
``` ```
Among them, `root_path` is the downloaded OpenCV source code path, and `install_path` is the installation path of OpenCV. After `make install` is completed, the OpenCV header file and library file will be generated in this folder for later source code compilation.
Among them, `root_path` is the downloaded OpenCV source path, `install_path` is the installation path of OpenCV, after the completion of `make install`, OpenCV header files and library files will be generated in this folder, which are used to compile the code that references the OpenCV library .
The final file structure under the OpenCV installation path is as follows. The final file structure under the installation path is as follows.
``` ```
opencv3/ opencv3/
......
doc/Install_CN.md
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...@@ -4,24 +4,54 @@ ...@@ -4,24 +4,54 @@
**强烈建议**您在**Docker内构建**Paddle Serving，请查看[如何在Docker中运行PaddleServing](Run_In_Docker_CN.md)。更多镜像请查看[Docker镜像列表](Docker_Images_CN.md)。 **强烈建议**您在**Docker内构建**Paddle Serving，请查看[如何在Docker中运行PaddleServing](Run_In_Docker_CN.md)。更多镜像请查看[Docker镜像列表](Docker_Images_CN.md)。
**提示**：目前paddlepaddle 2.1版本的默认GPU环境是Cuda 10.2，因此GPU Docker的示例代码以Cuda 10.2为准。镜像和pip安装包也提供了其余GPU环境，用户如果使用其他环境，需要仔细甄别并选择合适的版本。 **提示-1**：本项目仅支持<mark>**Python3.6/3.7/3.8**</mark>，接下来所有的与Python/Pip相关的操作都需要选择正确的Python版本。
**提示**：本项目仅支持Python3.6/3.7/3.8，接下来所有的与Python/Pip相关的操作都需要选择正确的Python版本。 **提示-2**：以下示例中GPU环境均为cuda10.2-cudnn7，如果您使用Python Pipeline来部署，并需要Nvidia TensorRT来优化预测性能，请参考[支持的镜像环境和说明](#4支持的镜像环境和说明)来选择其他版本。
## 1.启动开发镜像
<mark>**同时支持使用Serving镜像和Paddle镜像，1.1和1.2章节中的操作2选1即可。**</mark>
### 1.1 Serving开发镜像（CPU/GPU 2选1）
**CPU：**
``` ```
# 启动 CPU Docker # 启动 CPU Docker
docker pull registry.baidubce.com/paddlepaddle/serving:0.6.2-devel docker pull paddlepaddle/serving:0.7.0-devel
docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.6.2-devel bash docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-devel bash
docker exec -it test bash docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving git clone https://github.com/PaddlePaddle/Serving
``` ```
**GPU：**
``` ```
# 启动 GPU Docker # 启动 GPU Docker
nvidia-docker pull registry.baidubce.com/paddlepaddle/serving:0.6.2-cuda10.2-cudnn8-devel docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
nvidia-docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.6.2-cuda10.2-cudnn8-devel bash nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel bash
nvidia-docker exec -it test bash nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving git clone https://github.com/PaddlePaddle/Serving
``` ```
### 1.2 Paddle开发镜像（CPU/GPU 2选1）
**CPU：**
```
# 启动 CPU Docker
docker pull paddlepaddle/paddle:2.2.0
docker run -p 9292:9292 --name test -dit paddlepaddle/paddle:2.2.0 bash
docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving
# Paddle开发镜像需要执行以下脚本增加Serving所需依赖项
bash Serving/tools/paddle_env_install.sh
```
**GPU：**
```
# 启动 GPU Docker
docker pull paddlepaddle/paddle:2.2.0-cuda10.2-cudnn7
nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/paddle:2.2.0-cuda10.2-cudnn7 bash
nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving
# Paddle开发镜像需要执行以下脚本增加Serving所需依赖项
bash Serving/tools/paddle_env_install.sh
```
## 2.安装Paddle Serving相关Python库
安装所需的pip依赖 安装所需的pip依赖
``` ```
...@@ -30,13 +60,13 @@ pip3 install -r python/requirements.txt ...@@ -30,13 +60,13 @@ pip3 install -r python/requirements.txt
``` ```
```shell ```shell
pip3 install paddle-serving-client==0.6.2 pip3 install paddle-serving-client==0.7.0
pip3 install paddle-serving-server==0.6.2 # CPU pip3 install paddle-serving-server==0.7.0 # CPU
pip3 install paddle-serving-app==0.6.2 pip3 install paddle-serving-app==0.7.0
pip3 install paddle-serving-server-gpu==0.6.2.post102 #GPU with CUDA10.2 + TensorRT7 pip3 install paddle-serving-server-gpu==0.7.0.post102 #GPU with CUDA10.2 + TensorRT6
# 其他GPU环境需要确认环境再选择执行哪一条 # 其他GPU环境需要确认环境再选择执行哪一条
pip3 install paddle-serving-server-gpu==0.6.2.post101 # GPU with CUDA10.1 + TensorRT6 pip3 install paddle-serving-server-gpu==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.6.2.post11 # GPU with CUDA10.1 + TensorRT7 pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
``` ```
您可能需要使用国内镜像源（例如清华源, 在pip命令中添加`-i https://pypi.tuna.tsinghua.edu.cn/simple`）来加速下载。 您可能需要使用国内镜像源（例如清华源, 在pip命令中添加`-i https://pypi.tuna.tsinghua.edu.cn/simple`）来加速下载。
...@@ -47,26 +77,28 @@ paddle-serving-server和paddle-serving-server-gpu安装包支持Centos 6/7, Ubun ...@@ -47,26 +77,28 @@ paddle-serving-server和paddle-serving-server-gpu安装包支持Centos 6/7, Ubun
paddle-serving-client和paddle-serving-app安装包支持Linux和Windows，其中paddle-serving-client仅支持python3.6/3.7/3.8。 paddle-serving-client和paddle-serving-app安装包支持Linux和Windows，其中paddle-serving-client仅支持python3.6/3.7/3.8。
**最新的0.6.2的版本，已经不支持Cuda 9.0和Cuda 10.0，Python已不支持2.7和3.5。** ## 3.安装Paddle相关Python库
**当您使用`paddle_serving_client.convert`命令或者`Python Pipeline框架`时才需要安装。**
推荐安装2.1.0及以上版本的paddle
``` ```
# CPU环境请执行 # CPU环境请执行
pip3 install paddlepaddle==2.1.0 pip3 install paddlepaddle==2.2.0
# GPU Cuda10.2环境请执行 # GPU Cuda10.2环境请执行
pip3 install paddlepaddle-gpu==2.1.0 pip3 install paddlepaddle-gpu==2.2.0
``` ```
**注意**： 如果您的Cuda版本不是10.2，请勿直接执行上述命令，需要参考[Paddle-Inference官方文档-下载安装Linux预测库](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html#python)选择相应的GPU环境的url链接并进行安装。
**注意**： 如果您的Cuda版本不是10.2，请勿直接执行上述命令，需要参考[Paddle官方文档-多版本whl包列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-release) 例如Cuda 10.1的Python3.6用户，请选择表格当中的`cp36-cp36m`和`linux-cuda10.1-cudnn7.6-trt6-gcc8.2`对应的url，复制下来并执行
选择相应的GPU环境的url链接并进行安装，例如Cuda 10.1的Python3.6用户，请选择表格当中的`cp36-cp36m`和`cuda10.1-cudnn7-mkl-gcc8.2-avx-trt6.0.1.5`对应的url，复制下来并执行
``` ```
pip3 install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.0-gpu-cuda10.1-cudnn7-mkl-gcc8.2/paddlepaddle_gpu-2.1.0.post101-cp36-cp36m-linux_x86_64.whl pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.0/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.1_cudnn7.6.5_trt6.0.1.5/paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-linux_x86_64.whl
``` ```
由于默认的`paddlepaddle-gpu==2.1.0`是Cuda 10.2，并没有联编TensorRT，因此如果需要和在`paddlepaddle-gpu`上使用TensorRT，需要在上述多版本whl包列表当中，找到`cuda10.2-cudnn8.0-trt7.1.3`，下载对应的Python版本 ## 4.支持的镜像环境和说明
| 环境 | Serving开发镜像Tag | 操作系统 | Paddle开发镜像Tag | 操作系统 |
如果是其他环境和Python版本，请在表格中找到对应的链接并用pip安装。 | :--------------------------: | :-------------------------------: | :-------------: | :-------------------: | :----------------: |
| CPU | 0.7.0-devel | Ubuntu 16.04 | 2.2.0 | Ubuntu 18.04. |
| Cuda10.1+Cudnn7 | 0.7.0-cuda10.1-cudnn7-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda10.2+Cudnn7 | 0.7.0-cuda10.2-cudnn7-devel | Ubuntu 16.04 | 2.2.0-cuda10.2-cudnn7 | Ubuntu 16.04 |
| Cuda10.2+Cudnn8 | 0.7.0-cuda10.2-cudnn8-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda11.2+Cudnn8 | 0.7.0-cuda11.2-cudnn8-devel | Ubuntu 16.04 | 2.2.0-cuda11.2-cudnn8 | Ubuntu 18.04 |
对于**Windows 10 用户**，请参考文档[Windows平台使用Paddle Serving指导](Windows_Tutorial_CN.md)。 对于**Windows 10 用户**，请参考文档[Windows平台使用Paddle Serving指导](Windows_Tutorial_CN.md)。
doc/Install_EN.md
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...@@ -2,78 +2,106 @@ ...@@ -2,78 +2,106 @@
([简体中文](./Install_CN.md)|English) ([简体中文](./Install_CN.md)|English)
We **highly recommend** you to **run Paddle Serving in Docker**, please visit [Run in Docker](Run_In_Docker_EN.md). See the [document](./Docker_Images_EN.md) for more docker images. **Strongly recommend** you build **Paddle Serving** in Docker, please check [How to run PaddleServing in Docker](Run_In_Docker_CN.md). For more images, please refer to [Docker Image List](Docker_Images_CN.md).
**Attention:**: Currently, the default GPU environment of paddlepaddle 2.1 is Cuda 10.2, so the sample code of GPU Docker is based on Cuda 10.2. We also provides docker images and whl packages for other GPU environments. If users use other environments, they need to carefully check and select the appropriate version. **Tip-1**: This project only supports <mark>**Python3.6/3.7/3.8**</mark>, all subsequent operations related to Python/Pip need to select the correct Python version.
**Attention:** the following so-called 'python' or 'pip' stands for one of Python 3.6/3.7/3.8. **Tip-2**: The GPU environments in the following examples are all cuda10.2-cudnn7. If you use Python Pipeline to deploy and need Nvidia TensorRT to optimize prediction performance, please refer to [Supported Mirroring Environment and Instructions](#4.-Supported-Docker-Images-and-Instruction) to choose other versions.
## 1. Start the Docker Container
<mark>**Both Serving Dev Image and Paddle Dev Image are supported at the same time. You can choose 1 from the operation 2 in chapters 1.1 and 1.2.**</mark>
### 1.1 Serving Dev Images (CPU/GPU 2 choose 1)
**CPU:**
``` ```
# Run CPU Docker # Start CPU Docker Container
docker pull registry.baidubce.com/paddlepaddle/serving:0.6.0-devel docker pull paddlepaddle/serving:0.7.0-devel
docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.6.0-devel bash docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-devel bash
docker exec -it test bash docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving git clone https://github.com/PaddlePaddle/Serving
``` ```
**GPU:**
``` ```
# Run GPU Docker # Start GPU Docker Container
nvidia-docker pull registry.baidubce.com/paddlepaddle/serving:0.6.0-cuda10.2-cudnn8-devel docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
nvidia-docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.6.0-cuda10.2-cudnn8-devel bash nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel bash
nvidia-docker exec -it test bash nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving git clone https://github.com/PaddlePaddle/Serving
``` ```
install python dependencies ### 1.2 Paddle Dev Images (choose any codeblock of CPU/GPU)
``` **CPU:**
cd Serving
pip install -r python/requirements.txt
``` ```
# Start CPU Docker Container
docker pull paddlepaddle/paddle:2.2.0
docker run -p 9292:9292 --name test -dit paddlepaddle/paddle:2.2.0 bash
docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving
```shell # Paddle dev image needs to run the following script to increase the dependencies required by Serving
pip install paddle-serving-client==0.6.0 bash Serving/tools/paddle_env_install.sh
pip install paddle-serving-server==0.6.0 # CPU ```
pip install paddle-serving-app==0.6.0 **GPU:**
pip install paddle-serving-server-gpu==0.6.0.post102 #GPU with CUDA10.2 + TensorRT7 ```
# DO NOT RUN ALL COMMANDS! check your GPU env and select the right one # Start GPU Docker
pip install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6 docker pull paddlepaddle/paddle:2.2.0-cuda10.2-cudnn7
pip install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA10.1 + TensorRT7 nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/paddle:2.2.0-cuda10.2-cudnn7 bash
nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving
# Paddle development image needs to execute the following script to increase the dependencies required by Serving
bash Serving/tools/paddle_env_install.sh
```
You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to pip command) to speed up the download. ## 2. Install Paddle Serving related whl Packages
If you need install modules compiled with develop branch, please download packages from [latest packages list](./Latest_Packages_CN.md) and install with `pip install` command. If you want to compile by yourself, please refer to [How to compile Paddle Serving?](Compile_EN.md) Install the required pip dependencies
```
cd Serving
pip3 install -r python/requirements.txt
```
Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7, Ubuntu 16/18, Windows 10. ```shell
pip3 install paddle-serving-client==0.7.0
pip3 install paddle-serving-server==0.7.0 # CPU
pip3 install paddle-serving-app==0.7.0
pip3 install paddle-serving-server-gpu==0.7.0.post102 #GPU with CUDA10.2 + TensorRT6
# Other GPU environments need to confirm the environment before choosing which one to execute
pip3 install paddle-serving-server-gpu==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
```
Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python3.6/3.7/3.8. If you are in China, You may need to use a chinese mirror source (such as Tsinghua source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to the pip command) to speed up the download.
**For latest version, Cuda 9.0 or Cuda 10.0 are no longer supported, Python2.7/3.5 is no longer supported.** If you need to use the installation package compiled by the develop branch, please download the download address from [Latest installation package list](./Latest_Packages_CN.md), and use the `pip install` command to install. If you want to compile by yourself, please refer to [Paddle Serving Compilation Document](./Compile_CN.md).
Recommended to install paddle >= 2.1.0 The paddle-serving-server and paddle-serving-server-gpu installation packages support Centos 6/7, Ubuntu 16/18 and Windows 10.
The paddle-serving-client and paddle-serving-app installation packages support Linux and Windows, and paddle-serving-client only supports python3.6/3.7/3.8.
## 3. Install Paddle related Python libraries
**You only need to install it when you use the `paddle_serving_client.convert` command or the `Python Pipeline framework`. **
``` ```
# CPU users, please run # CPU environment please execute
pip install paddlepaddle==2.1.0 pip3 install paddlepaddle==2.2.0
# GPU Cuda10.2 please run # GPU Cuda10.2 environment please execute
pip install paddlepaddle-gpu==2.1.0 pip3 install paddlepaddle-gpu==2.2.0
``` ```
**Note**: If your Cuda version is not 10.2, please do not execute the above commands directly, you need to refer to [Paddle-Inference official document-download and install the Linux prediction library](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html#python) Select the URL link of the corresponding GPU environment and install it.
**Note**: If your Cuda version is not 10.2, please do not execute the above commands directly, you need to refer to [Paddle official documentation-multi-version whl package list For example, for Python3.6 users of Cuda 10.1, please select the URL corresponding to `cp36-cp36m` and `linux-cuda10.1-cudnn7.6-trt6-gcc8.2` in the table, copy it and execute
](https://www.paddlepaddle.org.cn/documentation/docs/en/install/Tables_en.html#multi-version-whl-package-list-release)
Select the url link of the corresponding GPU environment and install it. For example, for Python3.6 users of Cuda 10.1, please select `cp36-cp36m` and
The url corresponding to `cuda10.1-cudnn7-mkl-gcc8.2-avx-trt6.0.1.5`, copy it and run
``` ```
pip install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.0-gpu-cuda10.1-cudnn7-mkl-gcc8.2/paddlepaddle_gpu-2.1.0.post101-cp36-cp36m-linux_x86_64.whl pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.0/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.1_cudnn7.6.5_trt6.0.1.5/paddlepaddle_gpu-2.2.0.post101 -cp36-cp36m-linux_x86_64.whl
``` ```
## 4. Supported Docker Images and Instruction
the default `paddlepaddle-gpu==2.1.0` is Cuda 10.2 with no TensorRT. If you want to install PaddlePaddle with TensorRT. please also check the documentation-multi-version whl package list and find key word `cuda10.2-cudnn8.0-trt7.1.3`.
If it is other environment and Python version, please find the corresponding link in the table and install it with pip.
For **Windows Users**, please read the document [Paddle Serving for Windows Users](Windows_Tutorial_EN.md)
<h2 align="center">Quick Start Example</h2> | Environment | Serving Development Image Tag | Operating System | Paddle Development Image Tag | Operating System |
| :--------------------------: | :-------------------------------: | :-------------: | :-------------------: | :----------------: |
| CPU | 0.7.0-devel | Ubuntu 16.04 | 2.2.0 | Ubuntu 18.04. |
| Cuda10.1+Cudnn7 | 0.7.0-cuda10.1-cudnn7-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda10.2+Cudnn7 | 0.7.0-cuda10.2-cudnn7-devel | Ubuntu 16.04 | 2.2.0-cuda10.2-cudnn7 | Ubuntu 16.04 |
| Cuda10.2+Cudnn8 | 0.7.0-cuda10.2-cudnn8-devel | Ubuntu 16.04 | 无 | 无 |
| Cuda11.2+Cudnn8 | 0.7.0-cuda11.2-cudnn8-devel | Ubuntu 16.04 | 2.2.0-cuda11.2-cudnn8 | Ubuntu 18.04 |
This quick start example is mainly for those users who already have a model to deploy, and we also provide a model that can be used for deployment. in case if you want to know how to complete the process from offline training to online service, please refer to the AiStudio tutorial above. For **Windows 10 users**, please refer to the document [Paddle Serving Guide for Windows Platform](Windows_Tutorial_CN.md).
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...@@ -8,7 +8,7 @@ ...@@ -8,7 +8,7 @@
进入到Serving的git目录下，进入到`fit_a_line`例子 进入到Serving的git目录下，进入到`fit_a_line`例子
``` shell ``` shell
cd Serving/python/examples/fit_a_line cd Serving/examples/C++/fit_a_line
sh get_data.sh sh get_data.sh
``` ```
......
python/CMakeLists.txt
浏览文件 @ 15abbeaa
...@@ -73,7 +73,7 @@ if (SERVER) ...@@ -73,7 +73,7 @@ if (SERVER)
set(VERSION_SUFFIX 101) set(VERSION_SUFFIX 101)
elseif(CUDA_VERSION EQUAL 10.2) elseif(CUDA_VERSION EQUAL 10.2)
if(CUDNN_MAJOR_VERSION EQUAL 7) if(CUDNN_MAJOR_VERSION EQUAL 7)
set(VERSION_SUFFIX 1027) set(VERSION_SUFFIX 102)
elseif(CUDNN_MAJOR_VERSION EQUAL 8) elseif(CUDNN_MAJOR_VERSION EQUAL 8)
set(VERSION_SUFFIX 1028) set(VERSION_SUFFIX 1028)
endif() endif()
......
python/paddle_serving_server/server.py
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...@@ -429,7 +429,7 @@ class Server(object): ...@@ -429,7 +429,7 @@ class Server(object):
if device_type == "0": if device_type == "0":
device_version = self.get_device_version() device_version = self.get_device_version()
elif device_type == "1": elif device_type == "1":
if version_suffix == "101" or version_suffix == "1027" or version_suffix == "1028" or version_suffix == "112": if version_suffix == "101" or version_suffix == "102" or version_suffix == "1028" or version_suffix == "112":
device_version = "gpu-" + version_suffix device_version = "gpu-" + version_suffix
else: else:
device_version = "gpu-cuda" + version_suffix device_version = "gpu-cuda" + version_suffix
......
tools/paddle_env_install.sh 0 → 100644
浏览文件 @ 15abbeaa
unset GREP_OPTIONS
function install_trt(){
CUDA_VERSION=$(nvcc --version | egrep -o "V[0-9]+.[0-9]+" | cut -c2-)
if [ $CUDA_VERSION == "10.2" ]; then
wget https://paddle-ci.gz.bcebos.com/TRT/TensorRT6-cuda10.2-cudnn7.tar.gz --no-check-certificate
tar -zxf TensorRT6-cuda10.2-cudnn7.tar.gz -C /usr/local
cp -rf /usr/local/TensorRT-6.0.1.8/include/* /usr/include/ && cp -rf /usr/local/TensorRT-6.0.1.8/lib/* /usr/lib/
rm -rf TensorRT6-cuda10.2-cudnn7.tar.gz
elif [ $CUDA_VERSION == "11.2" ]; then
wget https://paddle-ci.gz.bcebos.com/TRT/TensorRT-8.0.3.4.Linux.x86_64-gnu.cuda-11.3.cudnn8.2.tar.gz --no-check-certificate
tar -zxf TensorRT-8.0.3.4.Linux.x86_64-gnu.cuda-11.3.cudnn8.2.tar.gz -C /usr/local
cp -rf /usr/local/TensorRT-8.0.3.4/include/* /usr/include/ && cp -rf /usr/local/TensorRT-8.0.3.4/lib/* /usr/lib/
rm -rf TensorRT-8.0.3.4.Linux.x86_64-gnu.cuda-11.3.cudnn8.2.tar.gz
else
echo "No Cuda Found, no need to install TensorRT"
fi
}
function env_install()
{
apt install -y libcurl4-openssl-dev libbz2-dev
wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && tar xf centos_ssl.tar && rm -rf centos_ssl.tar && mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
rm -rf /usr/local/go && wget -qO- https://paddle-ci.gz.bcebos.com/go1.15.12.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
mkdir /root/go && \
mkdir /root/go/bin && \
mkdir /root/go/src && \
echo "GOROOT=/usr/local/go" >> /root/.bashrc && \
echo "GOPATH=/root/go" >> /root/.bashrc && \
echo "PATH=/usr/local/go/bin:/root/go/bin:$PATH" >> /root/.bashrc
install_trt
}
env_install
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