Install the service whl package. There are three types of client, app and server. The server is divided into CPU and GPU. Choose one installation according to the environment.
- GPU with CUDA10.2 + Cudnn7 + TensorRT6(Recommended)
By default, the domestic Tsinghua mirror source is turned on to speed up the download. If you use a proxy, you can turn it off(`-i https://pypi.tuna.tsinghua.edu.cn/simple`).
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).
If you need to use the installation package compiled by the develop branch, please download the download address from [Download wheel packages](./Latest_Packages_EN.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).
The paddle-serving-server and paddle-serving-server-gpu installation packages support Centos 6/7, Ubuntu 16/18 and Windows 10.
For **Windows 10 users**, please refer to the document [Paddle Serving Guide for Windows Platform](Windows_Tutorial_CN.md).
## 5. Installation check
After the above steps are completed, you can use the command line to run the environment check function to automatically run the Paddle Serving related examples to verify the environment-related configuration.
```
python3 -m paddle_serving_server.serve check
```
For details, please refer to the [Environmental Check Documentation](./Check_Env_CN.md)
for most users, we do not need to read this section. But if you deploy your Paddle Serving on a machine without network, you will encounter a problem that the binary executable tar file cannot be downloaded. Therefore, here we give you all the download links for various environment.
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@@ -29,15 +29,15 @@ for most users, we do not need to read this section. But if you deploy your Padd