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>
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
pip install paddlepaddle==2.1.0
# CPU environment please execute
pip3 install paddlepaddle==2.2.0
# GPU Cuda10.2 please run
pip install paddlepaddle-gpu==2.1.0
# GPU Cuda10.2 environment please execute
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
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
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
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 |
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).
