update serving doc for CPU deployment

docs/zh_CN/inference_deployment/paddle_serving_deploy.md

@@ -19,23 +19,34 @@
 
 <a name="2"></a>
 ## 2. Serving 安装
-
 Serving 官网推荐使用 docker 安装并部署 Serving 环境。首先需要拉取 docker 环境并创建基于 Serving 的 docker。
 
 ```shell
+# 启动GPU docker
 docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
 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
+
+# 启动CPU docker
+docker pull paddlepaddle/serving:0.7.0-devel
+docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-devel bash
+docker exec -it test bash
 ```
 
 进入 docker 后，需要安装 Serving 相关的 python 包。
-
 ```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
-# 其他GPU环境需要确认环境再选择执行哪一条
+
+#若为CPU部署环境:
+pip3 install paddle-serving-server==0.7.0 # CPU
+pip3 install paddlepaddle==2.2.0          # CPU
+
+#若为GPU部署环境
+pip3 install paddle-serving-server-gpu==0.7.0.post102 # GPU with CUDA10.2 + TensorRT6
+pip3 install paddlepaddle-gpu==2.2.0     # GPU with CUDA10.2
+
+#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==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
 pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
 ```
@@ -43,11 +54,6 @@ pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + Tens
 * 如果安装速度太慢，可以通过 `-i https://pypi.tuna.tsinghua.edu.cn/simple` 更换源，加速安装过程。
 * 其他环境配置安装请参考: [使用Docker安装Paddle Serving](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Install_CN.md)
 
-* 如果希望部署 CPU 服务，可以安装 serving-server 的 cpu 版本，安装命令如下。
-
-```shell
-pip install paddle-serving-server
-```
 <a name="3"></a>
 
 ## 3. 图像分类服务部署