[Docs]Update hps README (#4403)

* update hps readme doc

* update hps readme doc

* update according to comment

* update docs according comments

* update docs according comments

* update docs according comments

* update docs according comments

* update docs according comments

deploy/hps/README.md

@@ -27,21 +27,23 @@ comments: true
 
 镜像构建步骤：
 
-1. 构建依赖收集镜像。
-2. 锁定依赖版本，提升部署镜像构建的可重现性。
+1. 构建依赖收集镜像。（可选）
+2. 锁定依赖版本，提升部署镜像构建的可重现性。（可选）
 3. 构建部署镜像，基于已锁定的依赖信息，构建最终的部署镜像，为后续的产线运行提供镜像支持。
 
-### 1.1 构建依赖收集镜像
+**如不需修改依赖相关信息，可跳转 [1.3 构建部署镜像](./README.md#13-镜像构建) 根据缓存的依赖信息直接构建部署镜像**。
 
-执行 `server_env` 目录下的依赖收集脚本。
+### 1.1 构建依赖收集镜像（可选）
+
+执行 `server_env` 目录下的构建依赖收集镜像脚本。
 
 ```bash
 ./scripts/prepare_rc_image.sh
 ```
 
-该脚本会为每种设备类型构建一个用于依赖收集的镜像，镜像包含 Python 3.10 以及 [pip-tools](https://github.com/jazzband/pip-tools) 工具。[1.2 锁定依赖](./README.md#12-锁定依赖) 将基于该镜像完成。构建完成后，将分别生成 `paddlex-hps-rc:gpu` 和 `paddlex-hps-rc:cpu` 两个镜像。如果遇到网络问题，可以通过 `-p` 参数指定其他 pip 源。如果不指定，默认使用 https://pypi.org/simple。
+该脚本会为每种设备类型构建一个用于依赖收集的镜像，镜像包含 Python 3.10 以及 [pip-tools](https://github.com/jazzband/pip-tools) 工具。[1.2 锁定依赖版本（可选）](./README.md#12-锁定依赖版本可选) 将基于该镜像完成。构建完成后，将分别生成 `paddlex-hps-rc:gpu` 和 `paddlex-hps-rc:cpu` 两个镜像。如果遇到网络问题，可以通过 `-p` 参数指定其他 pip 源。如果不指定，默认使用 https://pypi.org/simple。
 
-### 1.2 锁定依赖
+### 1.2 锁定依赖版本（可选）
 
 为了使构建结果的可重现性更强，本步骤将依赖锁定到精确版本。执行如下脚本：
 
@@ -53,7 +55,7 @@ comments: true
 
 ### 1.3 镜像构建
 
-在完成 1.2 锁定依赖后，如需构建 GPU 镜像，需提前将 [cuDNN 8.9.7-CUDA 11.x 安装包](https://developer.nvidia.cn/rdp/cudnn-archive) 和 [TensorRT 8.6.1.6-Ubuntu 20.04 安装包](https://developer.nvidia.com/nvidia-tensorrt-8x-download) 放在 `server_env` 目录下。对于 Triton Server，项目使用预先编译好的版本，将在构建镜像时自动下载，无需手动下载。以构建 GPU 镜像为例，执行以下命令：
+如需构建 GPU 镜像，需提前将 [cuDNN 8.9.7-CUDA 11.x 安装包](https://developer.nvidia.cn/rdp/cudnn-archive) 和 [TensorRT 8.6-Linux x86_64 GA 安装包](https://developer.nvidia.com/nvidia-tensorrt-8x-download) 放在 `server_env` 目录下。对于 Triton Server，项目使用预先编译好的版本，将在构建镜像时自动下载，无需手动下载。以构建 GPU 镜像为例，执行以下命令：
 
 ```bash
 ./scripts/build_deployment_image.sh -k gpu -t latest-gpu 

deploy/hps/README_en.md

@@ -4,7 +4,7 @@ comments: true
 
 # PaddleX High Stability Serving
 
-This project provides a high-stability serving solution, consisting of two main components: `server_env` and `sdk`.`server_env` is responsible for building multiple Docker images that include Triton Inference Server, providing the runtime environment for pipeline servers.`sdk` is used to package the pipeline SDK, including both server and client code for various model pipelines.As shown in the following figure:
+This project provides a high-stability serving solution, consisting of two main components: `server_env` and `sdk`.`server_env` is responsible for building multiple Docker images that include Triton Inference Server, providing the runtime environment for pipeline servers.`sdk` is used to package the pipeline SDK, including both server and client code for various model pipelines. As shown in the following figure:
 
 <img src="https://github.com/cuicheng01/PaddleX_doc_images/blob/main/images/hps/hps_workflow_en.png?raw=true"/>
 
@@ -15,7 +15,7 @@ This project provides a high-stability serving solution, consisting of two main
 - **Docker Version**: `>= 20.10.0` (Used for image building and deployment)
 - **CPU Architecture**: x86-64
 
-This  document  mainly introduces how to set up a high stability serving environment and package related materials using the scripts provided by this project.The overall process consists of two main stages:
+This  document  mainly introduces how to set up a high stability serving environment and package related materials using the scripts provided by this project. The overall process consists of two main stages:
 
 1. Image Building: Build Docker images that include Triton Inference Server. In this stage, requirement versions are locked to ensure reproducibility and stability of the deployment images.
 
@@ -30,24 +30,25 @@ This stage mainly introduces the overall process and key steps of image building
 
 Image Building Steps:
 
-1. Build a requirement collection image.
-2. Freeze requirement versions to improve the reproducibility of deployment image building. 
+1. Build a requirement collection image. (Optional)
+2. Freeze requirement versions to improve the reproducibility of deployment image building. (Optional)
 3. Build the deployment image based on the frozen requirement information to generate the final deployment image and provide image support for subsequent pipeline execution. 
 
+**If you do not need to modify requirement-related information, you can skip to [1.3 Building Image](./README_en.md#13-building-image) to build the deployment image using cached requirement information.**
 
-## 1.1 Build the Requirement Collection Image
+## 1.1 Build the Requirement Collection Image (Optional)
 
-Run the requirement collection script located in the `server_env` directory:
+Run follow script in the `server_env` directory to build the requirement collection image. 
 
 ```bash
 ./scripts/prepare_rc_image.sh
 ```
 
-This script builds a requirement collection image for each device type. The image includes Python 3.10 and [pip-tools](https://github.com/jazzband/pip-tools).[1.2 Freeze requirement](./README_en.md#12-freeze-requirement) will be based on this image.After the build is complete, two images: `paddlex-hps-rc:gpu` and `paddlex-hps-rc:cpu` will be generated.If you encounter network issues, you can specify other pip sources through the `-p` parameter.If not specified, the default source https://pypi.org/simple will be used.
+This script builds a requirement collection image for each device type. The image includes Python 3.10 and [pip-tools](https://github.com/jazzband/pip-tools). [1.2 Freeze Requirement (Optional)](./README_en.md#12-freeze-requirement-optional) will be based on this image. After the build is complete, two images: `paddlex-hps-rc:gpu` and `paddlex-hps-rc:cpu` will be generated. If you encounter network issues, you can specify other pip sources through the `-p` parameter. If not specified, the default source https://pypi.org/simple will be used.
 
-## 1.2 Freeze Requirement
+## 1.2 Freeze Requirement (Optional)
 
-To enhance the reproducibility of the build, this step freeze requirement to exact versions. run the following script:
+To enhance the reproducibility of the build, this step freeze requirement to exact versions, run the following script:
 
 ```bash
 ./scripts/freeze_requirements.sh
@@ -57,7 +58,7 @@ This script uses `pip-tools compile` to parse the source requirement files and g
 
 ## 1.3 Building Image
 
-After completing Step 1.2: Freeze Requirement, if you need to build the GPU image, make sure to place the following installation packages in the `server_env` directory in advance:[cuDNN 8.9.7-CUDA 11.x Tar](https://developer.nvidia.cn/rdp/cudnn-archive) and [TensorRT 8.6.1.6-Ubuntu 20.04 Tar Package](https://developer.nvidia.com/nvidia-tensorrt-8x-download).For Triton Inference Server, a precompiled version will be automatically downloaded during the build process, so manual download is not required.To build a GPU image , run the following command:
+If you need to build the GPU image, make sure to place the following installation packages in the `server_env` directory in advance:[cuDNN 8.9.7-CUDA 11.x Tar](https://developer.nvidia.cn/rdp/cudnn-archive) and [TensorRT 8.6-Linux x86_64 GA Tar Package](https://developer.nvidia.com/nvidia-tensorrt-8x-download). For Triton Inference Server, a precompiled version will be automatically downloaded during the build process, so manual download is not required. To build a GPU image, run the following command:
 
 ```bash
 ./scripts/build_deployment_image.sh -k gpu -t latest-gpu
@@ -151,7 +152,7 @@ After run successfully, the packaged  will be stored in the `/output` directory.
 
 **1. Failed to pull the base Docker image during build?**
 
-This issue may occur due to network connectivity problems or restricted access to Docker Hub. You can add trusted domestic mirror registry URLs to your local Docker configuration file at `/etc/docker/daemon.json` to improve download speed and stability.If this does not resolve the issue, consider manually downloading the base image from the official source or other trusted third-party source.
+This issue may occur due to network connectivity problems or restricted access to Docker Hub. You can add trusted domestic mirror registry URLs to your local Docker configuration file at `/etc/docker/daemon.json` to improve download speed and stability. If this does not resolve the issue, consider manually downloading the base image from the official source or other trusted third-party source.
 
 
 **2. Timeout when installing Python requirement during image build?**