Update docs (#3781)

docs/installation/installation.en.md

@@ -17,7 +17,7 @@ After installing PaddlePaddle (refer to the [PaddlePaddle Local Installation Tut
 > ❗ <b>Note</b>: Please ensure that PaddlePaddle is successfully installed before proceeding to the next step.
 
 ```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl[base]
 ```
 
 ### 1.2 Plugin Installation Mode
@@ -111,7 +111,7 @@ If the plugin you need to install is `PaddleXXX`, after installing PaddlePaddle
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleX.git
 cd PaddleX
-pip install -e .
+pip install -e .[base]
 paddlex --install PaddleXXX
 ```
 
@@ -186,7 +186,7 @@ cd PaddleX
 
 # Install PaddleX whl
 # -e: Install in editable mode, so changes to the current project's code will directly affect the installed PaddleX Wheel
-pip install -e .
+pip install -e .[base]
 ```
 
 * <b>If you choose plugin installation mode</b> and the plugin you need is named PaddleXXX (there can be multiple), execute the following commands:
@@ -196,7 +196,7 @@ cd PaddleX
 
 # Install PaddleX whl
 # -e: Install in editable mode, so changes to the current project's code will directly affect the installed PaddleX Wheel
-pip install -e .
+pip install -e .[base]
 
 # Install PaddleX plugins
 paddlex --install PaddleXXX
@@ -232,3 +232,42 @@ All packages are installed.
 ```
 
 For PaddleX installation on more hardware environments, please refer to the [PaddleX Multi-hardware Usage Guide](../other_devices_support/multi_devices_use_guide.en.md)
+
+Sure! Here's the English translation:
+
+---
+
+### 2.3 Selective Installation of Dependencies
+
+PaddleX offers a wide range of features, and different features require different dependencies. The features in PaddleX that can be used without installing plugins are categorized as "basic features." The official PaddleX Docker images have all dependencies required for these basic features preinstalled. Similarly, using the installation method introduced earlier—`pip install ...[base]`—will install all dependencies needed for the basic features.
+
+If you are only focused on a specific feature of PaddleX and want to minimize the size of the installed dependencies, you can selectively install them by specifying a "dependency group":
+
+```bash
+# For example, to install only the basic OCR features
+# Install the precompiled wheel package
+pip install /url/of/wheel[ocr]
+# Install from source
+pip install -e .[ocr]
+
+# You can also specify multiple dependency groups at once
+pip install -e .[ocr,cv]
+```
+
+PaddleX currently provides the following dependency groups:
+
+| Dependency Group | Corresponding Features |
+| - | - |
+| `base` | All basic features of PaddleX. |
+| `cv` | Basic features of computer vision pipelines (excluding multimodal pipelines). |
+| `multimodal` | Basic features of multimodal pipelines. |
+| `ie` | Basic features of information extraction pipelines. |
+| `ocr` | Basic features of OCR-related pipelines. |
+| `speech` | Basic features of speech pipeline.s |
+| `ts` | Basic features of time series pipelines. |
+| `video` | Basic features of video pipelines. |
+| `serving` | The serving feature. Installing this group is equivalent to installing the PaddleX serving plugin; the plugin can also be installed via the PaddleX CLI. |
+| `plugins` | All plugin-provided features that support installation via dependency groups. |
+| `all` | All basic features of PaddleX, as well as all plugin-provided features installable via dependency groups. |
+
+Each pipeline belongs to exactly one dependency group. You can refer to the tutorial of each pipeline to find out which dependency group it belongs to. For modules, you can access the related basic features by installing any dependency group that includes the module.

docs/installation/installation.md

@@ -19,8 +19,9 @@ PaddleX为您提供了两种安装模式：<b>Wheel包安装</b>和<b>插件安
 > ❗ 注：请务必保证 PaddlePaddle 安装成功，安装成功后，方可进行下一步。
 
 ```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0rc0-py3-none-any.whl[base]
 ```
+
 ### 1.2 插件安装模式
 若您使用PaddleX的应用场景为<b>二次开发</b> （例如重新训练模型、微调模型、自定义模型结构、自定义推理代码等），那么推荐您使用<b>功能更加强大</b>的插件安装模式。
 
@@ -193,7 +194,7 @@ cd PaddleX
 
 # 安装 PaddleX whl
 # -e：以可编辑模式安装，当前项目的代码更改，都会直接作用到已经安装的 PaddleX Wheel
-pip install -e .
+pip install -e .[base]
 ```
 <b>若您选择插件安装模式</b>，并且您需要的插件名称为 PaddleXXX（可以有多个），请执行以下命令：
 
@@ -202,7 +203,7 @@ cd PaddleX
 
 # 安装 PaddleX whl
 # -e：以可编辑模式安装，当前项目的代码更改，都会直接作用到已经安装的 PaddleX Wheel
-pip install -e .
+pip install -e .[base]
 
 # 安装 PaddleX 插件
 paddlex --install PaddleXXX
@@ -233,3 +234,37 @@ paddlex --install --platform gitee.com
 All packages are installed.
 ```
 更多硬件环境的PaddleX安装请参考[PaddleX多硬件使用指南](../other_devices_support/multi_devices_use_guide.md)
+
+### 2.3 选择性安装依赖
+
+PaddleX 的功能丰富，而不同的功能需要的依赖也不尽相同。将 PaddleX 中不需要安装插件即可使用的功能归类为“基础功能”。PaddleX 官方 Docker 镜像预置了基础功能所需的全部依赖；使用上文介绍的 `pip install ...[base]` 的安装方式也将安装基础功能需要的所有依赖。如果您只专注于 PaddleX 的某一项功能，且希望保持安装的依赖的体积尽可能小，可以通过指定“依赖组”的方式，选择性地安装依赖：
+
+```bash
+# 以仅安装 OCR 类基础功能为例
+# 安装预编译的 wheel 包
+pip install /url/of/wheel[ocr]
+# 从源码安装
+pip install -e .[ocr]
+
+# 也可以同时指定多个依赖组
+pip install -e .[ocr,cv]
+```
+
+PaddleX 目前提供如下依赖组：
+
+| 依赖组名称 | 对应的功能 |
+| - | - |
+| `base` | PaddleX 的所有基础功能。 |
+| `cv` | 除多模态产线外的 CV 产线的基础功能。 |
+| `multimodal` | 多模态产线的基础功能。 |
+| `ie` | 信息抽取产线的基础功能。 |
+| `ocr` | OCR 类产线的基础功能。 |
+| `speech` | 语音产线的基础功能。 |
+| `ts` | 时序产线的基础功能。 |
+| `video` | 视频产线的基础功能。 |
+| `serving` | 服务化部署功能。安装此依赖组等效于安装 PaddleX 服务化部署插件；也可以通过 PaddleX CLI 安装服务化部署插件。 |
+| `plugins` | 所有支持通过指定依赖组安装的插件提供的功能。 |
+| `all` | PaddleX 的所有基础功能，以及所有支持通过指定依赖组安装的插件提供的功能。 |
+
+
+每一条产线属于且仅属于一个依赖组；在各产线的使用文档中可以了解产线属于哪一依赖组。对于单功能模块，安装任意包含该模块的产线对应的依赖组后即可使用相关的基础功能。

docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.en.md

@@ -86,7 +86,7 @@ The pre-trained model pipelines provided by PaddleX allow for quick experimentat
 Online experience is currently not supported.
 
 ### 2.2 Local Experience
-> ❗ Before using the 3D multi-modal fusion detection pipeline locally, please ensure you have completed the PaddleX wheel package installation according to [the PaddleX Installation Tutorial](../../../installation/installation.md).
+> ❗ Before using the 3D multi-modal fusion detection pipeline locally, please ensure you have completed the PaddleX wheel package installation according to [the PaddleX Installation Tutorial](../../../installation/installation.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 Demo dataset download: You can use the following command to download the demo dataset to a specified folder:
 

docs/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.md

@@ -84,7 +84,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 暂不支持在线体验。
 
 ### 2.2 本地体验
-> ❗ 在本地使用3D多模态融合检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用3D多模态融合检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 

docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.en.md

@@ -152,7 +152,7 @@ The pre-trained model pipelines provided by PaddleX can be quickly experienced.
 Oneline Experience is not supported at the moment.
 
 ### 2.2 Local Experience
-> ❗ Before using the face recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the face recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 

docs/pipeline_usage/tutorials/cv_pipelines/face_recognition.md

@@ -153,7 +153,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在线体
 暂不支持在线体验
 
 ### 2.2 本地体验
-> ❗ 在本地使用人脸识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用人脸识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 

docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.en.md

@@ -129,7 +129,7 @@ Not supported yet.
 
 ### 2.2 Local Experience
 
-> ❗ Before using the general image recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the general image recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 

docs/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.md

@@ -129,7 +129,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 
 ### 2.2 本地体验
 
-> ❗ 在本地使用通用图像识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用通用图像识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 

docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.en.md

@@ -136,7 +136,7 @@ Not supported for online experience.
 
 ### 2.2 Local Experience
 
-> ❗ Before using the Human Keypoint Detection Pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the Human Keypoint Detection Pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 

docs/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.md

@@ -135,7 +135,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 
 ### 2.2 本地体验
 
-> ❗ 在本地使用人体关键点检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用人体关键点检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.en.md

@@ -75,7 +75,7 @@ This pipeline integrates the high-precision anomaly detection model STFPM, which
 ## 2. Quick Start
 PaddleX provides pre-trained models for the anomaly detection pipeline, allowing for quick experience of its effects. You can use the command line or Python to experience the image anomaly detection pipeline locally.
 
-Before using the image anomaly detection pipeline locally, ensure you have installed the PaddleX wheel package following the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md).
+Before using the image anomaly detection pipeline locally, ensure you have installed the PaddleX wheel package following the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 ### 2.1 Command-Line Experience
 You can quickly experience the image anomaly detection pipeline with just one command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md

@@ -79,7 +79,7 @@ comments: true
 ## 2. 快速开始
 PaddleX 所提供的模型产线均可以快速体验效果，您可以在本地使用命令行或 Python 体验图像异常检测产线的效果。
 
-在本地使用图像异常检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用图像异常检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 ### 2.1 命令行方式体验
 一行命令即可快速体验图像异常检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/image_classification.en.md

@@ -741,7 +741,7 @@ You can [experience online](https://aistudio.baidu.com/community/app/100061/webU
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the results, you can **fine-tune the models in the pipeline using your private data**. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code Platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-Before using the general image classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general image classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the image classification pipeline with a single command. Use [the test image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/image_classification.md

@@ -740,7 +740,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-在本地使用通用图像分类产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用图像分类产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验图像分类产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.en.md

@@ -107,7 +107,7 @@ You can [experience the image multi-label classification pipeline online](https:
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-> ❗ Before using the image multi-label classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the image multi-label classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the image multi-label classification pipeline effect with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.md

@@ -111,7 +111,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-❗ 在本地使用通用图像多标签分类产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+❗ 在本地使用通用图像多标签分类产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验图像多标签分类产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.en.md

@@ -216,7 +216,7 @@ You can [experience online](https://aistudio.baidu.com/community/app/100063/webU
 If you are satisfied with the pipeline's performance, you can directly integrate and deploy it. If not, you can also use your private data to <b>fine-tune the model within the pipeline</b>.
 
 ### 2.2 Local Experience
-> ❗ Before using the general instance segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the general instance segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 * You can quickly experience the instance segmentation pipeline effect with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.md

@@ -222,7 +222,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-> ❗ 在本地使用通用实例分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用通用实例分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 * 一行命令即可快速体验实例分割产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/object_detection.en.md

@@ -403,7 +403,7 @@ You can [experience the General Object Detection Pipeline online](https://aistud
 If you are satisfied with the pipeline's performance, you can directly integrate and deploy it. If not, you can also use your private data to <b>fine-tune the model within the pipeline</b>.
 
 ### 2.2 Local Experience
-Before using the general object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the effect of the object detection pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/object_detection.md

@@ -423,7 +423,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 如果您对产线运行的效果满意，可以直接对产线进行集成部署，如果不满意，您也可以利用私有数据<b>对产线中的模型进行在线微调</b>。
 
 ### 2.2 本地体验
-在本地使用通用目标检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用目标检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验目标检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.en.md

@@ -81,7 +81,7 @@ Open vocabulary object detection is an advanced object detection technology that
 ## 2. Quick Start
 
 ### 2.1 Local Experience
-> ❗ Before using the general open vocabulary detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md).
+> ❗ Before using the general open vocabulary detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `multimodal`.
 
 #### 2.1.1 Command Line Experience
 * You can quickly experience the open vocabulary detection pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.md

@@ -85,7 +85,7 @@ comments: true
 ## 2. 快速开始
 
 ### 2.1 本地体验
-> ❗ 在本地使用通用开放词汇检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用通用开放词汇检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `multimodal`。
 
 #### 2.1.1 命令行方式体验
 * 一行命令即可快速体验开放词汇检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.en.md

@@ -83,7 +83,7 @@ Open vocabulary segmentation is an image segmentation task that aims to segment
 ## 2. Quick Start
 
 ### 2.1 Local Experience
-> ❗ Before using the general open vocabulary segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the general open vocabulary segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `multimodal`.
 
 #### 2.1.1 Command Line Experience
 * You can quickly experience the open vocabulary segmentation pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.md

@@ -83,7 +83,7 @@ comments: true
 ## 2. 快速开始
 
 ### 2.1 本地体验
-> ❗ 在本地使用通用开放词汇分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用通用开放词汇分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `multimodal`。
 
 #### 2.1.1 命令行方式体验
 * 一行命令即可快速体验开放词汇分割产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.en.md

@@ -125,7 +125,7 @@ You can [experience the pedestrian attribute recognition pipeline online](https:
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-Before using the pedestrian attribute recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the pedestrian attribute recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the pedestrian attribute recognition pipeline with a single command. Use [the test image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.md

@@ -126,7 +126,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-在本地使用行人属性识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用行人属性识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验行人属性识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.en.md

@@ -80,7 +80,7 @@ Rotated object detection is a variant of the object detection module, specifical
 ## 2. Quick Start
 
 ### 2.1 Local Experience
-> ❗ Before using the rotated object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the rotated object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.1.1 Command Line Experience
 * You can quickly experience the rotated object detection pipeline with a single command. Use the [test image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.md

@@ -83,7 +83,7 @@ comments: true
 ## 2. 快速开始
 
 ### 2.1 本地体验
-> ❗ 在本地使用旋转目标检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+> ❗ 在本地使用旋转目标检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.1.1 命令行方式体验
 * 一行命令即可快速体验旋转目标检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.en.md

@@ -249,7 +249,7 @@ You can [experience online](https://aistudio.baidu.com/community/app/100062/webU
 If you are satisfied with the pipeline's performance, you can directly integrate and deploy it. If not, you can also use your private data to <b>fine-tune the model in the pipeline online</b>.
 
 ### 2.2 Local Experience
-> ❗ Before using the general semantic segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the general semantic segmentation pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 * You can quickly experience the semantic segmentation pipeline effect with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md

@@ -257,7 +257,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
->❗ 在本地使用通用语义分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+>❗ 在本地使用通用语义分割产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 * 一行命令即可快速体验语义分割产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection.en.md

@@ -100,7 +100,7 @@ You can [experience the small object detection pipeline online](https://aistudio
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-Before using the small object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the small object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 * You can quickly experience the small object detection pipeline effect with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/small_object_detection.md

@@ -105,7 +105,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-❗ 在本地使用小目标检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+❗ 在本地使用小目标检测产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 * 一行命令即可快速体验小目标检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.en.md

@@ -122,7 +122,7 @@ You can [experience the vehicle attribute recognition pipeline](https://aistudio
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-Before using the vehicle attribute recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the vehicle attribute recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the vehicle attribute recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg) and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.md

@@ -124,7 +124,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 
 ### 2.2 本地体验
 
-在本地使用车辆属性识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用车辆属性识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `cv`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验车辆属性识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.en.md

@@ -368,7 +368,7 @@ You can [experience online](https://aistudio.baidu.com/community/app/182491/webU
 If you are satisfied with the pipeline's performance, you can directly integrate and deploy it. If not, you can also use private data to **fine-tune the models in the pipeline online**.
 
 ### 2.2 Local Experience
-Before using the Document Scene Information Extraction v3 pipeline locally, ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md).
+Before using the Document Scene Information Extraction v3 pipeline locally, ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ie`.
 
 Before performing model inference, you need to prepare the API key for the large language model. PP-ChatOCRv3 supports calling the large model inference service provided by the [Baidu Cloud Qianfan Platform](https://console.bce.baidu.com/qianfan/ais/console/onlineService). You can refer to [Authentication and Authorization](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Um2wxbaps) to obtain the API key from the Qianfan Platform.
 

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.md

@@ -367,7 +367,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 如果您对产线运行的效果满意，可以直接对产线进行集成部署，如果不满意，您也可以利用私有数据<b>对产线中的模型进行在线微调</b>。
 
 ### 2.2 本地体验
-在本地使用文档场景信息抽取v3产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用文档场景信息抽取v3产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ie`。
 
 在进行模型推理之前，首先需要准备大语言模型的 api_key，PP-ChatOCRv3 支持调用 [百度云千帆平台](https://console.bce.baidu.com/qianfan/ais/console/onlineService) 提供的大模型推理服务，您可以参考[认证鉴权](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Um2wxbaps) 获取千帆平台的 api_key。
 

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.en.md

@@ -435,7 +435,7 @@ The RepSVTR text recognition model is a mobile-oriented text recognition model b
 The pre-trained pipelines provided by PaddleX allow for quick experience of their effects. You can locally use Python to experience the effects of the PP-ChatOCRv4-doc pipeline.
 
 ### 2.1 Local Experience
-Before using the PP-ChatOCRv4-doc pipeline locally, ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md).
+Before using the PP-ChatOCRv4-doc pipeline locally, ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ie`.
 
 Before performing model inference, you first need to prepare the API key for the large language model. PP-ChatOCRv4 supports large model services on the [Baidu Cloud Qianfan Platform](https://console.bce.baidu.com/qianfan/ais/console/onlineService) or the locally deployed standard OpenAI interface. If using the Baidu Cloud Qianfan Platform, refer to [Authentication and Authorization](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Um2wxbaps_en) to obtain the API key. If using a locally deployed large model service, refer to the [PaddleNLP Large Model Deployment Documentation](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/llm) for deployment of the dialogue interface and vectorization interface for large models, and fill in the corresponding `base_url` and `api_key`. If you need to use a multimodal large model for data fusion, refer to the OpenAI service deployment in the [PaddleMIX Model Documentation](https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples/ppdocbee) for multimodal large model deployment, and fill in the corresponding `base_url` and `api_key`.
 

docs/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.md

@@ -605,7 +605,7 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="">训练模
 PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可以在本地使用  Python 体验文档场景信息抽取v4产线的效果。
 
 ### 2.1 本地体验
-在本地使用文档场景信息抽取v4产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用文档场景信息抽取v4产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ie`。
 
 在进行模型推理之前，首先需要准备大语言模型的 api_key，PP-ChatOCRv4 支持在[百度云千帆平台](https://console.bce.baidu.com/qianfan/ais/console/onlineService)或者本地部署的标准 OpenAI 接口大模型服务。如果使用百度云千帆平台，可以参考[认证鉴权](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Um2wxbaps) 获取 api_key。如果使用本地部署的大模型服务，可以参考[PaddleNLP大模型部署文档](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/llm)进行大模型部署对话接口部署和向量化接口部署，并填写对应的 base_url 和 api_key 即可。如果需要使用多模态大模型进行数据融合，可以参考[PaddleMIX模型文档](https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples/ppdocbee)中的OpenAI服务部署进行多模态大模型部署，并填写对应的 base_url 和 api_key 即可。
 

docs/pipeline_usage/tutorials/ocr_pipelines/OCR.en.md

@@ -456,7 +456,7 @@ You can [experience the general OCR pipeline online](https://aistudio.baidu.com/
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-> ❗ Before using the general OCR pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the general OCR pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 #### 2.2.1 Command Line Experience
 * You can quickly experience the OCR pipeline with a single command. Use the [test image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/ocr_pipelines/OCR.md

@@ -468,7 +468,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-❗ 在本地使用通用OCR产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+❗ 在本地使用通用OCR产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 #### 2.2.1 命令行方式体验
 * 一行命令即可快速体验OCR产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.en.md

@@ -632,7 +632,7 @@ The ultra-lightweight cyrillic alphabet recognition model trained based on the P
 ## 2. Quick Start
 All the model pipelines provided by PaddleX can be quickly experienced. You can use the command line or Python on your local machine to experience the effect of the PP-StructureV3 pipeline.
 
-Before using the PP-StructureV3 pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the PP-StructureV3 pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 ### 2.1 Experiencing via Command Line
 

docs/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.md

@@ -592,7 +592,7 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 ## 2. 快速开始
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在本地使用命令行或 Python 体验通用通用版面解析v3产线的效果。
 
-在本地使用通用版面解析v3产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用版面解析v3产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 ### 2.1 命令行方式体验
 一行命令即可快速体验版面解析产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_structure_v3_demo.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.en.md

@@ -110,7 +110,7 @@ The document image preprocessing pipeline integrates two major functions: docume
 
 PaddleX supports experiencing the effects of the document image preprocessing pipeline locally via command line or Python.
 
-Before using the document image preprocessing pipeline locally, please ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md).
+Before using the document image preprocessing pipeline locally, please ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 ### 2.1 Local Experience
 

docs/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.md

@@ -107,7 +107,7 @@ comments: true
 ## 2. 快速开始
 PaddleX 支持在本地使用命令行或 Python 体验文档图像预处理产线的效果。
 
-在本地使用文档图像预处理产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用文档图像预处理产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 ### 2.1 本地体验
 

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.en.md

@@ -292,7 +292,7 @@ You can [experience the formula recognition pipeline online](https://aistudio.ba
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-> ❗ Before using the formula recognition pipelin locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the formula recognition pipelin locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the effect of the formula recognition pipeline with one command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/pipelines/general_formula_recognition_001.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.md

@@ -291,7 +291,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-❗ 在本地使用公式识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+❗ 在本地使用公式识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验公式识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/pipelines/general_formula_recognition_001.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.en.md

@@ -569,7 +569,7 @@ The ultra-lightweight cyrillic alphabet recognition model trained based on the P
 ## 2. Quick Start
 The pipelines provided by PaddleX allow for quick experience of their effects. You can use the command line or Python to experience the effects of the General Layout Parsing pipeline locally.
 
-Before using the General Layout Parsing pipeline locally, ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.md).
+Before using the General Layout Parsing pipeline locally, ensure you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 ### 2.1 Experience via Command Line
 You can quickly experience the effects of the Layout Parsing pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_demo.png) and replace `--input` with the local path for prediction:

docs/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.md

@@ -602,7 +602,7 @@ devanagari_PP-OCRv3_mobile_rec_infer.tar">推理模型</a>/<a href="https://padd
 ## 2. 快速开始
 PaddleX 所提供的模型产线均可以快速体验效果，你可以在本地使用命令行或 Python 体验通用通用版面解析产线的效果。
 
-在本地使用通用版面解析产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用版面解析产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 ### 2.1 命令行方式体验
 一行命令即可快速体验版面解析产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_parsing_demo.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.en.md

@@ -591,7 +591,7 @@ You can [experience the seal text recognition pipeline online](https://aistudio.
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using your private data</b>. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
 
 ### 2.2 Local Experience
-> ❗ Before using the seal text recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md).
+> ❗ Before using the seal text recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the seal text recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png), and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.md

@@ -561,7 +561,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-❗ 在本地使用印章文本识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+❗ 在本地使用印章文本识别产线前，请确保您已经按照[PaddleX安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验印章文本识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.en.md

@@ -634,7 +634,7 @@ You can [experience online](https://aistudio.baidu.com/community/app/91661/webUI
 If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the method in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can <b>fine-tune the models in the pipeline using private data</b>. If you have local training hardware resources, you can start training directly on your local machine; if not, the Baidu AI Studio provides a one-click training service. No code is required; simply upload your data to start the training task.
 
 ### 2.2 Local Experience
-Before using the General Table Recognition pipeline locally, ensure you have installed the PaddleX wheel package following the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the General Table Recognition pipeline locally, ensure you have installed the PaddleX wheel package following the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 ### 2.1 Command Line Experience
 You can quickly experience the table recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg), and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition.md

@@ -583,7 +583,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 如果您对产线运行的效果满意，可以直接进行集成部署。您可以选择从云端下载部署包，也可以参考[2.2节本地体验](#22-本地体验)中的方法进行本地部署。如果对效果不满意，您可以利用私有数据<b>对产线中的模型进行微调训练</b>。如果您具备本地训练的硬件资源，可以直接在本地开展训练；如果没有，星河零代码平台提供了一键式训练服务，无需编写代码，只需上传数据后，即可一键启动训练任务。
 
 ### 2.2 本地体验
-在本地使用通用表格识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用表格识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 ### 2.1 命令行方式体验
 一行命令即可快速体验表格识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.en.md

@@ -654,7 +654,7 @@ All model pipelines provided by PaddleX can be quickly experienced. You can use
 Online experience is not supported at the moment.
 
 ### 2.2 Local Experience
-Before using the General Table Recognition v2 Pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md).
+Before using the General Table Recognition v2 Pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Tutorial](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ocr`.
 
 ### 2.3 Command Line Experience
 You can quickly experience the table recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg)  and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.md

@@ -668,7 +668,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在本地
 暂不支持在线体验。
 
 ### 2.2 本地体验
-在本地使用通用表格识别v2产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用表格识别v2产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ocr`。
 
 ### 2.1 命令行方式体验
 一行命令即可快速体验表格识别产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/speech_pipelines/multilingual_speech_recognition.en.md

@@ -58,7 +58,7 @@ Speech recognition is an advanced tool that can automatically convert spoken lan
 ## 2. Quick Start
 PaddleX supports experiencing the multilingual speech recognition pipeline locally using the command line or Python.
 
-Before using the multilingual speech recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the multilingual speech recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `speech`.
 
 ### 2.1 Local Experience
 

docs/pipeline_usage/tutorials/speech_pipelines/multilingual_speech_recognition.md

@@ -58,7 +58,7 @@ comments: true
 ## 2. 快速开始
 PaddleX 支持在本地使用命令行或 Python 体验多语种语音识别产线的效果。
 
-在本地使用多语种语音识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了 PaddleX 的 wheel 包安装。
+在本地使用多语种语音识别产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了 PaddleX 的 wheel 包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `speech`。
 
 ### 2.1 本地体验
 

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.en.md

@@ -116,7 +116,7 @@ If you are satisfied with the pipeline's performance, you can directly integrate
 <b>Note</b>: Due to the close relationship between time series data and scenarios, the official built-in models for online experience of time series tasks are only model solutions for a specific scenario and are not universal. They are not applicable to other scenarios. Therefore, the experience mode does not support using arbitrary files to experience the effects of the official model solutions. However, after training a model for your own scenario data, you can select your trained model solution and use data from the corresponding scenario for online experience.
 
 ### 2.2 Local Experience
-Before using the general time-series anomaly detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general time-series anomaly detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ts`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the time-series anomaly detection pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv) and replace `--input` with the local path for prediction.

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.md

@@ -116,7 +116,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 <b>注</b>：由于时序数据和场景紧密相关，时序任务的在线体验官方内置模型仅是在一个特定场景下的模型方案，并非通用方案，不适用其他场景，因此体验方式不支持使用任意的文件来体验官方模型方案效果。但是，在完成自己场景数据下的模型训练之后，可以选择自己训练的模型方案，并使用对应场景的数据进行在线体验。
 
 ### 2.2 本地体验
-在本地使用通用时序异常检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用时序异常检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ts`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验时序异常检测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.en.md

@@ -84,7 +84,7 @@ If you are satisfied with the pipeline's performance, you can directly integrate
 Note: Due to the close relationship between time series data and scenarios, the official built-in model for online experience of time series tasks is only a model solution for a specific scenario and is not a general solution applicable to other scenarios. Therefore, the experience method does not support using arbitrary files to experience the effect of the official model solution. However, after training a model for your own scenario data, you can select your trained model solution and use data from the corresponding scenario for online experience.
 
 ### 2.2 Local Experience
-Before using the general time-series classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general time-series classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ts`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the time-series classification pipeline with a single command. Use [the test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.md

@@ -88,7 +88,7 @@ PaddleX 所提供的预训练的模型产线均可以快速体验效果，你可
 注：由于时序数据和场景紧密相关，时序任务的在线体验官方内置模型仅是在一个特定场景下的模型方案，并非通用方案，不适用其他场景，因此体验方式不支持使用任意的文件来体验官方模型方案效果。但是，在完成自己场景数据下的模型训练之后，可以选择自己训练的模型方案，并使用对应场景的数据进行在线体验。
 
 ### 2.2 本地体验
-在本地使用通用时序分类产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用时序分类产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ts`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验时序分类产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.en.md

@@ -123,7 +123,7 @@ Note: Due to the close relationship between time series data and scenarios, the
 
 ### 2.2 Local Experience
 
-Before using the general time-series forecasting pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general time-series forecasting pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `ts`.
 
 #### 2.2.1 Command Line Experience
 You can quickly experience the time-series forecasting pipeline with a single command. Use [the test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.md

@@ -135,7 +135,7 @@ PaddleX 所提供的模型产线均可以快速体验效果，你可以在星河
 注：由于时序数据和场景紧密相关，时序任务的在线体验官方内置模型仅是在一个特定场景下的模型方案，并非通用方案，不适用其他场景，因此体验方式不支持使用任意的文件来体验官方模型方案效果。但是，在完成自己场景数据下的模型训练之后，可以选择自己训练的模型方案，并使用对应场景的数据进行在线体验。
 
 ### 2.2 本地体验
-在本地使用通用时序预测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用时序预测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `ts`。
 
 #### 2.2.1 命令行方式体验
 一行命令即可快速体验时序预测产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/video_pipelines/video_classification.en.md

@@ -92,7 +92,7 @@ PP-TSM is a video classification model developed by Baidu PaddlePaddle's Vision
 
 PaddleX supports experiencing the pipeline's effects locally using command line or Python.
 
-Before using the general video classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX Local Installation Guide.
+Before using the general video classification pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX Local Installation Guide. If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `video`.
 
 #### 2.1 Command Line Experience
 You can quickly experience the video classification pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4) and replace `--input` with your local path for prediction.

docs/pipeline_usage/tutorials/video_pipelines/video_classification.md

@@ -96,7 +96,7 @@ PP-TSM是一种百度飞桨视觉团队自研的视频分类模型。该模型
 
 PaddleX 支持在本地使用命令行或 Python 体验产线的效果。
 
-在本地使用通用视频分类产线前，请确保您已经按照PaddleX本地安装教程完成了PaddleX的wheel包安装。
+在本地使用通用视频分类产线前，请确保您已经按照PaddleX本地安装教程完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `video`。
 
 #### 2.1 命令行方式体验
 一行命令即可快速体验视频分类产线效果，使用 [测试文件](https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4)，并将 `--input` 替换为本地路径，进行预测

docs/pipeline_usage/tutorials/video_pipelines/video_detection.en.md

@@ -39,7 +39,7 @@ YOWO is a single-stage network with two branches. One branch extracts spatial fe
 
 PaddleX supports experiencing the pipeline's effects locally using command line or Python.
 
-Before using the general video detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
+Before using the general video detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `video`.
 
 ### 2.1 Local Experience
 

docs/pipeline_usage/tutorials/video_pipelines/video_detection.md

@@ -41,7 +41,7 @@ YOWO是具有两个分支的单阶段网络。一个分支通过2D-CNN提取关
 
 PaddleX 支持在本地使用命令行或 Python 体验产线的效果。
 
-在本地使用通用视频检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。
+在本地使用通用视频检测产线前，请确保您已经按照[PaddleX本地安装教程](../../../installation/installation.md)完成了PaddleX的wheel包安装。如果您希望选择性安装依赖，请参考安装教程中的相关说明。该产线对应的依赖分组为 `video`。
 
 ### 2.1 本地体验