Doc update (#2479)

* update docs

* update index

README.md

@@ -41,7 +41,8 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
 ## 📣 近期更新
 
-🔥🔥 《PaddleX三大部署能力详解与实战》，高性能推理、服务化部署、端侧部署全面升级。高性能推理深度优化模型推理与处理性能，大幅减少模型推理耗时；服务化部署提供7种语言调用示例，提升系统灵活性、扩展性和可维护性；端侧部署升级支持 ARM CPU 与 Mali/Adreno GPU 部署，模型更轻量级、耗费资源更少、执行速度更快。**11月7日（周四）19：00**直播为您深度解析 PaddleX 3.0 Beta1 在部署方面的功能、优势与技巧。 [报名链接](https://www.wjx.top/vm/rpPVnwS.aspx?udsid=524776)
+🔥🔥 《PaddleX三大部署能力详解与实战》，高性能推理、服务化部署、端侧部署全面升级。高性能推理深度优化模型推理与处理性能，大幅减少模型推理耗时；服务化部署提供7种语言调用示例，提升系统灵活性、扩展性和可维护性；端侧部署升级支持 ARM CPU 与 Mali/Adreno GPU 部署，模型更轻量级、耗费资源更少、执行速度更快。**11月7日（周四）19：00**直播为您深度解析 PaddleX 3.0 Beta1 在部署方面的功能、优势与技巧。 [报名链接](https://www.wjx.top/vm/rpPVnwS.aspx?udsid=524776)。
+>[❗ 更多宝藏课程](https://aistudio.baidu.com/education/group/info/32160)
 
 🔥🔥 **2024.9.30**，PaddleX 3.0 Beta1 开源版正式发布，提供 **200+ 模型** 通过极简的 Python API 一键调用；实现基于统一命令的模型全流程开发，并开源 **PP-ChatOCRv3** 特色模型产线基础能力；支持 **100+ 模型高性能推理和服务化部署**（持续迭代中），**4条模型产线8个重点视觉模型端侧部署**；**100+ 模型开发全流程适配昇腾 910B**，**39+ 模型开发全流程适配昆仑芯和寒武纪**。
 
@@ -49,6 +50,7 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
 🔥 **2024.3.25**，PaddleX 3.0 云端发布，支持在 AI Studio 星河社区 以零代码的方式【创建产线】使用。
 
+
  ## 🔠 模型产线说明
 
  **PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。**

README_en.md

@@ -44,6 +44,8 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
 
 🔥🔥 **"PaddleX Document Information Personalized Extraction Upgrade"**, PP-ChatOCRv3 innovatively provides custom development functions for OCR models based on data fusion technology, offering stronger model fine-tuning capabilities. Millions of high-quality general OCR text recognition data are automatically integrated into vertical model training data at a specific ratio, solving the problem of weakened general text recognition capabilities caused by vertical model training in the industry. Suitable for practical scenarios in industries such as automated office, financial risk control, healthcare, education and publishing, and legal and government sectors. **October 24th (Thursday) 19:00** Join our live session for an in-depth analysis of the open-source version of PP-ChatOCRv3 and the outstanding advantages of PaddleX 3.0 Beta1 in terms of accuracy and speed. [Registration Link](https://www.wjx.top/vm/wpPu8HL.aspx?udsid=994465)
 
+> [❗ Get more courses for free](https://aistudio.baidu.com/education/group/info/32160)
+
 🔥🔥 **9.30, 2024**, PaddleX 3.0 Beta1 open source version is officially released, providing **more than 200 models** that can be called with a simple Python API; achieve model full-process development based on unified commands, and open source the basic capabilities of the **PP-ChatOCRv3** pipeline; support **more than 100 models for high-performance inference and service-oriented deployment** (iterating continuously), **more than 7 key visual models for edge-deployment**; **more than 70 models have been adapted for the full development process of Ascend 910B**, **more than 15 models have been adapted for the full development process of Kunlun chips and Cambricon**
 
 🔥 **6.27, 2024**, PaddleX 3.0 Beta open source version is officially released, supporting the use of various mainstream hardware for pipeline and model development in a low-code manner on the local side.

docs/index.en.md

@@ -112,18 +112,21 @@ PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePadd
 
 🔥🔥 <b>"PaddleX Document Information Personalized Extraction Upgrade"</b>, PP-ChatOCRv3 innovatively provides custom development functions for OCR models based on data fusion technology, offering stronger model fine-tuning capabilities. Millions of high-quality general OCR text recognition data are automatically integrated into vertical model training data at a specific ratio, solving the problem of weakened general text recognition capabilities caused by vertical model training in the industry. Suitable for practical scenarios in industries such as automated office, financial risk control, healthcare, education and publishing, and legal and government sectors. <b>October 24th (Thursday) 19:00</b> Join our live session for an in-depth analysis of the open-source version of PP-ChatOCRv3 and the outstanding advantages of PaddleX 3.0 Beta1 in terms of accuracy and speed. [Registration Link](https://www.wjx.top/vm/wpPu8HL.aspx?udsid=994465)
 
+>[❗ more courses](https://aistudio.baidu.com/education/group/info/32160)
+
 🔥🔥 <b>9.30, 2024</b>, PaddleX 3.0 Beta1 open source version is officially released, providing <b>more than 200 models</b> that can be called with a simple Python API; achieve model full-process development based on unified commands, and open source the basic capabilities of the <b>PP-ChatOCRv3</b> pipeline; support <b>more than 100 models for high-performance inference and service-oriented deployment</b> (iterating continuously), <b>more than 7 key visual models for edge-deployment</b>; <b>more than 70 models have been adapted for the full development process of Ascend 910B</b>, <b>more than 15 models have been adapted for the full development process of Kunlun chips and Cambricon</b>
 
 🔥 <b>6.27, 2024</b>, PaddleX 3.0 Beta open source version is officially released, supporting the use of various mainstream hardware for pipeline and model development in a low-code manner on the local side.
 
 🔥 <b>3.25, 2024</b>, PaddleX 3.0 cloud release, supporting the creation of pipelines in the AI Studio Galaxy Community in a zero-code manner.
 
+> [more](https://paddlepaddle.github.io/PaddleX/latest/en/CHANGLOG.html)
+
 ## 🔠 Explanation of Pipeline
 PaddleX is dedicated to achieving pipeline-level model training, inference, and deployment. A pipeline refers to a series of predefined development processes for specific AI tasks, which includes a combination of single models (single-function modules) capable of independently completing a certain type of task.
 
 ## 📊 What can PaddleX do？
 
-
 All pipelines of PaddleX support <b>online experience</b> on [AI Studio](https://aistudio.baidu.com/overview) and local <b>fast inference</b>. You can quickly experience the effects of each pre-trained pipeline. If you are satisfied with the effects of the pre-trained pipeline, you can directly perform [high-performance inference](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/high_performance_inference.html) / [serving deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/service_deploy.html) / [edge deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/edge_deploy.html) on the pipeline. If not satisfied, you can also <b>Custom Development</b> to improve the pipeline effect. For the complete pipeline development process, please refer to the [PaddleX pipeline Development Tool Local Use Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/pipeline_develop_guide.html).
 
 In addition, PaddleX provides developers with a full-process efficient model training and deployment tool based on a [cloud-based GUI](https://aistudio.baidu.com/pipeline/mine). Developers <b>do not need code development</b>, just need to prepare a dataset that meets the pipeline requirements to <b>quickly start model training</b>. For details, please refer to the tutorial ["Developing Industrial-level AI Models with Zero Barrier"](https://aistudio.baidu.com/practical/introduce/546656605663301).

docs/index.md

@@ -110,10 +110,14 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
 🔥🔥 <b>2024.9.30</b>，PaddleX 3.0 Beta1 开源版正式发布，提供 <b>200+ 模型</b> 通过极简的 Python API 一键调用；实现基于统一命令的模型全流程开发，并开源 <b>PP-ChatOCRv3</b> 特色模型产线基础能力；支持 <b>100+ 模型高性能推理和服务化部署</b>（持续迭代中），<b>4条模型产线8个重点视觉模型端侧部署</b>；<b>100+ 模型开发全流程适配昇腾 910B</b>，<b>39+ 模型开发全流程适配昆仑芯和寒武纪</b>。低成本完成一站式全流程开发，加速产业应用。新增文本图像智能分析利器，大小模型融合策略显著增强版面解析能力，实现高精度实时预测。<b>10月24日（周四）19：00<b>直播为您深度解析 PP-ChatOCRv3 开源版本以及 PaddleX 3.0 Beta1 在精度、速度方面的卓越优势。 [报名链接](https://www.wjx.top/vm/wpPu8HL.aspx?udsid=994465)
 
+>[❗ 更多宝藏课程](https://aistudio.baidu.com/education/group/info/32160)
+
 🔥 <b>2024.6.27</b>，PaddleX 3.0 Beta 开源版正式发布，支持以低代码的方式在本地端使用多种主流硬件进行产线和模型开发。
 
 🔥 <b>2024.3.25</b>，PaddleX 3.0 云端发布，支持在 AI Studio 星河社区 以零代码的方式【创建产线】使用。
 
+> [更多](https://paddlepaddle.github.io/PaddleX/latest/CHANGLOG.html)
+
 ## 🔠 模型产线说明
 
  <b>PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。</b>
@@ -121,7 +125,6 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
 
  ## 📊 能力支持
 
-
 PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行<b>在线体验</b>，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能推理](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)/[服务化部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/service_deploy.html)/[端侧部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/edge_deploy.html)，如果不满意，您也可以使用产线的<b>二次开发</b>能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/pipeline_develop_guide.html)或各产线使用[教程](#-文档)。
 
 

docs/quick_start.en.md

@@ -14,20 +14,20 @@ hide:
 
 ```bash
 # cpu
-python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
 
 # gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
 
 # gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
+python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
 ```
 > ❗For more PaddlePaddle versions, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html).
 
 * <b>Installing PaddleX</b>
 
 ```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b1-py3-none-any.whl
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl
 ```
 
 > ❗For more installation methods, refer to the [PaddleX Installation Guide](https://paddlepaddle.github.io/PaddleX/latest/en/installation/installation.html).

docs/quick_start.md

@@ -13,13 +13,13 @@ hide:
 * <b>安装 PaddlePaddle</b>
 ```bash
 # cpu
-python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
 
 # gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
 
 # gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
-python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
+python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
 ```
 > ❗ 更多飞桨 Wheel 版本请参考[飞桨官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation./docs/zh/install/pip/linux-pip.html)。
 
@@ -27,7 +27,7 @@ python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.
 * <b>安装PaddleX</b>
 
 ```bash
-pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b1-py3-none-any.whl
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl
 ```
 
 > ❗ 更多安装方式参考 [PaddleX 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)

mkdocs.yml

@@ -210,7 +210,7 @@ plugins:
             心跳监测时序数据分类应用教程: Heartbeat Monitoring Time Series Data Classification Application Tutorial
             用电量长期预测应用教程: Long-term Electricity Consumption Forecasting Application Tutorial
             FAQ: FAQ
-  - git-committers: # 显示贡献者
+            近期更新: Recently Update
       repository: PaddlePaddle/PaddleX #仓库名称
       branch: release/3.0-beta1 #仓库分支
   - git-revision-date-localized: #显示更新时间
@@ -412,3 +412,4 @@ nav:
        - 心跳监测时序数据分类应用教程: practical_tutorials/ts_classification.md
        - 用电量长期预测应用教程: practical_tutorials/ts_forecast.md
   - FAQ: FAQ.md
+  - 近期更新: CHANGLOG.md