[Docs] Cherry-pick VisualDL docs and fix typo (#4160)

* 【PaddleX No.1】PaddleX 文档优化 (#4064)

* 1. Add the documentation for VisualDL (Zh and En) 2. Modify the README.md document for PaddleX (update the En document simultaneously), mainly modifying the 'Documentation' section of this md file 3. Modify the FAQ document (Zh and En)

* 1. FAQ.md&FAQ.en.md: del the content about Quant;
2. VisualDL.en.md&VisualDL.md: del the logo and QR Code
3. mkdocs.yml: add the VisualDL
4. VisualDL: ready to add the content: How to use the log or weights produced by PaddleX with vdl. I am still in the process of learning.

* del the Prune content in FAQ

* Fix colon

---------

Co-authored-by: Echo-Nie <157974576+Echo-Nie@users.noreply.github.com>

README.md

@@ -716,13 +716,12 @@ for res in output:
 
 
 ## 📖 文档
-<details>
+<details open>
   <summary> <b> ⬇️ 安装 </b></summary>
 
   * [📦 PaddlePaddle 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/paddlepaddle_install.html)
   * [📦 PaddleX 安装教程](https://paddlepaddle.github.io/PaddleX/latest/installation/installation.html)
 
-
 </details>
 
 <details open>
@@ -733,74 +732,112 @@ for res in output:
 * <details open>
     <summary> <b> 📝 文本图像智能分析 </b></summary>
 
-   * [📄 文档场景信息抽取v3产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html)
-  </details>
+   * [📄 文档场景信息抽取v3产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html)
+
+   * [📄 文档场景信息抽取v4产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html)
+
+</details>
 
 * <details open>
     <summary> <b> 🔍 OCR </b></summary>
 
   * [📜 通用 OCR 产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/OCR.html )
+
   * [📊 通用表格识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html )
+
   * [🗂️ 通用表格识别v2产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html)
+
   * [📰 通用版面解析产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html)
+
   * [🗞️ 通用版面解析产线v3使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html)
   * [📐 公式识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)
-  * [🖋️ 印章文本识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
+  * [📝 印章文本识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
   * [🖌️ 文档图像预处理产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html)
+
 </details>
 
 * <details open>
     <summary> <b> 🎥 计算机视觉 </b></summary>
 
    * [🖼️ 通用图像分类产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_classification.html)
-   * [🎯 通用目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html)
+
+    * [🎯 通用目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/object_detection.html)
+
    * [📋 通用实例分割产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html)
-   * [🗣️ 通用语义分割产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html)
+
+    * [🗣️ 通用语义分割产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html)
+
    * [🏷️ 图像多标签分类产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html)
-   * [🔍 小目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html)
+
+    * [🔍 小目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html)
+
    * [🖼️ 图像异常检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html)
+
+    * [🌐 3D多模态融合检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html)
+
    * [🔍 人体关键点检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html)
-   * [📚 开放词汇检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html)
+
+    * [📚 开放词汇检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html)
+
    * [🎨 开放词汇分割产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html)
-   * [🔄 旋转目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html)
-   * [🌐 3D多模态融合检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html)
+
+    * [🔄 旋转目标检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html)
+
    * [🖼️ 通用图像识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html)
-   * [🆔人脸识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html)
+
+    * [🚶‍♀️ 行人属性识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html)
+
    * [🚗 车辆属性识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html)
-   * [🚶‍♀️ 行人属性识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html)
 
+    * [🆔人脸识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/cv_pipelines/face_recognition.html)
+
+</details>
 
 * <details open>
     <summary> <b> ⏱️ 时序分析</b> </summary>
 
    * [📈 时序预测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html)
+
    * [📉 时序异常检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html)
-   * [🕒 时序分类产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html)
-  </details>
+
+    * [🕒 时序分类产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html)
+
+</details>
 
 * <details open>
     <summary> <b> 🎤 语音识别</b> </summary>
 
     * [🌐 多语种语音识别产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/speech_pipelines/multilingual_speech_recognition.html)
 
+</details>
+
 * <details open>
     <summary> <b> 🎥 视频识别</b> </summary>
 
     * [📈 通用视频分类产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/video_pipelines/video_classification.html)
+
     * [🔍 通用视频检测产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/video_pipelines/video_detection.html)
 
+
+</details>
+
 * <details open>
     <summary> <b> 🌐 多模态视觉语言模型</b> </summary>
 
    * [📝 文档理解产线使用教程](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/tutorials/vlm_pipelines/doc_understanding.html)
-  </details>
 
-* <details>
+</details>
+
+* <details open>
     <summary> <b>🔧 相关说明文件</b> </summary>
 
    * [🖥️ PaddleX 产线命令行使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/instructions/pipeline_CLI_usage.html)
-   * [📝 PaddleX 产线 Python 脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/instructions/pipeline_python_API.html)
-  </details>
+
+  * [📝 PaddleX 产线 Python 脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/instructions/pipeline_python_API.html)
+
+  * [🔎 产线并行推理](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/instructions/parallel_inference.html)
+
+</details>
 
 </details>
 
@@ -811,116 +848,135 @@ for res in output:
   <summary> <b> 🔍 OCR </b></summary>
 
   * [📝 文本检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_detection.html)
+
   * [🔖 印章文本检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/seal_text_detection.html)
+
   * [🔠 文本识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_recognition.html)
+
   * [🗺️ 版面区域检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/layout_detection.html)
+
   * [📊 表格结构识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_structure_recognition.html)
-  * [📄 文档图像方向分类使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
-  * [🔧 文本图像矫正模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
-  * [📐 公式识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/formula_recognition.html)
+
   * [📊 表格单元格检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_cells_detection.html)
+
   * [📈 表格分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/table_classification.html)
+
+  * [📄 文档图像方向分类使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
+
+  * [🔧 文本图像矫正模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
+
   * [📝 文本行方向分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/textline_orientation_classification.html)
 
-  </details>
+  * [📐 公式识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/formula_recognition.html)
+
+
+</details>
+
 
 * <details open>
   <summary> <b> 🖼️ 图像分类 </b></summary>
 
   * [📂 图像分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/image_classification.html)
+
   * [🏷️ 图像多标签分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/image_multilabel_classification.html)
+
   * [👤 行人属性识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.html)
+
   * [🚗 车辆属性识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.html)
 
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> 🏞️ 图像特征 </b></summary>
 
     * [🔗 图像特征模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/image_feature.html)
+
     * [😁 人脸特征模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/face_feature.html)
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> 🎯 目标检测 </b></summary>
 
   * [🎯 目标检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/object_detection.html)
+
   * [📏 小目标检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/small_object_detection.html)
+
   * [🧑‍🤝‍🧑 人脸检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/face_detection.html)
+
   * [🔍 主体检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/mainbody_detection.html)
+
   * [🚶 行人检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/human_detection.html)
-  * [🚗 车辆检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/vehicle_detection.html)
-  * [🔄 旋转目标检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/rotated_object_detection.html)
 
-* <details open>
-  <summary> <b> 🌐 开放词汇目标检测 </b></summary>
+  * [🚶‍♂️ 人体关键点检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/human_keypoint_detection.html)
 
   * [🌐 开放词汇目标检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/open_vocabulary_detection.html)
-  </details>
-
-* <details open>
-  <summary> <b> 🎯 关键点检测 </b></summary>
-
-  * [🚶‍♂️ 人体关键点检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/human_keypoint_detection.html)
-  </details>
 
+</details>
 
 * <details open>
   <summary> <b> 🖼️ 图像分割 </b></summary>
 
   * [🗺️ 语义分割模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/semantic_segmentation.html)
+
   * [🔍 实例分割模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/instance_segmentation.html)
-  * [🚨 图像异常检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/anomaly_detection.html)
-  </details>
 
-* <details open>
-  <summary> <b> 🌐 开放词汇分割 </b></summary>
+  * [🚨 图像异常检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/anomaly_detection.html)
 
   * [🌐 开放词汇分割模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.html)
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> ⏱️ 时序分析 </b></summary>
 
   * [📈 时序预测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/time_series_modules/time_series_forecasting.html)
+
   * [🚨 时序异常检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.html)
-  * [🕒 时序分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/time_series_modules/time_series_classification.html)
-  </details>
 
-* <details open>
-  <summary> <b> 📦 3D </b></summary>
+  * [🕒 时序分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/time_series_modules/time_series_classification.html)
 
-  * [📦 3D多模态融合检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/3d_bev_detection.html)
-  </details>
+</details>
 
 * <details open>
   <summary> <b> 🎤 语音识别 </b></summary>
 
   * [🌐 多语种语音识别模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/speech_modules/multilingual_speech_recognition.html)
-  </details>
+
+</details>
 
 * <details open>
-  <summary> <b> 🎥 视频识别 </b></summary>
+  <summary> <b> 📦 3D </b></summary>
+
+  * [📦 3D多模态融合检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/cv_modules/3d_bev_detection.html)
 
-  * [📈 视频分类模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/video_modules/video_classification.html)
-  * [🔍 视频检测模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/video_modules/video_detection.html)
-  </details>
+</details>
 
 * <details open>
   <summary> <b> 🌐 多模态视觉语言模型 </b></summary>
 
   * [📝 文档类视觉语言模型模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/vlm_modules/doc_vlm.html)
+
   * [📈 图表解析模块使用教程](https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/vlm_modules/chart_parsing.html)
-  </details>
 
-* <details>
+</details>
+
+
+* <details open>
   <summary> <b> 📄 相关说明文件 </b></summary>
 
   * [📝 PaddleX 单模型 Python 脚本使用说明](https://paddlepaddle.github.io/PaddleX/latest/module_usage/instructions/model_python_API.html)
+
   * [📝 PaddleX 通用模型配置文件参数说明](https://paddlepaddle.github.io/PaddleX/latest/module_usage/instructions/config_parameters_common.html)
+
   * [📝 PaddleX 时序任务模型配置文件参数说明](https://paddlepaddle.github.io/PaddleX/latest/module_usage/instructions/config_parameters_time_series.html)
+
   * [📝 PaddleX 3d任务模型配置文件参数说明](https://paddlepaddle.github.io/PaddleX/latest/module_usage/instructions/config_parameters_3d.html)
-  </details>
+
+  * [📝 模型推理 Benchmark](https://paddlepaddle.github.io/PaddleX/latest/module_usage/instructions/benchmark.html)
+
+</details>
 
 </details>
 
@@ -930,8 +986,10 @@ for res in output:
   * [🚀 PaddleX 高性能推理指南](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)
   * [🖥️ PaddleX 服务化部署指南](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/serving.html)
   * [📱 PaddleX 端侧部署指南](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/edge_deploy.html)
+  * [🌐 获取 ONNX 模型](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/paddle2onnx.html)
 
 </details>
+
 <details open>
   <summary> <b> 🖥️ 多硬件使用 </b></summary>
 
@@ -940,26 +998,113 @@ for res in output:
   * [🔲 寒武纪 MLU 飞桨安装教程](https://paddlepaddle.github.io/PaddleX/latest/other_devices_support/paddlepaddle_install_MLU.html)
   * [💻 昇腾 NPU 飞桨安装教程](https://paddlepaddle.github.io/PaddleX/latest/other_devices_support/paddlepaddle_install_NPU.html)
   * [🔌 昆仑 XPU 飞桨安装教程](https://paddlepaddle.github.io/PaddleX/latest/other_devices_support/paddlepaddle_install_XPU.html)
+  * [📱 燧原 GCU 飞桨安装教程](https://paddlepaddle.github.io/PaddleX/latest/other_devices_support/paddlepaddle_install_GCU.html)
 
 </details>
 
-<details>
+<details open>
+<summary> <b> 📊 数据标注教程 </b></summary>
+
+- <details open>
+  <summary> <b> 💻 计算机视觉 </b></summary>
+
+  - [📂 图像分类任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/image_classification.html)
+
+  - [📂 图像特征任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/image_feature.html)
+
+  - [📂 实例分割任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/instance_segmentation.html)
+
+  - [📂 图像多标签分类模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/ml_classification.html)
+
+  - [📂 目标检测任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/object_detection.html)
+
+  - [📂 语义分割任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/cv_modules/semantic_segmentation.html)
+
+</details>
+
+- <details open>
+  <summary> <b> 🔍 OCR </b></summary>
+
+  - [📊 表格识别任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/ocr_modules/table_recognition.html)
+
+  - [📰 文本检测/识别任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/ocr_modules/text_detection_recognition.html)
+
+</details>
+
+- <details open>
+  <summary> <b> 📉 时序分析 </b></summary>
+
+  - [📈 时序异常检测任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/time_series_modules/time_series_anomaly_detection.html)
+
+  - [📉时序分类任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/time_series_modules/time_series_classification.html)
+
+  - [🕜 时序预测任务模块](https://paddlepaddle.github.io/PaddleX/latest/data_annotations/time_series_modules/time_series_forecasting.html)
+
+</details>
+
+</details>
+
+<details open>
+  <summary> <b> 📑 产线列表 </b></summary>
+
+  * [🖲️ PaddleX产线列表(CPU/GPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/pipelines_list.html)
+  * [🔲 PaddleX产线列表(DCU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/pipelines_list_dcu.html)
+  * [💻 PaddleX产线列表(MLU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/pipelines_list_mlu.html)
+  * [🔌 PaddleX产线列表(NPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/pipelines_list_npu.html)
+  * [📱 PaddleX产线列表(XPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/pipelines_list_xpu.html)
+
+</details>
+
+<details open>
+  <summary> <b> 📄 模型列表 </b></summary>
+
+  * [🖲️ PaddleX模型列表（CPU/GPU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/models_list.html)
+  * [🔲 PaddleX模型列表（海光 DCU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_dcu.html)
+  * [💻 PaddleX模型列表（寒武纪 MLU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_mlu.html)
+  * [🔌 PaddleX模型列表（昇腾 NPU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_npu.html)
+  * [📱 PaddleX模型列表（昆仑 XPU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_xpu.html)
+  * [📺 PaddleX模型列表（燧原 GCU）](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_gcu.html)
+
+</details>
+
+<details open>
   <summary> <b> 📝 产业实践教程&范例 </b></summary>
 
-* [📑 文档场景信息抽取v3模型产线———论文文献信息抽取应用教程](./docs/practical_tutorials/document_scene_information_extraction(layout_detection)_tutorial.md)
-* [📑 文档场景信息抽取v3模型产线———印章信息抽取应用教程](./docs/practical_tutorials/document_scene_information_extraction(seal_recognition)_tutorial.md)
+* [📑 文档场景信息抽取v3模型产线———论文文献信息抽取应用教程](https://paddlepaddle.github.io/PaddleX/3.0/practical_tutorials/document_scene_information_extraction%28layout_detection%29_tutorial.html)
+
+* [📑 文档场景信息抽取v3模型产线———印章信息抽取应用教程](https://paddlepaddle.github.io/PaddleX/3.0/practical_tutorials/document_scene_information_extraction%28seal_recognition%29_tutorial.html)
+
+* [📑 文档场景信息抽取v3模型产线———DeepSeek 篇](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/document_scene_information_extraction(deepseek)_tutorial.html)
+
+* [🚗 通用 OCR 模型产线———车牌识别教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ocr_det_license_tutorial.html)
+
+* [✍️ 通用 OCR 模型产线———手写中文识别教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ocr_rec_chinese_tutorial.html)
+
+* [🔍 公式识别模型产线实践教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/formula_recognition_tutorial.html)
+
+* [💻 版面区域检测模型使用实践教程———大模型训练数据构建教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/layout_detection.html)
+
+* [😊 人脸识别之卡通人脸识别实践教程———卡通人脸识别教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/face_recognition_tutorial.html)
+
 * [🖼️ 通用图像分类模型产线———垃圾分类教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/image_classification_garbage_tutorial.html)
+
 * [🧩 通用实例分割模型产线———遥感图像实例分割教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/instance_segmentation_remote_sensing_tutorial.html)
+
 * [👥 通用目标检测模型产线———行人跌倒检测教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/object_detection_fall_tutorial.html)
+
 * [👗 通用目标检测模型产线———服装时尚元素检测教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/object_detection_fashion_pedia_tutorial.html)
-* [🚗 通用 OCR 模型产线———车牌识别教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ocr_det_license_tutorial.html)
-* [✍️ 通用 OCR 模型产线———手写中文识别教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ocr_rec_chinese_tutorial.html)
+
 * [🗣️ 通用语义分割模型产线———车道线分割教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/semantic_segmentation_road_tutorial.html)
+
 * [🛠️ 时序异常检测模型产线———设备异常检测应用教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ts_anomaly_detection.html)
+
 * [🎢 时序分类模型产线———心跳监测时序数据分类应用教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ts_classification.html)
+
 * [🔋 时序预测模型产线———用电量长期预测应用教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/ts_forecast.html)
 
-  </details>
+* [🔧 产线部署实践教程](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/deployment_tutorial.html)
+
+</details>
 
 ## 🤔 FAQ
 

README_en.md

@@ -720,7 +720,7 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
 </details>
 
 ## 📖 Documentation
-<details>
+<details open>
   <summary> <b> ⬇️ Installation </b></summary>
 
   * [📦 PaddlePaddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/installation/paddlepaddle_install.html)
@@ -736,8 +736,10 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
 * <details open>
     <summary> <b> 📝 Information Extraction</b></summary>
 
-   * [📄 PP-ChatOCRv3 Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html)
-  </details>
+   * [📄 PP-ChatOCRv3 Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html)
+    * [📄 PP-ChatOCRv4 Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html)
+
+</details>
 
 * <details open>
     <summary> <b> 🔍 OCR </b></summary>
@@ -750,7 +752,8 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
     * [📐 Formula Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html)
     * [📝 Seal Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html)
     * [🖌️ Document Image Preprocessing](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html)
-  </details>
+
+</details>
 
 * <details open>
     <summary> <b> 🎥 Computer Vision </b></summary>
@@ -762,16 +765,17 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
    * [🏷️ Multi-label Image Classification Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html)
    * [🔍 Small Object Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html)
    * [🖼️ Image Anomaly Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html)
+   * [🌐 3D Bev Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html)
    * [🔍 Human Keypoint Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html)
    * [📚 Open Vocabulary Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html)
    * [🎨 Open Vocabulary Segmentation Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html)
    * [🔄 Rotated Object Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html)
-   * [🌐 3D Bev Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html)
    * [🖼️ Image Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html)
-   * [🆔 Face Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html)
-   * [🚗 Vehicle Attribute Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html)
    * [🚶‍♀️ Pedestrian Attribute Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute.html)
-  </details>
+   * [🚗 Vehicle Attribute Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute.html)
+   * [🆔 Face Recognition Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html)
+
+</details>
 
 * <details open>
     <summary> <b> ⏱️ Time Series Analysis</b> </summary>
@@ -779,7 +783,8 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
    * [📈 Time Series Forecasting Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html)
    * [📉 Time Series Anomaly Detection Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html)
    * [🕒 Time Series Classification Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html)
-  </details>
+
+</details>
 
 * <details open>
     <summary> <b> 🎤 Speech Recognition</b> </summary>
@@ -802,8 +807,10 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
     <summary> <b>🔧 Related Instructions</b> </summary>
 
    * [🖥️ PaddleX pipeline Command Line Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/instructions/pipeline_CLI_usage.html)
-   * [📝 PaddleX pipeline Python Script Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/instructions/pipeline_python_API.html)
-  </details>
+  * [📝 PaddleX pipeline Python Script Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/instructions/pipeline_python_API.html)
+  * [🔎 Line-Parallel Inference](https://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/instructions/parallel_inference.html)
+
+</details>
 
 </details>
 
@@ -818,31 +825,32 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
   * [🔠 Text Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_recognition.html)
   * [🗺️ Layout Parsing Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html)
   * [📊 Table Structure Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_structure_recognition.html)
-  * [📄 Document Image Orientation Classification Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
-  * [🔧 Document Image Unwarp Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
-  * [📐 Formula Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/formula_recognition.html)
   * [📊 Table Cell Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_cells_detection.html)
   * [📈 Table Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_classification.html)
+  * [📄 Document Image Orientation Classification Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/doc_img_orientation_classification.html)
+  * [🔧 Document Image Unwarp Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/text_image_unwarping.html)
   * [📝 Text Line Orientation Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/textline_orientation_classification.html)
-  </details>
+  * [📐 Formula Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/formula_recognition.html)
+
+</details>
 
 * <details open>
   <summary> <b> 🖼️ Image Classification </b></summary>
 
   * [📂 Image Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/image_classification.html)
   * [🏷️ Multi-label Image Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/image_multilabel_classification.html)
-
   * [👤 Pedestrian Attribute Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.html)
   * [🚗 Vehicle Attribute Recognition Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/vehicle_attribute_recognition.html)
 
-  </details>
+</details>
 
 * <details open>
   <summary> <b> 🏞️ Image Features </b></summary>
 
     * [🔗 Image Feature Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/image_feature.html)
     * [😁 Face_Feature Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/face_feature.html)
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> 🎯 Object Detection </b></summary>
@@ -852,25 +860,10 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
   * [🧑‍🤝‍🧑 Face Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/face_detection.html)
   * [🔍 Mainbody Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/mainbody_detection.html)
   * [🚶 Pedestrian Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/human_detection.html)
-  * [🚗 Vehicle Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/vehicle_detection.html)
-  * [🔄 Rotated Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/rotated_object_detection.html)
-
-  </details>
-
-* <details open>
-  <summary> <b> 🌐 Open-Vocabulary Object Detection </b></summary>
-
-  * [🌐 Open-Vocabulary Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_detection.html)
-</details>
-
-* <details open>
-  <summary> <b> 🎯 Keypoint Detection </b></summary>
-
   * [🚶‍♂️ Human Keypoint Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/human_keypoint_detection.html)
-   </details>
-
-
+  * [🌐 Open-Vocabulary Object Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_detection.html)
 
+</details>
 
 * <details open>
   <summary> <b> 🖼️ Image Segmentation </b></summary>
@@ -878,47 +871,40 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
   * [🗺️ Semantic Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/semantic_segmentation.html)
   * [🔍 Instance Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/instance_segmentation.html)
   * [🚨 Image Anomaly Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/anomaly_detection.html)
-  </details>
-
-* <details open>
-  <summary> <b> 🌐 Open-Vocabulary Segmentation </b></summary>
-
   * [🌐 Open-Vocabulary Segmentation Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/open_vocabulary_segmentation.html)
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> ⏱️ Time Series Analysis </b></summary>
 
   * [📈 Time Series Forecasting Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/time_series_modules/time_series_forecasting.html)
-  * [🚨 Time Series Anomaly Detection Module Tutorial](./docs/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.md)
+  * [🚨 Time Series Anomaly Detection Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/time_series_modules/time_series_anomaly_detection.html)
   * [🕒 Time Series Classification Module Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/time_series_modules/time_series_classification.html)
-  </details>
-
-* <details open>
-  <summary> <b> 📦 3D  </b></summary>
 
-  * [📦 3D Multimodal Fusion Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/3d_bev_detection.html)
-  </details>
+</details>
 
 * <details open>
   <summary> <b> 🎤 Speech Recognition </b></summary>
 
   * [🌐 Multilingual Speech Recognition Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/speech_modules/multilingual_speech_recognition.html)
-  </details>
+
+</details>
 
 * <details open>
-  <summary> <b> 🎥 Video Recognition </b></summary>
+  <summary> <b> 📦 3D  </b></summary>
 
-  * [📈 Video Classification Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/video_modules/video_classification.html)
-  * [🔍 Video Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/video_modules/video_detection.html)
-  </details>
+  * [📦 3D Multimodal Fusion Detection Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/cv_modules/3d_bev_detection.html)
+
+</details>
 
 * <details open>
   <summary> <b> 🌐 Multimodal Vision-Language Model </b></summary>
 
   * [📝 Document Vision-Language Model Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/vlm_modules/doc_vlm.html)
   * [📈 Chart Parsing Module Usage Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/vlm_modules/chart_parsing.html)
-  </details>
+
+</details>
 
 * <details open>
   <summary> <b> 📄 Related Instructions </b></summary>
@@ -927,7 +913,9 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
   * [📝 PaddleX General Model Configuration File Parameter Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/instructions/config_parameters_common.html)
   * [📝 PaddleX Time Series Task Model Configuration File Parameter Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/instructions/config_parameters_time_series.html)
   * [📝 PaddleX 3D Task Model Configuration File Parameter Instruction](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/instructions/config_parameters_3d.html)
-  </details>
+  * [📝 Model Inference Benchmark](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/instructions/benchmark.html)
+
+</details>
 
 </details>
 
@@ -937,36 +925,101 @@ To use the Python script for other pipelines, simply adjust the `pipeline` param
   * [🚀 PaddleX High-Performance Inference Guide](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/high_performance_inference.html)
   * [🖥️ PaddleX Serving Guide](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/serving.html)
   * [📱 PaddleX Edge Deployment Guide](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/edge_deploy.html)
+  * [🌐 Installation and Usage of the Paddle2ONNX Plugin](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/paddle2onnx.html)
 
 </details>
+
 <details open>
   <summary> <b> 🖥️ Multi-Hardware Usage </b></summary>
 
-  * [⚙️ Multi-Hardware Usage Guide](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/multi_devices_use_guide.html)
-  * [⚙️ DCU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_DCU.html)
-  * [⚙️ MLU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_MLU.html)
-  * [⚙️ NPU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_NPU.html)
-  * [⚙️ XPU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_XPU.html)
+  * [🔧 Multi-Hardware Usage Guide](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/multi_devices_use_guide.html)
+  * [🖲️ DCU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_DCU.html)
+  * [🔲 MLU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_MLU.html)
+  * [💻 NPU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_NPU.html)
+  * [🔌 XPU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_XPU.html)
+  * [📱 GCU Paddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/other_devices_support/paddlepaddle_install_GCU.html)
 
 </details>
 
-<details>
+<details open>
+  <summary> <b> 📊 Data Annotation Tutorials </b></summary>
+
+- <details open>
+  <summary> <b> 💻 计算机视觉 </b></summary>
+
+  - [📂 Image Classification Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/image_classification.html)
+  - [📂 Image Feature Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/image_feature.html)
+  - [📂 Instance Segmentation Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/instance_segmentation.html)
+  - [📂 Multi-Label Classification Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/ml_classification.html)
+  - [📂 Object Detection Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/object_detection.html)
+  - [📂 Semantic Segmentation Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/cv_modules/semantic_segmentation.html)
+
+</details>
+
+- <details open>
+  <summary> <b> 🔍 OCR </b></summary>
+
+  - [📊 Table Structure Recognition Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/ocr_modules/table_recognition.html)
+  - [📰 Text Detection/Text Recognition Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/ocr_modules/text_detection_recognition.html)
+
+</details>
+
+- <details open>
+  <summary> <b> 📉 时序分析 </b></summary>
+
+  - [📈 Time Series Anomaly Detection Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/time_series_modules/time_series_anomaly_detection.html)
+  - [📉Time Series Classification Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/time_series_modules/time_series_classification.html)
+  - [🕜 Time Series Forecasting Task Module](https://paddlepaddle.github.io/PaddleX/latest/en/data_annotations/time_series_modules/time_series_forecasting.html)
+
+</details>
+
+</details>
+
+<details open>
+  <summary> <b> 📑 Pipeline List </b></summary>
+
+  * [🖲️ PaddleX Pipelines (CPU/GPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/pipelines_list.html)
+  * [🔲 PaddleX Pipelines (DCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/pipelines_list_dcu.html)
+  * [💻 PaddleX Pipelines (MLU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/pipelines_list_mlu.html)
+  * [🔌 PaddleX Pipelines (NPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/pipelines_list_npu.html)
+  * [📱 PaddleX Pipelines (XPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/pipelines_list_xpu.html)
+
+</details>
+
+<details open>
+  <summary> <b> 📄 Model List </b></summary>
+
+  * [🖲️ PaddleX Model List (CPU/GPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/models_list.html)
+  * [🔲 PaddleX Model List (Hygon DCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_dcu.html)
+  * [💻 PaddleX Model List (Cambricon MLU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_mlu.html)
+  * [🔌 PaddleX Model List (Huawei Ascend NPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_npu.html)
+  * [📱 PaddleX Model List (Kunlun XPU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_xpu.html)
+  * [📺 PaddleX Model List (Enflame GCU)](https://paddlepaddle.github.io/PaddleX/latest/en/support_list/model_list_gcu.html)
+
+</details>
+
+<details open>
   <summary> <b> 📝 Tutorials & Examples </b></summary>
 
-* [📑 PP-ChatOCRv3 Model Line —— Paper Document Information Extract Tutorial](./docs/practical_tutorials/document_scene_information_extraction(layout_detection)_tutorial_en.md)
-* [📑 PP-ChatOCRv3 Model Line —— Seal Information Extract Tutorial](./docs/practical_tutorials/document_scene_information_extraction(seal_recognition)_tutorial_en.md)
+* [📑 PP-ChatOCRv3 Model Line —— Paper Document Information Extract Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/document_scene_information_extraction%28layout_detection%29_tutorial.html)
+* [📑 PP-ChatOCRv3 Model Line —— Seal Information Extract Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/document_scene_information_extraction%28seal_recognition%29_tutorial.html)
+* [📑 Document Scene Information Extraction v3 (PP-ChatOCRv3_doc) -- DeepSeek Edition](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/document_scene_information_extraction%28deepseek%29_tutorial.html)
+* [🚗 General OCR Model Line —— License Plate Recognition Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ocr_det_license_tutorial.html)
+* [✍️ General OCR Model Line —— Handwritten Chinese Character Recognition Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ocr_rec_chinese_tutorial.html)
+* [🔍 Practical Guide to Formula Recognition Model Production Line](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/formula_recognition_tutorial.html)
+* [💻 Layout Detection Model Pipeline Tutorial —— Large Model Training Data Construction Tutorial](https://paddlepaddle.github.io/PaddleX/latest/practical_tutorials/layout_detection.html)
+* [😊 Face Recognition Pipeline —— Cartoon Face Recognition Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/face_recognition_tutorial.html)
 * [🖼️ General Image Classification Model Line —— Garbage Classification Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/image_classification_garbage_tutorial.html)
 * [🧩 General Instance Segmentation Model Line —— Remote Sensing Image Instance Segmentation Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/instance_segmentation_remote_sensing_tutorial.html)
 * [👥 General Object Detection Model Line —— Pedestrian Fall Detection Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/object_detection_fall_tutorial.html)
 * [👗 General Object Detection Model Line —— Fashion Element Detection Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/object_detection_fashion_pedia_tutorial.html)
-* [🚗 General OCR Model Line —— License Plate Recognition Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ocr_det_license_tutorial.html)
-* [✍️ General OCR Model Line —— Handwritten Chinese Character Recognition Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ocr_rec_chinese_tutorial.html)
 * [🗣️ General Semantic Segmentation Model Line —— Road Line Segmentation Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/semantic_segmentation_road_tutorial.html)
 * [🛠️ Time Series Anomaly Detection Model Line —— Equipment Anomaly Detection Application Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ts_anomaly_detection.html)
 * [🎢 Time Series Classification Model Line —— Heartbeat Monitoring Time Series Data Classification Application Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ts_classification.html)
 * [🔋 Time Series Forecasting Model Line —— Long-term Electricity Consumption Forecasting Application Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/ts_forecast.html)
+* [🔧 Pipeline Deployment Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/practical_tutorials/deployment_tutorial.html)
 
-  </details>
+</details>
 
 
 

docs/FAQ.en.md

@@ -20,6 +20,28 @@ A: If your application scenario in using PaddleX mainly focuses on model inferen
 
 A: Baidu AIStudio Community's Zero-Code Pipeline is the cloud-based carrier of PaddleX, with its underlying code consistent with PaddleX, and can be considered as a cloud-based PaddleX. The design philosophy of Baidu AIStudio Community's Zero-Code Pipeline is to enable users to quickly build and deploy model applications without needing to delve deeply into programming and algorithm knowledge. On this basis, Baidu AIStudio Community's Zero-Code Pipeline also provides many special pipelines, such as training high-precision models with a small number of samples and solving complex time-series problems using multi-model fusion schemes. PaddleX, on the other hand, is a local development tool that provides users with powerful functions supporting more in-depth secondary development. This means developers can flexibly adjust and expand based on PaddleX to create solutions that better fit specific application scenarios. Additionally, PaddleX offers a rich set of model interfaces, supporting users in freely combining models for use.
 
+## <b>Q: How to continue training from a previously trained model?</b>
+
+A: To resume training from a saved checkpoint, set the `pretrain_weights` parameter to the path of the previously saved model when calling the `train` interface.
+
+## <b>Q: What are the differences between various models saved by PaddleX and how to distinguish them?</b>
+
+A: The purposes of different types of models are as follows:
+
+1. **Normally Trained Saved Model**: Suitable for loading predictions, serving as pre-training weights, or exporting deployment models.
+3. **Exported Deployment Model**: Designed for server-side deployment and cannot be used as pre-training weights.
+
+To distinguish between these models, check the `status` field in the `model.yml` file within the model directory:
+- `Normal`: Normally trained model
+- `Infer`: Deployment model
+
+## <b>Q: Every time I start a new training session, it tries to re-download the pretrained models. Can this be avoided?</b>
+
+A: Yes, you have two options:
+
+1. Manually manage the models as described previously.
+2. Set a global cache path for pretrained models, e.g., `paddlex.pretrain_dir='/usrname/paddlex'`. Models already downloaded to this directory will not be redownloaded.
+
 ## Q: When I encounter problems while using PaddleX, how should I provide feedback?
 
 A: Welcome to the [Discussion Area](https://github.com/PaddlePaddle/PaddleX/discussions) to communicate with a vast number of developers! If you find errors or deficiencies in PaddleX, you are also welcome to [submit an issue](https://github.com/PaddlePaddle/PaddleX/issues), and our on-duty team members will respond to your questions as soon as possible.

docs/FAQ.md

@@ -28,6 +28,33 @@ A：星河零代码产线是PaddleX 的云端载体，底层代码与PaddleX保
 
 
 
+## <b>Q：如何从之前训练的模型继续训练？</b>
+
+A：在调用 `train` 接口时，将 `pretrain_weights` 参数设置为之前保存的模型路径即可继续训练。
+
+
+
+## <b>Q：PaddleX保存的几种模型有什么区别？如何区分？</b>
+
+A：不同类型的模型用途如下：
+
+1. **正常训练保存模型**：可用于加载预测、作为预训练模型、导出部署模型；
+3. **导出部署模型**：用于服务端部署，不可用于预训练；
+
+区分方法：查看模型目录下的 `model.yml` 文件中的 `status` 字段：
+- `Normal`: 正常模型
+- `Infer`: 部署模型
+
+
+
+## <b>Q：每次训练都需要重新下载预训练模型，能否只下载一次？</b>
+
+A：可以：
+1. 参考上述方式手动管理模型；
+2. 设置全局预训练模型缓存路径，例如：`paddlex.pretrain_dir='/usrname/paddlex'`，已下载模型将不会重复下载。
+
+
+
 ## <b>Q：当我在使用PaddleX的过程中遇到问题，应该怎样反馈呢？</b>
 
 A：欢迎来[讨论区](https://github.com/PaddlePaddle/PaddleX/discussions)与海量开发者一起交流！若您发现了PaddleX的错误或不足，也欢迎向我们[提出issue](https://github.com/PaddlePaddle/PaddleX/issues)，值班同学将尽快为您解答问题。

docs/VisualDL.en.md

@@ -0,0 +1,332 @@
+[**中文**](./VisualDL.md)
+
+
+
+## Introduction to VisualDL
+
+VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters, and visualizes model structures, data samples, histograms of tensors, pr curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently.
+
+VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing the model structure, displaying the data sample, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more. Additionally, VisualDL provides VDL.service, which enables developers easily to save, track and share visualization results of experiments. For specific guidelines of each function, please refer to  [**VisualDL User Guide**](https://www.paddlepaddle.org.cn/documentation/docs/en/2.2/guides/03_VisualDL/visualdl_usage_en.html). Currently, VisualDL iterates rapidly and new functions will be continously added.
+
+VisualDL natively supports the use of Python. Developers can retrieve plentiful visualization results by simply adding a few lines of Python code into the model before training.
+
+## Contents
+
+* [Key Highlights](#Key-Highlights)
+* [Installation](#Installation)
+* [Usage Guideline](#Usage-Guideline)
+* [Function Preview](#Function-Preview)
+* [Contribution](#Contribution)
+* [More Details](#More-Details)
+* [Technical Communication](#Technical-Communication)
+
+
+
+## Key Highlights
+
+**Easy to Use**
+
+The high-level design of API makes it easy to use. Only one click can initiate the visualization of model structures.
+
+**Various Functions**
+
+The function contains the visualization of training parameters, data samples, graph structures, histograms of tensors, PR curves and high-dimensional data.
+
+**High Compatibility**
+
+VisualDL provides the visualization of the mainstream model structures such as Paddle, ONNX, Caffe, widely supporting visual analysis for diverse users.
+
+**Fully Support**
+
+By Integrating into PaddlePaddle and related modules, VisualDL allows developers to use different components unobstructed, and thus have the best experience in the PaddlePaddle ecosystem.
+
+## Installation
+
+It is recommended to use **pip installation**, which is simple and convenient. Just enter the following command in the terminal:
+
+```shell
+python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple
+```
+If you need to install from source code, you can use the following command to clone the repository and install:
+
+```
+git clone https://github.com/PaddlePaddle/VisualDL.git
+cd VisualDL
+
+python setup.py bdist_wheel
+pip install --upgrade dist/visualdl-*.whl
+```
+Please note that Python 2 is no longer maintained officially since January 1, 2020. VisualDL now only supports Python 3 in order to ensure the usability of codes.
+
+## Usage Guideline
+
+VisualDL stores the data, parameters and other information of the training process in a log file. Users can launch the panel to observe the visualization results.
+
+### 1. Log
+
+The Python SDK is provided at the back end of VisualDL, and a logger can be customized through LogWriter. The interface description is shown as follows:
+
+```python
+class LogWriter(logdir=None,
+                comment='',
+                max_queue=10,
+                flush_secs=120,
+                filename_suffix='',
+                write_to_disk=True,
+                **kwargs)
+```
+
+**Interface Parameters**
+
+| parameters      | type    | meaning                                                      |
+| --------------- | ------- | ------------------------------------------------------------ |
+| logdir          | string  | The path location of log file. VisualDL will create a log file under this path to record information generated by the training process. If not specified, the path will be  `runs/${CURRENT_TIME}`as default. |
+| comment         | string  | Add a suffix to the log folder name, which is invalid if logdir is already specified. |
+| max_queue       | int     | The maximum capacity of the data generated before recording in a log file. Default value is 10. If the capacity is reached, the data is immediately written into the log file. The cache is not used if set to 0. |
+| flush_secs      | int     | The maximum cache time of the data generated before recording in a log file. Default value is 120. When this time is reached, the data is immediately written to the log file. The cache is not used if set to 0. |
+| filename_suffix | string  | Add a suffix to the default log file name.                   |
+| write_to_disk   | boolean | Write into disk or not.                                      |
+| display_name  | string | Set the name of different runs when `logdir` is too long or needed to be hidden. If not set, the default name is `logdir`.|
+
+**Example**
+
+Create a log file and record scalar values:
+
+```python
+from visualdl import LogWriter
+
+# create a log file under `./log/scalar_test/train`
+with LogWriter(logdir="./log/scalar_test/train") as writer:
+    # use `add_scalar` to record scalar values
+    writer.add_scalar(tag="acc", step=1, value=0.5678)
+    writer.add_scalar(tag="acc", step=2, value=0.6878)
+    writer.add_scalar(tag="acc", step=3, value=0.9878)
+```
+
+### 2. Launch Panel
+
+In the above example, the log has recorded three sets of scalar values. Develpers can view the visualization results of the log file through launching the visualDL panel. There are two ways to launch a log file:
+
+#### a. Launch by Command Line
+
+Use the command line to launch the VisualDL panel：
+
+```python
+visualdl --logdir <dir_1, dir_2, ... , dir_n> --host <host> --port <port> --cache-timeout <cache_timeout> --language <language> --public-path <public_path> --api-only --component_tabs <tab_name1, tab_name2, ...>
+```
+
+Parameter details:
+
+| parameters      | meaning                                                      |
+| --------------- | ------------------------------------------------------------ |
+| --logdir        | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display the all experimental results. |
+| --host          | Specify IP address. The default value is·`127.0.0.1`.        |
+| --port          | Set the port. The default value is`8040`.                    |
+| --cache-timeout | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data is obtained from the cache. The default cache time is 20 seconds. |
+| --language      | The language of the VisualDL panel. Language can be specified as 'en' or 'zh', and the default is the language used by the browser. |
+| --public-path   | The URL path of the VisualDL panel. The default path is '/app', meaning that the access address is 'http://&lt;host&gt;:&lt;port&gt;/app'. |
+| --api-only      | Decide whether or not to provide only API. If this parameter is set, VisualDL will only provides API service without displaying the web page, and the API address is 'http://&lt;host&gt;:&lt;port&gt;/&lt;public_path&gt;/api'. Additionally, If the public_path parameter is not specified, the default address is 'http://&lt;host&gt;:&lt;port&gt;/api'. |
+| --component_tabs  | Decide which components are presented in page, currently support 15 components, i.e. `scalar`, `image`, `text`, `embeddings`, `audio`, `histogram`, `hyper_parameters`, `static_graph`, `dynamic_graph`, `pr_curve`, `roc_curve`, `profiler`, `x2paddle`, `fastdeploy_server`, `fastdeploy_client`. If this parameter is set, only specified components will be presented. If not set, and specify `--logdir` parameter, only components with data in vdlrecords log are presented. If both `--component_tabs` and `--logdir` are not set, only present `static_graph`, `x2paddle`, `fastdeploy_server`, `fastdeploy_client` components by default. |
+
+To visualize the log file generated in the previous step, developers can launch the panel through the command:
+
+```
+visualdl --logdir ./log
+```
+
+**Example:**
+
+```
+visualdl --logdir ./log --port 8089
+```
+
+Open your browser and enter: `localhost:8089` to view the VisualDL dashboard.
+
+#### b. Launch in Python Script
+
+Developers can start the VisualDL panel in Python script as follows:
+
+```python
+visualdl.server.app.run(logdir,
+                        host="127.0.0.1",
+                        port=8080,
+                        cache_timeout=20,
+                        language=None,
+                        public_path=None,
+                        api_only=False,
+                        open_browser=False)
+```
+
+Please note: since all parameters are indefinite except `logdir`, developers should specify parameter names when using them.
+
+The interface parameters are as follows:
+
+| parameters    | type                                               | meaning                                                      |
+| ------------- | -------------------------------------------------- | ------------------------------------------------------------ |
+| logdir        | string or list[string_1, string_2, ... , string_n] | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display the all experimental results. |
+| host          | string                                             | Specify IP address. The default value is·`127.0.0.1`.        |
+| port          | int                                                | Set the port. The default value is`8040`.                    |
+| cache_timeout | int                                                | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data is obtained from the cache. The default cache time is 20 seconds. |
+| language      | string                                             | The language of the VisualDL panel. Language can be specified as 'en' or 'zh', and the default is the language used by the browser. |
+| public_path   | string                                             | The URL path of the VisualDL panel. The default path is '/app', meaning that the access address is 'http://&lt;host&gt;:&lt;port&gt;/app'. |
+| api_only      | boolean                                            | Decide whether or not to provide only API. If this parameter is set, VisualDL will only provides API service without displaying the web page, and the API address is 'http://&lt;host&gt;:&lt;port&gt;/&lt;public_path&gt;/api'. Additionally, If the parameter public_path is not specified, the default address is 'http://&lt;host&gt;:&lt;port&gt;/api'. |
+| open_browser  | boolean                                            | Whether or not to open the browser. If this parameter is set as True, the browser will be openned automatically and VisualDL panel will be launched at the same time. If parameter api_only is specified as True,  parameter open_browser can be ignored. |
+| component_tabs | string or list[string_1, string_2, ... , string_n] | Decide which components are presented in page, currently support 15 components, i.e. `scalar`, `image`, `text`, `embeddings`, `audio`, `histogram`, `hyper_parameters`, `static_graph`, `dynamic_graph`, `pr_curve`, `roc_curve`, `profiler`, `x2paddle`, `fastdeploy_server`, `fastdeploy_client`. If this parameter is set, only specified components will be presented. If not set, and specify `--logdir` parameter, only components with data in vdlrecords log are presented. If both `--component_tabs` and `--logdir` are not set, only present `static_graph`, `x2paddle`, `fastdeploy_server`, `fastdeploy_client` components by default. |
+
+To visualize the log file generated in the previous step, developers can launch the panel through the command:
+
+```python
+from visualdl.server import app
+
+app.run(logdir="./log")
+```
+
+After launching the panel by one of the above methods, developers can see the visualization results on the browser shown as blow:
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/48054808/90868674-ba321f00-e3c9-11ea-83c1-f03c6dd19187.png" width="70%"/>
+</p>
+
+
+
+## Function Preview
+
+### 1. Scalar
+**Scalar** makes use of various charts to display how the parameters, such as accuracy, loss and learning rate, change during the training process. In this case, developers can observe not only the single but also the multiple groups of parameters in order to understand the training process and thus speed up the process of model tuning.
+
+#### a. Dynamic Display
+
+After the launchment of VisualDL Board, the LogReader will continuously record the data to display in the front-end. Hence, the changes of parameters can be visualized in real-time, as shown below:
+
+<p align="center">
+  <img src="https://visualdl.bj.bcebos.com/images/dynamic_display.gif" width="60%"/>
+</p>
+
+
+#### b. Comparison of Multiple Experiments
+
+Developers can compare with multiple experiments by specifying and uploading the path of each experiment at the same time so as to visualize the same parameters in the same chart.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/48054808/90869567-fdd95880-e3ca-11ea-9855-6c97ad5c8ae7.gif" width="100%"/>
+</p>
+
+
+### 2. Image
+**Image** provides real-time visualizations of the image data during the training process, allowing developers to observe the changes of images in different training stages and  to deeply understand the effects of the training process.
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/90869677-22353500-e3cb-11ea-9830-2334bdd8e52e.gif" width="60%"/>
+</p>
+
+### 3. Audio
+**Audio** aims to allow developers to listen to the audio data in real-time during the training process, helping developers to monitor the process of speech recognition and text-to-speech.
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/90869771-47c23e80-e3cb-11ea-8b2a-a38b6c33d64b.png" width="85%"/>
+</p>
+
+### 4. Text
+**Text** visualizes the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model.
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/28444161/106248340-cdd09400-624b-11eb-8ea9-5a07a239c365.png" width="85%"/>
+</p>
+
+### 5. Graph
+
+**Graph** enables developers to visualize model structures by only one click. Moreover, **Graph** allows developers to explore model attributes, node information, node input and output. aiding them analyze model structures quickly and understand the direction of data flow easily. Additionally, Graph supports the visualization of dynamic and static model graph respectively.
+
+- dynamic graph
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/175811841-64b44d99-7d48-4fe9-a679-01156d15af74.gif" width="85%"/>
+</p>
+
+- static graph
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/175811795-1fd21737-06f0-42fc-bea3-ef7a17216fc9.gif" width="85%"/>
+</p>
+
+
+### 6. Histogram
+
+**Histogram** displays how the trend of tensors (weight, bias, gradient, etc.) changes during the training process in the form of histogram. Developers can adjust the model structures accurately by having an in-depth understanding of the effect of each layer.
+
+- Offset Mode
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/90870121-bd2e0f00-e3cb-11ea-89cf-6622cb607b89.png" width="85%"/>
+</p>
+
+
+- Overlay Mode
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/90870194-cfa84880-e3cb-11ea-8a66-bebcad267a10.png" width="85%"/>
+</p>
+
+### 7. High Dimensional
+
+**High Dimensional** provides two approaches--T-SNE and PCA--to do the dimensionality reduction, allowing developers to have an in-depth analysis of the relationship between high-dimensional data and to optimize algorithms based on the analysis.
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/103188111-1b32ac00-4902-11eb-914e-c2368bdb8373.gif" width="85%"/>
+</p>
+
+### 8. Hyper Parameters
+
+**Hyper Parameters** visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way.
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/28444161/119247155-e9c0c280-bbb9-11eb-8175-58a9c7657a9c.gif" width="85%"/>
+</p>
+
+### 9. Performance Analysis
+**Performance Analysis**(Profiler) visualize the profiling data collected during your program runs, helping you identify program bottlenecks and optimize performance. Please refer to [VisualDL Profiler Guide](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/profiler/README.md).
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/185894151-53ffc60b-7203-4cb8-a289-5d97332d0691.gif" width="85%"/>
+</p>
+
+### 10. X2Paddle
+The X2Paddle component provides the functions of onnx model format visualization and transformation to paddle format.
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211203066-f2e43ef5-104f-436a-b44c-cad2b37ad518.gif" width="100%"/>
+</p>
+
+
+### 11. FastDeployServer
+The FastDeployServer component provides the functions of loading and editing the model repository, fastdeployserver service management and monitoring, and providing the client to test service. Please refer to [use VisualDL for fastdeploy serving deployment visualization](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_server/README.md).
+
+ <p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211196832-1a05bf80-5aaa-493f-bba2-27e819c18bb9.gif" width="100%"/>
+</p>
+
+
+### 12. FastDeployClient
+The FastDeployClient component is mainly used to quickly access the fastdeployserver service, to help users visualize prediction requests and results. Please refer to [use VisualDL as fastdeploy client for request visualization](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_client/README.md).
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211203852-059d5b98-6299-4057-97d8-5209805aa67f.gif" width="100%"/>
+</p>
+
+
+### 13. VDL.service
+
+**VDL.service** enables developers to easily save, track and share visualization results with anyone for free.
+
+<p align="center">
+<img src=https://user-images.githubusercontent.com/48054808/93731055-fbeafb00-fbfd-11ea-80f4-bbfd08a0fc35.png
+</p>
+
+## Contribution
+
+VisualDL, in which Graph is powered by [Netron](https://github.com/lutzroeder/netron), is an open source project supported by  [PaddlePaddle](https://www.paddlepaddle.org/) and [ECharts](https://echarts.apache.org/) . Developers are warmly welcomed to use, comment and contribute.
+
+
+## More Details
+
+For more details related to the use of VisualDL, please refer to [**VisualDL User Guide**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/README.md), [**VisualDL Profiler Guide**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/profiler/README.md), [**Use VisualDL for fastdeploy serving deployment visualization**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_server/README.md), [**Use VisualDL as fastdeploy client for request visualization**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_client/README.md).

docs/VisualDL.md

@@ -0,0 +1,350 @@
+ [**English**](./VisualDL.en.md)
+
+
+
+
+## VisualDL 介绍
+VisualDL是飞桨可视化分析工具，以丰富的图表呈现训练参数变化趋势、模型结构、数据样本、高维数据分布等。可帮助用户更清晰直观地理解深度学习模型训练过程及模型结构，进而实现高效的模型优化。
+
+VisualDL提供丰富的可视化功能，支持标量、图结构、数据样本可视化、直方图、PR曲线及高维数据降维呈现等诸多功能，同时VisualDL提供可视化结果保存服务，通过VDL.service生成链接，保存并分享可视化结果。具体功能使用方式，请参见 [**VisualDL使用指南**](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.2/guides/03_VisualDL/visualdl_usage_cn.html)。项目正处于高速迭代中，敬请期待新组件的加入。
+
+VisualDL支持浏览器种类：Chrome（81和83）、Safari 13、FireFox（77和78）、Edge（Chromium版）。
+
+VisualDL原生支持python的使用， 通过在模型的Python配置中添加几行代码，便可为训练过程提供丰富的可视化支持。
+
+## 目录
+
+* [核心亮点](#核心亮点)
+
+* [安装方式](#安装方式)
+
+* [使用方式](#使用方式)
+
+* [可视化功能概览](#可视化功能概览)
+
+* [开源贡献](#开源贡献)
+
+* [更多细节](#更多细节)
+
+## 一、核心亮点
+
+**简单易用**
+
+API设计简洁直观，使用门槛低，只需几步操作即可实现模型结构的一键可视化，降低学习成本和使用难度，让您快速上手并应用到实际项目中。
+
+**功能丰富**
+
+涵盖标量、数据样本、图结构、直方图、PR曲线及数据降维等多种可视化功能，全面支持模型训练过程中的参数变化趋势分析和高维数据的深入理解，满足不同场景下的多样化需求。
+
+**高兼容性**
+
+全面支持Paddle、ONNX、Caffe等市面上主流的模型结构可视化，轻松应对不同框架构建的模型，为您提供广泛的可视化分析支持，确保不同技术背景的用户都能顺利使用。
+
+**全面支持**
+
+与飞桨服务平台及工具组件全面打通，让您在模型开发、训练、部署等各个环节享受顺畅流程和强大支持。
+
+
+
+## 二、安装方式
+
+推荐使用**pip安装**，简单快捷，只需在命令行中输入以下命令：
+
+```shell
+python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple
+```
+如果您需要从源码安装，可以使用以下命令克隆仓库并安装：
+
+```
+git clone https://github.com/PaddlePaddle/VisualDL.git
+cd VisualDL
+
+python setup.py bdist_wheel
+pip install --upgrade dist/visualdl-*.whl
+```
+**需要注意：** 官方自2020年1月1日起不再维护Python2，为了保障代码可用性，VisualDL现仅支持Python3
+
+## 三、使用方式
+
+VisualDL将训练过程中的数据、参数等信息储存至日志文件中后，启动面板即可查看可视化结果。
+
+### 1. 记录日志
+
+VisualDL的后端提供了Python SDK，可通过LogWriter定制一个日志记录器，接口如下：
+
+```python
+class LogWriter(logdir=None,
+                comment='',
+                max_queue=10,
+                flush_secs=120,
+                filename_suffix='',
+                write_to_disk=True,
+                display_name='',
+                **kwargs)
+```
+
+**接口参数**
+
+| 参数            | 格式    | 含义                                                         |
+| --------------- | ------- | ------------------------------------------------------------ |
+| logdir          | string  | 日志文件所在的路径，VisualDL将在此路径下建立日志文件并进行记录，如果不填则默认为`runs/${CURRENT_TIME}` |
+| comment         | string  | 为日志文件夹名添加后缀，如果制定了logdir则此项无效           |
+| max_queue       | int     | 日志记录消息队列的最大容量，默认值为10，达到此容量则立即写入到日志文件，如果设置为0则不缓存   |
+| flush_secs      | int     | 日志记录消息队列的最大缓存时间，默认值为120，达到此时间则立即写入到日志文件，如果设置为0则不缓存 |
+| filename_suffix | string  | 为默认的日志文件名添加后缀                                   |
+| write_to_disk   | boolean | 是否写入到磁盘                                               |
+| display_name    | string  | 在面板中`选择数据流`位置显示此参数，如不指定则默认显示日志所在路径（当日志所在路径过长或想隐藏日志所在路径时可指定此参数） |
+
+**示例（设置日志文件并记录标量数据）：**
+
+```python
+from visualdl import LogWriter
+
+# 在`./log/scalar_test/train`路径下建立日志文件
+with LogWriter(logdir="./log/scalar_test/train") as writer:
+    # 使用scalar组件记录一个标量数据
+    writer.add_scalar(tag="acc", step=1, value=0.5678)
+    writer.add_scalar(tag="acc", step=2, value=0.6878)
+    writer.add_scalar(tag="acc", step=3, value=0.9878)
+```
+
+### 2. 启动 VisualDL 面板
+
+在上一个示例中，日志已记录三组标量数据。现在，您可以启动 VisualDL 面板来查看日志的可视化结果。启动方式有两种：命令行启动和 Python 脚本启动。
+
+#### a. 在命令行启动
+
+使用命令行启动VisualDL面板，命令格式如下：
+
+```python
+visualdl --logdir <dir_1, dir_2, ... , dir_n> --host <host> --port <port> --cache-timeout <cache_timeout> --language <language> --public-path <public_path> --api-only --component_tabs <tab_name1, tab_name2, ...>
+```
+
+参数详情：
+
+|      参数       |                                                                                             意义                                                                                             |
+| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --logdir        | 设定日志所在目录，可以指定多个目录，VisualDL将遍历并且迭代寻找指定目录的子目录，将所有实验结果进行可视化                                                                                     |
+| --model         | 设定模型文件路径(非文件夹路径)，VisualDL将在此路径指定的模型文件进行可视化，目前可支持PaddlePaddle、ONNX、Keras、Core ML、Caffe等多种模型结构，详情可查看[graph支持模型种类](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.2/guides/03_VisualDL/visualdl_usage_cn.html#id11) |
+| --host          | 设定IP，默认为`127.0.0.1`                                                                                                                                                                    |
+| --port          | 设定端口，默认为`8040`                                                                                                                                                                       |
+| --cache-timeout | 后端缓存时间，在缓存时间内前端多次请求同一url，返回的数据从缓存中获取，默认为20秒                                                                                                            |
+| --language      | VisualDL面板语言，可指定为'en'或'zh'，默认为浏览器使用语言                                                                                                                                   |
+| --public-path   | VisualDL面板URL路径，默认是'/app'，即访问地址为'http://&lt;host&gt;:&lt;port&gt;/app'                                                                                                                    |
+| --api-only      | 是否只提供API，如果设置此参数，则VisualDL不提供页面展示，只提供API服务，此时API地址为'http://&lt;host&gt;:&lt;port&gt;/&lt;public_path&gt;/api'；若没有设置public_path参数，则默认为'http://&lt;host&gt;:&lt;port&gt;/api' |
+| --component_tabs | 设定需要显示的组件，当前支持 `scalar`、`image`、`text`、`embeddings`、`audio`、`histogram`、`hyper_parameters`、`static_graph`、`dynamic_graph`、`pr_curve`、`roc_curve`、`profiler`、`x2paddle`、`fastdeploy_server`、`fastdeploy_client` 共 15 个组件。如果设置了此参数，将只展示所指定的组件。如果没有设置此参数，当指定了 `--logdir` 参数时，将会根据日志文件中拥有的数据类型来自动显示相应的组件。当没有指定 `--logdir` 参数，默认显示 `static_graph`、`x2paddle`、`fastdeploy_server`、`fastdeploy_client` 这四个名称代表的组件。 |
+
+
+针对上一步生成的日志，启动命令为：
+
+```
+visualdl --logdir ./log
+```
+
+**示例：**
+
+```
+visualdl --logdir ./log --port 8089
+```
+
+在浏览器输入：`localhost:8089` 即可查看VisualDL面板。
+
+
+#### b. 在Python脚本中启动
+
+支持在Python脚本中启动VisualDL面板，接口如下：
+
+```python
+visualdl.server.app.run(logdir,
+                        host="127.0.0.1",
+                        port=8080,
+                        cache_timeout=20,
+                        language=None,
+                        public_path=None,
+                        api_only=False,
+                        open_browser=False)
+```
+
+请注意：除`logdir`外，其他参数均为不定参数，传递时请指明参数名。
+
+接口参数具体如下：
+
+|     参数      |                       格式                       |                                                                                             含义                                                                                             |
+| ------------- | ------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| logdir        | string或list[string_1, string_2, ... , string_n] | 日志文件所在的路径，VisualDL将在此路径下递归搜索日志文件并进行可视化，可指定单个或多个路径                                                                                                   |
+| model         | string                                           | 模型文件路径(非文件夹路径)，VisualDL将在此路径指定的模型文件进行可视化                                                                                                   |
+| host          | string                                           | 指定启动服务的ip，默认为`127.0.0.1`                                                                                                                                                          |
+| port          | int                                              | 启动服务端口，默认为`8040`                                                                                                                                                                   |
+| cache_timeout | int                                              | 后端缓存时间，在缓存时间内前端多次请求同一url，返回的数据从缓存中获取，默认为20秒                                                                                                            |
+| language      | string                                           | VisualDL面板语言，可指定为'en'或'zh'，默认为浏览器使用语言                                                                                                                                   |
+| public_path   | string                                           | VisualDL面板URL路径，默认是'/app'，即访问地址为'http://&lt;host&gt;:&lt;port&gt;/app'                                                                                                                    |
+| api_only      | boolean                                          | 是否只提供API，如果设置此参数，则VisualDL不提供页面展示，只提供API服务，此时API地址为'http://&lt;host&gt;:&lt;port&gt;/&lt;public_path&gt;/api'；若没有设置public_path参数，则默认为'http://&lt;host&gt;:&lt;port&gt;/api' |
+| open_browser  | boolean                                          | 是否打开浏览器，设置为True则在启动后自动打开浏览器并访问VisualDL面板，若设置api_only，则忽略此参数                                                                                           |
+| --component_tabs | string或list[string_1, string_2, ... , string_n] | 设定需要显示的组件，当前支持 `scalar`、`image`、`text`、`embeddings`、`audio`、`histogram`、`hyper_parameters`、`static_graph`、`dynamic_graph`、`pr_curve`、`roc_curve`、`profiler`、`x2paddle`、`fastdeploy_server`、`fastdeploy_client` 共 15 个组件。如果设置了此参数，将只展示所指定的组件。如果没有设置此参数，当指定了 `--logdir` 参数时，将会根据日志文件中拥有的数据类型来自动显示相应的组件。当没有指定 `--logdir` 参数，默认显示 `static_graph`、`x2paddle`、`fastdeploy_server`、`fastdeploy_client` 这四个名称代表的组件。 |
+
+针对上一步生成的日志，我们的启动脚本为：
+
+```python
+from visualdl.server import app
+
+app.run(logdir="./log")
+```
+
+在使用任意一种方式启动VisualDL面板后，打开浏览器访问VisualDL面板，即可查看日志的可视化结果，如图：
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/48054808/82786044-67ae9880-9e96-11ea-8a2b-3a0951a6ec19.png" width="60%"/>
+</p>
+
+
+
+## 四、可视化功能概览
+
+### 1. Scalar（标量）
+以图表形式实时展示训练过程参数，如loss、accuracy。让用户通过观察单组或多组训练参数变化，了解训练过程，加速模型调优。具有两大特点：
+
+#### a. 动态展示
+
+在启动VisualDL Board后，LogReader将不断增量的读取日志中数据并供前端调用展示，因此能够在训练中同步观测指标变化，如下图：
+
+<p align="center">
+  <img src="https://visualdl.bj.bcebos.com/images/dynamic_display.gif" width="60%"/>
+</p>
+
+
+#### b. 多实验对比
+
+只需在启动VisualDL Board的时将每个实验日志所在路径同时传入即可，每个实验中相同tag的指标将绘制在一张图中同步呈现，如下图：
+
+<p align="center">
+  <img src="https://visualdl.bj.bcebos.com/images/multi_experiments.gif" width="100%"/>
+</p>
+
+
+### 2. Image（图像）
+实时展示训练过程中的图像数据，用于观察不同训练阶段的图像变化，进而深入了解训练过程及效果。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/90356439-24715980-e082-11ea-8896-01c27fc2fc9b.gif" width="85%"/>
+</p>
+
+### 3. Audio（音频）
+实时查看训练过程中的音频数据，监控语音识别与合成等任务的训练过程。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/87659138-b4746880-c78f-11ea-965b-c33804e7c296.png" width="85%"/>
+</p>
+
+### 4. Text（文本）
+展示文本任务任意阶段的数据输出，对比不同阶段的文本变化，便于深入了解训练过程及效果。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/28444161/106248340-cdd09400-624b-11eb-8ea9-5a07a239c365.png" width="85%"/>
+</p>
+
+### 5. Network Structure（网络结构）
+
+一键可视化模型的网络结构。可查看模型属性、节点信息、节点输入输出等，并支持节点搜索，辅助用户快速分析模型结构与了解数据流向，覆盖动态图与静态图两种格式。
+
+- 动态图
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/175770313-2509f7e9-041a-4654-9a0f-45f4bd76e1e8.gif" width="85%"/>
+</p>
+
+- 静态图
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/175770315-11e2c3f5-141c-4f05-be86-0e1e2785e11f.gif" width="85%"/>
+</p>
+
+### 6. Histogram（直方图）
+
+以直方图形式展示Tensor（weight、bias、gradient等）数据在训练过程中的变化趋势。深入了解模型各层效果，帮助开发者精准调整模型结构。
+
+- Offset模式
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/86551031-86647c80-bf76-11ea-8ec2-8c86826c8137.png" width="85%"/>
+</p>
+
+- Overlay模式
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/86551033-882e4000-bf76-11ea-8e6a-af954c662ced.png" width="85%"/>
+</p>
+
+### 7. PR Curve（精度-召回率曲线）
+
+精度-召回率曲线，帮助开发者权衡模型精度和召回率之间的平衡，设定最佳阈值。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/86738774-ee46c000-c067-11ea-90d2-a98aac445cca.png" width="85%"/>
+</p>
+
+### 8. High Dimensional（高维数据）
+
+将高维数据进行降维展示，目前支持T-SNE、PCA两种降维方式，用于深入分析高维数据间的关系，方便用户根据数据特征进行算法优化。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/82396340-3e4dd100-9a80-11ea-911d-798acdbc9c90.gif" width="85%"/>
+</p>
+
+### 9. Hyper Parameters（超参数）
+
+以丰富的视图多角度地可视化超参数与模型关键指标间的关系，便于快速确定最佳超参组合，实现高效调参。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/28444161/119247155-e9c0c280-bbb9-11eb-8175-58a9c7657a9c.gif" width="85%"/>
+</p>
+
+### 10. Performance Analysis（性能分析）
+
+通过多个视图可视化性能分析的数据，辅助用户定位性能瓶颈并进行优化。可参考[使用VisualDL做性能分析](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/profiler/README_CN.md)。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/22424850/185893177-a049c8d5-2310-4138-8dd5-844cf198e425.gif" width="85%"/>
+</p>
+
+### 11. X2Paddle
+
+提供onnx模型转paddle模型的可视化操作界面，帮助用户可视化onnx模型结构并且获取转换后的paddle模型结构和参数文件。
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211203066-f2e43ef5-104f-436a-b44c-cad2b37ad518.gif" width="100%"/>
+</p>
+
+
+### 12. FastDeployServer
+
+基于[FastDeploy](https://github.com/PaddlePaddle/FastDeploy)的Serving可视化部署，提供配置模型库、管理监控服务以及测试服务等功能。详细内容可参考[使用VisualDL进行Serving可视化部署](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_server/README_CN.md)。
+
+ <p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211196832-1a05bf80-5aaa-493f-bba2-27e819c18bb9.gif" width="100%"/>
+</p>
+
+
+### 13. FastDeployClient
+提供给用户访问fastdeployserver服务的客户端界面，进行一键预测和可视化结果。详细内容可参考[使用VisualDL作为fastdeployserver服务的客户端](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_client/README_CN.md)。
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/22424850/211203852-059d5b98-6299-4057-97d8-5209805aa67f.gif" width="100%"/>
+</p>
+
+
+### 14. VDL.service
+
+VisualDL可视化结果保存服务，以链接形式将可视化结果保存下来，方便用户快速、便捷的进行托管与分享。
+
+<p align="center">
+<img src="https://user-images.githubusercontent.com/48054808/93729521-72382f00-fbf7-11ea-91ff-6b6ab4b41e32.png" width="85%"/>
+</p>
+
+
+## 五、开源贡献
+
+VisualDL 是由 [PaddlePaddle](https://www.paddlepaddle.org/) 和 [ECharts](https://echarts.apache.org/) 合作推出的开源项目。 Graph 相关功能由 [Netron](https://github.com/lutzroeder/netron) 提供技术支持。 欢迎所有人使用，提意见以及贡献代码。
+
+
+## 六、更多细节
+
+想了解更多关于VisualDL可视化功能的使用详情介绍，请查看[**VisualDL使用指南**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/README_CN.md)，[**使用VisualDL做性能分析**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/profiler/README_CN.md)，[**使用VisualDL进行Serving可视化部署**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_server/README_CN.md)，[**使用VisualDL作为fastdeployserver服务的客户端**](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/components/fastdeploy_client/README_CN.md)。

mkdocs.yml

@@ -237,6 +237,7 @@ 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
             多语种语音识别模块: Multilingual Speech Recognition Task
+            可视化工具: VisualDL
             FAQ: FAQ
             近期更新: Recently Update
       repository: PaddlePaddle/PaddleX #仓库名称
@@ -479,5 +480,6 @@ nav:
        - 心跳监测时序数据分类应用实践教程: practical_tutorials/ts_classification.md
        - 用电量长期预测应用实践教程: practical_tutorials/ts_forecast.md
        - 产线部署实践教程: practical_tutorials/deployment_tutorial.md
+  - 可视化工具: VisualDL.md
   - FAQ: FAQ.md
   - 近期更新: CHANGELOG.md