[cherry-pick] rename: edge deployment -> on-device deployment (#4196)

Author: zhang-prog

.github/ISSUE_TEMPLATE/3_deploy.md

@@ -33,7 +33,7 @@ assignees: ''
     * 如果是多语言调用的问题，请给出调用示例子。
 
 3. 端侧部署
-    * 您是否完全按照[端侧部署文档教程](https://paddlepaddle.github.io/PaddleX/main/pipeline_deploy/edge_deploy.html)跑通了流程？
+    * 您是否完全按照[端侧部署文档教程](https://paddlepaddle.github.io/PaddleX/main/pipeline_deploy/on_device_deployment.html)跑通了流程？
 
     * 您使用的端侧设备是？对应的PaddlePaddle版本和PaddleLite版本分别是什么？
 

README.md

@@ -69,7 +69,7 @@ PaddleX 3.0 是基于飞桨框架构建的低代码开发工具，它集成了
  ## 📊 能力支持
 
 
-PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行**在线体验**，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/serving.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**二次开发**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)。
+PaddleX的各个产线均支持本地**快速推理**，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行**在线体验**，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/serving.html)/[端侧部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/on_device_deployment.html)，如果不满意，您也可以使用产线的**二次开发**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)。
 
 
 此外，PaddleX在[AI Studio星河社区](https://aistudio.baidu.com/overview)为开发者提供了基于[云端图形化开发界面](https://aistudio.baidu.com/pipeline/mine)的全流程开发工具, 点击【创建产线】，选择对应的任务场景和模型产线，就可以开启全流程开发。详细请参考[教程《零门槛开发产业级AI模型》](https://aistudio.baidu.com/practical/introduce/546656605663301)
@@ -986,7 +986,7 @@ 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)
+  * [📱 PaddleX 端侧部署指南](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/on_device_deployment.html)
   * [🌐 获取 ONNX 模型](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/paddle2onnx.html)
 
 </details>

README_en.md

@@ -74,7 +74,7 @@ PaddleX is dedicated to achieving pipeline-level model training, inference, and
 ## 📊 What can PaddleX do？
 
 
-All pipelines of PaddleX support **online experience** on [AI Studio]((https://aistudio.baidu.com/overview)) and local **fast inference**. 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](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/serving.html) / [edge deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/edge_deploy.html) on the pipeline. If not satisfied, you can also **Custom Development** 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).
+All pipelines of PaddleX support **online experience** on [AI Studio]((https://aistudio.baidu.com/overview)) and local **fast inference**. 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](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/serving.html) / [edge deployment](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/on_device_deployment.html) on the pipeline. If not satisfied, you can also **Custom Development** 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 **do not need code development**, just need to prepare a dataset that meets the pipeline requirements to **quickly start model training**. For details, please refer to the tutorial ["Developing Industrial-level AI Models with Zero Barrier"](https://aistudio.baidu.com/practical/introduce/546656605663301).
 
@@ -85,7 +85,7 @@ In addition, PaddleX provides developers with a full-process efficient model tra
         <th>Local Inference</th>
         <th>High-Performance Inference</th>
         <th>Serving</th>
-        <th>Edge Deployment</th>
+        <th>On-Device Deployment</th>
         <th>Custom Development</th>
         <th><a href="https://aistudio.baidu.com/pipeline/mine">Zero-Code Development On AI Studio</a></td>
     </tr>
@@ -924,7 +924,7 @@ 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)
+  * [📱 PaddleX On-Device Deployment Guide](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/on_device_deployment.html)
   * [🌐 Installation and Usage of the Paddle2ONNX Plugin](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_deploy/paddle2onnx.html)
 
 </details>

docs/pipeline_deploy/edge_deploy.en.md renamed to docs/pipeline_deploy/on_device_deployment.en.md

@@ -2,9 +2,9 @@
 comments: true
 ---
 
-# PaddleX Edge Deployment Demo Usage Guide
+# PaddleX On-Device Deployment Demo Usage Guide
 
-- [PaddleX Edge Deployment Demo Usage Guide](#paddlex-edge-deployment-demo-usage-guide)
+- [PaddleX On-Device Deployment Demo Usage Guide](#paddlex-on-device-deployment-demo-usage-guide)
   - [Installation Process and Usage](#installation-process-and-usage)
     - [Environment Preparation](#environment-preparation)
     - [Material Preparation](#material-preparation)

docs/pipeline_deploy/edge_deploy.md renamed to docs/pipeline_deploy/on_device_deployment.md

@@ -170,7 +170,7 @@ In addition, PaddleX also provides three other deployment methods, with detailed
 
 ☁️ <b>Serving</b>: Serving is a common deployment strategy in real-world production environments. By encapsulating inference functions into services, clients can access these services via network requests to obtain inference results. PaddleX supports various solutions for serving pipelines. For detailed pipeline serving procedures, please refer to the [PaddleX Pipeline Serving Guide](../pipeline_deploy/serving.md).
 
-📱 <b>Edge Deployment</b>: Edge deployment is a method that places computing and data processing capabilities on user devices themselves, allowing devices to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. Refer to the [PaddleX Edge Deployment Guide](../pipeline_deploy/edge_deploy.en.md) for detailed edge deployment procedures.
+📱 <b>On-Device Deployment</b>: Edge deployment is a method that places computing and data processing capabilities on user devices themselves, allowing devices to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. Refer to the [PaddleX On-Device Deployment Guide](../pipeline_deploy/on_device_deployment.en.md) for detailed edge deployment procedures.
 
 Choose the appropriate deployment method for your model pipeline based on your needs, and proceed with subsequent AI application integration.
 

docs/pipeline_usage/pipeline_develop_guide.en.md

@@ -172,7 +172,7 @@ PaddleX 也提供了其他三种部署方式，详细说明如下：
 
 ☁️ <b>服务化部署</b>：服务化部署是实际生产环境中常见的一种部署形式。通过将推理功能封装为服务，客户端可以通过网络请求来访问这些服务，以获取推理结果。PaddleX 支持多种产线服务化部署方案，详细的产线服务化部署流程请参考[PaddleX服务化部署指南](../pipeline_deploy/serving.md)。
 
-📱 <b>端侧部署</b>：端侧部署是一种将计算和数据处理功能放在用户设备本身上的方式，设备可以直接处理数据，而不需要依赖远程的服务器。PaddleX 支持将模型部署在 Android 等端侧设备上，详细的端侧部署流程请参考[PaddleX端侧部署指南](../pipeline_deploy/edge_deploy.md)。
+📱 <b>端侧部署</b>：端侧部署是一种将计算和数据处理功能放在用户设备本身上的方式，设备可以直接处理数据，而不需要依赖远程的服务器。PaddleX 支持将模型部署在 Android 等端侧设备上，详细的端侧部署流程请参考[PaddleX端侧部署指南](../pipeline_deploy/on_device_deployment.md)。
 您可以根据需要选择合适的方式部署模型产线，进而进行后续的 AI 应用集成。
 
 PaddleX 提供了将 Paddle 模型转换为 ONNX 模型的能力，详细说明请参考[Paddle2ONNX 插件的安装与使用](../pipeline_deploy/paddle2onnx.md)

docs/pipeline_usage/pipeline_develop_guide.md

@@ -498,7 +498,7 @@ print(result["detectedObjects"])
 </details>
 <br/>
 
-📱 <b>Edge Deployment</b>: Edge deployment is a method of placing computing and data processing functions on the user's device itself, allowing the device to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the [PaddleX Edge Deployment Guide](../../../pipeline_deploy/edge_deploy.md).
+📱 <b>On-Device Deployment</b>: Edge deployment is a method of placing computing and data processing functions on the user's device itself, allowing the device to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the [PaddleX On-Device Deployment Guide](../../../pipeline_deploy/on_device_deployment.md).
 
 You can choose an appropriate deployment method for your model pipeline based on your needs, and then proceed with subsequent AI application integration.
 

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

@@ -490,7 +490,7 @@ print(result["detectedObjects"])
 </details>
 <br/>
 
-📱 <b>端侧部署</b>：端侧部署是一种将计算和数据处理功能放在用户设备本身上的方式，设备可以直接处理数据，而不需要依赖远程的服务器。PaddleX 支持将模型部署在 Android 等端侧设备上，详细的端侧部署流程请参考[PaddleX端侧部署指南](../../../pipeline_deploy/edge_deploy.md)。
+📱 <b>端侧部署</b>：端侧部署是一种将计算和数据处理功能放在用户设备本身上的方式，设备可以直接处理数据，而不需要依赖远程的服务器。PaddleX 支持将模型部署在 Android 等端侧设备上，详细的端侧部署流程请参考[PaddleX端侧部署指南](../../../pipeline_deploy/on_device_deployment.md)。
 
 您可以根据需要选择合适的方式部署模型产线，进而进行后续的 AI 应用集成。
 

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

@@ -1100,7 +1100,7 @@ pprint.pp(result_infer["faces"])
 </details>
 <br/>
 
-📱 <b>Edge Deployment</b>: Edge deployment is a method of placing computing and data processing capabilities on the user's device itself, allowing the device to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the [PaddleX Edge Deployment Guide](../../../pipeline_deploy/edge_deploy.en.md).
+📱 <b>On-Device Deployment</b>: Edge deployment is a method of placing computing and data processing capabilities on the user's device itself, allowing the device to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the [PaddleX On-Device Deployment Guide](../../../pipeline_deploy/on_device_deployment.en.md).
 You can choose the appropriate method to deploy the model pipeline according to your needs, and then proceed with subsequent AI application integration.