#115 #122 (#123)

* update integration images

* register_callbacks

Author: Zeyi-Lin

en/guide_cloud/integration/integration-ultralytics.md

@@ -4,7 +4,7 @@
 
 [Ultralytics](https://github.com/ultralytics/ultralytics) YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds on the success of previous YOLO versions and introduces new features and improvements to further enhance performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a variety of object detection and tracking, instance segmentation, image classification, and pose estimation tasks.
 
-![ultralytics](/assets/ig-ultralytics.png)
+![ultralytics](./ultralytics/logo.png)
 
 You can use Ultralytics to quickly train computer vision models while using SwanLab for experiment tracking and visualization.
 

en/guide_cloud/integration/ultralytics/logo.png

@@ -77,6 +77,10 @@ if accuracy > 0.95:
     )
 ```
 
+## Register plugins externally
+
+<!--@include: ./shared-snippet.md-->
+
 ## Limitations
 
 • The training completion/error notifications of the DingTalk notification plugin use the `on_stop` lifecycle callback of `SwanKitCallback`. Therefore, if your process is abruptly `killed` or the training machine shuts down unexpectedly, the `on_stop` callback will not be triggered, resulting in no DingTalk notification being sent.

en/plugin/notification-dingtalk.md

@@ -75,6 +75,10 @@ if accuracy > 0.95:
     )
 ```
 
+## Register plugins externally
+
+<!--@include: ./shared-snippet.md-->
+
 ## Limitations
 
 • The training completion/error notification of the email notification plugin relies on the `on_stop` lifecycle callback of `SwanKitCallback`. Therefore, if your process is abruptly `killed` or the training machine shuts down unexpectedly, the `on_stop` callback will not be triggered, and no email notification will be sent.

en/plugin/notification-email.md

@@ -78,6 +78,10 @@ if accuracy > 0.95:
     )
 ```
 
+## Register plugins externally
+
+<!--@include: ./shared-snippet.md-->
+
 ## Limitations
 
 • The training completion/error notification of the Lark notification plugin relies on the `on_stop` lifecycle callback of `SwanKitCallback`. Therefore, if your process is abruptly `killed` or the training machine shuts down unexpectedly, the `on_stop` callback will not be triggered, and no Lark notification will be sent.

en/plugin/notification-lark.md

@@ -80,6 +80,10 @@ if accuracy > 0.95:
     )
 ```
 
+## Register plugins externally
+
+<!--@include: ./shared-snippet.md-->
+
 
 ## Limitations
 

en/plugin/notification-wxwork.md

@@ -0,0 +1,8 @@
+If you are using the integration of SwanLab with other frameworks and thus find it difficult to locate `swanlab.init`, you can use the `swanlab.register_callbacks` method to pass in plugins externally:
+
+```python
+import swanlab
+
+# Equivalent to swanlab.init(callbacks=[...])
+swanlab.register_callbacks([...])
+```

en/plugin/shared-snippet.md

@@ -72,4 +72,8 @@ for epoch in range(2, epochs):
 
 # [Optional] Finish training, which is necessary in notebook environments
 swanlab.finish()
-```
+```
+
+## Register plugins externally
+
+<!--@include: ./shared-snippet.md-->

en/plugin/writer-csv.md

@@ -4,7 +4,7 @@
 
 [Ultralytics](https://github.com/ultralytics/ultralytics) YOLOv8 是一款尖端、最先进的 （SOTA） 模型，它建立在以前 YOLO 版本的成功基础上，并引入了新功能和改进，以进一步提高性能和灵活性。YOLOv8 设计为快速、准确且易于使用，使其成为各种对象检测和跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
 
-![ultralytics](/assets/ig-ultralytics.png)
+![ultralytics](./ultralytics/logo.png)
 
 你可以使用Ultralytics快速进行计算机视觉模型训练，同时使用SwanLab进行实验跟踪与可视化。