[Docs] Add the usage of clearml (#1180)

docs/en/common_usage/visualize_training_log.md

@@ -1,6 +1,6 @@
 # Visualize Training Logs
 
-MMEngine integrates experiment management tools such as [TensorBoard](https://www.tensorflow.org/tensorboard), [Weights & Biases (WandB)](https://docs.wandb.ai/), and [MLflow](https://mlflow.org/docs/latest/index.html), making it easy to track and visualize metrics like loss and accuracy.
+MMEngine integrates experiment management tools such as [TensorBoard](https://www.tensorflow.org/tensorboard), [Weights & Biases (WandB)](https://docs.wandb.ai/), [MLflow](https://mlflow.org/docs/latest/index.html) and [ClearML](https://clear.ml/docs/latest/docs), making it easy to track and visualize metrics like loss and accuracy.
 
 Below, we'll show you how to configure an experiment management tool in just one line, based on the example from [15 minutes to get started with MMEngine](../get_started/15_minutes.md).
 
@@ -71,3 +71,31 @@ runner.train()
 ```
 
 ## MLflow (WIP)
+
+## ClearML
+
+Before using ClearML, you need to install the `clearml` dependency library and refer to [Connect ClearML SDK to the Server](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps#connect-clearml-sdk-to-the-server) for configuration.
+
+```bash
+pip install clearml
+clearml-init
+```
+
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `ClearMLVisBackend`.
+
+```python
+runner = Runner(
+    model=MMResNet50(),
+    work_dir='./work_dir',
+    train_dataloader=train_dataloader,
+    optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
+    train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy),
+    visualizer=dict(type='Visualizer', vis_backends=[dict(type='ClearMLVisBackend')]),
+)
+runner.train()
+```
+
+![image](https://github.com/open-mmlab/mmengine/assets/58739961/d68e1dd2-9e82-40fb-ad81-00a647549adc)

docs/zh_cn/common_usage/visualize_training_log.md

@@ -1,6 +1,6 @@
 # 可视化训练日志
 
-MMEngine 集成了 [TensorBoard](https://www.tensorflow.org/tensorboard?hl=zh-cn)、[Weights & Biases (WandB)](https://docs.wandb.ai/) 和 [MLflow](https://mlflow.org/docs/latest/index.html) 实验管理工具，你可以很方便地跟踪和可视化损失及准确率等指标。
+MMEngine 集成了 [TensorBoard](https://www.tensorflow.org/tensorboard?hl=zh-cn)、[Weights & Biases (WandB)](https://docs.wandb.ai/)、[MLflow](https://mlflow.org/docs/latest/index.html) 和 [ClearML](https://clear.ml/docs/latest/docs) 实验管理工具，你可以很方便地跟踪和可视化损失及准确率等指标。
 
 下面基于[15 分钟上手 MMENGINE](../get_started/15_minutes.md)中的例子介绍如何一行配置实验管理工具。
 
@@ -71,3 +71,31 @@ runner.train()
 ```
 
 ## MLflow (WIP)
+
+## ClearML
+
+使用 ClearML 前需安装依赖库 `clearml` 并参考 [Connect ClearML SDK to the Server](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps#connect-clearml-sdk-to-the-server) 进行配置。
+
+```bash
+pip install clearml
+clearml-init
+```
+
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `ClearMLVisBackend`。
+
+```python
+runner = Runner(
+    model=MMResNet50(),
+    work_dir='./work_dir',
+    train_dataloader=train_dataloader,
+    optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
+    train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
+    val_dataloader=val_dataloader,
+    val_cfg=dict(),
+    val_evaluator=dict(type=Accuracy),
+    visualizer=dict(type='Visualizer', vis_backends=[dict(type='ClearMLVisBackend')]),
+)
+runner.train()
+```
+
+![image](https://github.com/open-mmlab/mmengine/assets/58739961/d68e1dd2-9e82-40fb-ad81-00a647549adc)