[Docs] Introduce the use of wandb and tensorboard (#912)

* [Docs] Introduce the use of wandb and tensorboard

* fix link

* Update docs/en/common_usage/visualize_training_log.md

docs/en/common_usage/set_random_seed.md

-# How to Set Random Seed
+# Set Random Seed
 
 As described in [PyTorch REPRODUCIBILITY](https://pytorch.org/docs/stable/notes/randomness.html), there are 2 factors affecting the reproducibility of an experiment, namely random number and nondeterministic algorithms.
 

docs/en/common_usage/visualize_training_log.md

+# 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.
+
+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).
+
+## TensorBoard
+
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `TensorboardVisBackend`.
+
+```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='TensorboardVisBackend')]),
+)
+runner.train()
+```
+
+## WandB
+
+Before using WandB, you need to install the `wandb` dependency library and log in to WandB.
+
+```bash
+pip install wandb
+wandb login
+```
+
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `WandbVisBackend`.
+
+```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='WandbVisBackend')]),
+)
+runner.train()
+```
+
+![image](https://user-images.githubusercontent.com/58739961/217226120-0c45267c-c45f-4fce-bdd5-a99c8c393006.png)
+
+You can click on [WandbVisBackend API](mmengine.visualization.WandbVisBackend) to view the configurable parameters for `WandbVisBackend`. For example, the `init_kwargs` parameter will be passed to the [wandb.init](https://docs.wandb.ai/ref/python/init) method.
+
+```python
+runner = Runner(
+    ...
+    visualizer=dict(
+        type='Visualizer',
+        vis_backends=[
+            dict(
+                type='WandbVisBackend',
+                init_kwargs=dict(project='toy-example')
+            ),
+        ],
+    ),
+    ...
+)
+runner.train()
+```
+
+## MLflow (WIP)

docs/en/index.rst

@@ -23,6 +23,7 @@ You can switch between Chinese and English documents in the lower-left corner of
    common_usage/resume_training.md
    common_usage/speed_up_training.md
    common_usage/save_gpu_memory.md
+   common_usage/visualize_training_log.md
    common_usage/set_random_seed.md
    common_usage/debug_tricks.md
    common_usage/model_analysis.md

docs/zh_cn/common_usage/visualize_training_log.md

+# 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。
+
+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。
+
+## TensorBoard
+
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `TensorboardVisBackend`。
+
+```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='TensorboardVisBackend')]),
+)
+runner.train()
+```
+
+## WandB
+
+使用 WandB 前需安装依赖库 `wandb` 并登录至 wandb。
+
+```bash
+pip install wandb
+wandb login
+```
+
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `WandbVisBackend`。
+
+```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='WandbVisBackend')]),
+)
+runner.train()
+```
+
+![image](https://user-images.githubusercontent.com/58739961/217226120-0c45267c-c45f-4fce-bdd5-a99c8c393006.png)
+
+可以点击 [WandbVisBackend API](mmengine.visualization.WandbVisBackend) 查看 `WandbVisBackend` 可配置的参数。例如 `init_kwargs`，该参数会传给 [wandb.init](https://docs.wandb.ai/ref/python/init) 方法。
+
+```python
+runner = Runner(
+    ...
+    visualizer=dict(
+        type='Visualizer',
+        vis_backends=[
+            dict(
+                type='WandbVisBackend',
+                init_kwargs=dict(project='toy-example')
+            ),
+        ],
+    ),
+    ...
+)
+runner.train()
+```
+
+## MLflow (WIP)

docs/zh_cn/index.rst

@@ -23,6 +23,7 @@
    common_usage/resume_training.md
    common_usage/speed_up_training.md
    common_usage/save_gpu_memory.md
+   common_usage/visualize_training_log.md
    common_usage/set_random_seed.md
    common_usage/debug_tricks.md
    common_usage/model_analysis.md