mmengine/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/)、[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。

## 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)

## 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)