74 lines
2.5 KiB
Markdown
74 lines
2.5 KiB
Markdown
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# Visualize Training Logs
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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.
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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).
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## TensorBoard
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Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `TensorboardVisBackend`.
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```python
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runner = Runner(
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model=MMResNet50(),
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work_dir='./work_dir',
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train_dataloader=train_dataloader,
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optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
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train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
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val_dataloader=val_dataloader,
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val_cfg=dict(),
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val_evaluator=dict(type=Accuracy),
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visualizer=dict(type='Visualizer', vis_backends=[dict(type='TensorboardVisBackend')]),
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)
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runner.train()
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```
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## WandB
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Before using WandB, you need to install the `wandb` dependency library and log in to WandB.
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```bash
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pip install wandb
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wandb login
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```
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Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `WandbVisBackend`.
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```python
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runner = Runner(
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model=MMResNet50(),
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work_dir='./work_dir',
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train_dataloader=train_dataloader,
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optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
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train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
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val_dataloader=val_dataloader,
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val_cfg=dict(),
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val_evaluator=dict(type=Accuracy),
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visualizer=dict(type='Visualizer', vis_backends=[dict(type='WandbVisBackend')]),
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)
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runner.train()
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```
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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.
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```python
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runner = Runner(
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...
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visualizer=dict(
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type='Visualizer',
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vis_backends=[
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dict(
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type='WandbVisBackend',
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init_kwargs=dict(project='toy-example')
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),
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],
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),
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...
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)
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runner.train()
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```
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## MLflow (WIP)
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