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), [ClearML](https://clear.ml/docs/latest/docs) and [Neptune](https://docs.neptune.ai/), 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).
Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [TensorboardVisBackend](mmengine.visualization.TensorboardVisBackend).
Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [WandbVisBackend](mmengine.visualization.WandbVisBackend).
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.
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.
Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [ClearMLVisBackend](mmengine.visualization.ClearMLVisBackend).
Configure the `Runner` in the initialization parameters of the Runner, and set `vis_backends` to [NeptuneVisBackend](mmengine.visualization.NeptuneVisBackend).
Please note: If the `project` and `api_token` are not specified, neptune will be set to offline mode and the generated files will be saved to the local `.neptune` file.
It is recommended to specify the `project` and `api_token` during initialization as shown below.