> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-mintlify-bbaa8558.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# MMEngine

> Use W&B with OpenMMLab's MMEngine through the WandbVisBackend to log training metrics, configs, and visual records.

MMEngine by [OpenMMLab](https://github.com/open-mmlab) is a foundational library for training deep learning models based on PyTorch. MMEngine implements a next-generation training architecture for the OpenMMLab algorithm library, providing a unified execution foundation for over 30 algorithm libraries within OpenMMLab. Its core components include the training engine, evaluation engine, and module management.

[W\&B](https://wandb.ai/site) is directly integrated into MMEngine through a dedicated [`WandbVisBackend`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.visualization.WandbVisBackend.html#mmengine.visualization.WandbVisBackend) that can be used to

* log training and evaluation metrics.
* log and manage experiment configs.
* log additional records such as graph, images, scalars, etc.

## Get started

Install `openmim` and `wandb`.

<Tabs>
  <Tab title="Command Line">
    ```bash theme={null}
    pip install -q -U openmim wandb
    ```
  </Tab>

  <Tab title="Notebook">
    ```bash theme={null}
    !pip install -q -U openmim wandb
    ```
  </Tab>
</Tabs>

Next, install `mmengine` and `mmcv` using `mim`.

<Tabs>
  <Tab title="Command Line">
    ```bash theme={null}
    mim install -q mmengine mmcv
    ```
  </Tab>

  <Tab title="Notebook">
    ```bash theme={null}
    !mim install -q mmengine mmcv
    ```
  </Tab>
</Tabs>

## Use the `WandbVisBackend` with MMEngine Runner

This section demonstrates a typical workflow using `WandbVisBackend` using [`mmengine.runner.Runner`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.Runner.html#mmengine.runner.Runner).

1. Define a `visualizer` from a visualization config.

   ```python theme={null}
   from mmengine.visualization import Visualizer

   # define the visualization configs
   visualization_cfg = dict(
       name="wandb_visualizer",
       vis_backends=[
           dict(
               type='WandbVisBackend',
               init_kwargs=dict(project="mmengine"),
           )
       ],
       save_dir="runs/wandb"
   )

   # get the visualizer from the visualization configs
   visualizer = Visualizer.get_instance(**visualization_cfg)
   ```

   <Note>
     You pass a dictionary of arguments for [W\&B run initialization](/models/ref/python/functions/init) input parameters to `init_kwargs`.
   </Note>

2. Initialize a `runner` with the `visualizer`, and call `runner.train()`.

   ```python theme={null}
   from mmengine.runner import Runner

   # build the mmengine Runner which is a training helper for PyTorch
   runner = Runner(
       model,
       work_dir='runs/gan/',
       train_dataloader=train_dataloader,
       train_cfg=train_cfg,
       optim_wrapper=opt_wrapper_dict,
       visualizer=visualizer, # pass the visualizer
   )

   # start training
   runner.train()
   ```

## Use the `WandbVisBackend` with OpenMMLab computer vision libraries

The `WandbVisBackend` can also be used easily to track experiments with OpenMMLab computer vision libraries such as [MMDetection](https://mmdetection.readthedocs.io/).

```python theme={null}
# inherit base configs from the default runtime configs
_base_ = ["../_base_/default_runtime.py"]

# Assign the `WandbVisBackend` config dictionary to the
# `vis_backends` of the `visualizer` from the base configs
_base_.visualizer.vis_backends = [
    dict(
        type='WandbVisBackend',
        init_kwargs={
            'project': 'mmdet',
            'entity': 'geekyrakshit'
        },
    ),
]
```