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MMEngine by OpenMMLab 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 is directly integrated into MMEngine through a dedicated 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.

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Get started

Install openmim and wandb.
  • Command Line
  • Notebook
pip install -q -U openmim wandb
Next, install mmengine and mmcv using mim.
  • Command Line
  • Notebook
mim install -q mmengine mmcv

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Use the WandbVisBackend with MMEngine Runner

This section demonstrates a typical workflow using WandbVisBackend using mmengine.runner.Runner.
  1. Define a visualizer from a visualization config. 
    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)
    You pass a dictionary of arguments for W&B run initialization input parameters to init_kwargs.
  2. Initialize a runner with the visualizer, and call runner.train(). 
    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()

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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. 
# 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'
        },
    ),
]
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