MMEngine

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

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.

Get started

Install openmim and wandb.

Command Line

pip install -q -U openmim wandb

Notebook

!pip install -q -U openmim wandb

Next, install mmengine and mmcv using mim.

Command Line

mim install -q mmengine mmcv

Notebook

!mim install -q mmengine mmcv

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()
   

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'
        },
    ),
]