MMEngine

• Log training and evaluation metrics.
• Log and manage experiment configs.
• Log additional records such as graphs, images, and scalars.

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Get started

pip install -q -U openmim wandb

!pip install -q -U openmim wandb

mim install -q mmengine mmcv

!mim install -q mmengine mmcv

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Use the WandbVisBackend with MMEngine runner

1. Define a visualizer from a visualization config. The visualizer is what the runner uses to dispatch logs to the configured backend.
   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() to start training. The runner uses the visualizer to stream metrics and configs to W&B.
   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

# 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'
        },
    ),
]