clearml-docs/docs/integrations/mmengine.md

title
MMEngine

:::tip If you are not already using ClearML, see Getting Started for setup instructions. :::

MMEngine is a library for training deep learning models based on PyTorch. MMEngine supports ClearML through a builtin logger: It automatically logs experiment environment information, such as required packages and uncommitted changes, and supports reporting scalars, parameters, and debug samples.

Integrate ClearML with the following steps:

1. Instantiate a ClearMLVisBackend object. This creates a ClearML Task that logs the experiment’s environment information.

   from mmengine.visualization import ClearMLVisBackend
   vis_backend = ClearMLVisBackend(
       artifact_suffix=('.py', 'pth'), 
       init_kwargs=dict(
           project_name='examples',
           task_name='OpenMMLab cifar10',
           output_uri=True
       )
   )
   

   You can specify the following parameters:

   • init_kwargs – A dictionary that contains the arguments to pass to ClearML's Task.init().
   • artifact_suffix – At the end of training, artifacts with these suffixes will be uploaded to the task's output_uri. Defaults to (.py, pth).

2. Log experiment parameters using ClearMLVisBackend.add_config(). Under the config parameter, input a dictionary of parameter key-value pairs.

   cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
   vis_backend.add_config(config=cfg)
   

   The parameters will be displayed in the ClearML WebApp, under the experiment’s Hyperparameters

3. Log your experiment’s scalars using either ClearMLVisBackend.add_scalar() for single values or ClearMLVisBackend.add_scalars() for multiple values:

   vis_backend.add_scalar(name='mAP', value=0.6, step=1)
   vis_backend.add_scalars(scalar_dict={'loss': 0.1,'acc':0.8}, step=1)
   

   The scalars are displayed in the experiment's Scalars tab.

4. Report images to your experiment using ClearMLVisBackend.add_image(). Under the image parameter, input the image to be reported as an np.ndarray in RGB format:

   img = np.random.randint(0, 256, size=(10, 10, 3))
   vis_backend.add_image(name='img.png', image=img, step=1)
   

   The images will be displayed in the experiment's Debug Samples

5. Once you've finished training, make sure to run ClearMLVisBackend.close() so that ClearML can mark the task as completed. This will also scan the directory for relevant artifacts with the suffixes input when instantiating ClearMLVisBackend and log the artifacts to your experiment.

   vis_backend.close()