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MLflowVisBackend

class mmengine.visualization.MLflowVisBackend(save_dir, exp_name=None, run_name=None, tags=None, params=None, tracking_uri=None, artifact_suffix=('.json', '.log', '.py', 'yaml'), tracked_config_keys=None)[source]

MLflow visualization backend class.

It can write images, config, scalars, etc. to a mlflow file.

Examples

>>> from mmengine.visualization import MLflowVisBackend
>>> from mmengine import Config
>>> import numpy as np
>>> vis_backend = MLflowVisBackend(save_dir='temp_dir')
>>> img = np.random.randint(0, 256, size=(10, 10, 3))
>>> vis_backend.add_image('img.png', img)
>>> vis_backend.add_scalar('mAP', 0.6)
>>> vis_backend.add_scalars({'loss': 0.1,'acc':0.8})
>>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
>>> vis_backend.add_config(cfg)
Parameters:
  • save_dir (str) – The root directory to save the files produced by the backend.

  • exp_name (str, optional) – The experiment name. Defaults to None.

  • run_name (str, optional) – The run name. Defaults to None.

  • tags (dict, optional) – The tags to be added to the experiment. Defaults to None.

  • params (dict, optional) – The params to be added to the experiment. Defaults to None.

  • tracking_uri (str, optional) – The tracking uri. Defaults to None.

  • artifact_suffix (Tuple[str] or str, optional) – The artifact suffix. Defaults to (‘.json’, ‘.log’, ‘.py’, ‘yaml’).

  • tracked_config_keys (dict, optional) – The top level keys of config that will be added to the experiment. If it is None, which means all the config will be added. Defaults to None. New in version 0.7.4.

add_config(config, **kwargs)[source]

Record the config to mlflow.

Parameters:

config (Config) – The Config object

Return type:

None

add_image(name, image, step=0, **kwargs)[source]

Record the image to mlflow.

Parameters:
  • name (str) – The image identifier.

  • image (np.ndarray) – The image to be saved. The format should be RGB.

  • step (int) – Global step value to record. Default to 0.

Return type:

None

add_scalar(name, value, step=0, **kwargs)[source]

Record the scalar data to mlflow.

Parameters:
  • name (str) – The scalar identifier.

  • value (int, float, torch.Tensor, np.ndarray) – Value to save.

  • step (int) – Global step value to record. Default to 0.

Return type:

None

add_scalars(scalar_dict, step=0, file_path=None, **kwargs)[source]

Record the scalar’s data to mlflow.

Parameters:
  • scalar_dict (dict) – Key-value pair storing the tag and corresponding values.

  • step (int) – Global step value to record. Default to 0.

  • file_path (str, optional) – Useless parameter. Just for interface unification. Defaults to None.

Return type:

None

close()[source]

Close the mlflow.

Return type:

None

property experiment

Return MLflow object.

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