Description
This library provides an abstraction to perform Model Versioning using Weight & Biases.
Features
- Version a new trained model
- Promote a model to another stage (e.g production)
Example of usage
The following code snippet shows how to promote a newly trained model to the best_model on the validation set. First, it creates the model providing the checkpoint path, the artifact name and type, a description, and the metadata, where the metrics or any desired information is stored.
The comparision_type indicates how to compare the metrics between the two models. E.g if smaller, the smaller the metric from the new model the better
run = wandb.init(...)
# Create the desired artifact
artifact = versioner.create_artifact(
checkpoint='model.ckpt',
artifact_name='prueba',
artifact_type='model',
description='Prueba Wandb-MV',
metadata={
'val_metric': 78.0,
'test_metric': 0.0
})
# Promote the desired artifact to the 'best_model' tag
versioner.promote_model(new_model=artifact,
artifact_name='prueba',
artifact_type='model',
comparision_metric='val_metric',
promotion_alias='best_model',
comparision_type='smaller'
)
This code snippet shows how to promote a trained model to production after being validated on the test set. The already_deployed parameter indicates that the model is already logged, so it only needs to be updated.
versioner = Versioner(run)
versioner.promote_model(new_model=artifact,
artifact_name='prueba',
artifact_type='model',
comparision_metric='test_metric',
promotion_alias='production',
comparision_type='smaller',
already_deployed=True
)
Next features
- Allow providing a custom comparision function to promote a model
- ...
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