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
- ...
482 Dec 26, 2022
77 Jan 05, 2023
93 Dec 28, 2022
731 Jan 02, 2023
99 Dec 14, 2022
252 Nov 10, 2022
36 Oct 19, 2022
91 Dec 26, 2022
1.3k Dec 28, 2022
0 May 04, 2022
3 Dec 13, 2021
127 Dec 20, 2022
69 Dec 30, 2022
170 Jan 03, 2023
97 Dec 02, 2022
20.2k Jan 05, 2023
1.5k Jan 02, 2023
95 Dec 23, 2022
17 Oct 15, 2022
1 Nov 19, 2021