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
- ...
5 Dec 08, 2022
1 Jan 10, 2022
76 Dec 21, 2022
187 Dec 28, 2022
839 Dec 29, 2022
546 Dec 17, 2022
14 Dec 17, 2022
3.9k Dec 30, 2022
14 Dec 18, 2022
37 Dec 20, 2022
236 Jan 01, 2023
26 Nov 18, 2022
25 Oct 26, 2022
34 Dec 29, 2022
44 Dec 06, 2022
82 Dec 22, 2022
13 Dec 15, 2022
19 Dec 19, 2022
101 Nov 30, 2022
49 Nov 28, 2022