Importance of Artifact Management in Machine Learning
In terms of artifact management, logging models as wandb.Artifacts provided a great way to track versions and ensure reproducibility. It also allowed for easier comparison between different configurations, making it clear which model performed best under specific hyperparameters.
Created on February 13|Last edited on February 17
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Reproducibility:
Managing artifacts ensures that we can replicate the entire training process, including models, datasets, and evaluation results. By storing models as wandb.Artifact, we create a snapshot of the exact experiment, making it possible to recreate the same results in the future. For instance, in this report, I logged the model with the best performance (using a learning rate of 0.001) as an artifact. This guarantees that anyone can access the exact model and replicate the experiment at any time.
Version Control:
Artifacts such as models and datasets are assigned unique versions, enabling us to monitor modifications and easily revert to previous versions when necessary. In this experiment, I saved each model trained with different learning rates as separate versions within the same artifact. This approach facilitates direct comparisons between models trained under different configurations, with no risk of version conflicts.
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