Create a custom alias

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Use aliases as pointers to specific versions. By default, Run.log_artifact adds the latest alias to the logged version.
An artifact version v0 is created and attached to your artifact when you log an artifact for the first time. Weights & Biases checksums the contents when you log again to the same artifact. If the artifact changed, Weights & Biases saves a new version v1.
For example, if you want your training script to pull the most recent version of a dataset, specify latest when you use that artifact. The proceeding code example demonstrates how to download a recent dataset artifact named bike-dataset that has an alias, latest:
import wandb
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run = wandb.init(project='<example-project>')
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artifact = run.use_artifact('bike-dataset:latest')
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artifact.download()
You can also apply a custom alias to an artifact version. For example, if you want to mark that model checkpoint is the best on the metric AP-50, you could add the string 'best-ap50' as an alias when you log the model artifact.
artifact = wandb.Artifact('run-3nq3ctyy-bike-model', type='model')
artifact.add_file('model.h5')
run.log_artifact(artifact, aliases=['latest','best-ap50'])
Update artifacts
Create new artifact versions
Last modified 27d ago
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