Create a custom alias
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
run = wandb.init(project='<example-project>')
artifact = run.use_artifact('bike-dataset:latest')
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'])