Skip to main content
This page describes creating lineage graphs in the legacy W&B Model Registry. To learn about lineage graphs in W&B Registry, refer to Create and view lineage maps.
W&B will transition assets from the legacy W&B Model Registry to the new W&B Registry. This migration will be fully managed and triggered by W&B, requiring no intervention from users. The process is designed to be as seamless as possible, with minimal disruption to existing workflows. Refer to Migrate from legacy Model Registry.
A useful feature of logging model artifacts to W&B are lineage graphs. Lineage graphs show artifacts logged by a run as well as artifacts used by specific run. This means that, when you log a model artifact, you at a minimum have access to view the W&B run that used or produced the model artifact. If you track a dependency, you also see the inputs used by the model artifact. For example, the proceeding image shows artifacts created and used throughout an ML experiment:
Model lineage graph
From left to right, the image shows:
  1. The jumping-monkey-1 W&B run created the mnist_dataset:v0 dataset artifact.
  2. The vague-morning-5 W&B run trained a model using the mnist_dataset:v0 dataset artifact. The output of this W&B run was a model artifact called mnist_model:v0.
  3. A run called serene-haze-6 used the model artifact (mnist_model:v0) to evaluate the model.

​
Track an artifact dependency

Declare an dataset artifact as an input to a W&B run with the use_artifact API to track a dependency. The proceeding code snippet shows how to use the use_artifact API: 
# Initialize a run
run = wandb.init(project=project, entity=entity)

# Get artifact, mark it as a dependency
artifact = run.use_artifact(artifact_or_name="name", aliases="<alias>")
Once you have retrieved your artifact, you can use that artifact to (for example), evaluate the performance of a model.
⌘I