Organize data with tags
Use tags to organize tags in ZenML.
Last updated
Use tags to organize tags in ZenML.
Last updated
Organizing and categorizing your machine learning artifacts and models can streamline your workflow and enhance discoverability. ZenML enables the use of tags as a flexible tool to classify and filter your ML assets. In this guide, we'll demonstrate how to assign tags to both artifacts and models within the ZenML ecosystem.
If you want to tag the artifact versions of a step or pipeline that is executed repeatedly, you can use the tags
property of ArtifactConfig
to assign an arbitrary number of tags to the created artifacts:
This will assign tags sklearn
and pre-training
to all artifacts created by this step, which can later be used to filter and organize these artifacts.
Note that ZenML Pro users can tag artifacts directly in the cloud dashboard.
Just like artifacts, you can also tag your models to organize them semantically. Here's how to use tags with models in the ZenML Python SDK and CLI (or in the ZenML Pro Dashboard directly).
When creating a model version using the Model
object, you can specify tags as key-value pairs that will be attached to the model version upon creation.
During pipeline run a model can be also implicitly created (if not exists), in such cases it will not get the tags
from the Model
class. You can manipulate the model tags using SDK (see below) or the ZenML Pro UI.
You can also assign tags when creating or updating models with the Python SDK:
To add tags to existing models and their versions using the ZenML CLI, you can use the following commands: