nterfaces:

To list artifacts: zenml artifact listThe ZenML Pro dashboard offers advanced visualization features for artifact exploration.

To prevent visual clutter, make sure to assign names to your most important artifacts that you would like to explore visually.

Versioning artifacts manually

ZenML automatically versions all created artifacts using auto-incremented numbering. I.e., if you have defined a step creating an artifact named iris_dataset as shown above, the first execution of the step will create an artifact with this name and version "1", the second execution will create version "2", and so on.

While ZenML handles artifact versioning automatically, you have the option to specify custom versions using the ArtifactConfig. This may come into play during critical runs like production releases.

from zenml import step, ArtifactConfig

@step

def training_data_loader() -> (

Annotated[

pd.DataFrame,

# Add `ArtifactConfig` to control more properties of your artifact

ArtifactConfig(

name="iris_dataset",

version="raw_2023"

),

):

...

The next execution of this step will then create an artifact with the name iris_dataset and version raw_2023. This is primarily useful if you are making a particularly important pipeline run (such as a release) whose artifacts you want to distinguish at a glance later.

Since custom versions cannot be duplicated, the above step can only be run once successfully. To avoid altering your code frequently, consider using a YAML config for artifact versioning.

After execution, iris_dataset and its version raw_2023 can be seen using:

To list versions: zenml artifact version list

The Cloud dashboard visualizes version history for your review.

Add metadata and tags to artifacts

If you would like to extend your artifacts with extra metadata or tags you can do so by following the patterns demonstrated below:

from zenml import step, get_step_context, ArtifactConfig

from typing_extensions import Annotated

# below we annotate output with `ArtifactConfig` giving it a name,