ehensive list of available information.

ArtifactsEach step of a pipeline run can have multiple output and input artifacts that we can inspect via the outputs and inputs properties.

To inspect the output artifacts of a step, you can use the outputs attribute, which is a dictionary that can be indexed using the name of an output. Alternatively, if your step only has a single output, you can use the output property as a shortcut directly:

# The outputs of a step are accessible by name

output = step.outputs["output_name"]

# If there is only one output, you can use the `.output` property instead

output = step.output

# use the `.load()` method to load the artifact into memory

my_pytorch_model = output.load()

Similarly, you can use the inputs and input properties to get the input artifacts of a step instead.

Check out this page to see what the output names of your steps are and how to customize them.

Note that the output of a step corresponds to a specific artifact version.

Fetching artifacts directly

If you'd like to fetch an artifact or an artifact version directly, it is easy to do so with the Client:

from zenml.client import Client

# Get artifact

artifact = Client().get_artifact('iris_dataset')

artifact.versions  # Contains all the versions of the artifact

output = artifact.versions['2022']  # Get version name "2022"

# Get artifact version directly:

# Using version name:

output = Client().get_artifact_version('iris_dataset', '2022')

# Using UUID

output = Client().get_artifact_version('f429f94c-fb15-43b5-961d-dbea287507c5')

loaded_artifact = output.load()

Artifact information

Regardless of how one fetches it, each artifact contains a lot of general information about the artifact as well as datatype-specific metadata and visualizations.

Metadata