ment_uri", is_deployment_artifact=True)],

]:

...The ArtifactConfig object allows configuring model linkage directly on the artifact, and you specify whether it's for a model or deployment by using the is_model_artifact and is_deployment_artifact flags (as shown above) else it will be assumed to be a data artifact.

Saving intermediate artifacts

It is often handy to save some of your work half-way: steps like epoch-based training can be running slow, and you don't want to lose any checkpoints along the way if an error occurs. You can use the save_artifact utility function to save your data assets as ZenML artifacts. Moreover, if your step has the Model context configured in the @pipeline or @step decorator it will be automatically linked to it, so you can get easy access to it using the Model Control Plane features.

from zenml import step, Model

from zenml.artifacts.utils import save_artifact

import pandas as pd

from typing_extensions import Annotated

from zenml.artifacts.artifact_config import ArtifactConfig

@step(model=Model(name="MyModel", version="1.2.42"))

def trainer(

trn_dataset: pd.DataFrame,

) -> Annotated[

ClassifierMixin, ArtifactConfig("trained_model", is_model_artifact=True)

]:  # this configuration will be applied to `model` output

"""Step running slow training."""

...

for epoch in epochs:

checkpoint = model.train(epoch)

# this will save each checkpoint in `training_checkpoint` artifact

# with distinct version e.g. `1.2.42_0`, `1.2.42_1`, etc.

# Checkpoint artifacts will be linked to `MyModel` version `1.2.42`

# implicitly.

save_artifact(

data=checkpoint,

name="training_checkpoint",

version=f"1.2.42_{epoch}",

...

return model

Link artifacts explicitly

If you would like to link an artifact to a model not from the step context or even outside a step, you can use the link_artifact_to_model function. All you need is ready to link artifact and the configuration of a model.

from zenml import step, Model, link_artifact_to_model, save_artifact

from zenml.client import Client