Run an individual step

If you want to run just an individual step on your stack, you can simply call the step as you would with a normal Python function. ZenML will internally create a pipeline with just your step and run it on the active stack.

The pipeline run that will happen to execute your step will be unlisted, which means it will not be associated with any pipeline. You can still see it in the "Runs" tab of the dashboard.

from zenml import step
import pandas as pd
from sklearn.base import ClassifierMixin
from sklearn.svm import SVC

# Configure the step to use a step operator. If you're not using
# a step operator, you can remove this and the step will run on
# your orchestrator instead.
@step(step_operator="<STEP_OPERATOR_NAME>")
def svc_trainer(
    X_train: pd.DataFrame,
    y_train: pd.Series,
    gamma: float = 0.001,
) -> Tuple[
    Annotated[ClassifierMixin, "trained_model"],
    Annotated[float, "training_acc"],
]:
    """Train a sklearn SVC classifier."""

    model = SVC(gamma=gamma)
    model.fit(X_train.to_numpy(), y_train.to_numpy())

    train_acc = model.score(X_train.to_numpy(), y_train.to_numpy())
    print(f"Train accuracy: {train_acc}")

    return model, train_acc


X_train = pd.DataFrame(...)
y_train = pd.Series(...)

# Call the step directly. This will internally create a
# pipeline with just this step, which will be executed on
# the active stack.
model, train_acc = svc_trainer(X_train=X_train, y_train=y_train)

Run the underlying step function directly

If you instead want to run your step function without ZenML getting involved, you can use the entrypoint(...) method of a step:

X_train = pd.DataFrame(...)
y_train = pd.Series(...)

model, train_acc = svc_trainer.entrypoint(X_train=X_train, y_train=y_train)

If you want to make this the default behavior when calling a step, you can set the ZENML_RUN_SINGLE_STEPS_WITHOUT_STACK environment variable to True. Once you do that, calling svc_trainer(...) will simply call the underlying function and not use your ZenML stack.