If you want to run just a single step remotely from a notebook, 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.
When defining a step that should be run remotely in a notebook, make sure you're aware of all the limitations that apply.
from zenml import stepimport pandas as pdfrom sklearn.base import ClassifierMixinfrom 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>")defsvc_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_accX_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)