⛓️Build a pipeline

Building pipelines is as simple as adding the `@step` and `@pipeline` decorators to your code.

@step  # Just add this decorator
def load_data() -> dict:
    training_data = [[1, 2], [3, 4], [5, 6]]
    labels = [0, 1, 0]
    return {'features': training_data, 'labels': labels}


@step
def train_model(data: dict) -> None:
    total_features = sum(map(sum, data['features']))
    total_labels = sum(data['labels'])

    # Train some model here

    print(f"Trained model using {len(data['features'])} data points. "
          f"Feature sum is {total_features}, label sum is {total_labels}")


@pipeline  # This function combines steps together 
def simple_ml_pipeline():
    dataset = load_data()
    train_model(dataset)

You can now run this pipeline by simply calling the function:

simple_ml_pipeline()

When this pipeline is executed, the run of the pipeline gets logged to the ZenML dashboard where you can now go to look at its DAG and all the associated metadata. To access the dashboard you need to have a ZenML server either running locally or remotely. See our documentation on this here.

DAG representation in the ZenML Dahboard.

Check below for more advanced ways to build and interact with your pipeline.

Configure pipeline/step parameters

Name and annotate step outputs

Control caching behavior

Run pipeline from a pipeline

Control the execution order of steps

Customize the step invocation ids

Name your pipeline runs

Use failure/success hooks

Hyperparameter tuning

Attach metadata to steps

Fetch metadata within steps

Fetch metadata during pipeline composition

Version pipelines

Enable or disable logs storing

Special Metadata Types

Access secrets in a step