Steps & Pipelines

Steps and Pipelines are the core building blocks of ZenML

Steps and Pipelines are the fundamental building blocks of ZenML. A Step is a reusable unit of computation, and a Pipeline is a directed acyclic graph (DAG) composed of steps. Together, they allow you to define, version, and execute machine learning workflows.

The Relationship Between Steps and Pipelines

In ZenML, steps and pipelines work together in a clear hierarchy:

1. Steps are individual functions that perform specific tasks, like loading data, processing it, or training models

2. Pipelines orchestrate these steps, connecting them in a defined sequence where outputs from one step can flow as inputs to others

3. Each step produces artifacts that are tracked, versioned, and can be reused across pipeline runs

Think of a step as a single LEGO brick, and a pipeline as the complete structure you build by connecting many bricks together.

Basic Steps

Creating a Simple Step

A step is created by applying the @step decorator to a Python function:

from zenml import step

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

Step Inputs and Outputs

Steps can take inputs and produce outputs. These can be simple types, complex data structures, or custom objects.

@step
def process_data(data: dict) -> dict:
    # Input: data dictionary with features and labels
    # Process the input data
    processed_features = [feature * 2 for feature in data['features']]
    
    # Output: return processed data and statistics
    return {
        'processed_features': processed_features,
        'labels': data['labels'],
        'num_samples': len(data['features']),
        'feature_sum': sum(map(sum, data['features']))
    }

In this example:

• The step takes a dict as input containing features and labels

• It processes the features and computes some statistics

• It returns a new dict as output with the processed data and additional information

Custom Output Names

You can name your step outputs using the Annotated type:

from typing import Annotated
from typing import Tuple

@step
def divide(a: int, b: int) -> Tuple[
    Annotated[int, "quotient"],
    Annotated[int, "remainder"]
]:
    return a // b, a % b

By default, step outputs are named output for single output steps and output_0, output_1, etc. for steps with multiple outputs.

Basic Pipelines

Creating a Simple Pipeline

A pipeline is created by applying the @pipeline decorator to a Python function that composes steps together:

from zenml import pipeline

@pipeline
def simple_ml_pipeline():
    dataset = load_data()
    train_model(dataset)

Running Pipelines

You can run a pipeline by simply calling the function:

simple_ml_pipeline()

The run is automatically logged to the ZenML dashboard where you can view the DAG and associated metadata.

End-to-End Example

Here's a simple end-to-end example that demonstrates the basic workflow:

import numpy as np

from typing import Tuple

from zenml import step, pipeline

# Create steps for a simple ML workflow
@step
def get_data() -> Tuple[np.ndarray, np.ndarray]:
    # Generate some synthetic data
    X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
    y = np.array([0, 1, 0, 1])
    return X, y

@step
def process_data(data: Tuple[np.ndarray, np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
    X, y = data
    # Apply a simple transformation
    X_processed = X * 2
    return X_processed, y

@step
def train_and_evaluate(processed_data: Tuple[np.ndarray, np.ndarray]) -> float:
    X, y = processed_data
    # Simplistic "training" - just compute accuracy based on a rule
    predictions = [1 if sum(sample) > 10 else 0 for sample in X]
    accuracy = sum(p == actual for p, actual in zip(predictions, y)) / len(y)
    return accuracy

# Create a pipeline that combines these steps
@pipeline
def simple_example_pipeline():
    raw_data = get_data()
    processed_data = process_data(raw_data)
    accuracy = train_and_evaluate(processed_data)
    print(f"Model accuracy: {accuracy}")

# Run the pipeline
if __name__ == "__main__":
    simple_example_pipeline()

Parameters and Artifacts

Understanding the Difference

ZenML distinguishes between two types of inputs to steps:

1. Artifacts: Outputs from other steps in the same pipeline

   • These are tracked, versioned, and stored in the artifact store

   • They are passed between steps and represent data flowing through your pipeline

   • Examples: datasets, trained models, evaluation metrics

2. Parameters: Direct values provided when invoking a step

   • These are typically simple configuration values passed directly to the step

   • They're not tracked as separate artifacts but are recorded with the pipeline run

   • Examples: learning rates, batch sizes, model hyperparameters

This example demonstrates the difference:

@pipeline
def my_pipeline():
    int_artifact = some_other_step()  # This is an artifact
    # input_1 is an artifact, input_2 is a parameter
    my_step(input_1=int_artifact, input_2=42)

Parameter Types

Parameters can be:

1. Primitive types: int, float, str, bool

2. Container types: list, dict, tuple (containing primitives)

3. Custom types: As long as they can be serialized to JSON using Pydantic

Parameters that cannot be serialized to JSON should be passed as artifacts rather than parameters.

Parameterizing Workflows

Step Parameterization

Steps can take parameters like regular Python functions:

@step
def train_model(data: dict, learning_rate: float = 0.01, epochs: int = 10) -> None:
    # Use learning_rate and epochs parameters
    print(f"Training with learning rate: {learning_rate} for {epochs} epochs")

Pipeline Parameterization

Pipelines can also be parameterized, allowing values to be passed down to steps:

@pipeline
def training_pipeline(dataset_name: str = "default_dataset", learning_rate: float = 0.01):
    data = load_data(dataset_name=dataset_name)
    train_model(data=data, learning_rate=learning_rate, epochs=20)

You can then run the pipeline with specific parameters:

training_pipeline(dataset_name="custom_dataset", learning_rate=0.005)

Step Type Handling & Output Management

Type Annotations

While optional, type annotations are highly recommended and provide several benefits:

• Artifact handling: ZenML uses type annotations to determine how to serialize, store, and load artifacts. The type information guides ZenML to select the appropriate materializer for saving and loading step outputs.

• Type validation: ZenML validates inputs against type annotations at runtime to catch errors early.

• Code documentation: Types make your code more self-documenting and easier to understand.

from typing import Tuple

@step
def square_root(number: int) -> float:
    return number ** 0.5

@step
def divide(a: int, b: int) -> Tuple[int, int]:
    return a // b, a % b

When you specify a return type like -> float or -> Tuple[int, int], ZenML uses this information to determine how to store the step's output in the artifact store. For instance, a step returning a pandas DataFrame with the annotation -> pd.DataFrame will use the pandas-specific materializer for efficient storage.

If you want to enforce type annotations for all steps, set the environment variable ZENML_ENFORCE_TYPE_ANNOTATIONS to True.

Multiple Return Values

Steps can return multiple artifacts:

from typing import Tuple
from sklearn.base import ClassifierMixin
from typing import Annotated

@step
def train_classifier(X_train, y_train) -> Tuple[
    Annotated[ClassifierMixin, "model"],
    Annotated[float, "accuracy"]
]:
    model = SVC(gamma=0.001)
    model.fit(X_train, y_train)
    accuracy = model.score(X_train, y_train)
    return model, accuracy

ZenML uses the following convention to differentiate between a single output of type Tuple and multiple outputs:

• When the return statement is followed by a tuple literal (e.g., return 1, 2 or return (value_1, value_2)), it's treated as a step with multiple outputs

• All other cases are treated as a step with a single output of type Tuple

Conclusion

Steps and Pipelines provide a flexible, powerful way to build machine learning workflows in ZenML. This guide covered the basic concepts of creating steps and pipelines, managing inputs and outputs, and working with parameters.

For more advanced features, check out the Advanced Features guide. For configuration using YAML files, see Configuration with YAML.