Get started.

Build your first pipeline

1. 01

   Install ZenML

   Get ZenML up and running in minutes. You just need to install it

   bashCopy

   pip install 'zenml[local]'

2. 02

   Track inputs and outputs

   Wire two steps into a training pipeline. ZenML tracks every input and output as a versioned artifact:

   pythonCopy

   from sklearn.base import ClassifierMixin
   from sklearn.datasets import load_iris
   from sklearn.svm import SVC
   from zenml import step, pipeline
   
   
   @step
   def load_data() -> tuple[list, list]:
       X, y = load_iris(return_X_y=True)
       return X, y
   
   
   @step
   def train_model(X: list, y: list) -> ClassifierMixin:
       # The returned model is versioned + tracked as an artifact automatically.
       return SVC().fit(X, y)
   
   
   @pipeline
   def training_pipeline():
       X, y = load_data()
       train_model(X, y)
   
   
   if __name__ == "__main__":
       training_pipeline()

3. 03

   Run your pipeline locally

   Run it locally. The pipeline executes, artifacts are versioned, and the run shows up in your dashboard.

   bashCopy

   python run.py