Functional API

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Functional API

Build production ML pipelines from the simple step interface.

The Functional ZenML API is defined by the primitive @step and @pipeline decorators. These make it easy to quickly take your functional code into a ZenML pipeline. If you want more control over the different steps the Class based API is the way to go.
A user may also mix-and-match the Functional API with the Class Based API: All standard data types and steps that are applicable in both of these approaches.
In order to illustrate how the Functional API functions, we'll do a simple exercise to create a training pipeline from scratch (without any High Level components) and take it all the way to deployment.
Here is what we'll do in this guide:
Create a MNIST pipeline that trains using TensorFlow (Keras) (similar to the Quickstart).
Swap out implementations of the trainer and evaluator steps with scikit-learn.
Persist our interim data artifacts in SQL tables rather than on files.
Read from a dynamically changing datasource rather than a static one.
Deploy the pipeline on Kubeflow Pipelines.
If you just want to see all the code for each chapter of the guide, head over to the GitHub version​
If not, then get your environment ready and follow along!
Set up locally
In order to run the chapters of the guide, you need to install and initialize ZenML:
pip install zenml 
zenml integration install tensorflow
zenml integration install sklearn
pip install sqlalchemy 
​
# pull example
zenml example pull functional_api
cd zenml_examples/functional_api
​
# initialize
zenml init
In general, to run each chapter you can do:
python chapter_*.py  # for the chapter of your choice
Note before executing each chapter, make sure to clean the old chapter artifact and metadata store:
rm -rf .zen
zenml init  # start again
Clean up
In order to clean up, delete the remaining zenml references.
rm -rf zenml_examples
Press next to start the first chapter!

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Outline

Set up locally

Clean up