Overview of Kubeflow Fairing

Build, train, and deploy your ML training jobs remotely

Out of date

This guide contains outdated information pertaining to Kubeflow 1.0. This guide needs to be updated for Kubeflow 1.1.

Beta

This Kubeflow component has beta status. See the Kubeflow versioning policies. The Kubeflow team is interested in your feedback about the usability of the feature.

Kubeflow Fairing streamlines the process of building, training, and deploying machine learning (ML) training jobs in a hybrid cloud environment. By using Kubeflow Fairing and adding a few lines of code, you can run your ML training job locally or in the cloud, directly from Python code or a Jupyter notebook. After your training job is complete, you can use Kubeflow Fairing to deploy your trained model as a prediction endpoint.

Getting started

Use the following guides to get started with Kubeflow Fairing:

1. To set up your development environment, follow the guide to installing Kubeflow Fairing.
2. To ensure that Kubeflow Fairing can access your Kubeflow cluster, follow the guide to configuring your development environment with access to Kubeflow.
3. To learn more about how to use Kubeflow Fairing in your environment, follow the Kubeflow Fairing tutorials.

What is Kubeflow Fairing?

Kubeflow Fairing is a Python package that makes it easy to train and deploy ML models on Kubeflow. Kubeflow Fairing can also been extended to train or deploy on other platforms. Currently, Kubeflow Fairing has been extended to train on Google AI Platform.

Kubeflow Fairing packages your Jupyter notebook, Python function, or Python file as a Docker image, then deploys and runs the training job on Kubeflow or AI Platform. After your training job is complete, you can use Kubeflow Fairing to deploy your trained model as a prediction endpoint on Kubeflow.

The following are the goals of the Kubeflow Fairing project:

• Easily package ML training jobs: Enable ML practitioners to easily package their ML model training code, and their code’s dependencies, as a Docker image.
• Easily train ML models in a hybrid cloud environment: Provide a high-level API for training ML models to make it easy to run training jobs in the cloud, without needing to understand the underlying infrastructure.
• Streamline the process of deploying a trained model: Make it easy for ML practitioners to deploy trained ML models to a hybrid cloud environment.

Next steps

• Learn how to set up a Jupyter notebooks instance on your Kubeflow cluster.

Last modified 18.08.2020: Fix bad link in Fairing Docs (#2139) (5dc68c87)