Overview of Kubeflow Fairing

Build, train, and deploy your ML training jobs remotely

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 deployingmachine learning (ML) training jobs in a hybrid cloud environment. By usingKubeflow Fairing and adding a few lines of code, you can run your ML trainingjob locally or in the cloud, directly from Python code or a Jupyternotebook. After your training job is complete, you can use Kubeflow Fairing todeploy your trained model as a prediction endpoint.

Getting started

Use the following guides to get started with Kubeflow Fairing:

• To set up your development environment, follow the guide to installingKubeflow Fairing.
• To ensure that Kubeflow Fairing can access your Kubeflow cluster, followthe guide to configuring your development environment with accessto Kubeflow.
• 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 MLmodels on Kubeflow. Kubeflow Fairing can also been extended totrain or deploy on other platforms. Currently, Kubeflow Fairing has beenextended to train on Google AI Platform.

Kubeflow Fairing packages your Jupyter notebook, Python function, or Pythonfile as a Docker image, then deploys and runs the training job on Kubeflowor AI Platform. After your training job is complete, you can use KubeflowFairing 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 Kubeflowcluster.

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Last modified 04.02.2020: Kubeflow v1.0: Versioning policy and app stability indicators (#1308) (627bae1b)