Configure Kubeflow Fairing

In order to use Kubeflow Fairing to train or deploy a machine learning model on Kubeflow, you must configure your development environment with access to your container image registry and your Kubeflow cluster. This guide describes how to configure Kubeflow Fairing to run training jobs on Kubeflow.

Additional configuration steps are required to access Kubeflow when it is hosted on a cloud environment. Use the following guides to configure Kubeflow Fairing with access to your hosted Kubeflow environment.

• To use Kubeflow Fairing to train and deploy on Kubeflow on Google Kubernetes Engine, follow the guide to configuring Kubeflow Fairing with access to Google Cloud Platform.

Prerequisites

Before you configure Kubeflow Fairing, you must have a Kubeflow environment and Kubeflow Fairing installed in your development environment.

• If you do not have a Kubeflow cluster, follow the getting started with Kubeflow guide to set one up.
• If you have not installed Kubeflow Fairing, follow the installing Kubeflow Fairing guide.

Using Kubeflow Fairing with Kubeflow notebooks

The standard Kubeflow notebook images include Kubeflow Fairing and come preconfigured to run training jobs on your Kubeflow cluster. No additional configuration is required.

If you built your Kubeflow notebook server from a custom Jupyter Docker image, follow the instruction in this guide to configure your notebooks environment with access to your Kubeflow environment.

Configure Docker with access to your container image registry

Authorize Docker to access your container image registry by following the instructions in the docker login reference guide.

Configure access to your Kubeflow cluster

Use the following instructions to configure kubeconfig with access to your Kubeflow cluster.

1. Kubeflow Fairing uses kubeconfig to access your Kubeflow cluster. This guide uses kubectl to set up your kubeconfig. To check if you have kubectl installed, run the following command:

   which kubectl
   

   The response should be something like this:

   /usr/bin/kubectl
   

   If you do not have kubectl installed, follow the instructions in the guide to installing and setting up kubectl.

2. Follow the guide to configuring access to Kubernetes clusters, to update your kubeconfig with appropriate credentials and endpoint information to access your Kubeflow cluster.

Next steps

• Follow the samples and tutorials to learn more about how to run training jobs remotely with Kubeflow Fairing.