Configure Kubeflow Fairing

Configuring your Kubeflow Fairing development environment with access to Kubeflow

In order to use Kubeflow Fairing to train or deploy a machine learningmodel on Kubeflow, you must configure your development environment with accessto your container image registry and your Kubeflow cluster. This guidedescribes 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 cloudenvironment. Use the following guides to configure Kubeflow Fairing with accessto your hosted Kubeflow environment.

Prerequisites

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

Using Kubeflow Fairing with Kubeflow notebooks

The standard Kubeflow notebook images include Kubeflow Fairing and comepreconfigured to run training jobs on your Kubeflow cluster. No additionalconfiguration 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 environmentwith access to your Kubeflow environment.

Configure Docker with access to your container image registry

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

Configure access to your Kubeflow cluster

Use the following instructions to configure kubeconfig with access to yourKubeflow cluster.

  • Kubeflow Fairing uses kubeconfig to access your Kubeflow cluster. Thisguide uses kubectl to set up your kubeconfig. To check if you havekubectl installed, run the following command:
  1. which kubectl

The response should be something like this:

  1. /usr/bin/kubectl

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

Next steps

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