Getting Started with Kubeflow

Before you begin

This document provides information about setting up Kubeflow in various environments.

It’s important that you have some knowledge of the following systems and tools:

• Kubernetes
• kustomize

If you plan to deploy Kubeflow on an existing Kubernetes cluster, review these Kubernetes system requirements.

Installing Kubeflow

There are various ways to install Kubeflow. Choose one of the following options to suit your environment (desktop or server, existing Kubernetes cluster or public cloud):

• Installing Kubeflow on a desktop or server:

  • To use Kubeflow on Windows, follow the Windows deployment guide.
  • To use Kubeflow on MacOS, follow the MacOS deployment guide.
  • To use Kubeflow on Linux, follow the Linux deployment guide.

• Installing Kubeflow on a existing Kubernetes cluster or a public cloud:

  • Installing Kubeflow on a Kubernetes cluster, follow the guide to deploying Kubeflow on Kubernetes.
  • To use Kubeflow on Google Cloud Platform (GCP) and Kubernetes Engine (GKE), follow the GCP deployment guide.
  • To use Kubeflow on Amazon Web Services (AWS), follow the AWS deployment guide.
  • To use Kubeflow on Microsoft Azure Kubernetes Service (AKS), follow the AKS deployment guide.
  • To use Kubeflow on IBM Cloud Private (ICP), follow the ICP deployment guide.

Installing command line tools

The following information is useful if you need or prefer to use command line tools for deploying and managing Kubeflow:

• Download the kfctl binary from the Kubeflow releases page.

• Follow the kubectl installation and setup instructions from the Kubernetes documentation. As described in the Kubernetes documentation, your kubectl version must be within one minor version of the Kubernetes version that you use in your Kubeflow cluster.

• Follow the kustomize installation and setup instructions from the guide to kustomize in Kubeflow.

Troubleshooting

See the Kubeflow troubleshooting guide.

Next steps

• Read the documentation for in-depth instructions on using Kubeflow.
• Explore the tutorials and codelabs for learning and trying out Kubeflow.
• Build machine-learning pipelines with the Kubeflow Pipelines SDK.

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