website/content/docs/started/getting-started.md

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title = "Getting Started with Kubeflow"
description = "Get your machine-learning workflow up and running on Kubeflow"
weight = 1
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There are various ways to install Kubeflow. Choose one of the following options
to suit your environment (cloud, on premises (on prem), or local):
* To use Kubeflow on Google Cloud Platform (GCP) and Kubernetes Engine (GKE),
follow the [GCP deployment guide](/docs/gke/deploy/).
* To use Kubeflow on Amazon Web Services (AWS),
follow the [AWS deployment guide](/docs/aws/deploy/).
* If you have an existing Kubernetes cluster or want to use Kubeflow on prem,
follow the [guide to deploying Kubeflow on
Kubernetes](/docs/started/getting-started-k8s/).
* If you want to run Kubernetes locally in a virtual machine (VM), choose one of
the following options:
* [MiniKF setup](/docs/started/getting-started-minikf/)
* MiniKF is a fast and easy way to get started with Kubeflow.
* It installs with just two commands and then you are up for
experimentation, and for running complete Kubeflow Pipelines.
* MiniKF runs on all major operating systems (Linux, macOS, Windows).
* [Minikube setup](/docs/started/getting-started-minikube/)
* Minikube uses virtualization applications like
[VirtualBox](https://www.virtualbox.org/) or [VMware
Fusion](https://www.vmware.com/products/fusion.html) to host the VM
and provides a CLI that you can use outside the VM.
* Minikube defines a fully-baked
[ISO image](https://en.wikipedia.org/wiki/ISO_image) that contains a
minimal operating system and Kubernetes already installed.
* This option may be useful if you are just starting to learn and already
have one of the virtualization applications installed.
* [MicroK8s setup](/docs/started/getting-started-multipass/)
* [MicroK8s](https://microk8s.io/) can provide the following benefits:
- A small, fast, secure, single node Kubernetes installation that installs on any
Linux system as a [snap](https://snapcraft.io/microk8s).
- Strong isolation and update semantics - your cluster
is updated within a short period after upstream Kubernetes
releases.
- Built-in support to enable an installed GPU:
`microk8s.enable gpu`
* MicroK8s requires Linux. If you are not on a Linux machine, or you want
to confine your Kubeflow to a disposable machine, the installation guide
show you how to use
[Multipass](https://github.com/CanonicalLtd/multipass) to launch a VM.
Benefits include:
- [Ubuntu Cloud Images](http://cloud-images.ubuntu.com/) already
integrated.
- Lightweight hypervisor using native operating system mechanisms
(for example, [Hypervisor
Framework](https://developer.apple.com/documentation/hypervisor) on
macOS, [Hyper-V on Windows
10](https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/quick-start/enable-hyper-v), or
QEMU/KVM for Linux).
- No need to install a separate virtualization application.
- Use of `cloud-init` to customize the VM.
## Troubleshooting
See the [Kubeflow troubleshooting guide](/docs/other-guides/troubleshooting/).
## Resources
* The [documentation](/docs/) provides in-depth instructions for using Kubeflow.
* Self-paced scenarios for learning and trying out Kubeflow:
* [Codelabs](https://codelabs.developers.google.com/?cat=tensorflow)
* [Introduction to Kubeflow on Google Kubernetes
Engine](https://codelabs.developers.google.com/codelabs/kubeflow-introduction/index.html)
* [Kubeflow End to End: GitHub Issue
Summarization](https://codelabs.developers.google.com/codelabs/cloud-kubeflow-e2e-gis/index.html)
* [Kubeflow Pipelines: GitHub Issue
Summarization](https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis/index.html)
* [Katacoda](https://www.katacoda.com/kubeflow)
* [Deploying GitHub Issue Summarization with
Kubeflow](https://www.katacoda.com/kubeflow/scenarios/deploying-github-issue-summarization)
* [Deploying
Kubeflow](https://www.katacoda.com/kubeflow/scenarios/deploying-kubeflow)
* [Deploying Kubeflow with
Ksonnet](https://www.katacoda.com/kubeflow/scenarios/deploying-kubeflow-with-ksonnet)
* [Deploying Pytorch with
Kubeflow](https://www.katacoda.com/kubeflow/scenarios/deploy-pytorch-with-kubeflow)
* [Qwiklabs](https://qwiklabs.com/catalog?keywords=kubeflow)
* [Introduction to Kubeflow on Google Kubernetes
Engine](https://qwiklabs.com/focuses/960?locale=en&parent=catalog)
* [Kubeflow End to End: GitHub Issue
Summarization](https://qwiklabs.com/focuses/1257?locale=en&parent=catalog)