# Overview

In this self-paced exercise, you will first configure and provision a GPU enabled Amazon EKS cluster. You will then follow that up by deploying a workload that will use the GPU and review the integrated dashboards for GPUs.

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## What Will You Do by Part

| Part | What will you do? |
| --- | --- |
| 1 | [Setup](https://docs.rafay.co/learn/quickstart/eks/gpu/setup/) and Configuration |
| 2 | [Provision](https://docs.rafay.co/learn/quickstart/eks/gpu/provision/) an Amazon EKS Cluster with GPUs |
| 3 | Create a Cluster [Blueprint](https://docs.rafay.co/learn/quickstart/eks/gpu/blueprint/) with the Nvidia GPU Operator |
| 4 | Deploy a Machine Learning [Workload](https://docs.rafay.co/learn/quickstart/eks/gpu/workload/) to GPU enabled Amazon EKS Cluster |
| 5 | [Deprovision](https://docs.rafay.co/learn/quickstart/eks/gpu/deprovision/) the EKS cluster |

Watch a video of this entire exercise below.

Deploy NVIDIA GPUs on Amazon EKS Clusters with Rafay | GPU Kubernetes Setup - YouTube

[Deploy NVIDIA GPUs on Amazon EKS Clusters with Rafay | GPU Kubernetes Setup](https://www.youtube.com/watch?v=TBJ715lakw8)

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## Assumptions

- You have access to an [Amazon AWS account](https://aws.amazon.com/) with privileges to create an IAM Role with the default [Full IAM Policy](https://docs.rafay.co/clusters/eks/full_iam_policy/) to allow provisioning of resources on your behalf as part of the EKS cluster lifecycle.
- You have a Git client on your laptop.