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title: KubeCon + CloudNativeCon 2023 EU

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# KubeCon + CloudNativeCon 2023 EU
# Title -  .

## Speakers
Geetika Batra

Geetika is a Senior MTS at VMware. At VMware, she is part of the Cluster API provider for vSphere. She has been working in OpenSource for a long time now. Previous to VMware, she was part of orgs like Red Hat. She has contributed to OpenSource org such as Openstack, Fabric Analytics, and Kubernetes and has been an LFX program mentor. She is the author of this feature. She is now part of Core Edge Engineering Team.

Sagar Muchhal

## Benefits to the Ecosystem

Kubernetes can be used to run GPU based workloads. Take an example of
Alex who is an Engineer at a retail organization that requires GPU node. 
This talk is for all the Alex's present who want to leverage the power of GPU's on top of kubernetes using cluster API. 

Relevant links:
* https://github.com/kubernetes-sigs/cluster-api-provider-vsphere/blob/bc235d336e4da091aea670e8e5e769f5ff8286d4/docs/proposal/20210823-gpu-support.md#gpu-and-pci-passthrough-support-in-capv
* https://github.com/kubernetes-sigs/cluster-api-provider-vsphere/pull/1264

The above design and proposal was implemented by the authors hence they are the best people to deliver this talk as they know ins and out of the feature.

There is no other way to support GPU workloads on a kubernetes cluster, hence this topic needs attention from community. The talk will give insights to a lot of people who are trying to run AI/ML models on top of GPU based resources.


## Description


Cluster API is  Kubernetes Special Interest Group (SIG) Cluster Lifecycle project, the Cluster API project uses Kubernetes-style APIs and patterns to automate cluster lifecycle management for platform operators. 
All of the other hyper-scale cloud providers (AWS, Azure, & GCP) offer GPU-accelerated virtualization and Kubernetes platforms. Many of them additionally offer robust vertically integrated AI/ML solutions building on open source technology. Enterprises need to apply a multi-cloud strategy to combat the lock-in effect imposed by the hyperscalers, but that is extremely difficult as it basically inhibits using most of these SaaS platform capabilities, as they are differentiators and thus different.
The number of edge locations is likely tens of thousands in the US alone. And when it comes to the edge, location matters! That's kind of the reason for hyperscalers being not a very good solution for it!
With this capability, users can deploy GPU accelerated workloads on the Kubernetes cluster provisioned via CAPV(vSphere) on insfrastructure like vSPhere.

This talk would specifically talk about attaching GPU via PCI. The talk will also give insights to the listener about ways they can attach PCI devices other than GPU's.
Following points would be covered and demonstration would be provided: 

1) Identify your PCI connect device. Make sure it reflects in vSphere
2) Gather information about the PCI device like **DeviceName** and **vendor ID**. 
3) Run Nvidia operator on your workload cluster
4) Apply template with specific PCI device information to use.
5) Run CUDA vector calculations to verify that it works as expected.


Audience will gather knowledge about GPU's, PCI devices and how to leverage them to run any CUDA calculations or AI/ML workloads.
