Nvidia Jetson Nano Kubernetes

Nvidia jetson nano delivered gpu power in an amazingly small package.

Nvidia jetson nano kubernetes. Nvidia jetson nano and nvidia jetson agx xavier for kubernetes k8s and machine learning ml for smart iot. Nvidia jetpack includes nvidia container runtime with docker integration enabling gpu accelerated. So my kubernetes cluster are multi platform. Nvidia jetson nano developer board with the developer kit installed.

K3s is a certified kubernetes distribution designed for production workloads in unattended resource constrained remote locations or inside iot appliances. Helmut hoffer von ankershoffen né oertel. It turned out however i had to jump through a couple of hoops to get it working. Kubernetes cluster setup based on amd64 nvidia jetson tx2.

Run tensorflow as a kubernetes pod on jetson nano. Among many many similar devices its key selling point is a fully featured gpu. Deploy gpu enabled kubernetes pod on nvidia jetson nano jerry liang. I finally got mine in the mailbox and couldn t wait to add it to my raspberry pi k8s cluster to take up gpu workload.

Nvidia has published a set of container images that are optimized for jetpack to run at the edge. As you can see hooking up a jetson nano to a kubernetes cluster is a pretty simple and straightforward process. K3s is the right solution for you.

Thanks to luxas create the kubernetes on arm project. As you may know jetson nano is a low cost 99 single board computer intended for iot type of use cases. If you are looking out for lightweight kubernetes which is easy to install and perfect for edge iot ci and arm then look no further. With the kubernetes infrastructure available we will try to run tensorflow 2 x as a pod in our single node cluster powered by k3s.

But my project has some different i have a vm on x86 as master node and two nvidia tx2 development kits as work node. The k3s github repository has already crossed 9000 stars.