k2tf VS k8s-device-plugin

This might be what you are looking for: https://github.com/sl1pm4t/k2tf

... and if you're an opinionated person, like me, and you value consolidated infrastructure atomicity as a whole along side locks for everything. You'd port cherry-picked helm charts as terraform modules with k2tf, and build every docker container from scratch, with forced layer invalidation to perform security updates for every image, using the docker and kubernetes providers respectively.

Pro tip: If you want to convert k8s yaml files to .tf, you can use k2tf (repo) that is able to convert the resource types of the yaml top their appropriated counterparts of the k8s provider for terraform. To install it, just:

Almost all is created as manifest/helm in K8S world, too much toil to convert (tool like https://github.com/sl1pm4t/k2tf help but exists corners cases)

https://github.com/NVIDIA/k8s-device-plugin/issues/332#issue...

K8s-device-plugin

This framework allows the use of external devices (e.g., NVIDIA GPUs, AMD GPUS, SR-IOV NICs) without modifying core Kubernetes components.

Not talking about MIG. NVIDIA device plugin. https://github.com/NVIDIA/k8s-device-plugin

GPU Operator Device plugin

If you ever tried to use GPU-based instances with AWS ECS, or on EKS using the default Nvidia plugin, you would know that it's not possible to make a task/pod shared the same GPU on an instance. If you want to add more replicas to your service (for redundancy or load balancing), you would need one GPU for each replica.

Some notes on Plex/Emby/Kodi and transcoding. If you want true transcoding with GPU acceleration, you have to have Nvidia GPU or be a k8s device plugin genius. The whole idea of mounting elastic devices in k8s is fairly new and rather complex. In the mean time transcoding is best done on a beefy device with a proper CPU (eg i7) or specifically Nvidia GPU because there are numerous pre-made plugins. I just run Plex and Emby on an old ATX gaming machine without GPU acceleration and it works totally fine. They were barely usable for just me when running on the RPis, wouldn't recommend it unless you can figure out how to mount the correct devices in the pod using a custom raspberry pi device plugin . . . lol good luck! - Arm labs device manager: https://community.arm.com/developer/research/b/articles/posts/a-smarter-device-manager-for-kubernetes-on-the-edge - Deis labs Akri device manager: https://github.com/deislabs/akri - Nvidia GPU plugin: https://github.com/NVIDIA/k8s-device-plugin