k8s-device-plugin VS charts

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

Spinning up workloads in kubernetes is much different than just spinning up a container in docker or even with docker compose. If someone has not already packaged it in a helm chart or some other kubernetes workload you'll have to develop one yourself. There are some nice library charts you can use as a base that should handle just about any random docker image you want to deploy. https://github.com/bjw-s/helm-charts/tree/main/charts/library/common there is also a repo of pre developed charts for common images. https://github.com/k8s-at-home/charts but be aware it was recently deprecated so it won't be receiving any updates.

Take a look at https://github.com/k8s-at-home/charts (recently deprecated but still a fantastic resource) - there are charts for the popular Arrs , tools, etc. You could deploy each chart individually into a namespace, or you could create yourself an "umbrella" chart which pulls in all the necessary charts as dependencies.

Library charts. A very thorough example can be seen here and usages of it here.

Could it be related to this? https://github.com/k8s-at-home/charts/pull/1435/files

I'd submitted a helm chart at https://github.com/k8s-at-home/charts/tree/master/charts/stable/firefly-iii if you want to try out

I know /u/stefantigro means well but the way you are both doing the helm charts is not ideal, helm charts are meant to be shared, not as a means to install apps into your cluster from a local folder. While they can be, it's not a good pattern. Take the helm chart from here for example. This is a published helm chart you can install using the commands in the Readme and you only need to provide the configuration for your instance from the values.yaml file. You can take a look at the values I use for this helm chart here. You can also see I'm using an custom Sonarr image, this image is tailored to running in Kubernetes

There is a working Helm chart for k8s-at-home that should be a good starting point. The biggest hurdle I see is that homebridge can conflict with SCALE's mDNS service as seen in this linked post.

There's an actual helm chart published here.