terraform-provider-kubernetes VS aws-virtual-gpu-device-plugin

Now, to be honest, I'm not entirely sure/confident how this works. When I've used this kind of setup, I had two separate workspaces: one for setting up EKS and one for setting up Kubernetes within EKS. I'd apply the EKS workspace, first, then use its outputs for the Kubernete's workspace. You can see this pattern is specifically outlined in this EKS/k8s example. The Kubernetes provider docs also explicitly warns against creating the cluster in the same module as the Kubernetes provider. So it appears this may work, but it isn't recommended.

It is a pretty important distinction. Terraform and Kubernetes are fundamentally different in how they work. If you ever try to manage kubernetes state from terraform, it the differences become very obvious: https://github.com/hashicorp/terraform-provider-kubernetes/issues/1367

I'm using the terraform kubernetes-provider and I'd like to translate something like this kubectl command into TF:

After the resources be provisioned, you might want to run terraform apply -refresh-only to refresh your local state as the creation of some resource change the state of others within AWS. Also, state differences on metadata.resource_version of k8s resources almost always show up after an apply. This seems to be related to this issue.

You can open an issue here: https://github.com/hashicorp/terraform-provider-kubernetes/issues

We want to do that, but this issue has been a huge blocker for us. You might not hit it unless you’re using AKS, though.

This project (available here) uses the k8s device plugin described by this AWS blog post to make GPU-based nodes publish the amount of GPU resource they have available. Instead of the amount of VRAM available or some abstract metric, this plugin advertises the amount of pods/processes that can be connected to the GPU. This is controlled by what is called by NVIDIA as Multi-Process Service (MPS).

AWS has apparently already started using this type of tech as of this year (see lost below). They mention virtual gpus but this particular solution probably won't help OP unfortunately. https://aws.amazon.com/blogs/opensource/virtual-gpu-device-plugin-for-inference-workload-in-kubernetes/

The post explores GPU device plugin to address how to set fractional number of GPU resource for each pod by implementing the Kubernetes device plugin and Nvidia MPS. This project has been open sourced on GitHub.