I like to find out the current best practice for setting up a kubernetes cluster on a Dell Alienware Aurora workstation running Ubuntu 18.04 LTS for GPU based tensorflow workload. This will be a staging ground for my services/containers before I deploy them to a full-blown k8s cluster. I am not sure what the correct strategy for such a setup looks like. Here are some possibilities:

1. Minikube with virtualbox driver, worker node in VM
2. Minikube with --vm-driver=none, relying on docker
3. Kubeadm with scheduling pods on master enabled
4. Kubeadm-dind (docker in docker)

Update: added kubeadm options. Can someone also comment on the docker in docker solution. Will services/pods work seamlessly from docker in docker setup to multi-node remote machines/cloud instance setups?

Would love to hear from the kubernetes experts or someone familiar with tensorflow/GPU workloads on a single physical machine.