Yep, we've been using GPUs for quite a while (even before the alpha support in Kube), both the K80s in Azure and some Pascals in our own clusters. With the support in Kube now it's quite seamless.
Late reply, but Kube meant Kubernetes not Kubeflow.
Alpha GPU support landed in 1.6 if my memory serves me right.
Before that you had to do a bunch of stuff manually to make GPU work, mostly around scheduling etc.
Since 1.6, Kubernetes will automatically detect the GPUs on your node and thus correctly assign the workloads where they fit.
Kubeflow is an abstraction layer on top of that, that helps a lot when you want to do things such as distributed TensorFlow training. It also helps a bit for simpler jobs by (almost) removing the need to manually mount the NVIDIA drivers from the host into the container for example.