Kubernetes supports GPUs as an experimental feature. Does it work in google container engine? Do I need to have some special configuration to enable it? I want to be able to run machine learning workloads, but want to use Python 3 which isn't available in CloudML.
I've not tested it, but as long as GPU vm are just machine types I would say that doing these two steps should make it feasible:
UPDATE: Main site for Custom Machine Types: https://cloud.google.com/custom-machine-types/
1- Create a GPU Custom Machine Type: https://cloud.google.com/compute/docs/gpus/
You can add GPUs to any non-shared-core predefined machine type or custom machine type that you are able to create in a zone
2- When creating nodes, chose your custom machine type in your cluster or node pool: https://cloud.google.com/container-engine/docs/clusters/operations
--machine-type: The Google Compute Engine machine type (e.g. n1-standard-1) to use for instances in this container cluster. If unspecified, the default machine type is n1-standard-1
GPUs on Google Container Engine are now available in Alpha. Sign up form.
Beware that alpha cluster limitations apply: they cannot be upgraded, and they will be auto-deleted in 30 days.
Disclaimer: I work at GCP.
I am afraid this is not supported out of the box. When creating a regular instance in Google Compute Engine (GCE) you are able to select GPU specs for your machine. On the other side, when creating a cluster, these options are not available. I imagine that this will be available sooner or later, but not at the moment.
As an alternative, you can create several GCE instances and build a cluster using tools like kubeadm or following guides like Kubernetes the hard way: https://github.com/kelseyhightower/kubernetes-the-hard-way