GKE Concepts

Caliban makes it easy to create your own GKE Cluster - similar to your own personal copy of AI Platform - in your Cloud project, and submit jobs to that cluster. The advantage over AI Platform currently is that you can get more quota, often 10x what you have available in AI Platform, and many features are supported in GKE much earlier than they are in AI Platform.

The quota disparity is particularly notable with TPUs. AI Platform currently only allows 8 TPUs, while a GKE cluster lets you specify 32, 64, etc TPUs for a given job.

A good collection of GKE documentation can be found here


A cluster is a collection of cloud machines, combining a set of nodes that run your processing jobs, and control plane (also referred to as a cluster master) that manages these worker nodes and handles scheduling your jobs and creating worker nodes to run them.

Cluster Master

A cluster master is the controller for the cluster and all its resources. It handles creating and deleting worker nodes, and scheduling jobs submitted by users.


A node is a worker machine (a cloud compute engine instance) that actually performs the work your job requires. The cluster control plane creates and manages these instances.

Node Pool

A node pool is a collection of identical nodes (cpu, memory, gpu, tpu).


A job is a task that is to be run to completion using cluster resources. The cluster control plane manages the resources the job needs and handles restarting the job in case of failure or preemption. A job probably matches the concept you have in mind when you think of a job you submit to AI platform. A job is a top-level task, which may be run on multiple machines/containers, which in GKE are referred to as pods, described below.


A pod is a single, ephemeral, running execution of your container. A job may run on several pods.