gke-compute-class-creator

Generate GKE ComputeClass manifests with autoscaling priorities and GPU/TPU fields.

163|81|Updated Jun 19, 2025

Search Tags:#gpu #autoscaling #gke #computeclass #node-pool #spot

Ready to run

npx skills add https://github.com/GoogleCloudPlatform/gke-mcp --skill gke-compute-class-creator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gke-compute-class-creator
Source: https://github.com/GoogleCloudPlatform/gke-mcp/tree/main/skills/gke-compute-class-creator
Command: npx skills add https://github.com/GoogleCloudPlatform/gke-mcp --skill gke-compute-class-creator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Creating and managing ComputeClass resources in GKE can be complex because it requires defining an ordered set of autoscaling priorities and optional fields for GPUs, TPUs, and location, which this skill guides.

Core Features & Use Cases

Declarative ComputeClass manifests with ordered priorities to control workload placement and scaling.
Support for nodePoolAutoCreation, activeMigration, and hardware configurations like GPUs/TPUs.
Use cases include cost-optimized clusters with Spot VMs, high-performance workloads with specific machine families, and AI/ML workloads requiring GPUs or TPUs.

Quick Start

Draft a ComputeClass manifest and apply it with kubectl apply -f <filename>.yaml.

Frequently Asked Questions about gke-compute-class-creator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I configure GKE autoscaling with custom hardware priorities?▼

A ComputeClass resource controls GKE autoscaling by letting you define an ordered set of priorities for workload placement. You specify hardware constraints like GPUs or Spot VMs to manage node pool scaling and configuration.

What is a ComputeClass in GKE and when do I need it?▼

A ComputeClass in GKE is a custom resource definition that manages node configurations and autoscaling. You need it when deploying cost-optimized Spot VM clusters, performance-driven workloads, or AI/ML scenarios requiring specific GPUs and TPUs.

Can I use nodePoolAutoCreation and activeMigration in my GKE ComputeClass manifest?▼

Yes, nodePoolAutoCreation and activeMigration are supported fields in the ComputeClass CRD specification. You can include them in your manifest to automatically create node pools and migrate workloads when applying the configuration via kubectl.

How do I create a GKE manifest for AI/ML workloads requiring GPUs?▼

To create a GKE manifest for AI/ML workloads requiring GPUs, define a ComputeClass resource with optional GPU and TPU fields. Specify location constraints and hardware priorities to generate a valid manifest ready for kubectl apply.

What is the best way to optimize GKE cluster costs using Spot VMs?▼

The best way to optimize GKE cluster costs using Spot VMs is defining a ComputeClass manifest with cost-optimized autoscaling priorities. This declarative approach targets Spot VMs for workload placement, reducing overall compute expenses.