Creating GKE ComputeClasses

This skill helps you construct ComputeClass resources for Google Kubernetes Engine (GKE). ComputeClasses allow for declarative node configuration and sophisticated autoscaling behaviors like fallback priorities and active migration.

Workflow

1. •Analyze Requirements: Determine the user's goals (Cost optimization? Specific hardware? High availability?).
2. •Select Strategy:
   • •Cost Optimization: Use spot: true as a high priority, with spot: false as a fallback.
   • •Performance: Select specific machineFamily (e.g., c3, c4) or machineType.
   • •AI/ML: Configure gpu or tpu fields.
3. •Construct YAML: Use the references below to build the ComputeClass manifest.
4. •Validate: Ensure all fields comply with the specification.
5. •Apply: Provide the user with the kubectl apply -f <filename>.yaml command.

References

• •Specification: Detailed breakdown of the ComputeClass CRD fields (priorities, machineFamily, gpu, etc.).
• •Examples: Copy-pasteable YAML patterns for common scenarios (Spot fallback, GPU, Zonal).

Usage Tips

• •Active Migration: If the user wants to automatically move back to Spot VMs when they become available, ensure spec.activeMigration.optimizeRulePriority is set to true.
• •Node Selection: Remind the user that to use the class, their Pods must specify:
  yaml

  nodeSelector:
    cloud.google.com/compute-class: "<class-name>"
  

• •Conflict Warning: Advise against mixing ComputeClass selection with other hard node selectors (like cloud.google.com/gke-spot) as this can lead to scheduling conflicts.