ComputeClass: Provisioning Methods & Binding

node pool auto-creation vs. Manual Node Pools

1. Node pool is a GKE API resource, not a Kubernetes CRD.
2. On regional clusters, auto-created node pools are regional by default
3. No way to set a prefix or custom name for auto-created node pools

Custom Node Initialization

ComputeClass node pool auto-creation dynamically manages nodes and does not natively support custom UserData or startup scripts via the nodePoolConfig. To initialize nodes:

1. Privileged DaemonSets (Recommended): Deploy a DaemonSet with an initContainer to perform host-level setup or install proprietary monitoring agents.
2. Custom OS Images: GKE supports custom OS images via the gke-custom-image-builder (Private preview; contact account team), though DaemonSets are the primary K8s-native workaround.

Hybrid Strategy

Put manual pools at the top for zero-latency scheduling; use node pool auto-creation fallbacks below for infinite scale.

Stateful Workloads & Storage

For Zonal PVs, use volumeBindingMode: WaitForFirstConsumer in the StorageClass to avoid cross-zone deadlocks between disks and autoscaled nodes.

Recommended (GKE 1.35.3-gke.1290000+): the built-in dynamic-rwo StorageClass (type: dynamic, pd-type: pd-balanced, hyperdisk-type: hyperdisk-balanced, use-allowed-disk-topology: "true"). use-allowed-disk-topology makes the Cluster Autoscaler disk-topology-aware — it reads the workload's disk requirements and scales up only disk-compatible node options ("machine serenity"), skipping incompatible-generation priorities instead of provisioning a node that then fails PV attach. This is what lets a stateful ComputeClass keep a broad priorities[] fallback list across machine families/generations safely (see Gen Isolation exception). Built-in on supported clusters (reference by name; no need to create); asset dynamic-rwo-storageclass.yaml documents its contents. NOTE: this is the data-PV StorageClass, distinct from priorities[].storage.bootDiskType (the node boot disk).

Intent-based vs. Strict Configuration

• Intent-based (Preferred): machineFamily: n4, minCores: 16. Allows GKE to find best-fit shape or substitute families.
• Strict: machineType: n4-standard-16. Pins to exact SKU.

Binding Manual Pools to ComputeClass

Manual pools must be labeled/tainted to be eligible for a ComputeClass (unless it's the cluster default).

gcloud container node-pools update <POOL> \
    --node-labels="cloud.google.com/compute-class=<CLASS-NAME>" \
    --node-taints="cloud.google.com/compute-class=<CLASS-NAME>:NoSchedule"

When using node pool auto-creation, ComputeClasses auto-tolerate these taints; workloads do not need matching tolerations.

Default Class Selection

• Cluster Default: Create ComputeClass named default + enable feature on cluster.
• Namespace Default: Label NS cloud.google.com/default-compute-class=<name>.
• Workload Selection: nodeSelector: cloud.google.com/compute-class: <name>.

Integration with Kueue (Batch/Job Queuing)

For AI/ML batch workloads, use Kueue to manage quotas and job admission, while relying on ComputeClasses to handle hardware provisioning (fallback routing between Spot, DWS, and On-Demand).

To map a Kueue ResourceFlavor to a ComputeClass, use the node label in the flavor definition:

apiVersion: kueue.x-k8s.io/v1beta1
kind: ResourceFlavor
metadata:
  name: "ccc-flavor"
spec:
  nodeLabels:
    cloud.google.com/compute-class: "your-compute-class-name"

When Kueue admits the job, it automatically injects this nodeSelector into the Pod. The GKE Autoscaler will then provision hardware according to the ComputeClass's prioritized fallback list.