# Gke Batch Hpc

> Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

- Skill: `google/gke-batch-hpc` (Agent Skill)
- Install (CLI): `npx skillmds@latest add google/gke-batch-hpc`
- Raw SKILL.md: https://api.skillmd.com/api/skills/google/gke-batch-hpc/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra, Containers & Kubernetes
- Tags: Batch Processing, Gke, Hpc, Jobset, Kubernetes Jobs, Kueue, Mpi, Spot Vms
- Author: Google (https://skillmd.com/u/google), verified publisher
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/google/gke-batch-hpc

---


# GKE Batch & HPC Workloads

This reference covers running batch processing and high-performance computing
(HPC) workloads on GKE.

> **MCP Tools:** `apply_k8s_manifest`, `get_k8s_resource`,
> `describe_k8s_resource`, `get_k8s_logs`, `delete_k8s_resource`,
> `list_k8s_events`

## When to Use

-   Running batch data processing pipelines
-   HPC simulations (CFD, molecular dynamics, financial modeling)
-   Large-scale parallel computation (MPI, MapReduce)
-   ML training jobs
-   CI/CD build farms

## Batch Processing on GKE

### Kubernetes Jobs

```yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: batch-job
spec:
  parallelism: 10
  completions: 100
  backoffLimit: 3
  template:
    spec:
      containers:
      - name: worker
        image: <IMAGE>
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
      restartPolicy: Never
```

### JobSet (for Complex Multi-Job Workflows)

The golden path enables JobSet monitoring (`JOBSET` in monitoringConfig).

```yaml
apiVersion: jobset.x-k8s.io/v1alpha2
kind: JobSet
metadata:
  name: training-job
spec:
  replicatedJobs:
  - name: workers
    replicas: 4
    template:
      spec:
        parallelism: 1
        completions: 1
        template:
          spec:
            containers:
            - name: worker
              image: <IMAGE>
              resources:
                requests:
                  cpu: "4"
                  memory: "8Gi"
```

### Kueue (Job Queuing)

Kueue manages job scheduling and resource allocation for batch workloads:

```bash
# Install Kueue
kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/latest/download/manifests.yaml
```

```yaml
# Define a ClusterQueue
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: batch-queue
spec:
  namespaceSelector: {}
  resourceGroups:
  - coveredResources: ["cpu", "memory"]
    flavors:
    - name: default
      resources:
      - name: "cpu"
        nominalQuota: 100
      - name: "memory"
        nominalQuota: "200Gi"
---
# Allow a namespace to use the queue
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  name: batch-local
  namespace: batch-jobs
spec:
  clusterQueue: batch-queue
```

## HPC on GKE

### Compact Placement (Low-Latency Networking)

For tightly-coupled HPC workloads that need low-latency inter-node
communication:

```bash
# Standard clusters: create node pool with compact placement
gcloud container node-pools create hpc-pool \
  --cluster <CLUSTER_NAME> --region <REGION> \
  --machine-type c3-standard-44 \
  --placement-type COMPACT \
  --num-nodes 8 \
  --enable-autoscaling --min-nodes 0 --max-nodes 16 \
  --quiet
```

### MPI Workloads

Use the MPI Operator for MPI-based HPC applications:

```bash
# Install MPI Operator
kubectl apply -f https://raw.githubusercontent.com/kubeflow/mpi-operator/master/deploy/v2beta1/mpi-operator.yaml
```

```yaml
apiVersion: kubeflow.org/v2beta1
kind: MPIJob
metadata:
  name: hpc-simulation
spec:
  slotsPerWorker: 4
  mpiReplicaSpecs:
    Launcher:
      replicas: 1
      template:
        spec:
          containers:
          - name: launcher
            image: <MPI_IMAGE>
            command: ["mpirun", "-np", "32", "./simulation"]
            resources:
              requests:
                cpu: "1"
                memory: "2Gi"
              limits:
                cpu: "2"
                memory: "4Gi"
    Worker:
      replicas: 8
      template:
        spec:
          containers:
          - name: worker
            image: <MPI_IMAGE>
            resources:
              requests:
                cpu: "4"
                memory: "8Gi"
              limits:
                cpu: "8"
                memory: "16Gi"
```

## Cost Optimization for Batch/HPC

### Spot VMs for Batch

Batch workloads are ideal Spot VM candidates (interruptible, can checkpoint).
Use a ComputeClass with Spot-first priority and `activeMigration` to return to
Spot when available. See the `gke-compute-classes` skill for the
Spot-with-fallback pattern.

### Scale-to-Zero

For batch clusters, allow node pools to scale to zero when no jobs are running:

-   Autopilot (golden path): Automatic, nodes scale to zero when no pods are
    scheduled
-   Standard: Set `--min-nodes 0` on batch node pools

## Best Practices & Production Guidelines

-   **Resource Quotas**: Always specify resource requests and limits (CPU,
    memory, and optionally GPU/TPU) for all batch/HPC manifests. This is
    critical for Kueue admission, autoscaling, and preventing resource
    starvation in the cluster.
-   **TPU/Spot Cluster Maintenance**: For long-running AI training runs on Spot
    VMs/TPUs, advise using **GKE maintenance exclusions** to block automatic
    cluster upgrades/reboots during the active training window to minimize
    unnecessary preemption.
-   **MPI Workloads**: Use the **Kubeflow Training Operator** to orchestrate
    distributed MPI applications via the `MPIJob` custom resource.
-   **Kueue & JobSet**: Use **Kueue** for multi-tenant job queueing and fair
    sharing; use **JobSet** for multi-component tightly coupled workloads.
-   **Resilience**: Always set a `backoffLimit` on Jobs, and implement
    application-level checkpointing (e.g., using Orbax or PyTorch checkpointing)
    to survive Spot VM preemption.