trainer/examples/yaml

Kubeflow Trainer YAML Examples

Standalone manifests that can be applied directly with kubectl. For an end-to-end conceptual walkthrough, see the Kubeflow Trainer documentation.

Prerequisites

• Kubernetes cluster with Kubeflow Trainer installed

• kubectl configured against the cluster

• The default ClusterTrainingRuntimes installed (shipped with Kubeflow Trainer):

  kubectl get clustertrainingruntimes
  

Examples

File	Description
01-multi-node.yaml	Multi-node distributed PyTorch training launched with torchrun
02-runtime-patches.yaml	Pod customization with the runtimePatches API (nodeSelector, tolerations, serviceAccountName, labels, annotations)
03-kueue-integration.yaml	Queue-based scheduling with Kueue
04-volcano-integration.yaml	Gang scheduling with Volcano
05-multi-step.yaml	Dataset-initializer step running before the trainer

Quick start

kubectl apply -f 01-multi-node.yaml
kubectl get trainjob multi-node-example
kubectl get pods -l jobset.sigs.k8s.io/jobset-name=multi-node-example
kubectl logs -l jobset.sigs.k8s.io/jobset-name=multi-node-example
kubectl delete trainjob multi-node-example

See also

• Runtime guide
• Job scheduling guide
• Python SDK examples