

kubectl logs -n kubeflow transformers-pytorchjob-worker-0 -f
After the training job completes, the trained model can be copied from the PVC or storage location. When you are done
with the job, the PyTorchJob resource can be deleted from the cluster using kubectl delete -f pytorchjob.yaml.
Summary
This guide covered running distributed PyTorch training jobs using multiple CPUs on bare metal and on a Kubernetes
cluster. Both cases utilize Intel Extension for PyTorch and Intel oneCCL Bindings for PyTorch for optimal training
performance, and can be used as a template to run your own workload on multiple nodes.