PyTorch Training (PyTorchJob)
This page describes PyTorchJob for training a machine learning model with PyTorch.
The PyTorchJob is a Kubernetes
custom resource
to run PyTorch training jobs on Kubernetes. The Kubeflow implementation of
the PyTorchJob is in the training-operator.
Note: PyTorchJob doesn’t work in a user namespace by default because of
Istio automatic sidecar injection.
In order to get it running, it needs the annotation sidecar.istio.io/inject: "false"
to disable it for either PyTorchJob pods or the namespace.
To view an example of how to add this annotation to your yaml file,
see the TFJob documentation.
Creating a PyTorch training job
You can create a training job by defining a PyTorchJob config file. See the manifests for the distributed MNIST example.
You may change the config file based on your requirements.
Deploy the PyTorchJob resource to start training:
kubectl create -f https://raw.githubusercontent.com/kubeflow/training-operator/master/examples/pytorch/simple.yaml
You should now be able to see the created pods matching the specified number of replicas.
kubectl get pods -l training.kubeflow.org/job-name=pytorch-simple -n kubeflow
Training takes 5-10 minutes on a cpu cluster. Logs can be inspected to see its training progress.
PODNAME=$(kubectl get pods -l training.kubeflow.org/job-name=pytorch-simple,training.kubeflow.org/replica-type=master,training.kubeflow.org/replica-index=0 -o name -n kubeflow)
kubectl logs -f ${PODNAME} -n kubeflow
Monitoring a PyTorchJob
kubectl get -o yaml pytorchjobs pytorch-simple -n kubeflow
See the status section to monitor the job status. Here is sample output when the job is successfully completed.
apiVersion: kubeflow.org/v1
kind: PyTorchJob
metadata:
clusterName: ""
creationTimestamp: 2018-12-16T21:39:09Z
generation: 1
name: pytorch-tcp-dist-mnist
namespace: default
resourceVersion: "15532"
selfLink: /apis/kubeflow.org/v1/namespaces/default/pytorchjobs/pytorch-tcp-dist-mnist
uid: 059391e8-017b-11e9-bf13-06afd8f55a5c
spec:
cleanPodPolicy: None
pytorchReplicaSpecs:
Master:
replicas: 1
restartPolicy: OnFailure
template:
metadata:
creationTimestamp: null
spec:
containers:
- image: gcr.io/kubeflow-ci/pytorch-dist-mnist_test:1.0
name: pytorch
ports:
- containerPort: 23456
name: pytorchjob-port
resources: {}
Worker:
replicas: 3
restartPolicy: OnFailure
template:
metadata:
creationTimestamp: null
spec:
containers:
- image: gcr.io/kubeflow-ci/pytorch-dist-mnist_test:1.0
name: pytorch
ports:
- containerPort: 23456
name: pytorchjob-port
resources: {}
status:
completionTime: 2018-12-16T21:43:27Z
conditions:
- lastTransitionTime: 2018-12-16T21:39:09Z
lastUpdateTime: 2018-12-16T21:39:09Z
message: PyTorchJob pytorch-tcp-dist-mnist is created.
reason: PyTorchJobCreated
status: "True"
type: Created
- lastTransitionTime: 2018-12-16T21:39:09Z
lastUpdateTime: 2018-12-16T21:40:45Z
message: PyTorchJob pytorch-tcp-dist-mnist is running.
reason: PyTorchJobRunning
status: "False"
type: Running
- lastTransitionTime: 2018-12-16T21:39:09Z
lastUpdateTime: 2018-12-16T21:43:27Z
message: PyTorchJob pytorch-tcp-dist-mnist is successfully completed.
reason: PyTorchJobSucceeded
status: "True"
type: Succeeded
replicaStatuses:
Master: {}
Worker: {}
startTime: 2018-12-16T21:40:45Z
Next steps
Learn about distributed training in the Training Operator.
See how to run a job with gang-scheduling.
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