PaddlePaddle Training (PaddleJob)
This page describes PaddleJob for training a machine learning model with PaddlePaddle.
PaddleJob is a Kubernetes
custom resource
to run PaddlePaddle training jobs on Kubernetes. The Kubeflow implementation of
PaddleJob is in training-operator.
Note: PaddleJob doesn’t work in a user namespace by default because of
Istio automatic sidecar injection.
In order to get it running, it needs annotation sidecar.istio.io/inject: "false"
to disable it for either PaddleJob pods or namespace.
To view an example of how to add this annotation to your yaml file,
see the TFJob documentation.
Creating a PaddlePaddle training job
You can create a training job by defining a PaddleJob config file. See the manifests for the distributed example. You may change the config file based on your requirements.
Deploy the PaddleJob resource to start training:
kubectl create -f https://raw.githubusercontent.com/kubeflow/training-operator/master/examples/paddlepaddle/simple-cpu.yaml
You should now be able to see the created pods matching the specified number of replicas.
kubectl get pods -l job-name=paddle-simple-cpu -n kubeflow
Training takes several minutes on a cpu cluster. Logs can be inspected to see its training progress.
PODNAME=$(kubectl get pods -l job-name=paddle-simple-cpu,replica-type=worker,replica-index=0 -o name -n kubeflow)
kubectl logs -f ${PODNAME} -n kubeflow
Monitoring a PaddleJob
kubectl get -o yaml paddlejobs paddle-simple-cpu -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: PaddleJob
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"kubeflow.org/v1","kind":"PaddleJob","metadata":{"annotations":{},"name":"paddle-simple-cpu","namespace":"kubeflow"},"spec":{"paddleReplicaSpecs":{"Worker":{"replicas":2,"restartPolicy":"OnFailure","template":{"spec":{"containers":[{"args":["-m","paddle.distributed.launch","run_check"],"command":["python"],"image":"registry.baidubce.com/paddlepaddle/paddle:2.4.0rc0-cpu","imagePullPolicy":"Always","name":"paddle","ports":[{"containerPort":37777,"name":"master"}]}]}}}}}}
creationTimestamp: "2022-10-24T03:47:45Z"
generation: 3
name: paddle-simple-cpu
namespace: kubeflow
resourceVersion: "266235056"
selfLink: /apis/kubeflow.org/v1/namespaces/kubeflow/paddlejobs/paddle-simple-cpu
uid: 7ef4f92f-0ed4-4a35-b10a-562b79538cc6
spec:
paddleReplicaSpecs:
Worker:
replicas: 2
restartPolicy: OnFailure
template:
spec:
containers:
- args:
- -m
- paddle.distributed.launch
- run_check
command:
- python
image: registry.baidubce.com/paddlepaddle/paddle:2.4.0rc0-cpu
imagePullPolicy: Always
name: paddle
ports:
- containerPort: 37777
name: master
protocol: TCP
status:
completionTime: "2022-10-24T04:04:43Z"
conditions:
- lastTransitionTime: "2022-10-24T03:47:45Z"
lastUpdateTime: "2022-10-24T03:47:45Z"
message: PaddleJob paddle-simple-cpu is created.
reason: PaddleJobCreated
status: "True"
type: Created
- lastTransitionTime: "2022-10-24T04:04:28Z"
lastUpdateTime: "2022-10-24T04:04:28Z"
message: PaddleJob kubeflow/paddle-simple-cpu is running.
reason: JobRunning
status: "False"
type: Running
- lastTransitionTime: "2022-10-24T04:04:43Z"
lastUpdateTime: "2022-10-24T04:04:43Z"
message: PaddleJob kubeflow/paddle-simple-cpu successfully completed.
reason: JobSucceeded
status: "True"
type: Succeeded
replicaStatuses:
Worker:
labelSelector:
matchLabels:
group-name: kubeflow.org
job-name: paddle-simple-cpu
training.kubeflow.org/job-name: paddle-simple-cpu
training.kubeflow.org/operator-name: paddlejob-controller
training.kubeflow.org/replica-type: Worker
succeeded: 2
startTime: "2022-10-24T03:47:45Z"
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