PaddlePaddle Training (PaddleJob)
Beta
This Kubeflow component has beta status. See the Kubeflow versioning policies. The Kubeflow team is interested in your feedback about the usability of the feature.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.
Installing Paddle Operator
If you haven’t already done so please follow the Getting Started Guide to deploy Kubeflow.
By default, Paddle Operator will be deployed as a controller in training operator.
If you want to install a standalone version of the training operator without Kubeflow, see the kubeflow/training-operator’s README.
Verify that PaddleJob support is included in your Kubeflow deployment
Check that the Paddle custom resource is installed:
kubectl get crd
The output should include paddlejobs.kubeflow.org like the following:
NAME CREATED AT
...
paddlejobs.kubeflow.org 2022-10-21T05:37:53Z
...
Check that the Training operator is running via:
kubectl get pods -n kubeflow
The output should include job-name-worker-<rank> like the following:
NAME READY STATUS RESTARTS AGE
training-operator-worker-0 1/1 Running 0 4m37s
training-operator-worker-1 1/1 Running 0 4m37s
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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