Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK. The Kubeflow pipelines service has the following goals:
- End to end orchestration
- Easy experimentation
- Easy re-use
Kubeflow Pipelines consist of the following components:
- API Server
- Persistence Agent
- Scheduled Workflow Controller
- CRD Viewer
- Visualization Server
- UI
Please refer to the following commands to perform a health check or investigation into the Kubeflow Pipeline.
KFP API Server
1. Check the status of the KFP API Server Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline -o wide
2. Inspect the events on the KFP API Server Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline
3. Check the status of the KFP API Server Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline -o wide
4. View KFP API Server logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline -f
KFP Persistence Agent
1. Check the status of the KFP Persistence Agent Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline-persistenceagent -o wide
2. Inspect the events on the KFP Persistence Agent Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline-persistenceagent
3. Check the status of the KFP Persistence Agent Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline-persistenceagent -o wide
4. View KFP Persistence Agent logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline-persistenceagent -f
KFP Scheduled Workflow Controller
1. Check the status of the KFP Scheduled Workflow Controller Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline-scheduledworkflow -o wide
2. Inspect the events on the KFP Scheduled Workflow Controller Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline-scheduledworkflow
3. Check the status of the KFP Scheduled Workflow Controller Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline-scheduledworkflow -o wide
4. View KFP Scheduled Workflow Controller logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline-scheduledworkflow -f
KFP CRD Viewer
1. Check the status of the KFP CRD Viewer Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline-viewer-crd -o wide
2. Inspect the events on the KFP CRD Viewer Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline-viewer-crd
3. Check the status of the KFP CRD Viewer Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline-viewer-crd -o wide
4. View KFP CRD Viewer logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline-viewer-crd -f
KFP Visualization Server
1. Check the status of the KFP Visualization Server Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline-visualizationserver -o wide
2. Inspect the events on the KFP Visualization Server Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline-visualizationserver
3. Check the status of the KFP Visualization Server Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline-visualizationserver -o wide
4. View KFP Visualization Server logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline-visualizationserver -f
KFP UI
1. Check the status of the KFP UI Deployment:
$ kubectl get deployment -n kubeflow ml-pipeline-ui -o wide
2. Inspect the events on the KFP UI Deployment:
$ kubectl describe deployment -n kubeflow ml-pipeline-ui
3. Check the status of the KFP UI Pod:
$ kubectl get pods -n kubeflow -l app=ml-pipeline-ui -o wide
4. View KFP UI logs:
$ kubectl logs -n kubeflow deploy/ml-pipeline-ui -f
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