Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Kubernetes. It provides a set of tools for orchestrating ML workflows, enabling data scientists and engineers to automate and manage the end-to-end ML lifecycle.
When working with Kubeflow Pipelines, you might encounter an error message stating WorkflowNotFound. This indicates that the system is unable to locate the specified workflow. This error can be frustrating, especially when you are certain that the workflow should exist.
The WorkflowNotFound error occurs when the Kubeflow Pipelines system cannot find the workflow you are trying to access. This can happen due to several reasons, such as an incorrect workflow ID or name, or the workflow not being created successfully.
To resolve the WorkflowNotFound error, follow these steps:
Ensure that the workflow ID or name you are using is correct. Double-check for any typographical errors. You can list all workflows to verify:
kubectl get workflows -n kubeflow
This command will list all workflows in the kubeflow namespace. Ensure the workflow you are looking for is listed.
If the workflow is not listed, it may not have been created. Check the logs of the pipeline run to ensure there were no errors during creation:
kubectl logs -n kubeflow
Replace <pipeline-run-pod> with the actual pod name of your pipeline run.
If the workflow was previously available, it might have been deleted. Review the audit logs or check with team members to confirm if the workflow was intentionally removed.
For more information on managing workflows in Kubeflow Pipelines, refer to the official Kubeflow Pipelines Documentation. If you continue to experience issues, consider reaching out to the Kubeflow community for support.
By following these steps, you should be able to resolve the WorkflowNotFound error and continue working with your Kubeflow Pipelines efficiently.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
