Every kubeflow image should be scanned for security vulnerabilities.
It would be great to have a periodic security report.
Each of these images with vulnerability should be patched and updated.

What steps did you take

Code gets stuck in infinite loop is SageMaker training job gets stopped (unhandled use case)

What happened:

https://github.com/kubeflow/pipelines/blob/master/components/aws/sagemaker/train/src/sagemaker_training_component.py#L57-L66

Above code only caters for training job status Completed or Failed, so if the training job status is marked as `Stopped

/kind feature

Describe the solution you'd like

In pkg/apis/serving/v1beta1/inference_service_defaults.go the default InferenceService resource requests and limits are hard coded to be 1 cpu and 2Gi memory. These are reasonable defaults. However, the entire existence of these defaults should be disablable. Moreover, administrators should be able to quickly adjust defaults globally via t

Is your feature request related to a problem? Please describe.
We currently only create and push tags in our release script. To bring Elyra more in line with similar projects (such as JupyterLab), we could consider adding Github releases as well.

Current:
<img width="164" alt="Screen Shot 2021-10-08 at 3 20 36 PM" src="https://user-images.githubusercontent.com/31816267/136620475-35651a55-

Summary

What change needs making?
Example script on how to use created secret successfully.
I have tried myself using the following code from test
It creates a secret successfully but fails to echo the secrets value.

Use Cases

When would you use this?
when I n

arena should support clean-task-policy "All" when training via tfjobs.
currently, only supports "Running"(clean RUNNING pod) or None(do nothing).
when job is done, often we need to remove ps, chief, evaluator and worker.