IBM Data and AI Ideas Portal for Customers


Shape the future of IBM!

We invite you to shape the future of IBM, including product roadmaps, by submitting ideas that matter to you the most. Here's how it works:

Post your ideas

Post ideas and requests to enhance a product or service. Take a look at ideas others have posted and upvote them if they matter to you,

  1. Post an idea

  2. Upvote ideas that matter most to you

  3. Get feedback from the IBM team to refine your idea

Help IBM prioritize your ideas and requests

The IBM team may need your help to refine the ideas so they may ask for more information or feedback. The product management team will then decide if they can begin working on your idea. If they can start during the next development cycle, they will put the idea on the priority list. Each team at IBM works on a different schedule, where some ideas can be implemented right away, others may be placed on a different schedule.

Receive notification on the decision

Some ideas can be implemented at IBM, while others may not fit within the development plans for the product. In either case, the team will let you know as soon as possible. In some cases, we may be able to find alternatives for ideas which cannot be implemented in a reasonable time.

Additional Information

To view our roadmaps: http://ibm.biz/Data-and-AI-Roadmaps

Reminder: This is not the place to submit defects or support needs, please use normal support channel for these cases

IBM Employees:

The correct URL for entering your ideas is: https://hybridcloudunit-internal.ideas.aha.io


Status Not under consideration
Workspace Cloud Pak for Data
Created by Guest
Created on Nov 11, 2021

RStudio or Python Runtime environments are automatically restarted when memory limits are reached

RStudio or Python Runtime environments are automatically restarted when memory limits are reached without any information to the data scientist and all results are gone. A better behavior would be if the application RStudio or Jupither/lab would give an "Out of memory" message and the runtime environment would remain stable.

Needed By Quarter
  • Guest
    Nov 21, 2021

    Hi Yalon, we have a problem that some of my data scientist colleagues tried to analyze large datasets (about 40 million records) with Rstudio. they started with a 16 GB runtime environment, but it was restarted very quickly and all the intermediate results were gone. Then the colleagues increased the runtime environment continuously: 32 GB, 64 GB, 96 GB and 128 GB. Only with a size of 128 GB RAM the analysis worked. The colleagues were very frustrated that all the intermediate results were always lost during the restarts and it took a lot of time to perform this analysis.

    I have to admit that I am not very familiar with Kubernetes, but isn't there a possibility to set limits within the runtime environment in the form of ulimits, cgroups or application specific limits in Rstudio or Jupyter/Lab? So that it does not come to situation that the application reaches the limit of the runtime environment.

  • Admin
    Yalon Gordon
    Nov 12, 2021

    This is a Kubernetes feature where memory is a non-compressible resource. If it crosses the limit the only way Kubernetes can keep the system safe is to kill the pod. Pods are typically throttled when CPU goes beyond limits. My recommendation is to ensure you have enough capacity and size your workload properly. Let me know if you have further questions