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IBM Data and AI Ideas Portal for Customers


This portal is to open public enhancement requests against products and services offered by the IBM Data & AI organization. To view all of your ideas submitted to IBM, create and manage groups of Ideas, or create an idea explicitly set to be either visible by all (public) or visible only to you and IBM (private), use the IBM Unified Ideas Portal (https://ideas.ibm.com).


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IBM Employees should enter Ideas at https://hybridcloudunit-internal.ideas.aha.io/


Status Delivered
Workspace Watson Studio
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.

  • Guest
    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