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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 (

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Status Planned for future release
Created by Guest
Created on Nov 16, 2021

Ability to determine through Monitoring whether or not sufficient resources to start an R or Notebook runtime environment are available per node

As Data Scientist I frequently need to start runtime environments. Often these are small enough and they quite naturally fit the available resources. Sometimes however, there is a very large environment needed. We would like to easily see and determine whether there are sufficient resources available on one or many nodes of our cluster. Since the resources are constrained by nodes in the cluster this requires a view of available resources per node.

Ideally this should happen through the monitoring page. However that is currently lacking the information how many resources are available for each node. It should be enhanced so that it captures not just used or free resources for the entire cluster, but break this information down onto node level, because that is the relevant metric for starting new pods as they are constrained by a node.

Currently one would try to start a new runtime environment not knowing whether that succeeds or causing a failure due to lack of resources. The benefit thus would be that the Data Scientist doesn't even try to start a new runtime environment when there aren't sufficient resources available.

Needed By Quarter
  • Guest
    Apr 12, 2022

    In addition to showing information about the available resources per node in the user interface as requested in the original post, we would like to be able to make a request to a REST api endpoint to get this information. This way, we can include prioritizing different workloads in our scheduling workflows without starting a runtime when there aren't sufficient resources available.