Support dynamically sized (elastic) jobs
Opened this issue · 13 comments
We should have a clear path towards support spark and other dynamically sized jobs. Another example of this is Ray.
One related aspect is to support dynamically updating the resource requirements of a workload, we can probably limit that to support changing the count of a PodSet in QueuedWorkload (in Spark, the number of workers could change during the runtime of the job, but not the resource requirements of a worker).
One idea is to model it in a way similar to "in-place update to pod resources" [1], but in our case it would be the count that is mutable. The driver pod in spark would be watching for the corresponding QueuedWorkload instance and adjusts the number of workers when the new count is admitted.
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This will be easier to support with https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/3521-pod-scheduling-readiness
I am interested in working on this -- this probably needs some sort of design doc, will work with @alculquicondor and see if I can put something together in the next few weeks
/assign
I am interested in working on this -- this probably needs some sort of design doc, will work with @alculquicondor and see if I can put something together in the next few weeks
/assign
Hi @andrewsykim! Is there any progress?
@tenzen-y I was planning to work on this in a couple weeks during the holiday season, but feel free to start working on this if you're interested.
@andrewsykim Thanks. I also don't have enough time now. So, when I can get enough time, I will ask for progress again.
FYI @vicentefb and I are working on a proposal in a google doc, we will share it here soon when it's ready
/reopen
@tenzen-y: Reopened this issue.
In response to this:
/reopen
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Hi I would love to work on this issue, especially the ray autoscaling support. Would resuming #1852 be a good starting point?
/assign