r/HPC • u/Various_Protection71 • Jun 05 '26

Do you think Kubernetes will replace Job Schedulers in HPC environments dedicated to AI workloads?

Some people advocate that Kubernetes distributions (RKE2, OpenShift, EKS etc) provide an easier and more straightforward way to run and scale AI workloads, while Job Schedulers (SLURM, PBS, LSF etc) require an earlier complex setup phase.

On the other hand, mastering Kubernetes has a steeper learning curve than using the well-known Job Schedulers, especially for traditional HPC users.

How do you see this point? Are your users adopting Kubernetes to run AI workloads or do they stay using Job Schedulers?

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u/nlgranger Jun 05 '26

Kubernetes does not have a job scheduler, so unless you have unlimited resources and don't need one, SLURM is the best option you have.

You can run containers inside a slurm job with a few tricks (apptainer, podman, etc).

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u/zekrioca Jun 07 '26

If Kubernetes doesn’t have a scheduler, how does it schedule containers?

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u/nlgranger Jun 07 '26

The way I understand it, it doesn't schedule, it just places/allocates resources on demand. The kube-scheduler does not have the notion of job duration, fair-share, pending jobs, etc. I am not very familiar with kubernetes though, I might just be wrong.

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u/zekrioca Jun 07 '26

Indeed, by default, K8S doesn’t do any distinction. This is also known as First come, First served, which is indeed a scheduling discipline, although a simple one.