Overview

SLURM is widely used for managing machine learning (ML) workloads. It is dedicated to efficient resource management, allowing users to split large jobs into many steps, then run them in parallel for distributed ML training. As high-performance computing (HPC) environments evolve, there’s an increasing demand to bridge the gap between traditional HPC job schedulers and modern cloud-native infrastructure.

With Rafay, you can deploy Slinky based SLURM clusters on Kubernetes clusters providing high availability and automatic scaling, to save computing resources and costs. This enables organizations to deploy and operate Slurm-based workloads on Kubernetes clusters allowing them to leverage the best of both worlds: Slurm’s mature, job-centric HPC scheduling model and Kubernetes’s scalable, cloud-native runtime environment.