Running Slurm on Amazon EKS with Slinky

Running Slurm on Amazon EKS with Slinky A primer on Slurm The Slinky Project The use case for Slinky Architecture overview of Slurm on EKS with Slinky Slinky Slurm cluster components Benefits of running Slurm on EKS with Slinky Alternatives for running Slurm on AWS AWS ParallelCluster AWS Parallel Computing Service Amazon SageMaker HyperPod Alternative Kubernetes native job schedulers Volcano Apache YuniKorn Kueue When Slurm on EKS is right for you About the authors When building an AI infrastructure stack for pre-training, fine-tuning, or inference workloads, both Slurm and Kubernetes can be used as compute orchestration platforms to meet the needs of different teams and address different stages of the AI development lifecycle. However, traditionally this would result in managing disparate clusters of accelerated compute capacity, potentially duplicating operational overhead and risking resource underuse. But what if you could deploy a Slurm cluster as a Kubernetes service to get the best of both worlds? Think of Kubernetes as a large, modern office building providing shared resources (for example, electricity, internet, security, HVAC) for its tenants. When a specialized lab moves in, needing dedicated resources such as specific power and temperature control, you don’t build a new building. Instead, you integrate the lab into the existing building infrastructure, allowing it to use shared services while maintaining its own precise controls for high-performance work. In the same way, Slurm can be ran inside a Kubernetes environment such as Amazon Elastic Kubernetes Service (Amazon EKS) using the open source Slinky Project. In this post, we introduce the Slinky Project, discuss its benefits, explore some alternatives, and leave you with a bit of homework to go deploy the Slurm on EKS blueprint , which uses the Slinky Slurm operator. Slurm is an open source, highly scalable workload manager and job scheduler designed for managing compute resources on compute clusters of all sizes. It provides three core functions: allocating access to compute resources, providing a framework for launching and monitoring parallel computing jobs, and managing queues of pending work to resolve resource contention. Slurm is widely used in traditional High-Performance Computing (HPC) environments and in AI training to manage and schedule large-scale accelerated compute workloads across multi-node clusters. Slurm allows researchers and engineers to efficiently allocate CPU, GPU, and memory resources for distributed training jobs with fine-grained control over resource types and job priorities. Slurm’s reliability, advanced scheduling features, and integration with both on-premises and cloud environments make it a preferred choice for handling the scale, throughput, and reproducibility that modern AI research and industry demand.