Design a GPU Cluster Management System for ML Training — System Design Interview Practice
Design a GPU cluster management system that handles job scheduling, resource allocation, multi-tenancy, preemption, and cost optimization for ML training workloads. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Schedule training jobs across GPU nodesRequirement
- Implement fair-share resource allocationRequirement
- Support job preemption and priority queuesRequirement
- Handle multi-tenancy with resource quotasRequirement
- Track GPU utilization and idle resourcesRequirement
- Implement gang scheduling for distributed trainingRequirement
- Support spot/preemptible instance integrationRequirement
- Provide cost allocation and chargeback reportsRequirement
- mlopsConcept to explore
- gpu clusterConcept to explore
- schedulingConcept to explore
- resource managementConcept to explore
- kubernetesConcept to explore
- infrastructureConcept to explore