Lilly Tech Systems
GPU Scheduling in Kubernetes
Master GPU resource management for AI and ML workloads on Kubernetes. Learn device plugins, time-slicing, Multi-Instance GPU (MIG), scheduling policies, and production best practices for maximizing GPU utilization.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand why GPU scheduling matters in Kubernetes, the challenges of GPU resource management, and the landscape of tools available.
2. Device Plugins
Learn how Kubernetes device plugins expose GPU resources to pods, install the NVIDIA device plugin, and configure GPU resource requests.
3. Time-Slicing
Configure GPU time-slicing to share a single GPU across multiple pods, enabling cost-effective utilization for lightweight workloads.
4. Multi-Instance GPU (MIG)
Partition NVIDIA A100 and H100 GPUs into isolated instances using MIG for guaranteed compute, memory, and fault isolation.
5. Scheduling Policies
Implement node affinity, taints, tolerations, priority classes, and topology-aware scheduling for optimal GPU placement.
6. Best Practices
Production patterns for GPU monitoring, cost optimization, multi-tenant clusters, autoscaling, and troubleshooting GPU workloads.
What You'll Learn
By the end of this course, you'll be able to:
Configure GPU Resources
Set up device plugins, resource requests, and limits to expose and allocate GPUs to Kubernetes pods effectively.
Maximize Utilization
Use time-slicing and MIG to share GPUs across workloads, reducing costs while maintaining performance guarantees.
Optimize Scheduling
Apply advanced scheduling policies including affinity rules, topology awareness, and priority-based preemption.
Monitor & Scale
Implement GPU monitoring with DCGM, set up autoscaling, and manage multi-tenant GPU clusters in production.