When running artificial intelligence (AI) and machine learning (ML) model training and inference on Kubernetes, dynamic scaling up and down becomes a critical element. In addition to requiring high-bandwidth storage and networking to ingest data, AI model training also needs substantial (and expensive) compute, mostly from GPUs or other specialized processors. By Ilya Krutov of F5.
In this blog, we cover the three most common ways to scale AI/ML workloads on Kubernetes so you can achieve optimal performance, cost savings, and adaptability for dynamic scaling in diverse environments:
- Three scaling modalities for AI/ML workloads on Kubernetes
- HPA use cases
- VPA use cases
- Cluster Autoscaler use cases
- HPA, VPA, and Cluster Autoscaler Each Have a Role
HPA, VPA and Cluster Autoscaler complement each other in managing AI/ML workloads in Kubernetes. Cluster Autoscaler ensures there are enough nodes to meet workload demands, HPA efficiently distributes workloads across multiple pods, and VPA optimizes the resource allocation of these pods. Together, they provide a comprehensive scaling and resource management solution for AI/ML applications in Kubernetes environments. Nice one!
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