Optimizing AI Workloads on Kubernetes with an AI Computer

General Discussions
1

Hello Kubernetes community,

I’m currently running AI workloads on Kubernetes using an AI computer with a powerful GPU. The system is performing well, but I’m looking for advice to further optimize it for AI-related tasks on an AI computer.

Questions I’m Working On

  1. GPU Optimization:
  • The NVIDIA device plugin is active, and the GPU is accessible. Are there additional configurations or tools to ensure the GPU is used efficiently for AI tasks?
  1. Fast Storage Access:
  • I’ve set up persistent volumes to handle AI model data. Are there specific storage classes or configurations recommended for large AI workloads?
  1. Scaling AI Workloads:
  • Horizontal scaling works, but I want to improve initialization times for tasks involving large AI models. Are there ways to prewarm nodes or optimize pod scheduling for this purpose?

Steps Taken

  • Configured NVIDIA’s device plugin to enable GPU usage.
  • Used dynamic provisioning for persistent storage.
  • Adjusted resource requests and limits in deployment YAML files to match workload needs.

Request for Suggestions

If anyone has experience deploying AI workloads with Kubernetes on an AI computer, I’d appreciate any tips on GPU utilization, storage performance, or workload scaling. Would frameworks like Kubeflow or similar tools help streamline these processes?

Thank you for sharing your insights!