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Tutorial: Build, Operate, and Use a Multi-Tenant AI Cluster Based Entirely on Open Source

on YouTube 01h 15m
Talkat KubeCon + CloudNativeCon Europe 2025 · on Wednesday, 2025-04-02· at 16:15· Level 1 | Hall Entrance N11· [machine-learning, kubernetes]

speakers

name:C. Misale
links:none on file
name:O. Tardieu
links:none on file
name:D. Grove
links:none on file

resources

video:https://youtu.be/Ab7mRoJYsMo
slides:none on file

summary

Open-source stack shows a concrete recipe: deploy Nvidia GPU Operator, Kueue for workload-aware queuing and quotas, scheduler plugins for gang and topology-aware placement, and app wrapper plus autopilot for automated health checks and fault recovery. Demos run on a 24‑GPU test cluster and IBM Research reports operating ~1,200 GPUs with near-100% utilization by letting teams borrow unused quota, preempting or suspending jobs, and automatically migrating workloads off failing GPUs without human intervention.

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chapters (13)

  1. 0:00Multi-tenant AI GPU clusters
  2. 2:25AI workloads and hardware needs
  3. 6:20Kubernetes and platform overview
  4. 10:32Cluster setup prerequisites
  5. 12:17Cluster config and team onboarding
  6. 18:18Queuing and quota with Kueue
  7. 20:03Kueue demo and preemption
  8. 32:14Fault detection with Autopilot
  9. 47:59Fault recovery with App Wrappers
  10. 55:01Kubeflow trainer example
  11. 57:32KubeRay fine-tuning example
  12. 1:06:28Batch inference example
  13. 1:09:59Key takeaways and results

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