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The Future of AI Workloads with Slinky
Manage episode 477383602 series 3446189
Content provided by open.intel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by open.intel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode, Marlow Warnicke, lead for the Slinky project, and Tim Wickberg, CTO of SchedMD, join us to discuss their work integrating HPC scheduler Slurm with Kubernetes. They provide background on Slurm's origins, its open source nature, and its evolution into Slinky to address Kubernetes's limitations in scheduling AI and HPC workloads. The discussion touches on the unique challenges in the MLOps space, the need for fine-grained resource control, and their collaborative efforts with various communities to enhance Kubernetes's efficiency. They also share the roadmap for Slinky and avenues for community collaboration and contribution.
00:00 Introduction and Guest Introductions
00:39 Overview of Slurm and Its Evolution
01:44 The Fusion of Slurm and Kubernetes: Slinky
04:14 Challenges in Kubernetes Scheduling
09:07 Unique Challenges in MLOps
12:58 Community Collaboration and Future Plans
16:41 Getting Involved and Final Thoughts
100 episodes
Share
Manage episode 477383602 series 3446189
Content provided by open.intel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by open.intel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode, Marlow Warnicke, lead for the Slinky project, and Tim Wickberg, CTO of SchedMD, join us to discuss their work integrating HPC scheduler Slurm with Kubernetes. They provide background on Slurm's origins, its open source nature, and its evolution into Slinky to address Kubernetes's limitations in scheduling AI and HPC workloads. The discussion touches on the unique challenges in the MLOps space, the need for fine-grained resource control, and their collaborative efforts with various communities to enhance Kubernetes's efficiency. They also share the roadmap for Slinky and avenues for community collaboration and contribution.
00:00 Introduction and Guest Introductions
00:39 Overview of Slurm and Its Evolution
01:44 The Fusion of Slurm and Kubernetes: Slinky
04:14 Challenges in Kubernetes Scheduling
09:07 Unique Challenges in MLOps
12:58 Community Collaboration and Future Plans
16:41 Getting Involved and Final Thoughts
100 episodes
All episodes
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Similar to Open at Intel
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continue reading
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