Training Machine Learning (ML) models on Kubernetes

Kubernetes Bytes

In this episode of the Kubernetes Bytes podcast, Bhavin sits down with Bernie Wu, VP Strategic Partnerships and AI/CXL/Kubernetes Initiatives at Memverge. They discuss about how Kubernetes is the most popular platform to run AI model training and model inferencing jobs. The discussion dives into model training, talking about different phases of a DAG, and then talk about how Memverge can help users with efficient and cost-effective model checkpoints. The discussion goes into topics like saving costs by using spot instances, hot restart of training jobs, reclaiming unused GPU resources, etc.

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Episode Sponsor: Nethopper

• Learn more about KAOPS: @nethopper.io
• For a supported-demo: [email protected]
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Cloud Native News:

• https://www.aquasec.com/blog/linguistic-lumberjack-understanding-cve-2024-4323-in-fluent-bit/
• https://kubernetes.io/blog/2024/05/20/completing-cloud-provider-migration/
• https://thenewstack.io/introducing-aks-automatic-managed-kubernetes-for-developers/
• https://www.harness.io/blog/harness-to-acquire-split

Show Links:

• https://www.linkedin.com/in/berniewu/
• https://criu.org/Main_Page
• https://memverge.com/
• https://youtu.be/tY8YOMRuqWI?si=yB3hHqLUpYPZ-KWN
• https://youtu.be/ND4seSKpJHI?si=shh0iuA9qC-dO6eb

Timestamps:

• 01:04 Cloud Native News
• 08:47 Interview with Bernie
• 51:40 Key takeaways