Kubernetes Bytes

Training Machine Learning (ML) models on Kubernetes

May 31, 2024

Bernie Wu from Memverge discusses training ML models on Kubernetes, including cost-saving tips with spot instances, efficient model checkpoints, hot restarts, and reclaiming GPU resources. They delve into topics like DAG phases, transparent checkpointing, and GPU snapshotting for AI workloads.

55:29

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Episode guests

Bernie Wu

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Efficient model checkpointing enhances AI training reliability and recovery, optimizing resource management in Kubernetes.

Transitioning to stateful workloads on Kubernetes requires efficient data management and complex operations handling for AI models.

Deep dives

Transition from Stateless to Stateful Workloads on Kubernetes

The discussion highlighted the evolution of workloads on Kubernetes from stateless to stateful, showcasing a shift towards managing more data and complex operations efficiently on the platform.

Introduction

2min

Transition to External Plugin Architecture in Kubernetes and AKS Automatic Solution

2min

Trends in Containerized Applications for Kubernetes Clusters and Recent Acquisition News

4min

Evolution of Kubernetes for AI Workloads

13min

Optimizing Machine Learning Model Training on Kubernetes with Checkpoints

21min

AI Technologies, Project Creo, and Kubernetes Operator Deployment

6min

Exploring Kubernetes Security and Developer Shift Left Strategies

2min

Evolution of Kubernetes towards Stateful Data Management and GPU Resource Optimization

4min

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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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

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Kubernetes Bytes

Training Machine Learning (ML) models on Kubernetes

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Transition from Stateless to Stateful Workloads on Kubernetes

Importance of Model Checkpointing for AI Workloads

Efficient Utilization and Resilience with Transparent Checkpointing

Practical Implementation with Kubernetes Operators

Remember Everything You Learn from Podcasts