KubeRay maintainers Andrew Sy Kim from Google and Kai-Hsun Chen from Anyscale present an in-depth look at scaling generative AI workloads using KubeRay and Kubernetes. Their talk addresses how this integration provides a lightweight, flexible solution for diverse infrastructure requirements in AI deployments.
The presentation covers crucial integrations with the Kubernetes ecosystem and cloud providers, focusing on essential features for training and fine-tuning. These include gang scheduling, distributed checkpointing, and retries. The speakers explore KubeRay's capabilities in supporting both online and offline inference through features like Ray Autoscaler and fault tolerance, along with its compatibility with various hardware accelerators including GPUs, TPUs, and CPUs.
The session includes current KubeRay project updates and developments, highlighting Kubernetes community enhancements such as hierarchical scheduling and dynamic resource allocation (DRA). This comprehensive overview demonstrates how KubeRay and Kubernetes work together to scale AI infrastructure across multi-cloud, production environments.
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Interested in more?
- Watch the full Day 1 Keynote: https://youtu.be/jwZHJthQvXo
- Watch the full Day 2 Keynote https://youtu.be/Lury2ad6KG8
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