Managing Ray clusters and workloads on Kubernetes can be challenging. KubeRay provides an open-source toolkit to manage the lifecycle of Ray clusters and simplify the deployment process of Ray Applications on Kubernetes. In this talk, we will talk about:
1. How KubeRay integrates with the Kubernetes ecosystem? (e.g. observability, ingress, prototyping, scheduling)
2. What capabilities exist in KubeRay? (e.g. autoscaling, fault-tolerance, high-available serving deployment, batch job)
3. Performance/Stability/Scalability benchmarks
Find the slide deck here: https://drive.google.com/file/d/1ejdCpUKMjFvJKizpN5COvQCMoAZ_S71_/view?usp=drive_link
About Anyscale
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Anyscale is the AI Application Platform for developing, running, and scaling AI.
https://www.anyscale.com/
If you're interested in a managed Ray service, check out:
https://www.anyscale.com/signup/
About Ray
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Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
https://docs.ray.io/en/latest/
#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai