It can often be useful to leverage short-lived Ray clusters as a part of a broader ML workflow (ex: to perform distributed training as part of a pipeline with multiple data and modeling steps). Using these single-purpose, ephemeral Ray clusters can unlock opportunities for improved reproducibility, efficiency, and observability. KubeRay provides a natural way to manage these ephemeral Ray clusters on Kubernetes. Sematic (sematic.dev) shares learnings from leveraging KubeRay & Ray in this way.
Find the slide deck here: drive.google.com/file/d/1LhLO...
About Anyscale
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Anyscale is the AI Application Platform for developing, running, and scaling AI.
www.anyscale.com/
If you're interested in a managed Ray service, check out:
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.
docs.ray.io/en/latest/
#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai
Негізгі бет On-Demand Ray Clusters in ML Workflows via KubeRay & Sematic
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