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Paige Bailey

Richard Liaw

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Jian Xiao

Chandler Gibbons

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Ray 1.12: Ray AI Runtime (alpha), usage data collection, and more

Juan Navas

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How to distribute hyperparameter tuning using Ray Tune

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How to tune hyperparameters on XGBoost

Matthew Deng

Amog Kamsetty

Will Drevo

Distributed deep learning with Ray Train is now in Beta

Qingyun Wu

Chi Wang

Antoni Baum

Michael Galarnyk

FLAML XGBOOST Ray Tune

Fast AutoML with FLAML + Ray Tune

Ray Lightning

Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy

Kai Fricke

XGBoost-Ray

Introducing Distributed XGBoost Training with Ray

Hao Zhang

Sender Receiver (Introducing Collective Communication Primitive APIs in Ray)

Introducing Collective Communication Primitive APIs in Ray

Robert Nishihara

PyTorch + Ray

Getting Started with Distributed Machine Learning with PyTorch and Ray

Bill Chambers

Jieru Hu

Hydra+Ray (Anyscale)

Configuring and Scaling ML with Hydra + Ray

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Hyperparameter Search with Hugging Face Transformers

BlogBiolexis on Anyscale (4)

Biolexis Boosts Their New AI/ML Drug Discovery Platform Using Anyscale’s Fully-Managed Ray Platform

Ray For Success

Four Reasons Why Leading Companies Are Betting On Ray

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Ray Summit, the Industry Event for Scalable AI, Unveils New Innovations in Ray and the Anyscale Platform; Features Speakers from Uber, IBM, OpenAI, Shopify, Dow and Numerous Global Organizations

Blog Adtriba on Anyscale (4)

Adtriba Accelerates and Advances Media Mix Modeling Using the Anyscale Fully-Managed Ray Platform

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Products / Libraries

From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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Scalable AI and Python

Posts by Richard Liaw

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