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Ray version 1.6 is here. Highlights include: Ray Datasets for large-scale data loading, Ray Lightning for distributed training on PyTorch Lightning, TPU support in Ray Autoscaler, and Runtime Environments goes GA.

Ikigai Labs provides AI-charged spreadsheets: an AI augmented data processing and analytics collaborative, cloud platform that can be used with an ease of spreadsheet. While the platform supports various features, they all revolve around the data pro...

Ray Lightning is a new plugin that makes running multi-node GPU training with PyTorch Lightning fast and easy.

Ray is a fast, simple distributed execution framework that makes it easy to scale your applications and to leverage state of the art machine learning libraries. Using Ray, you can take Python code that runs sequentially and transform it into a distri...

LightGBM is a gradient boosting framework based on tree-based learning algorithms. Compared to XGBoost, it is a relatively new framework, but one that is quickly becoming popular in both academic and production use cases.  We’re excited to announce a...

An overview of some of the best reinforcement learning talks presented at the second Ray Summit

An overview of some of the best machine learning talks presented at Ray Summit 2021.

Learn how Ray can be paired with Apache Kafka to power streaming applications.

Ray is a general-purpose distributed system. One of Ray's goals is to seamlessly integrate data processing libraries (e.g., Dask, Spark) into distributed applications. As part of this goal, Ray provides a robust distributed memory manager. The goal...

XGBoost-Ray is a novel backend for distributed XGBoost training. It features multi node and multi GPU training, distributed data loading, advanced fault tolerance such as elastic training, and a seamless integration with hyperparameter optimization f...