All Posts

ray-1-6
08 . 23 . 2021

Ray version 1.6 is released

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.

ikigaiDashboard
08 . 19 . 2021

How Ikigai Labs Serves Interactive AI Workflows at Scale using Ray Serve

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
08 . 19 . 2021

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

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

Ray Dashboard 8 Core
08 . 12 . 2021

Writing your First Distributed Python Application with Ray

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...

Ray + LightGBM
08 . 10 . 2021

Introducing Distributed LightGBM Training with Ray

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...

Neural MMO
07 . 22 . 2021

Best Reinforcement Learning Talks from Ray Summit 2021

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

ML Platform Panel
07 . 20 . 2021

Best Machine Learning Talks from Ray Summit 2021

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

Kafka + Ray
07 . 13 . 2021

Serverless Kafka Stream Processing with Ray

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

Dask+Ray
06 . 29 . 2021

Analyzing memory management and performance in Dask-on-Ray

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
06 . 16 . 2021

Introducing Distributed XGBoost Training with Ray

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...