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This article reviews some common options for parallelizing Python code including process-based parallelism, specialized libraries, ipython parallel, and Ray.

Anastasia.ai provides a powerful platform that enables organizations to operate AI capacities at scale with a fraction of the resources and effort traditionally required. This post covers a demand prediction problem we had and how using Ray to solve...

An introductory tutorial on reinforcement learning with OpenAI Gym, RLlib, and Google Colab.

FLAML is a lightweight Python library from Microsoft Research that finds accurate machine learning models in an efficient and economical way using cutting edge algorithms designed to be resource-efficient and easily parallelizable. FLAML can also uti...

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