All Posts

With and without NumPy Ray
09 . 02 . 2021

Parallelizing Python Code

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

ML Platform
08 . 31 . 2021

How Anastasia accelerated their ML processes 9x with Ray and Anyscale

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...

Introduction to Reinforcement Learning
08 . 26 . 2021

An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab

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

FLAML XGBOOST Ray Tune
08 . 24 . 2021

Fast AutoML with FLAML + Ray Tune

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-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