Featured Posts
Introducing Distributed XGBoost Training with Ray
XGBoost-Ray is a novel backend for distributed XGBoost training. It features mul...
Introducing Ray Lightning: Multi-node PyTorch Lightning training made easy
Ray Lightning is a new plugin that makes running multi-node GPU training with Py...
Analyzing memory management and performance in Dask-on-Ray
Ray is a general-purpose distributed system. One of Ray's goals is to seamlessl...
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The Third Generation of Production ML Architectures
As technology has advanced, production ML architectures have evolved. One way to see it is in terms of generations: The first generation involved “fixed function” pipelines, while the second generation involved programmability within the pipeline of...
Building Highly Available and Scalable Online Applications on Ray at Ant Group
Ant Group has developed Ant Ray Serving which is an online service framework based on Ray, which provides users with a Serverless platform to publish Java/Python code as online services. The platform provides users with basic capabilities such as dep...
Parallelizing Python Code
This article reviews some common options for parallelizing Python code including process-based parallelism, specialized libraries, ipython parallel, and Ray.
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...
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.
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 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.
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...
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.
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...