Posts by Richard Liaw
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...
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.
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...
Introducing Collective Communication Primitive APIs in Ray
In Ray 1.2.0, we’ve added a library for “collective communication primitives” to Ray. These primitives can be used in your Ray program to simplify the exchange of information across many distributed processes at the same time, speeding up certain dis...
Getting Started with Distributed Machine Learning with PyTorch and Ray
Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.
Configuring and Scaling ML with Hydra + Ray
Hydra, from Facebook AI, is a framework for elegantly configuring complex applications. Since its initial release, Hydra has become a popular framework adopted by researchers and practitioners. We are happy to announce that users can now scale and la...
