Posts by Richard Liaw

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 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.

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...

Sender Receiver (Introducing Collective Communication Primitive APIs in Ray)
05 . 28 . 2021

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...

PyTorch + Ray
03 . 02 . 2021

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.

Hydra+Ray (Anyscale)
01 . 26 . 2021

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...

ray-hf-header
11 . 03 . 2020

Hyperparameter Search with Hugging Face Transformers