All Posts

Online Resource Allocation with Ray at Ant Group

Executing a distributed shuffle without a MapReduce system

How to Speed Up Pandas with Modin

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.

Data Processing Support in Ray

Retrieval Augmented Generation with Huggingface Transformers and Ray

Huggingface Transformers recently added the Retrieval Augmented Generation (RAG) model, a new NLP architecture that leverages external documents (like Wikipedia) to augment its knowledge and achieve state of the art results on knowledge-intensive tasks. In this blog post, we introduce the integration of Ray, a library for building scalable applications, into the RAG contextual document retrieval mechanism. This speeds up retrieval calls by 2x and improves the scalability of RAG distributed fine-tuning.

How to Speed up Scikit-Learn Model Training

Configuring and Scaling ML with Hydra + Ray

Reinforcement Learning with RLlib in the Unity Game Engine

Ray & MLflow: Taking Distributed Machine Learning Applications to Production