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Data loading & preprocessing can easily become the performance bottleneck in ML pipelines. With Ray Data, data loading can be fast, flexible and scalable.

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Fast, Flexible and Scalable Data Loading for ML Training

Fast, flexible, and scalable data loading for ML training with Ray Data

This blog post delves into how Ray Data streaming works and how to use it for your own ML pipelines distributed across both CPU and GPU devices.

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How to Stream Distributed Execution Across CPUs & GPUs

Streaming distributed execution across CPUs and GPUs

Jiao Dong

Dmitri Gekhtman

Sang Cho

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Ray 1.13: Improving support for shuffling terabyte-scale and larger datasets

In this blog, we compare the memory management and performance of Dask versus Ray (or "Dask-on-Ray'') with its built-in scheduler.

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Analyzing Memory Management & Performance in Dask-on-Ray

Analyzing memory management and performance in Dask-on-Ray

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Executing a distributed shuffle without a MapReduce system

Alex Wu

Clark Zinzow

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Data Processing Support in Ray

Intel and Anyscale have integrated Ray with Gaudi3. Gaudi3 is AI-optimized for efficient training and other machine learning workloads.

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Accelerating AI: Harnessing Intel(R) Gaudi(R) 3 with Ray 2.10

Anyscale is teaming with NVIDIA to combine the developer productivity of Ray Serve and RayLLM with the cutting-edge optimizations from NVIDIA Triton Inference Server software and the NVIDIA TensorRT-LLM library.

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Low-latency Generative AI Model Serving with Ray, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM

Based on the popular “Needle In a Haystack” benchmark and RAG, we share our process of creating a problem-specific fine-tuning dataset to extend the context of models to build better RAG systems.

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Fine-tuning LLMs for longer context and better RAG systems

Generating embeddings is a critical task for developing successful RAG applications, and Anyscalew with Pinecone is the most cost-efficient solution for this workflow.

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RAG at Scale: 10x Cheaper Embedding Computations with Anyscale and Pinecone

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