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Jiao Dong

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Edward Oakes

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Multi-model composition with Ray Serve deployment graphs

Archit Kulkarni

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Handling files and packages on your cluster with Ray runtime environments

Leonnardo Rabello

Lucas Machado

The live traffic pattern is variable and predictable. Serve’s replica autoscaling drives the cost down further during idle time.

Cheaper and 3X Faster Parallel Model Inference with Ray Serve

Michael Galarnyk

Where Ray Serve Fits In

Serving ML Models in Production: Common Patterns

This is part 2 of our generative AI blog series. Here, we cover how Ray empowers large language models (LLM) frameworks such as Alpa. 

Figure 5: Technical integration layered stack for LLM


Figure-5

High performance LLM training at 1000 GPU scale with Alpa and Ray

Training 175B Parameter Language Models at 1000 GPU scale with Alpa and Ray

This is part 1 of our generative AI blog series. In this blog, we talk about how to use Ray to deploy common generative model workloads in production. An upcoming blog will deep dive into why projects like Alpa are using Ray to scale large models.

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Ray solves Generative AI and LLM Infrastructure Challenges

How Ray solves common production challenges for generative AI infrastructure

Exoshuffle is now the most cost-efficient way to sort 100TB of data on the public cloud, using only $0.97 per terabyte. Learn how this is possible with Ray.

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Ray Is the World’s Most Cost-Efficient Sorting System at $1/TB

Ray breaks the $1/TB barrier as the world’s most cost-efficient sorting system

More companies are needing to train & deploy many small ML models, often hundreds or thousands. Learn how Ray and Anyscale make that possible.

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Training 1 Million ML Models in Record Time | Anyscale

Training One Million Machine Learning Models in Record Time with Ray

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

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Scalable AI and Python

Posts by Edward Oakes

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