We recently moved to weekly Ray releases to ship features to our community faster. 🚀 Doing so required us to fix flaky tests and completely revamp our release process. 👊 Read more here: anyscale.com/blog/ray-spotl…

FP8 for vLLM cuts latency nearly in half.

🎊Excited to announce Brandon Leonardo , Co-founder of Instacart, as a keynote speaker at Ray Summit! Brandon will share insights on scaling tech, AI innovations, and building resilient systems. Don’t miss this chance to learn from a leading tech mind! 🚀 Sign up here:

We don't hear the term *exabyte* too frequently. This is an impressive use case. aws.amazon.com/blogs/opensour…

This migration began 4 years ago 🫢 Not our typical Ray use case, but so impressive and it illustrates Ray's versatility. Also, it was worth it because they're saving over $100 million annually 😇 Some fascinating excerpts. 2016: Amazon aims to remove all dependencies on

Canva just acquired Leonardo AI 🤯

🚨The Ray Summit Training Guide is here!🚨 Check it out and make the most out of your experience. 🎓 Explore all the classes available, 🍎 Discover personalized learning paths (LLMs, Ray Core, and more), 🎯 Reserve your spot early to avoid missing out! 🚀 #RaySummit Training

Running hands on training sessions is something we started in the very early days of Ray. We got this idea of course from the Apache Spark community. Here are two photos from the early days 1. The first Ray meetup, hosted at OpenAI. 2. A tutorial we ran at an O'Reilly Media

We're running a one day event for the AI infrastructure community as a warm up for #RaySummit.

1000 contributors!!! 🤯🤯 One of the earliest contributors to Ray was Ant Group / Alipay. They were the first serious Ray user, and contributed a lot to the hardening of Ray in production. Today, they use Ray for a huge range of workloads ranging from batch inference to model

1/11 🥁Anyscale now offers DPO as part of its LLM training suite! This enables refinement of LLMs for domain-specific preferences, which can outperform frontier closed-models & SFT baselines. We demo the capability through a case study on summarization 🧵

🚨Anyscale Leadership Speaker Session Alert 🚨 Ready to break down the AI complexity wall? Join Robert Nishihara, Co-Founder of Anyscale, at #RaySummit as he reveals how companies can overcome the toughest AI challenges. Don’t miss out on strategies that could redefine your AI

Tutorial on computing CLIP scores for assessing image / text pair quality. Really enjoyed reading this blog post and example code. "I would suggest mastering Ray locally at first." I like this quote, and despite Ray being designed for scale, we really optimize single-machine

uv is one of the most important projects that's been created recently, and the team at Astral is very very good. It's hard to overstate how important getting dependency management right is for iteration velocity. My favorite thing about uv is that when installing a new

attentive 💬 is creating a better, personalized shopping experience for consumers – built on a foundation of scalable ML infrastructure. 🙌 Check out how they're taking personalized marketing to the next level with Anyscale 👉 anyscale.com/resources/case… "Before Anyscale, we didn’t

From processing satellite imagery for 35,000 hectares in 3 months to analyzing 24 million in just 1 hour — Agreena’s 10,000x efficiency gain is redefining climate tech, all while cutting costs by 78%. Using AI and ML, they're turning massive geospatial data into real-time

The AI compute software stack consists of 3 specialized layers: 🔧🔧🔧 Layer 1: Training & Inference Framework (PyTorch + vLLM) • Runs models efficiently on GPUs • Handles model optimization and model parallelism strategies • Manages accelerator memory and automatic

The Ray Summit CfP is closing on July 14! We'll be back in San Francisco from Nov 3-5 for Ray Summit and want to showcase your work. Whether it's scaling smoother, building GenAI workflows, or creating complex ML systems - if you've built it with Ray, we want to hear about it.

Anyscale has contributed Ray to #PyTorch Foundation, joining #PyTorch, vLLM, and DeepSpeed under open governance to advance scalable, distributed AI infrastructure. Janakiram MSV (Forbes) on how this model reduces fragmentation and fosters transparent collaboration ⬇️

The RL training infra for Composer 2 was built with Ray. Very impressive work.