Anyscale

⚡Anyscale Webinar Alert! ⚡ Join us on Feb 26 for an Introduction to Anyscale and Ray AI Libraries, a webinar designed to help you get started with Ray’s AI libraries for distributed machine learning. Our training instructors will walk you through: 🔹 How Anyscale addresses today’s AI challenges 🔹 An overview of Ray AI Libraries (Data, Train, Tune, Serve) 🔹 A full ML workflow covering all MLOps stages Don't miss this chance to learn directly from experts and see how Ray AI Libraries can supercharge your ML workflows. 🔗 Register now: https://lnkd.in/dbK8PFw5

Really enjoyed visiting the ByteDance headquarters today and learning about their AI infrastructure! They have been shipping a ton of models recently: 🎥 OmniHuman-1 for high quality deepfake videos 🎹 Seed-Music for music generation Today they hosted a great meetup and went into detail on how they use Ray for their multimodal data pipelines (audio and video processing) and post-training (RLHF). Also got to catch up with some friends 😊

⚡ Speed wins! ⚡ The groupby map_groups operation runs 30% faster, all thanks to the efficiency of #NumPy powered by Ray Data 🙌 Awesome to have a vibrant community making Ray the best AI compute engine! 🚀

Where is AI headed? Anyscale Chairman Ion Stoica joins TechCrunch Equity podcast to explain why open source is shaping the future of AI—lowering barriers to innovation and preventing control by a few players. He also shares how Microsoft’s early move to host DeepSeek on Azure strengthens its AI position by backing open-source advancements. Listen to full episode hereListen here 🎧🔗 https://lnkd.in/gfmD8E-6

⭐ Anyscale Webinar Alert ⭐ Join us on Feb 26 for Introduction to Anyscale and Ray AI Libraries, a webinar designed to help you get started with Ray’s AI libraries for distributed machine learning. Our technical instructors will cover: • How Anyscale's platform handles modern day AI challenges • Ray AI Libraries (Data, Train, Tune, Serve) for distributed ML • End-to-end workflow spanning all stages of the MLOps lifecycle Don't miss this chance to learn directly from experts and see how Ray AI Libraries can supercharge your ML workflows 🔥 🔗 Register here: https://lnkd.in/gcawHtJz #MachineLearning #RayAI #DistributedComputing

This team does incredible work building systems that power AI at Apple. If you're interested in contributing to open source software for AI, consider joining them!

💙 💙 Integrating Ray Data and Apache Hudi enables users to run ML workflows with Ray Data using datasets directly from open #lakehouse platforms. Getting started is easy: import ray ds = ray.data.read_hudi(table_uri="/hudi/trips") Read all about in the documentation: https://lnkd.in/dg9C5sKT 👇

Serving DeepSeek R1 on Anyscale's Enterprise vLLM adds high availability, fault tolerance, zero downtime upgrades, automated performance tuning, and limitless scale. Check out our interactive demo to see how we deployed DeepSeek-R1-Distill-Llama-8B on Anyscale in just 5 minutes and had complete control to run on any GPU👉🔗 https://lnkd.in/g5_Ecbhk

In the new year, I've had 11 conversations with AI startups using large H100 reservations who share a common pain point: multitenancy (sharing the GPU reservation among multiple workloads of varying priorities). And multitenancy is fundamentally about cost. In a typical elastic cloud environment, this is not a problem. Each workload will independently spin up whatever compute resources it needs and shut them down when it's done. However, high-end GPUs are often purchased via fixed-size reservations. These might be used for a combination of training, inference, and data pipelines. Here's a typical scenario. - When my big training job is running, the training job should get all the GPUs it needs. - However, when the training job finishes or the researcher goes on vacation, the GPUs are idle. - I want some kind of background job that can act as a "sponge" and soak up all the unused compute, otherwise I'm wasting money. - A good candidate for this background job is often data processing (typically batch inference) because there's often a big backlog of data to process. - The data processing workload may also use other cloud instance types outside of the GPU reservation. - When new training jobs come online, they need to take resources away from the background job. This is also one of the reasons companies like OpenAI offer cheaper batch APIs because these workloads can be scheduled with more flexibility when the resources are available and can therefore even out overall compute utilization. The tools we're building with Ray and our platform at Anyscale are geared toward solving these challenges (and other complexities around managing and scaling compute-intensive AI workloads). And yes, I generated an image based on this post (can you tell which provider?).

Join us this Thursday for Anyscale’s Ray Meetup in San Jose with ByteDanceOSS! We’ll cover Ray’s latest feature developments, large-scale multimodal data processing, AI infrastructure trends, GPU workload heterogeneity, and RL for LLMs 🚀 Hear from experts and connect with the community ⚡ In-person space is limited and available on a first-come, first-serve basis. Register now 👉 https://lu.ma/ji7atxux