Scalable compute for AI and Python Anyscale enables developers of all skill levels to easily build applications that run at any scale, from a laptop to a data center.
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The Anyscale Platform offers key advantages over Ray open source. It provides a seamless user experience for developers and AI teams to speed development, and deploy AI/ML workloads at scale. Companies using Anyscale benefit from rapid time-to-market and faster iterations across the entire AI lifecycle.
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Anyscale reposted this
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! 🚀
🚀🚀🚀 Second update on my winter optimization to the #RayData library (2.42 release): The groupby-map_groups operation now runs 30% faster, all thanks to the efficiency of #numpy. Ray Data excels in scaling up complex user-defined functions beyond what SQL can handle. If you are already leveraging multiprocessing, Spark's parallelize(), or navigating through 20+ mapping functions in Spark, give Ray Data a try. It's a game-changer for boosting developer experience and compute performance. #speedwins #opensourcesoftware
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
Anyscale reposted this
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!
We are looking for a senior manager to lead our Ray as a Service team. Ray is a key technology in our stack to enable a unified AI and Data ecosystem #ray #apple #ai #data #ml #opensource Reza Shiftehfar Robert Nishihara https://lnkd.in/g-FKnxDg
💙 💙 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 👇
Apache Hudi + Ray Data = 💙 We are excited to introduce the latest integration of Apache Hudi with Ray Data. Ray Data is a scalable data processing library for ML workloads, particularly suited for offline batch inference and preprocessing/ingesting data for ML training. With this new integration, users can now run ML workflows with Ray Data, using datasets directly from open #lakehouse platforms such as Hudi. This enables users to take advantage of Hudi’s transactional capabilities and Ray Data’s streaming execution to efficiently process large datasets and build reliable and scalable ML pipelines. How to get started? import ray ds = ray.data.read_hudi(table_uri="/hudi/trips") Read all about in the documentation: https://lnkd.in/dg9C5sKT #dataengineering #softwareengineering
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
Anyscale reposted this
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
Huge thanks to everyone who joined us last night for an incredible evening with our friends at Arize AI! 🙌 Special shoutout to Robert Nishihara & Jason Lopatecki for sharing their insights on scaling AI systems & ML observability. Great conversations, great community! 🔥 #AI #GenAI #ML #Ray #ScalableAI