AI has a problem – it is difficult to scale applications without a complex distributed architecture. Companies have to hire specialized engineers to build this architecture, linking things like AWS or Azure cloud instances with Spark and distribution management tools like Kubernetes.
Scaling even a simple application across a cluster involves thousands of lines of code, must define communication protocols, message serialization and deserialization strategies, and various data handling strategies.
One of Ray’s goals is to enable practitioners to turn a prototype algorithm that runs on a laptop into a high-performance distributed application that runs efficiently on a cluster (or on a single multi-core machine) with relatively few additional lines of code. Such a framework should include the performance benefits of a hand-optimized system without requiring the user to reason about scheduling, data transfers, and machine failures.
Ray uses an actor abstraction to encapsulate mutable state shared between multiple tasks – something very useful in machine learning.
“Intel IT has been leveraging Ray to scale Python workloads with minimal code modifications,” said Moty Fania, Principal Engineer and Chief Technology Officer for Intel IT’s Enterprise and Platform Group. “With the implementation into Intel’s manufacturing and testing processes, we have found that Ray helps increase the speed and scale of our hyperparameter selection techniques and auto modeling processes used for creating personalized chip tests. For us, this has resulted in reduced costs, additional capacity and improved quality.”
“As the adoption of Ray has grown, we’ve seen it become the open-source project of choice for scaling complex distributed applications from a laptop to a datacenter. As distributed computing continues to grow, the natural next step is to bring Ray to more organizations that can benefit from its capabilities,” said Robert Nishihara, co-founder and CEO, Anyscale. “Our mission is to help more developers, enterprises and organizations solve their problems without having to worry about scalable infrastructure and without needing to be experts in distributed computing. With this investment, we’ll fortify our ability to continuously improve Ray and grow our team to make this mission a reality.”
“Ray is one of the fastest-growing open-source projects we’ve ever tracked, and it’s being used in production at some of the largest and most sophisticated companies,” said Ben Horowitz, cofounder and general partner, Andreessen Horowitz. “Its massive popularity is both a testament to the importance of the problem it is tackling and how well the team behind it has executed on building a product that works and does what it claims. We look forward to working with Robert, Philipp, and Ion to bringing Anyscale to users around the world.”
In case this approach seems a lot like serverless to you, Ion Stoica, a professor of computer science at the University of California, Berkeley and company cofounder has this to say, “These serverless platforms are notoriously bad at supporting scalable AI. We are excelling in that aspect.”
One of our definitions of the future of work is the democratization of AI – in this case, AI is being made more accessible to programmers so we believe the definition holds.
The financial model for the company is similar to Red Hat and how they interact with Linux.
Similarly – the opportunity is huge as AI will impact all organizations – making them more productive and efficient.
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