Ray is an open-source distributed framework that makes it easy to scale applications and to leverage machine learning libraries. The project was developed by the distributed programming platform company Anyscale.
Ray includes three libraries for accelerating machine learning workloads: Tune, RLlib and Distributed Training. According to the company, the machine learning libraries give developers the ability to include hyperparameter search, reinforcement learning, training and serving.
It also offers the ability to scale anywhere since users can run the same code on their laptop, on a powerful multi-core machine, on any cloud provider, or on a Kubernetes cluster, the team explained.
“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,” said Robert Nishihara, co-founder and CEO of Anyscale.
With the power of Ray, Anyscale has been able to simplify distributed programming, according to the founders. Applications built with Ray can be scaled out from a laptop to a cluster, eliminating the need for in-house distributed computing expertise and resources.
“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.”
The founders of the open-source project also announced a $20.6 million round of funding for Anyscale. The series A funding was led by Adreessen Horowitz with participation from NEA, Intel Capital, Ant Financial, Amplify Patners, 11.2 Capital and The House Fund. The company plans to use the funding to expand its leadership team and contribution to the open-source community.
“With this investment, we’ll fortify our ability to continuously improve Ray and grow our team to make this mission a reality,” said Nishihara.
