In today's dynamic machine learning landscape, Ray has emerged as an essential platform, powering demanding tasks like training ChatGPT at OpenAI and processing terabytes of data everyday at Amazon. This talk unveils Ray's pivotal role in addressing the exponential growth of modern ML workloads.
We will take a deep dive into Ray internal scalability, covering tasks, actors, objects and nodes, offering concrete examples to guide you in developing scalable code that maximizes Ray's potential.
Furthermore, we will explore the latest post-Ray 2.0 enhancements on health checks, resource broadcasting, and asynchronous actor creation. Join us on this exciting journey as we discuss the challenges and opportunities of buidling an unprecedented 4000-node cluster.
• Help the audience understand Ray's scalability and improvements after 2.0.
Yi Cheng is a software engineer at Anyscale and a committer for the Ray project. He is interested in building efficient and reliable computation systems. He recently focused on Ray's reliability and scalability.
Come connect with the global community of thinkers and disruptors who are building and deploying the next generation of AI and ML applications.