Emmy Li
Emmy is a technical trainer at Anyscale Inc. She holds a B.Sc in Physics from Stanford University where she contributed toward computational astrophysics research at the Stanford Linear Accelerator Laboratory and NASA’s Jet Propulsion Laboratory. Emmy is passionate about creating high quality educational materials and sharing them with the broader Ray community.
Sessions
Machine learning (ML) pipelines involve a variety of computationally intensive stages. As state-of-the-art models and systems demand more compute, there is an urgent need for adaptable tools to scale ML workloads. This idea drove the creation of Ray—an open source, distributed ML compute framework that not only powers systems like ChatGPT but also pushes theoretical computing benchmarks. Ray AIR is especially useful for parallelizing ML workloads such as pre-processing images, model training and finetuning, and batch inference. In this tutorial, participants will learn about AIR’s composable APIs through hands-on coding exercises.