Richard Liaw

Team Lead, Anyscale

Richard Liaw is currently a team lead at Anyscale, where he leads a team working on training libraries and integrations on top of Ray. He was previously a PhD candidate in the UC Berkeley RISELab working with Ion Stoica and Joseph Gonzalez, where he focused on problems in distributed deep learning, reinforcement learning, and ML cluster scheduling.

Past sessions

Summit Europe 2020 Ray and Its Growing Ecosystem

November 17, 2020 04:00 PM PT

Ray (https://github.com/ray-project/ray) is a framework developed at UC Berkeley and maintained by Anyscale for building distributed AI applications. Over the last year, the broader machine learning ecosystem has been rapidly adopting Ray as the primary framework for distributed execution. In this talk, we will overview how libraries such as Horovod (https://horovod.ai/), XGBoost, and Hugging Face Transformers, have integrated with Ray. We will then showcase how Uber leverages Ray and these ecosystem integrations to simplify critical production workloads at Uber. This is a joint talk between Anyscale and Uber.

Speakers: Travis Addair and Richard Liaw

Reinforcement learning (RL) algorithms involve the deep nesting of distinct components, where each component typically exhibits opportunities for distributed computation. Current RL libraries offer parallelism at the level of the entire program, coupling all the components together and making existing implementations difficult to extend, combine, and reuse.

We argue for building composable RL components by encapsulating parallelism and resource requirements within individual components, which can be achieved by building on top of a flexible task-based programming model. We demonstrate this principle by building Ray RLLib on top of the the Ray distributed execution engine and show that we can implement a wide range of state-of-the-art algorithms by composing and reusing a handful of standard components. This composability does not come at the cost of performance --- in our experiments, RLLib matches or exceeds the performance of highly optimized reference implementations.

Session hashtag: #AI3SAIS

Richard Liaw