Ray Meetup

Ray Meetup is Back in the New Year 2022

Thursday, January 20, 2:00AM UTC

Welcome to our first Ray meetup in the New Year 2022, after two years of absence due to the Covid-19 pandemic. We are super excited to be back in a hybrid-format meetup: an in-person (for the Bay Area Ray community) and online (for the global community).

If you are attending in person, you will be required to show proof of vaccination and wear a mask in the common Meetup area. Please RSVP and indicate whether you are planning to attend online or in person.

We’ll see you there!

RSVP for Ray Meetup >>>


6:00 PM-6:05 PM: Kickoff Welcome remarks & agenda by Jules Damji
6:05 PM - 6:15 PM: “The year 2021 in Review of Ray” by Robert Nishihara
6:15 - 6:40 PM: “What’s New in Ray 1.9 and Beyond” by Zhe Zhang
6:45 - 7:15 PM: “Unifying Preprocessing and Training at Scale with Ray Datasets" by Alex Wu and Clark Zinzow
7:25 PM - 7:50 PM: Ray Community Talk by TBD

Talk 1: The 2021 Year in Review of Ray
We will reflect back on Ray’s major milestones, Ray’s ecosystems of ML native and integrated libraries, and community growth and contributions.
Bio: Robert Nishihara is the co-creator of Ray and CEO and co-founder of Anyscale, the company behind Ray.

Talk 2: What’s New in Ray 1.9 and Beyond
We share what’s new in Ray, what's coming in the near future and roadmap, and get involved & contribute
Bio: Zhe Zhang leads and spearheads the Ray OSS project at Anyscale.
Talk 3: Unifying Preprocessing and Training at Scale with Ray Datasets
ML tasks such as distributed training and batch inference stretch the abstractions of modern data processing systems. In this talk, we’ll discuss the wide-ranging problems that the Python community faces when building large-scale preprocessing and training pipelines. Some of these problems are caused by the complexity of stitching together distributed systems that weren’t designed to be compatible. For example, creating a pipeline with Dask and Horovod that can efficiently use the CPUs and GPUs in a cluster. Other problems -- like per-epoch dataset shuffling, show a gap between what operations ML practitioners want and what data processing libraries are capable of doing efficiently. We’ll also introduce Ray Datasets, a simple, scalable, and pythonic way of solving these problems.
Bio(s): Alex Wu and Clark Zinzow are both software engineers at Anyscale, working in the Ray Core and cloud computing infrastructure teams.

COVID-19 safety measures
- Masks requiredCOVID 19 vaccination requiredEvent will be indoors
- Health regulations for COVID-19 require you to provide legitimate proof of vaccinations and wear masks at the venue.
- Safety measures instituted by the event host. Meetup is not responsible for ensuring that precautions are followed.