Ray Community Talks

It’s time for our May meetup, a monthly series, where we get together to discuss Ray and Ray’s native libraries for scaling machine learning workloads. This month, we have invited Ray community speakers to share how they use Ray to solve challenging ML problems.

Join us if you are interested in hearing from the community of Ray users.

# Agenda

(The times are not strict; they will vary slightly.)

• 6:00 PM Welcome remarks, announcements & agenda by Jules Damji, Anyscale
• 6:05 PM Talk 1: Deep Learning for protein engineering with Ray, Stanley Bishop DeepChem.io
• 6:35 PM Q&A
• 6:40 PM Talk 2: Introduction to KubeRay, Dmitri Gekhtman Anyscale & Jiaxin Shan, ByteDance
• 7:20 PM Q&A
• 7:25 PM Talk 3: AutoML with PyTorch and Ray, Aniket Maurya, Grid.ai
• 7:50 PM Q & A

Talk 1: Deep Learning for protein engineering with Ray
Abstract:
We will discuss Ray as an active learning orchestrator for protein engineering in the drug/medicine discovery process.
In particular, we will look at the deployment of systems that involve active-learning feedback between sequence-to-sequence transformers, Alphafold driven sequence-to-structure prediction, and, more broadly, how these two deep learning methods are revolutionizing the field.
Bio:
Stanley Bishop is an ML-nerd contributor to the open source-project [DeepChem.io](https://deepchem.io/), which works to democratize deep learning for science.

Talk 2: Introduction to KubeRay
Abstract:
In this introductory session, we will introduce the KubeRay, which is a Ray cluster management tool on top of Kubernetes.
We will talk about the motivation behind KubeRay, the difference between ray-operator in the Ray core, recent v0.2.0 features, and future updates.

Bios:
Dmitri Gekhtman is a software engineer on the Infrastructure team at Anyscale. His areas of focus include autoscaling and the integration of Ray into Kubernetes environments.

Jiaxin Shan is a software engineer focusing on serverless infrastructure and cloud-native adoption at Bytedance.

Talk 3: AutoML with PyTorch and Ray
Abstract:
Gradsflow is an open-source AutoML Library based on PyTorch. It provides automatic model building and training for various Deep Learning tasks like Image Classification and Text Classification. Furthermore, it leverages Ray for Hyperparameter tuning and scaling the training on a laptop or a cluster of machines.
In this talk, we will look at the internals of Gradsflow with Ray and how to tune hyperparameters of your model quickly.

Bio: Aniket is a Machine Learning-Software Engineer and currently a Developer Advocate at Grid.ai.