Ray Meetup

Ray Meetup @ SF Bay ACM: Scaling ML/AI workloads with the Ray Ecosystem

Tuesday, July 26, 2:00AM UTC

Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference with Ray — all from a single node to a cluster, with tangible improvements in performance.

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from UC Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It is now used in many production deployments.

This talk will give an overview of Ray and Ray's architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray's native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and dive into Ray’s growing ecosystem.

Key takeaways:

  • Learn Ray architecture, core concepts, and Ray primitives and patterns

  • Find out why distributed computing will be the norm, not an exception

  • See how to scale your ML workloads with Ray libraries:

    • Training on a single node vs. Ray cluster, using XGBoost with/without Ray

    • Hyperparameter search and tuning, using XGBoost with Ray Tune

    • Inferencing at scale, using XGBoost with/without Ray

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Jules Damji

Jules Damji

Lead developer advocate, Anyscale

Jules S. Damji is a lead developer advocate at Anyscale and an MLflow contributor. He is a hands-on developer with over 20+ years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/Loudcloud, VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science (from Oregon State University and Cal State, Chico respectively), and an MA in Political Advocacy and Communication (from Johns Hopkins University).