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 U.C. 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 cover Ray’s overview, architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem.

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

The takeaways from this talk are :

  • Learn Ray architecture, core concepts, and Ray primitives and patterns
  • Why Distributed computing will be the norm not an exception
  • 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 and Ray Tune
    • Inferencing at scale, using XGBoost with/without Ray

Jules Damji

Lead Developer Advocate | Anyscale

Jules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow contributor, and co-author of Learning Spark, 2nd Edition. He is a hands-on developer with over 25 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).

Jules Damji