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

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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