Ray: A Framework for Scaling and Distributing Python & ML Applications
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