Scaling 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 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’s native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem to scale your Python or ML workloads.
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
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[@LynnLangit]

exciting work - can't wait to try this out with my customers

[@duongkstn]

great ! thanks for sharing

[@normalchannel4747]

Genius