Fast and Scalable Model Training with PyTorch and Ray

Organizations are making substantial investments in GenAI and LLMs, and Anyscale is at the forefront of this innovation. Our Virtual AI Tutorial Series introduces core concepts of modern AI applications, emphasizing large-scale computing, cost-effectiveness, and ML models.

In this episode, we focus on distributed model training with PyTorch and Ray. You'll learn how to migrate your code from pure PyTorch to Ray Train and Ray Data, enabling scalable and efficient AI workflows.

Join this session to learn about:

  • How to migrate your code from PyTorch ecosystem libraries to Ray Train to enable large scale model training or fine-tuning

  • Review reference implementations for common PyTorch+Ray scenarios

  • Common performance and cost efficiency optimizations for distributed model training on Anyscale

Who Should Attend?

This technical webinar is ideal for AI Engineers looking to operationalize generative AI models at scale. Infrastructure Engineers aiming to support GenAI use cases in their organizations will also benefit greatly.

Don't miss this opportunity to gain deep technical insights and practical knowledge from industry experts.

Ready to try Anyscale?

Access Anyscale today to see how companies using Anyscale and Ray benefit from rapid time-to-market and faster iterations across the entire AI lifecycle.