Learn Ray and Anyscale

Learn how to build scalable LLMs and AI apps with Ray and Anyscale.

Large Language Models

Intro to LLM Applications

Explore a micro illustration of a common LLM app pattern, using Python and OpenAI's GPT-3.5-Turbo model via API. Understand the key ideas behind tools like vector databases, Langchain/LlamaIndex, data extraction, and function calling.

LLM Endpoints Lab

Familiarize yourself with Anyscale Endpoints, a service similar to the OpenAI API but with open models.

LLM Endpoints Lab Solution

Look here for solutions to the endpoints lab exercises.

More on LLMs

Generative AI

Fine-tuning Stable Diffusion

Optimize your text-to-image model with Ray's distributed fine-tuning and gain insights from personalized diffusion techniques, as inspired by Dreambooth.

Serving Stable Diffusion

Discover how to effortlessly deploy your fine-tuned Stable Diffusion model using Ray Serve for scalable and production-grade serving.

Introduction to Ray AI Libraries

Quick End-to-end Example

Get introduced to the Ray AI Libraries in this overview. These libraries are built on top of Ray Core and provide a unified framework for machine learning tasks with a focus on performance and scalability.

Plain Hugging Face Image Classification

While this notebook focuses on using Hugging Face without Ray, it sets the foundation for subsequent notebooks, where Ray Train, Ray Data, and Ray Serve will be introduced for scalable machine learning. Explore the basics of fine-tuning ViTs for image classification, building a strong starting point for more advanced tasks in the following notebooks.

Adding Ray Train for Distributed Fine-tuning

Leverage the power of Ray Train for distributed machine learning training in this notebook focused on ViT image classification. Explore the capabilities of Ray Train, which enables distributed training and efficient resource utilization.