---
title: "Ray: AI Compute Engine"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Ray: AI Compute Engine

[Ray](https://www.ray.io/) is an open-source distributed computing framework designed to simplify the development and scaling of machine learning (ML) and Python applications. It provides a unified platform for executing a wide range of tasks, from data processing to model training and serving, across multiple nodes and hardware accelerators.

Key Features and Components:
	1.	Ray Core:
	•	Tasks and Actors: Ray introduces two primary abstractions: tasks (stateless functions executed in parallel) and actors (stateful workers). These abstractions enable developers to parallelize and distribute Python code efficiently. (GitHub)
	2.	Ray AI Libraries:
	•	Ray Data: Facilitates scalable data loading and transformation, supporting various stages such as training, tuning, and prediction.
	•	Ray Train: Enables distributed model training across multiple nodes and cores, incorporating fault tolerance mechanisms that integrate seamlessly with popular training libraries.
	•	Ray Tune: Provides scalable hyperparameter tuning to optimize model performance.
	•	Ray Serve: Offers scalable and programmable model serving for online inference, with optional micro-batching to enhance performance.
	•	Ray RLlib: Supports scalable distributed reinforcement learning workloads. (Ray Documentation)
	3.	Scalability and Flexibility:
	•	Ray is designed to scale applications from a single machine to large clusters, managing resources such as CPUs, GPUs, and TPUs. It supports dynamic scaling, allowing applications to adapt to varying workloads efficiently. (Ray)
	4.	Integration with Existing Ecosystems:
	•	Ray integrates seamlessly with popular ML frameworks like PyTorch and TensorFlow, as well as MLOps tools such as MLflow and Weights & Biases. This compatibility ensures that developers can incorporate Ray into their existing workflows without significant modifications. (GitHub)
	5.	Deployment Options:
	•	Ray can be deployed on various platforms, including local machines, on-premises clusters, and cloud environments. It also supports deployment on Kubernetes through KubeRay, combining Kubernetes’ orchestration capabilities with Ray’s optimized features for ML workloads. (Ray Documentation)

Use Cases:
	•	Distributed Training: Ray enables efficient distribution of training workloads across multiple nodes and GPUs, facilitating the fine-tuning of large models that may not fit into the memory of a single device. (GitHub)
	•	Hyperparameter Tuning: With Ray Tune, users can perform scalable hyperparameter optimization, leading to improved model performance. (Ray Documentation)
	•	Model Serving: Ray Serve allows for scalable and programmable model serving, handling high-throughput, low-latency inference workloads effectively. (Ray Documentation)
	•	Reinforcement Learning: Ray RLlib provides tools for scalable reinforcement learning, supporting complex training scenarios and environments. (Ray Documentation)

Community and Support:

Ray boasts an active open-source community, offering extensive documentation, tutorials, and examples to assist users in leveraging its capabilities. Regular updates and contributions from the community ensure that Ray continues to evolve, incorporating the latest advancements in distributed computing and machine learning. (GitHub)

In summary, Ray is a versatile and powerful framework that simplifies the development and scaling of distributed applications, particularly in the realm of machine learning. Its comprehensive suite of tools and seamless integration with existing ecosystems make it a valuable asset for developers and data scientists aiming to build scalable and efficient applications.

## History
Ray is an open-source distributed computing framework developed to address the challenges of scaling machine learning (ML) and artificial intelligence (AI) applications. Its development has been marked by several key milestones:

2017: Inception and Initial Release

Ray was introduced by researchers from the University of California, Berkeley’s RISELab in December 2017. The foundational paper, “Ray: A Distributed Framework for Emerging AI Applications,” outlined Ray’s architecture and its ability to handle dynamic task graphs and actor-based computations. (ArXiv)

2018: Performance Enhancements and Community Adoption

In 2018, Ray’s architecture was refined to improve scalability and fault tolerance. The system employed a distributed scheduler and a fault-tolerant store to manage control state, achieving a throughput of over 1.8 million tasks per second. (ArXiv) This performance attracted attention from the ML and AI communities, leading to increased adoption and contributions.

2019-2020: Expansion of Ecosystem and Industry Adoption

During this period, Ray’s ecosystem expanded with the introduction of libraries tailored for specific ML tasks:
	•	Ray Tune: For scalable hyperparameter tuning.
	•	Ray Serve: For model serving.
	•	Ray RLlib: For reinforcement learning.

These additions enhanced Ray’s versatility, making it a comprehensive platform for various ML workloads. Companies like OpenAI and Ant Group began leveraging Ray for large-scale AI applications. (Ray)

2021-Present: Continued Growth and Integration

Ray’s development has continued with a focus on improving usability, performance, and integration with other tools. The community has grown, contributing to a rich ecosystem of integrations and applications. Ray now supports deployment on various platforms, including Kubernetes, and integrates with popular ML frameworks like PyTorch and TensorFlow. (Ray Documentation)

In summary, Ray has evolved from a research project into a robust, widely-adopted framework for distributed computing in ML and AI, continually adapting to meet the needs of modern AI applications.

## Recent Publications

There are recent publications that provide comprehensive insights into Ray, the open-source distributed computing framework:
	1.	“[Learning Ray: Flexible Distributed Python for Machine Learning](https://www.amazon.com/Learning-Ray-Flexible-Distributed-Machine/dp/1098117220/ref=sr_1_1)” by Max Pumperla, Edward Oakes, and Richard Liaw (Published March 2023)
This book offers a practical guide for Python programmers, data engineers, and data scientists to leverage Ray for scaling compute-intensive Python workloads. It covers building machine learning applications with Ray, including distributed training, hyperparameter tuning, and model serving. (O’Reilly Media)
	2.	“[Scaling Python with Ray](https://www.amazon.com/Scaling-Python-Ray-Adventures-Serverless/dp/1098118804/ref=sr_1_2)” by Holden Karau and Boris Lublinsky (Published August 2022)
This publication delves into scaling Python applications using Ray, focusing on distributed computing concepts and practical implementations. It explores Ray’s core components, including its scheduler, distributed data storage, and actor system, providing insights into building scalable and resilient applications. (O’Reilly Media)

These resources are valuable for understanding and effectively utilizing Ray in distributed computing and machine learning projects.
