# Ray

Ray is an open-source distributed computing framework designed to scale AI and Python workloads, enabling parallel execution across clusters for machine learning and large-scale applications.

Ray is an open-source distributed computing framework designed to scale artificial intelligence and Python workloads. It provides a unified programming model for building and running distributed applications, from data processing to model training and serving. Ray was initially developed at the [Berkeley AI Research](https://www.wikiprompt.org/wiki/berkeley-ai-research) lab and the [University of Toronto](https://www.wikiprompt.org/wiki/university-of-toronto), with its first public release in 2018. It has become a foundational tool in the [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) ecosystem, used by organizations such as [openai](https://www.wikiprompt.org/wiki/openai), [anthropic](https://www.wikiprompt.org/wiki/anthropic), and [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services) to manage complex, parallel tasks.

The framework simplifies distributed computing by abstracting away low-level details like cluster management and task scheduling. Developers write code that looks sequential but executes in parallel across a cluster of machines. Ray supports a range of libraries built on top of its core, including Ray Tune for hyperparameter tuning, Ray Serve for model serving, and Ray Data for distributed data processing. Its design emphasizes fault tolerance, dynamic task scheduling, and efficient memory sharing, making it suitable for both research and production environments.

## Architecture and Core Components

Ray's architecture consists of several key components. The **Ray Core** provides the fundamental primitives: tasks, actors, and objects. Tasks are stateless functions executed remotely, while actors are stateful worker processes that can maintain state across calls. Objects are immutable values stored in a distributed object store, enabling efficient data sharing between tasks. The **Raylet** is the node-level component that manages resources, schedules tasks, and coordinates with other nodes. The **Global Control Store** (GCS) maintains cluster metadata and serves as the central coordination point.

Ray also includes **Ray Clusters**, which can be launched on a single machine, on-premises, or on cloud platforms like [google-cloud](https://www.wikiprompt.org/wiki/google-cloud) and [azure](https://www.wikiprompt.org/wiki/azure). The framework integrates with popular cluster managers such as Kubernetes and YARN, allowing seamless scaling from a laptop to thousands of nodes. This flexibility has made Ray a popular choice for [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) workloads that require massive parallelism.

## Libraries and Ecosystem

Ray hosts a rich ecosystem of libraries tailored to different stages of the AI lifecycle. **Ray Tune** automates hyperparameter optimization, supporting distributed execution and integration with frameworks like [pytorch](https://www.wikiprompt.org/wiki/pytorch) and TensorFlow. **Ray Serve** enables scalable model serving, handling request routing, batching, and autoscaling. **Ray Data** provides a distributed data processing API, allowing users to load, transform, and feed large datasets into training pipelines. Additionally, **Ray RLlib** is a library for reinforcement learning, offering scalable algorithms and support for multi-agent environments.

These libraries are built on Ray Core, ensuring consistency and interoperability. For example, a user can preprocess data with Ray Data, train a model with a framework like [tensorflow](https://www.wikiprompt.org/wiki/tensorflow), tune hyperparameters with Ray Tune, and deploy the model with Ray Serve, all within the same cluster. This unified approach reduces operational overhead and accelerates development.

## Adoption and Industry Use

Ray has been widely adopted across industry and academia. Companies like [openai](https://www.wikiprompt.org/wiki/openai) and [anthropic](https://www.wikiprompt.org/wiki/anthropic) use Ray to train and serve [large language models](https://www.wikiprompt.org/wiki/large-language-model), leveraging its ability to handle distributed training across hundreds of GPUs. [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services) offers Amazon SageMaker Ray, a managed service that integrates Ray with its ML platform. [google-cloud](https://www.wikiprompt.org/wiki/google-cloud) and [azure](https://www.wikiprompt.org/wiki/azure) also provide support for Ray clusters, making it accessible to a broad range of users.

In addition to tech giants, Ray is used in finance, healthcare, and autonomous driving. For instance, [waymo](https://www.wikiprompt.org/wiki/waymo) has employed Ray for simulation and data processing, while [intuitive-surgical](https://www.wikiprompt.org/wiki/intuitive-surgical) uses it for medical imaging analysis. The framework's flexibility has also made it a staple in academic research, with publications from [mit-csail](https://www.wikiprompt.org/wiki/mit-csail), [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), and [carnegie-mellon-university](https://www.wikiprompt.org/wiki/carnegie-mellon-university) citing Ray in distributed systems and AI research.

## Performance and Scalability

Ray is designed for high performance and scalability. It uses a distributed scheduler that can handle millions of tasks per second, with low latency and high throughput. The object store uses shared memory and zero-copy serialization to minimize data transfer overhead. Ray also supports dynamic resource allocation, allowing tasks to request specific resources like GPUs or memory, and can scale down to zero when idle.

Benchmarks have shown that Ray can achieve near-linear scaling for many workloads. For example, training a [neural-network](https://www.wikiprompt.org/wiki/neural-network) model with Ray Tune can reduce hyperparameter search time by an order of magnitude compared to sequential execution. In production, Ray clusters have been scaled to thousands of nodes, as demonstrated by deployments at companies like uber and [netflix](https://www.wikiprompt.org/wiki/netflix).

## Community and Development

Ray is developed as an open-source project under the Apache 2.0 license, with a vibrant community of contributors. The project is hosted on GitHub, where it has received thousands of stars and contributions from developers worldwide. The core team, originally from the RISELab at UC Berkeley, continues to drive innovation, with regular releases adding new features and improvements. The community maintains extensive documentation, tutorials, and examples, making it accessible to newcomers.

The governance of Ray is overseen by the Ray Project, which includes a steering committee and technical advisory board. Major releases are announced on the project's blog, and the community holds annual conferences, such as Ray Summit, to share best practices and use cases. This active development ensures that Ray remains at the forefront of distributed computing for AI.

## Conclusion

Ray has established itself as a leading open-source framework for distributed computing in the AI domain. Its combination of simplicity, scalability, and a rich ecosystem makes it a go-to choice for researchers and engineers alike. As AI models grow in size and complexity, Ray's role in enabling efficient parallel computation is likely to expand, solidifying its position in the modern AI infrastructure stack.

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Source: https://www.wikiprompt.org/wiki/ray
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-09T01:55:33.026623+00:00