// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Ray
An open-source framework that simplifies building and running distributed applications, especially for AI and machine learning workloads.
TECHNICAL DEFINITION
Ray is an open-source distributed computing framework that provides a simple API for parallelizing Python applications and building distributed systems, widely used for scaling AI workloads like reinforcement learning, hyperparameter tuning, and distributed training across clusters.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Ray Project
- Distributed Python
- AI Scaling Framework
- Distributed Computing
USAGE NOTE
Essential for scaling complex AI computations and ML workflows across multiple machines.
DEVELOPERS
Organizations developing technology related to Ray.
The company founded by the creators of Ray, offering a managed platform for scaling AI and Python applications using the Ray framework.
Meta AI extensively uses and contributes to the Ray framework for large-scale deep learning research, model training, and production deployments.
Intel contributes to the Ray ecosystem, optimizing the framework for performance on Intel hardware and participating in the open-source community.
Microsoft Azure Machine Learning integrates with Ray, providing support for scalable machine learning workloads and contributing to the distributed AI ecosystem.
Google Cloud provides infrastructure and services that support running Ray workloads, and its AI teams may leverage or contribute to Ray integrations.
Amazon Web Services (AWS) supports running Ray on its cloud infrastructure, including services like Amazon EKS and SageMaker, contributing to its broad adoption.
The academic origin of the Ray project, researchers at UC Berkeley continue to advance the state of the art in distributed systems for AI.