Ray
Ray is an open-source Python-native framework designed to scale AI, machine learning, and Python applications across distributed infrastructure ranging from laptops to thousands of nodes. It supports end-to-end workflows including data processing, model training, fine-tuning, and inference for workloads such as simulations, multimodal data processing, generative AI, and large language model serving. Ray provides a core distributed runtime alongside high-level libraries to orchestrate compute on any accelerator, with tools for cluster deployment, debugging, optimization, and integration with popular frameworks. Ray is used by organizations like OpenAI to power large-scale AI models including ChatGPT, enabling faster iteration and flexible scaling without requiring code rewrites. The framework offers workload observability, profiling tools for distributed debugging, and fault-tolerant cluster deployment with features like auto-scaling, spot instance management, and cost governance. Ray is open-source with a large active community, reflected in its GitHub repository with over 41,000 stars and more than 1,000 contributors.
Ray is an open-source Python framework for scaling AI and machine learning applications across distributed infrastructure.
Distributed AI Model Training
Scaling machine learning model training across multiple nodes and accelerators to reduce training time.
Multimodal Data Processing
Processing and analyzing large datasets containing images, videos, and audio in parallel.
Large Language Model Serving
Deploying and fine-tuning large language models for inference in production environments.
pip install ray.ray start --head or use the Anyscale platform for managed clusters.@ray.remote decorator to define tasks and actors for distributed execution.