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Overview

Ray is an open-source AI compute engine designed to scale AI and Python applications. It provides a unified framework for parallel and distributed computing, enabling developers to build and deploy applications that can seamlessly scale from a laptop to a large cluster. Ray supports a variety of AI workloads, including model training, model serving, reinforcement learning, and data processing. It offers Python-native APIs and integrates with popular ML frameworks like PyTorch, TensorFlow, and XGBoost. Ray's architecture includes a core distributed scheduler, object store, and actor model. It supports fine-grained, independent scaling of heterogeneous compute resources, such as GPUs and CPUs, optimizing resource utilization and reducing costs. Ray facilitates end-to-end GenAI workflows, including multimodal models and RAG applications, ensuring efficiency and scalability across the entire AI lifecycle.

Common tasks

Distributed Training Model Serving Reinforcement Learning Data Processing LLM Inference LLM Fine Tuning

FAQ

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What is Ray?

Ray is an open-source framework for building distributed applications. It provides a simple, universal API for building applications that scale from a single machine to large clusters.

What are the key components of Ray?

The key components of Ray include the Ray Core, Ray Libraries (Ray Data, Ray Train, Ray Serve, Ray RLlib), and Ray Clusters.

What types of applications can I build with Ray?

Ray can be used to build a wide range of applications, including machine learning, reinforcement learning, data processing, and general-purpose distributed computing.

How does Ray scale?

Ray scales by distributing tasks and data across a cluster of machines. It uses a distributed scheduler and object store to manage resources and data.

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