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.
