# Ray: full documentation

> Ray is an open-source unified compute framework for scaling AI and Python workloads, including data processing, model training, model serving, hyperparameter tuning, and reinforcement learning. The full documentation lives at https://docs.ray.io/.

Full page text grouped by section, one file each (large sections are split into sub-shards, nested below). Each entry gives the section, its page count, and what it covers — fetch only what you need:

- [APIs](https://docs.ray.io/en/latest/apis/llms-full.txt) (1 pages): Entry point for Ray's API reference, with per-library API documentation for Core, Data, Train, Tune, Serve, and RLlib.
- [Ray Clusters](https://docs.ray.io/en/latest/cluster/llms-full.txt) (8 pages): Deploy and scale Ray clusters from a laptop to the cloud, with native support for Kubernetes (KubeRay), AWS, GCP, and Azure VMs, plus autoscaling.
  - [Ray on Kubernetes](https://docs.ray.io/en/latest/cluster/kubernetes/llms-full.txt) (84 pages): Deploy and manage Ray clusters on Kubernetes with the KubeRay operator: RayCluster, RayJob, and RayService CRDs, autoscaling, and GPU support.
  - [Application guide](https://docs.ray.io/en/latest/cluster/running-applications/llms-full.txt) (9 pages): Run Ray applications on a cluster using the Ray Jobs API and CLI, and reference autoscaling configuration for production workloads.
  - [Ray on Cloud VMs](https://docs.ray.io/en/latest/cluster/vms/llms-full.txt) (22 pages): Launch and manage Ray clusters on cloud VMs across AWS, GCP, and Azure, with autoscaling and heterogeneous compute.
- [Ray Data](https://docs.ray.io/en/latest/data/llms-full.txt) (38 pages): Scalable data processing for AI workloads — a streaming engine for batch inference, preprocessing, and ML training ingest across CPUs and GPUs.
- [Ray for ML Infrastructure](https://docs.ray.io/en/latest/ray-air/llms-full.txt) (1 pages): Overview of using Ray as ML infrastructure, covering how Ray Train, Ray Data, and Ray Serve compose end-to-end ML workflows on a unified compute runtime.
- [Developer Guides](https://docs.ray.io/en/latest/ray-contribute/llms-full.txt) (18 pages): Entry point to Ray's developer and contributor guides, linking to API stability guarantees, API policy, the contribution workflow, configuration, and architecture whitepapers. Start here to navigate the documentation for developing and contributing to Ray itself.
- [Ray Core](https://docs.ray.io/en/latest/ray-core/llms-full.txt) (91 pages): Ray's distributed computing primitives — tasks, actors, and objects — with examples for turning Python functions and classes into distributed apps.
- [Project Governance](https://docs.ray.io/en/latest/ray-governance/llms-full.txt) (2 pages): Ray's technical governance: the roles of contributors, committers, and the Technical Steering Committee, and how project decisions are made.
- [More Libraries](https://docs.ray.io/en/latest/ray-more-libs/llms-full.txt) (18 pages): Additional Ray integrations: Joblib, multiprocessing, Ray Collective, Dask-on-Ray, RayDP (Spark), Mars-on-Ray, and Modin.
- [Monitoring and Debugging](https://docs.ray.io/en/latest/ray-observability/llms-full.txt) (23 pages): Monitor and debug Ray applications and clusters with logs, metrics, events, dashboards, and the distributed debugger.
- [Overview](https://docs.ray.io/en/latest/ray-overview/llms-full.txt) (17 pages): Introduction to Ray, the open-source framework for scaling AI and Python workloads — its core primitives, AI libraries, and cluster integrations.
- [Glossary](https://docs.ray.io/en/latest/ray-references/llms-full.txt) (2 pages): Alphabetical definitions of key Ray terminology, from tasks, actors, and objects to reinforcement learning terms.
- [Security](https://docs.ray.io/en/latest/ray-security/llms-full.txt) (2 pages): Security model and best practices for Ray, covering the Dashboard/Jobs/Client trust model, gRPC communication, and token authentication.
- [Ray RLlib](https://docs.ray.io/en/latest/rllib/llms-full.txt) (27 pages): Industry-grade, scalable reinforcement learning with a unified API for single-agent, multi-agent, and offline RL training and deployment.
- [Ray Serve](https://docs.ray.io/en/latest/serve/llms-full.txt) (16 pages): Scalable, framework-agnostic model serving for online inference APIs, with LLM serving, model composition, response streaming, and request batching.
  - [Advanced Guides](https://docs.ray.io/en/latest/serve/advanced-guides/llms-full.txt) (18 pages): Advanced Ray Serve guides: autoscaling tuning, asyncio, performance, request batching, gRPC, replica and gang scheduling, and custom routing.
  - [Ray Serve API](https://docs.ray.io/en/latest/serve/api/llms-full.txt) (1 pages): Ray Serve API reference: the Python API for writing and running applications, configs and schemas, request router, CLI, and REST API.
  - [Serving LLMs](https://docs.ray.io/en/latest/serve/llm/llms-full.txt) (29 pages): Deploy LLMs with Ray Serve LLM: OpenAI-compatible API, multi-model serving, tensor/pipeline parallelism, LoRA, and vLLM/SGLang backends.
  - [Production Guide](https://docs.ray.io/en/latest/serve/production-guide/llms-full.txt) (7 pages): Run Ray Serve in production on Kubernetes via KubeRay RayService or directly on a Ray cluster, covering config files, health checking, and failure recovery.
  - [Serve an Inference Model on AWS NeuronCores Using FastAPI (Experimental)](https://docs.ray.io/en/latest/serve/tutorials/llms-full.txt) (24 pages): Experimental tutorial serving an inference model on AWS NeuronCores with Ray Serve and FastAPI.
- [Ray Train](https://docs.ray.io/en/latest/train/llms-full.txt) (46 pages): Distributed model training and fine-tuning at scale across PyTorch, Lightning, Hugging Face Transformers, XGBoost, and JAX.
- [Ray Tune](https://docs.ray.io/en/latest/tune/llms-full.txt) (29 pages): Hyperparameter tuning and experiment execution at any scale, with algorithms like PBT and ASHA and integrations for PyTorch, XGBoost, and Optuna.