{
  "slug": "ray",
  "name": "Ray",
  "aliases": ["Ray Core", "Ray Serve", "Ray Data", "Ray Train", "Ray Tune", "RLlib"],
  "vendor": { "name": "Ray project (Anyscale and contributors)", "slug": "ray-project", "website": "https://www.ray.io/" },
  "homepage_url": "https://www.ray.io/",
  "docs_url": "https://docs.ray.io/en/latest/",
  "repo_url": "https://github.com/ray-project/ray",
  "license_url": "https://github.com/ray-project/ray/blob/master/LICENSE",
  "product_type": "framework",
  "product_status": "active",
  "summary": "Open-source Python framework for distributing tasks, actors and ML workloads across a cluster, with libraries for data processing, training, tuning, serving and reinforcement learning",
  "description": "Ray is an open-source distributed computing framework for Python, created at UC Berkeley's RISELab and now developed by Anyscale and a large contributor community under the Apache 2.0 license. Ray Core turns ordinary functions and classes into remote tasks and stateful actors with a decorator, schedules them across the cores and machines of a cluster, and passes results through a shared object store. On top of that sit domain libraries: Ray Data for parallel data loading and batch inference, Ray Train for distributed PyTorch or other training, Ray Tune for hyperparameter search, Ray Serve for deploying model endpoints, and RLlib for reinforcement learning.\n\nA cluster is a head node plus workers started with the `ray` CLI, the built-in cluster launcher for AWS, GCP and Azure, or the KubeRay operator on Kubernetes; Anyscale sells a managed version. Ray runs on Linux (x86_64 and ARM) and on Apple Silicon Macs, with Windows support in beta. Recent releases enable token authentication by default for clusters.\n\nTypical users are ML engineers scaling a training or inference pipeline past one machine, data teams running embarrassingly parallel Python jobs, and platform teams serving many models behind one API. On a cluster of Macs it is one of the few general-purpose options for fanning Python work out across nodes, although Ray documents multi-node Apple Silicon clusters as untested.\n\nLimits: it is Python-centric (Java and C++ APIs are experimental), carries operational overhead compared with a job scheduler for simple batch jobs, and its object store and scheduler add complexity that only pays off beyond a single machine.",
  "capabilities": ["Remote tasks and actors from plain Python functions and classes", "Distributed object store for passing results between workers", "Ray Data for parallel loading and batch inference", "Ray Train and Ray Tune for distributed training and hyperparameter search", "Ray Serve for scalable model-serving endpoints", "Cluster launchers for cloud providers and KubeRay for Kubernetes", "Dashboard for jobs, actors and resources"],
  "workflows": ["Parallelize a Python simulation across all cores and nodes", "Run batch LLM inference over a large dataset", "Serve several models behind one HTTP endpoint with autoscaling", "Launch a hyperparameter sweep on a cluster"],
  "advantages": ["One API from laptop to cluster", "Rich ML-specific libraries on a general core", "Apache 2.0 license and large community", "Managed option available through Anyscale"],
  "limitations": ["Python-centric; other language APIs are experimental", "Operational overhead for simple batch workloads", "Multi-node clusters on Apple Silicon and Windows are untested or beta", "Memory pressure from the object store needs tuning"],
  "prerequisites": ["Python 3.10 or newer", "Network access between nodes for a multi-machine cluster"],
  "audiences": ["ML engineers scaling training and inference", "Data scientists parallelizing Python jobs", "Platform teams serving models"],
  "alternatives": ["dask", "apache-spark", "slurm", "open-mpi"],
  "comparison_notes": "Dask is the closest relative: also Python-native and task-based, but centred on parallel DataFrame and array collections rather than actors and ML serving. Apache Spark is a JVM-based data-processing engine with a SQL/DataFrame focus; Ray targets general Python and ML workloads and the two are often combined. Slurm is a batch scheduler that allocates nodes to jobs without a programming model; Open MPI is a message-passing library for tightly coupled HPC codes. Ray sits between them as a runtime with its own scheduler and higher-level libraries.",
  "categories": [{ "slug": "distributed-computing", "primary": true }, { "slug": "model-training" }, { "slug": "local-inference" }],
  "tags": ["python", "distributed-computing", "open-source", "llm", "kubernetes", "model-serving"],
  "use_cases": ["serve-models", "fine-tune-models", "distribute-computation"],
  "operating_systems": ["linux", "macos", "windows"],
  "execution_modes": ["local", "self-hosted", "cloud"],
  "interfaces": ["library", "cli", "api", "web"],
  "deployment_models": ["on-premises", "managed-cloud", "hybrid"],
  "integrations": ["PyTorch", "Hugging Face Transformers", "vLLM", "Kubernetes (KubeRay)", "AWS", "Google Cloud", "Azure", "Anyscale"],
  "data_formats": ["Parquet", "CSV", "JSON", "Arrow", "NumPy"],
  "languages": ["Python", "Java (experimental)", "C++ (experimental)"],
  "source_model": "open-source",
  "license_spdx": "Apache-2.0",
  "pricing_model": ["open-source", "free"],
  "self_hostable": true,
  "sources": [
    { "url": "https://www.ray.io/", "type": "official_site", "title": "Ray", "supports": ["name", "summary"], "checked_at": "2026-11-02" },
    { "url": "https://docs.ray.io/en/latest/", "type": "official_docs", "title": "Ray documentation", "supports": ["capabilities", "integrations", "description"], "checked_at": "2026-11-02" },
    { "url": "https://docs.ray.io/en/latest/ray-overview/installation.html", "type": "official_docs", "title": "Installing Ray", "supports": ["operating_systems", "prerequisites", "limitations"], "checked_at": "2026-11-02" },
    { "url": "https://github.com/ray-project/ray/blob/master/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }
  ],
  "confidence": "high",
  "discovery_method": "editorial-research",
  "depth": "deep",
  "editorial_status": "reviewed",
  "visibility": "public",
  "featured": false,
  "author": "batch-C"
}