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    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

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Try Managed Ray

Site Navigation

  • Get Started

  • Use Cases

  • Example Gallery

  • Library

    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

  • Docs

  • Resources

    • Discussion ForumGet your Ray questions answered

    • TrainingHands-on learning

    • BlogUpdates, best practices, user-stories

    • EventsWebinars, meetups, office hours

    • Success StoriesReal-world workload examples

    • EcosystemLibraries integrated with Ray

    • CommunityConnect with us

Try Managed Ray
  • Overview
  • Getting Started
  • Installation
  • Use Cases
    • Ray for ML Infrastructure
  • Examples
    • Multi-modal AI pipeline
      • Batch inference
      • Distributed training
      • Online serving
    • LLM training and inference
    • Audio batch inference
    • Distributed XGBoost pipeline
      • Distributed training of an XGBoost model
      • Model validation using offline batch inference
      • Scalable online XGBoost inference with Ray Serve
    • Time-series forecasting
      • Distributed training of a DLinear time-series model
      • DLinear model validation using offline batch inference
      • Online serving for DLinear model using Ray Serve
    • Scalable video processing
      • Fine-tuning a face mask detection model with Faster R-CNN
      • Object detection batch inference on test dataset and metrics calculation
      • Video processing with object detection using batch inference
      • Host an object detection model as a service
    • Distributed RAG pipeline
      • Build a Regular RAG Document Ingestion Pipeline (No Ray required)
      • Scalable RAG Data Ingestion and Pagination with Ray Data
      • Deploy LLM with Ray Serve LLM
      • Build Basic RAG App
      • Improve RAG with Prompt Engineering
      • Evaluate RAG with Online Inference
      • Evaluate RAG using Batch Inference with Ray Data LLM
    • Deploy MCP servers
      • Deploying a custom MCP in Streamable HTTP mode with Ray Serve
      • Deploy an MCP Gateway with existing Ray Serve apps
      • Deploying an MCP STDIO Server as a scalable HTTP service with Ray Serve
      • Deploying multiple MCP services with Ray Serve
      • Build a Docker image for an MCP server
    • Build a tool-using agent
    • Build a multi-agent system with the A2A protocol
  • Ecosystem
  • Ray Core
    • Key Concepts
    • User Guides
      • Tasks
        • Nested Remote Functions
      • Actors
        • Named Actors
        • Terminating Actors
        • AsyncIO / Concurrency for Actors
        • Limiting Concurrency Per-Method with Concurrency Groups
        • Utility Classes
        • Out-of-band Communication
        • Actor Task Execution Order
      • Objects
        • Serialization
        • Object Spilling
      • Environment Dependencies
      • Scheduling
        • Use labels to control scheduling
        • Resources