# Ray MCP Server

> Distributed computing framework integration for managing Ray clusters, jobs, and actors with real-time monitoring. Enables scaling machine learning training from single machine to cloud clusters with LLM-enhanced output support.

- **URL:** https://lobehub.com/mcp/pradeepiyer-ray-mcp
- **Category:** AI Integration MCP Servers
- **Tags:** Distributed Computing, Machine Learning, Scale
- **Updated:** 2026-03-22 20:23
- **Canonical page:** https://mcpserver.ever.works/ru/items/ray-mcp-server

## Details

## Overview

A Model Context Protocol (MCP) server for managing Ray clusters, jobs, and distributed computing workflows. Ray is an open-source, high-performance distributed execution framework designed for scalable and parallel Python and machine learning applications.

## Features

- **Multi-Node Cluster Management**: Manage Ray clusters across multiple nodes
- **Job Management**: Submit, monitor, and control Ray jobs
- **Actor Management**: Manage Ray actors for distributed computation
- **Real-Time Monitoring**: Monitor cluster health and resource usage
- **Workflow Orchestration**: Coordinate distributed workflows
- **LLM-Enhanced Output**: Optional AI-enhanced output via environment variable
- **Scheduling**: Advanced job scheduling capabilities

## Ray Capabilities

### Ray Train
- Scalable machine learning library for distributed training
- Scale model training from single machine to cluster with 1 line of code
- Compatible with PyTorch, TensorFlow, XGBoost
- Distributed deep learning training across CPUs/GPUs

### Ray Core
- Distributed execution framework
- Parallel Python applications
- Actor-based computation model
- Data pipeline processing

## Use Cases

- Distributed machine learning training
- Large-scale model fine-tuning
- Gen AI foundation model training
- Time series model training
- Data pipeline orchestration
- Batch inference at scale
- Hyperparameter tuning
- Multi-node computation

## Example Workflows

- **distributed_training.py**: Distributed ML training examples
- **basic_ray_jobs**: Simple Ray job submission
- **multi_node_clusters**: Cluster management
- **actor_based_computation**: Actor pattern examples
- **data_pipelines**: Data processing workflows

## Integration Benefits

- Natural language cluster management
- AI-assisted job scheduling
- Simplified distributed computing
- Reduced complexity for scaling
- Automated resource management
- Conversational monitoring

## Technical Implementation

Built on Ray's distributed runtime with abstractions for:
- Task parallelism
- Actor model
- Distributed scheduling
- Object store

## Compatibility

Works with Claude, Cursor, and any MCP-compatible AI assistant. Requires Ray cluster deployment.

## Pricing

Ray is open-source. Anyscale provides managed Ray services with enterprise features.

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