Ray MCP Server

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.