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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.