Can I use Rayatouille with multiple Ray clusters?

Yes, Rayatouille supports configurable profiles, enabling you to easily add, manage, and switch between multiple Ray cluster configurations.

Rayatouille is a terminal UI (TUI) designed to monitor and manage Ray clusters directly from your command line, eliminating the need for a web browser.

What Ray cluster components can Rayatouille monitor?

It provides comprehensive dashboards and detailed monitoring for Ray jobs, nodes, actors, Ray Serve applications, and cluster events, including logs and resource usage.

Does Rayatouille support keyboard navigation?

Yes, Rayatouille is fully keyboard-driven, featuring vim-style keybindings, a fuzzy-search command palette, and the ability to set custom keybindings for a tailored experience.

Can Rayatouille integrate with AI agents?

Absolutely. It includes an integrated MCP (Model Context Protocol) server, allowing AI agents like Claude Code to access and interact with your Ray cluster's data programmatically.

Rayatouille

Name: Rayatouille
Author: GangGreenTemperTatum

byGangGreenTemperTatum

0•

分析と監視

デプロイメントとDevOps

データサイエンスとML

Monitor and manage Ray clusters directly from your terminal with a comprehensive UI.

Rayatouille delivers a powerful, keyboard-driven terminal user interface (TUI) for real-time monitoring and management of Ray clusters, eliminating the need for a browser. It provides a comprehensive dashboard view of cluster health, detailed insights into jobs, nodes, actors, and Serve applications, along with an event timeline. Additionally, it integrates an MCP server to expose Ray cluster data and operational tools to AI agents, enhancing automated observability and interaction for machine learning and distributed computing workflows.

主な機能

01Comprehensive Ray cluster dashboard with resource bars and health indicators

02Detailed monitoring of jobs, nodes, actors, Serve applications, and cluster events

03Keyboard-driven navigation with vim-style keybindings and a fuzzy-search command palette

04Configurable profiles for managing multiple Ray clusters and custom keybindings

05Integrated MCP server to expose Ray cluster data to AI agents via Model Context Protocol

060 GitHub stars

ユースケース

01Real-time performance monitoring and health checks of Ray clusters.

02Debugging and troubleshooting distributed Ray applications and workflows through detailed logs.

03Enabling AI agents to query and interact with Ray cluster operational data for automated management.