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autoraysearch

Automate ML hyperparameter tuning and architecture search with Ray.

Updated Mar 15, 2026
Search Tags:#machine learning#distributed computing#hyperparameter tuning#ray#model optimization#autonomous research
One-click install
npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill autoraysearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoraysearch
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/custom/autoraysearch
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill autoraysearch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the iterative process of hyperparameter tuning and architectural search for machine learning models, significantly accelerating the research and development cycle.

Core Features & Use Cases

  • Autonomous ML Iteration: Automatically generates training code, runs experiments, and iterates on model improvements using Ray for distributed computing.
  • Boilerplate Generation: Creates train.py from a user-provided model.py, handling Ray and PyTorch setup.
  • Parallel Experimentation: Can run multiple experiments concurrently across a Ray cluster to explore the search space more efficiently.
  • Use Case: A data scientist has a PyTorch model (model.py) and wants to find the optimal architecture and hyperparameters. They can use /autoraysearch:plan to guide the process, and then /autoraysearch to autonomously iterate and improve the model's performance.

Quick Start

Use the autoraysearch skill to plan and launch an autonomous ML research loop for your model.

Frequently Asked Questions about autoraysearch

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate hyperparameter tuning and architectural search for PyTorch models?▼

Automate hyperparameter tuning by generating training boilerplate from your model.py and using Ray to run distributed experiments. This autonomous iteration refines model architectures based on performance metrics.

Can I run parallel machine learning experiments concurrently using Ray?▼

Yes, you can run parallel machine learning experiments concurrently across a Ray cluster. This allows you to explore the hyperparameter search space more efficiently than sequential execution.

How do I generate Ray and PyTorch training boilerplate from an existing model file?▼

Generate training boilerplate by providing a PyTorch model.py file. The system automatically creates a train.py script that handles the underlying Ray and PyTorch setup for distributed execution.

What is the best way to distribute autonomous ML research across a cluster?▼

Distribute autonomous ML research by using Ray for distributed computing to manage parallel experiments. The system iteratively refines model architectures and optimizes hyperparameters based on performance metrics.

Do I need a Ray cluster to optimize machine learning hyperparameters autonomously?▼

You need Ray for distributed execution to fully optimize machine learning hyperparameters autonomously. The system supports both sequential and parallel execution modes for efficient hyperparameter optimization.

Does autonomous ML iteration support both sequential and parallel execution modes?▼

Autonomous ML iteration supports both sequential and parallel execution modes. This flexibility ensures efficient hyperparameter optimization and architectural search across your available computing resources.

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