autoraysearch

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.