tree-autoresearch

What problem does it solve?

This Skill automates the complex and iterative process of machine learning and deep learning research, enabling systematic exploration of model configurations and hyperparameters without constant human intervention.

Core Features & Use Cases

• Tree-based Experiment Management: Organizes research iterations in a tree structure, allowing for parallel exploration of different research branches.
• Reproducible Research: Leverages Git worktrees to capture the exact code state for each experiment iteration.
• Autonomous Iteration: The system proposes, executes (or guides execution), and records results, deciding on the next steps based on performance.
• Use Case: When exploring optimal hyperparameters for a new neural network architecture, this Skill can autonomously test various learning rates, batch sizes, and optimizer settings, pruning unpromising paths and expanding on successful ones.

Quick Start

Use the tree-autoresearch skill to add a baseline experiment node with the command 'python train.py --model resnet50'.