pyexp-guide

What problem does it solve?

This Skill provides a comprehensive guide to structuring and managing machine learning experiments efficiently, ensuring reproducibility and simplifying complex workflows.

Core Features & Use Cases

• Experiment Structuring: Learn to define experiments using the @pyexp.experiment decorator.
• Configuration Management: Understand how to use Config objects, merge, sweep, and load_config for flexible parameterization.
• Dependency Management: Implement DAG-based dependencies between experiments using depends_on.
• Result Handling: Access and analyze experiment results through the Runs collection and Result dataclass.
• Use Case: When you need to set up a new machine learning project and want to ensure all your experiments are organized, reproducible, and easy to analyze, consult this guide.

Quick Start

Use the pyexp-guide skill to learn how to define a minimal experiment with the @pyexp.experiment decorator.