ml-config-manager

What problem does it solve?

ML experiments get slowed down when configuration files (model, data, trainer, logger, and sweeps) are hand-written inconsistently, causing Hydra composition issues and repeatable experiment setup to be error-prone.

Core Features & Use Cases

• Hydra config generation for full ML workflows: produce structured config files for model, data, trainer, logger, and end-to-end experiment composition.
• Hyperparameter sweep templates: create sweep configuration (including Optuna-based Bayesian/TPESampler setups) for systematic search and multirun execution.
• Modular best-practice structure: guides consistent naming, DRY defaults composition, and resolution-friendly _target_ patterns for PyTorch Lightning and Hydra.

Quick Start

Tell the AI: “Create a Hydra config for a ResNet model on CIFAR-10 with a W&B logger and an Optuna hyperparameter sweep optimizing val/loss.”