ml-hydra-config

What problem does it solve?

Hydra configuration management solves the problem of scattered, hard-to-maintain ML experiment settings by letting you compose hierarchical configs and override parameters from the command line without editing code.

Core Features & Use Cases

• Hierarchical config composition: Organize configs into groups (model, data, trainer, callbacks, logger) and compose them at runtime using defaults and overrides.
• Dynamic instantiation via target: Create models, optimizers, trainers, callbacks, and loggers directly from config definitions using the target field.
• Experiment reproducibility + scaling: Run single experiments and multiruns for hyperparameter sweeps while keeping per-run directories and config snapshots.
• Optuna hyperparameter optimization: Use Hydra’s Optuna sweeper integration to define search spaces and run HPO without rewriting training logic.
• Lightning integration patterns: Fit cleanly into a PyTorch Lightning training script pattern (instantiate datamodule/model/callbacks/logger/trainer from configs).

Use case example: You can reproduce and sweep a Lightning training setup by composing a base config with experiment-specific groups and then sweeping learning rates and batch sizes using multirun or Optuna.

Quick Start

Run the Hydra-based training entrypoint with a specific model and data override (for example: change the model to vit and the dataset to imagenet) to verify your composed configuration instantiates correctly.