Lightning Hydra Boilerplate

Structured experiment configuration via Hydra, training and evaluation loops with PyTorch Lightning, checkpointing and logging integrations, and utilities for hyperparameter sweeping and reproducibility.

Designed for local development and compute clusters; it works with accelerators supported by PyTorch Lightning such as GPUs and TPUs, subject to available drivers and backends.

Hydra-based hierarchical configurations allow modular config groups and overrides so users can extend or compose configs for new models, datasets, or training stages.

Provides seeding conventions, checkpointing, and structured logging to capture experiment state; exact reproducibility depends on deterministic settings and external factors like nondeterministic device operations.