Edit (by @deruyter92): this PR was split into several sub-PRs to facilitate a smooth transition. An overview is kept in #3193.

Summary

• Introduce new configuration classes for inference, logging, model, pose, project, runner, and training settings.
• Refactore data loading mechanisms to utilize new configuration structures.
• Move the multithreading and compilation options in inference configuration to the config module.
• Typed configuration for logging.
• Update dataset loaders to accept model configurations directly or via file paths.

Why Typed & Structured Configuration (OmegaConf + Pydantic)

• Strong guarantees for correctness

  • Runtime type safety ensures invalid configs fail fast with clear errors instead of silently producing incorrect training runs.
  • Schema-validated configs dramatically reduce debugging time for users and maintainers.

• Static typing improves developer velocity

  • IDE autocomplete and inline documentation make configs discoverable and self-documenting.
  • Refactors become safer: config changes are more likely to be caught at development time.

• Hierarchical, composable configuration

  • Natural representation of DeepLabCut’s nested project/model/training settings.
  • Easy composition and merging from multiple sources (base config, model presets, experiment overrides).

• Cleaner overrides and defaults.

• Structured configs make it easier to define parameter ranges for tuning and automation.

• Config schemas can be versioned and evolve safely over time while preserving backward compatibility.

• Full, validated configuration can be saved alongside results, which improves reproducibility and transparency.

• Builds on well-maintained, widely adopted libraries (OmegaConf, Pydantic).

Resources for knowing more about structured configs:

• hydra
• MIT Responsible AI's hydra-zen
• Pydantic
• Omegaconf
• Soklaski, R., Goodwin, J., Brown, O., Yee, M. and Matterer, J., 2022. Tools and practices for responsible AI engineering. arXiv preprint arXiv:2201.05647.

Future Work

• Currently default model definitions are still stored as yaml files in the package. Moving to LazyConfig as in Detectron 2 would improve things significantly.

More things that could be done ( @deruyter92 ):

• I think we need to make sure that everytime a model is used, all the changes to the project's config.yaml are reflected in the model's configuration under metadata as well.
• There might be a better way to handle things in deeplabcut/pose_estimation_pytorch/data/base.py.