CompoConf

Thu, 19 Jun 2025 00:00:00 +0000

CompoConf - modular configuration for modular systems

(see also the lengthy version aided by ChatGPT)

Library Code: https://github.com/kpoeppel/compoconf

Composition over Inheritance

“Composition over Inheritance” is one of the phrases you might have heard many, many times in software engineering. Self-contained units, modules, are at the core; they can be tested separately and can be easily replaced when they satisfy a common interface. This potential for switching out components makes it easier to compare models, ablate models (an important scientific practice), test on new datasets, and perform hyperparameter optimization. Early on, an important feature of PyTorch was its nn.Module system, enabling a lot of flexibility and easy re-use of components, making it the leading framework for Machine Learning today. Still, common practice in Machine Learning is to hard-code many default values and then put the remaining configuration variables into a large flat configuration file, as seen in Hugging Face’s transformers. Moving around configuration options forces some reduction in configurational complexity, because who wants to keep track of numerous hyperparameters for various components. This compression is beneficial once configurations stabilize.

CompoConf - the lengthy version

Thu, 19 Jun 2025 00:00:00 +0000

CompoConf: Embracing Composition Over Inheritance in Deep Learning Configurations

(see also the self-written version)

Library Code: https://github.com/kpoeppel/compoconf

Docs: https://compoconf.readthedocs.org

Deep learning projects often juggle complex model architectures and their configurations. Two prevalent approaches have emerged for managing these configurations: inheritance-based configs (as seen in libraries like Hugging Face Transformers) and composition-based design (as encouraged by PyTorch’s modular nn.Module patterns). In this post, we introduce CompoConf, a new Python library that brings the “composition over inheritance” principle to configuration management. We’ll explore why this principle matters for machine learning configs, how Hugging Face and PyTorch differ in their philosophies, and how CompoConf bridges a gap by enabling clean, type-safe, and composable configurations in Python.

Korbinian Pöppel

CompoConf

CompoConf - modular configuration for modular systems

Composition over Inheritance

CompoConf - the lengthy version

CompoConf: Embracing Composition Over Inheritance in Deep Learning Configurations