Skorch is a scikit-learn compatible neural network library that wraps PyTorch.
Skorch solves the problem of integrating deep learning models into scikit-learn workflows by providing a wrapper around PyTorch that implements the scikit-learn estimator interface. This allows PyTorch neural networks to be used with standard scikit-learn tools like pipelines, grid search, and cross-validation without requiring custom integration code. The approach wraps PyTorch modules in estimator classes such as NeuralNetClassifier that expose fit, predict, and predict_proba methods familiar to scikit-learn users.
Skorch suits projects where teams already use scikit-learn and want to add neural networks without abandoning their existing workflow patterns. It is particularly valuable for practitioners who need to combine neural networks with traditional preprocessing steps, hyperparameter tuning via grid search, or model selection via cross-validation within a single pipeline. The tool works best when you have PyTorch models you want to integrate into scikit-learn code, rather than as a replacement for direct PyTorch development.
The project maintains active test coverage and documentation, with integration testing against Hugging Face models. Development activity shows consistent attention to both the core library and its ecosystem compatibility, particularly around maintaining alignment with scikit-learn conventions and supporting integration with external machine learning frameworks.
