Skorch is a deep learning workflow manager and tensor-based model interface. It provides a consistent API for training and predicting with neural networks within standard machine learning workflows, acting as a hyperparameter optimizer for finding optimal network configurations. The library specializes in wrapping PyTorch neural networks in a scikit-learn compatible interface. This allows tensor-based models to be used within traditional machine learning pipelines and grid search tools, including the mapping of parameter grids to model configurations. The framework covers training lifecycle
Debugging, monitoring and visualization for Python Machine Learning and Data Science
The main features of graal-research/pytoune are: Developer Tools, Training and Experimentation, Training and Utilities.
Open-source alternatives to graal-research/pytoune include: ecs-vlc/torchbearer — torchbearer: A model fitting library for PyTorch. pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… catalyst-team/catalyst — Accelerated deep learning R&D. dnouri/skorch — Skorch is a deep learning workflow manager and tensor-based model interface. It provides a consistent API for training… microsoft/tensorwatch — Debugging, monitoring and visualization for Python Machine Learning and Data Science. bloodaxe/pytorch-toolbelt — PyTorch extensions for fast R&D prototyping and Kaggle farming.
