Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a
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
torchbearer: A model fitting library for PyTorch
Les fonctionnalités principales de ecs-vlc/torchbearer sont : Outils de développement, Training and Experimentation, Training and Utilities, PyTorch Utilities.
Les alternatives open-source à ecs-vlc/torchbearer incluent : catalyst-team/catalyst — Accelerated deep learning R&D. pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… graal-research/pytoune. awwong1/torchprof — PyTorch layer-by-layer model profiler. graal-research/poutyne — A simplified framework and utilities for PyTorch. dnouri/skorch — Skorch is a deep learning workflow manager and tensor-based model interface. It provides a consistent API for training…
