Open-source alternatives to Torchlite

Train AI models efficiently on medical images using any framework

pytorch-summary is a collection of utilities for PyTorch neural networks designed to generate model summaries, calculate memory requirements, and visualize layer-by-layer tensor shapes. It functions as a reporting tool that provides detailed breakdowns of network layers and output shapes to assist with model debugging and inspection. The project provides specialized capabilities for estimating the total memory usage of forward and backward passes based on input dimensions and parameter counts. It generates human-readable visualizations of model structures to verify architectural designs and i

PyTorch layer-by-layer model profiler

PyTorch extensions for fast R&D prototyping and Kaggle farming

Accelerated deep learning R&D

🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

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

Super simple fit method for PyTorch Modules

Accelerate is a PyTorch distributed training library that abstracts the boilerplate required to run models across multiple GPUs, TPUs, and CPUs. It functions as a deep learning model scaler and distributed hardware orchestrator, allowing the same training script to run on different hardware backends without modifying the core logic. The project provides a distributed training command line interface for configuring compute environments and launching jobs across single or multi-node clusters. It includes a mixed precision training framework to implement FP16 and BF16 precision, reducing memory

C++ extensions in PyTorch

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