Open-source alternatives to Pytoune

C++ extensions in PyTorch

Submission to https://pytorch.org/hub/

Adaptive Experimentation Platform

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

PyTorch extensions for fast R&D prototyping and Kaggle farming

PyTorch layer-by-layer model profiler

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

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

This project is a machine learning experiment tracker and event file generator that enables the recording of scalars, images, and histograms to monitor model performance. It functions as an integration bridge that allows training metrics from PyTorch to be logged into files compatible with the TensorBoard dashboard. The system includes a remote log synchronizer designed to stream experiment data to cloud services. This allows for the remote management and analysis of training results and the comparison of datasets across different training runs. The utility covers a broad range of monitoring

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

Train AI models efficiently on medical images using any framework

A simplified framework and utilities for PyTorch