Open-source alternatives to Torchprof

Train AI models efficiently on medical images using any framework

torchbearer: A model fitting library for PyTorch

PyTorch to Keras model convertor

Debugging, monitoring and visualization for Python Machine Learning and Data Science

A simplified framework and utilities for PyTorch

PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics

hessian in pytorch

CVXPYlayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and MLX using CVXPY. A convex optimization layer solves a parametrized convex optimization problem in the forward pass to produce a solution. It computes the derivative of the solution…

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

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

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

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

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

pytorch-OpCounter is a profiling utility for PyTorch neural networks designed to quantify model efficiency by calculating floating point operations and multiply-accumulate counts. It functions as a complexity analyzer to measure the computational cost and theoretical workload of different model architectures. The tool allows for the definition of custom operation counting rules to support third-party modules not covered by default. It uses forward hooks to intercept module calls and recursive traversal of the module tree to aggregate operations across child sub-modules. The project provides

PyTorch extensions for fast R&D prototyping and Kaggle farming