Projects sharing features with Pytorch2keras

PyTorch Lightning is a high-level deep learning framework for PyTorch that automates training loops and removes repetitive engineering boilerplate. It functions as a structured pipeline for managing machine learning experiments, providing a distributed training orchestrator and tools for mixed-precision training. The framework decouples scientific model architecture from the engineering required for infrastructure and scaling. This separation allows the same model code to execute across CPUs, GPUs, or TPUs through a hardware-agnostic execution engine and a centralized trainer that manages the

Accelerated deep learning R&D

torchbearer: A model fitting library for PyTorch

Super simple fit method for PyTorch Modules

Train AI models efficiently on medical images using any framework

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

Keras-js is a JavaScript inference engine and browser-based machine learning framework designed to execute pre-trained Keras neural networks. It allows for client-side model inference in web browsers or Node.js environments without the requirement of a backend server. The library utilizes a WebGL tensor accelerator to map mathematical operations to the graphics processor for hardware acceleration. To maintain user interface responsiveness during heavy computations, it incorporates a web worker inference runtime that executes neural network processing in background threads. The system support

PyTorch layer-by-layer model profiler

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…

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

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

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

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

This was the home of the Move language from inception to ~2022. This repository is no longer maintained, but development continues in https://github.com/move-language/move-on-aptos and https://github.com/move-language/move-sui.

A multi-model machine learning feature embedding database

Torch7 -> CoreML

This project is a curated directory of command line applications and utilities designed to enhance developer productivity and streamline technical workflows. It serves as a comprehensive index of open-source software, categorizing tools that assist with system administration, development automation, and personal task management. The repository distinguishes itself by providing a structured collection of terminal-based software that spans diverse functional domains. It includes resources for managing infrastructure and cloud resources, performing code maintenance, and customizing terminal envi

ANEE is an experimental dynamic inference wrapper for pretrained Transformer language models (currently GPT-2). Instead of always running all layers, ANEE exposes an energy_budget and performs early exit inside the model’s forward pass.

Graphback - Out of the box GraphQL server and client