Projects sharing features with Higher

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

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…

A simplified framework and utilities for PyTorch

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

hessian in pytorch

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

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

A multi-model machine learning feature embedding database

PyTorch layer-by-layer model profiler

Accelerated deep learning R&D

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

torchbearer: A model fitting library for PyTorch

Super simple fit method for PyTorch Modules

This project is a TensorFlow meta-learning framework and research toolkit designed to implement and train learned optimizers. It provides a library of tools for developing neural networks that learn how to optimize other models, replacing traditional gradient-based optimization algorithms. The framework includes a problem ensemble manager that allows multiple distinct optimization tasks to be combined into a single weighted loss function for simultaneous training. It uses a factory pattern for network instantiation and supports the definition of custom objective functions and loss graphs as t

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.

A helpful Chrome extension for WebGL development

Track and manage build artifacts from multiple programming languages.

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Graphback - Out of the box GraphQL server and client

The autonomous, self-improving AI agent. Single Rust binary. Every channel.

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