Open-source alternatives to Torchani

Riemannian Adaptive Optimization Methods with pytorch optim

Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators

A CV toolkit for my papers.

Differentiable rendering without approximation.

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…

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

Kaolin is a PyTorch 3D deep learning library providing a comprehensive suite of tools for 3D geometry processing, physics simulation, data visualization, and gradient-based rendering for computer vision. The library includes a differentiable 3D renderer and a geometry processing toolkit for converting and transforming 3D representations such as meshes and point clouds. It also features a 3D physics simulation engine to calculate physical interactions and collisions between three-dimensional objects and scenes. The toolkit provides utilities for 3D data visualization, including the creation o

A multi-model machine learning feature embedding database

torchdiffeq is a PyTorch ODE solver library designed for solving initial value problems and building neural ODE frameworks. It provides a differentiable ODE integrator that allows deep learning models to simulate continuous depth by integrating dynamics functions over time. The library features an adjoint method gradient calculator for memory-efficient backpropagation. By solving an augmented adjoint system backwards in time, it computes parameter gradients without storing every intermediate solver state. The project covers numerical integration with adaptive and fixed-step solvers, incorpor

This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v

Abseil is a common utility library for C++ that provides foundational building blocks for applications. It serves as a collection of optimized utility functions and data structures that augment the C++ standard library across different compiler versions. The library is distinguished by its high-performance containers, including SIMD-accelerated hash maps and sets for efficient key-value lookups. It also provides a comprehensive framework for computing absolute time points, durations, and timestamps across global time zones. The project covers a broad range of capability areas, including conc

Track and manage build artifacts from multiple programming languages.

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

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