#pytorch

Support PyTorch model conversion with LiteRT.

Crater is a cloud-native AI training & inference platform.

The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.

Compatibility layer for common array libraries to support the Array API

Open3D: A Modern Library for 3D Data Processing

Tools for handling multimodal data in machine learning projects.

Tensor Fusion is a state-of-the-art GPU virtualization and pooling solution designed to optimize GPU cluster utilization to its fullest potential.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Multi-dimensional arrays (tensors) and numerical definitions for Elixir

torchax is a PyTorch frontend for JAX. It gives JAX the ability to author JAX programs using familiar PyTorch syntax. It also provides JAX-Pytorch interoperability, meaning, one can mix JAX & Pytorch syntax together when authoring ML programs, and run it in every hardware JAX can run.

Visualizer for neural network, deep learning and machine learning models

An open source implementation of CLIP.

A Data Streaming Library for Efficient Neural Network Training

AI on the way. An auto deep learning pipe dream. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.

End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.