TensorPipe provides a tensor-aware channel to transfer rich objects from one process to another while using the fastest transport for the tensors contained therein.

Captum is a model interpretability and understanding library for PyTorch. Captum contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.

This package provides a collection of ordinal regression models for machine learning in Python. They are intended to be used with scikit-learn and are compatible with its API.

cleanlab automatically finds and fixes errors in any ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs).

cleanlab automatically finds and fixes errors in any ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

This Python library provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

This package provides provides an implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

This package provides logging utilities for the SpaCy natural language processing framework.

This is a small self-contained low-precision general matrix multiplication (GEMM) library. It is not a full linear algebra library. Low-precision means that the input and output matrix entries are integers on at most 8 bits. To avoid overflow, results are internally accumulated on more than 8 bits, and at the end only some significant 8 bits are kept.

fastText is a library for efficient learning of word representations and sentence classification.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

This is a package for hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores for natural language processing.

The GeomLoss library provides efficient GPU implementations for:

• Kernel norms (also known as Maximum Mean Discrepancies).

• Hausdorff divergences, which are positive definite generalizations of the Chamfer-ICP loss and are analogous to log-likelihoods of Gaussian Mixture Models.

• Debiased Sinkhorn divergences, which are affordable yet positive and definite approximations of Optimal Transport (Wasserstein) distances.

TensorFlow is a flexible platform for building and training machine learning models. This package provides the "lite" variant for mobile devices.

Gloo is a collective communications library. It comes with a number of collective algorithms useful for machine learning applications. These include a barrier, broadcast, and allreduce.

Random Jungle is an implementation of Random Forests. It is supposed to analyse high dimensional data. In genetics, it can be used for analysing big Genome Wide Association (GWA) data. Random Forests is a powerful machine learning method. Most interesting features are variable selection, missing value imputation, classifier creation, generalization error estimation and sample proximities between pairs of cases.

DeepXDE is a library for scientific machine learning and physics-informed learning. It includes implementations for the PINN (physics-informed neural networks), DeepONet (deep operator network) and MFNN (multifidelity neural network) algorithms.

This package provides common Python utilities and GitHub Actions for the Lightning suite of libraries.

DLPack is an in-memory tensor structure for sharing tensors among frameworks.

ONNX Interface for Framework Integration is a cross-platform API for loading and executing ONNX graphs on optimized backends. This package contains facebook extensions and is used by PyTorch.

Dlib is a modern C++ toolkit containing machine learning algorithms and tools. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.

This package provides a Python implementation of the CMA-ES algorithm and a few related numerical optimization tools.