ONNX Runtime is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models, with an open extensible architecture to continually address the latest developments in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX standard with complete implementation of all ONNX operators, and supports all ONNX releases (1.2+) with both future and backwards compatibility.

This package provides a GStreamer plugin that wraps Kaldi's SingleUtteranceNnet2Decoder. It requires iVector-adapted DNN acoustic models. The iVectors are adapted to the current audio stream automatically.

This library is used internally as header-only library by PyTorch.

Scikit-learn provides simple and efficient tools for data mining and data analysis.

Lantern provides a C API to the libtorch machine learning library.

OneAPI Deep Neural Network Library (oneDNN) is a cross-platform performance library of basic building blocks for deep learning applications.

Interpretable ML (iML) is a set of data type objects, visualizations, and interfaces that can be used by any method designed to explain the predictions of machine learning models (or really the output of any function). It currently contains the interface and IO code from the Shap project, and it will potentially also do the same for the Lime project.

PyTorch Lightning is just organized PyTorch; Lightning disentangles PyTorch code to decouple the science from the engineering.

This package provides easy download of thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

• Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.

• Images, for tasks like image classification, object detection, and segmentation.

• Audio, for tasks like speech recognition and audio classification.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

This package provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community. At the same time, each Python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them.

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.

DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.

This package provides a reimplementation of OpenAI's Whisper model using CTranslate2, which is a inference engine for transformer models.

Uniform Manifold Approximation and Projection is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction.

This package is a toolbox for optimization on Riemannian manifolds with support for automatic differentiation.

ML Collections is a library of Python collections designed for Machine Learning usecases.

LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators).

Scikit-learn provides simple and efficient tools for data mining and data analysis.

This package provides the Magic-Wormhole Transit Relay server, which helps clients establish bulk-data transit connections even when both are behind NAT boxes. Each side makes a TCP connection to this server and presents a handshake. Two connections with identical handshakes are glued together, allowing them to pretend they have a direct connection.

This package provides the main server that Magic-Wormhole clients connect to. The server performs store-and-forward delivery for small key-exchange and control messages. Bulk data is sent over a direct TCP connection, or through a transit-relay.

Magic-Wormhole is a library and a command-line tool named wormhole, which makes it possible to securely transfer arbitrary-sized files and directories (or short pieces of text) from one computer to another. The two endpoints are identified by using identical "wormhole codes": in general, the sending machine generates and displays the code, which must then be typed into the receiving machine.

The codes are short and human-pronounceable, using a phonetically-distinct wordlist. The receiving side offers tab-completion on the codewords, so usually only a few characters must be typed. Wormhole codes are single-use and do not need to be memorized.

Python module that implements DKIM (DomainKeys Identified Mail) email signing and verification (RFC6376). It also provides helper scripts for command line signing and verification. It supports DKIM signing/verifying of ed25519-sha256 signatures (RFC 8463). It also supports the RFC 8617 Authenticated Received Chain (ARC) protocol.

pan is a Usenet newsreader that's good at both text and binaries. It supports offline reading, scoring and killfiles, yEnc, NZB, PGP handling, multiple servers, and secure connections.

This package provides extra tables, filters, and various other addons for OpenSMTPD to extend its functionality.

Alot is a terminal-based mail user agent based on the Notmuch mail indexer. It is written in Python using the urwid toolkit and features a modular and command prompt driven interface to provide a full mail user agent (MUA) experience as an alternative to the Emacs mode shipped with Notmuch.