This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

Thread-pool Controls provides Python helpers to limit the number of threads used in the threadpool-backed of common native libraries used for scientific computing and data science (e.g. BLAS and OpenMP).

This package provides an implementation of today’s most used tokenizers, with a focus on performance and versatility.

Brian is a simulator for spiking neural networks written in Python. It is therefore designed to be easy to learn and use, highly flexible and easily extensible.

This package provides a fast (zero-copy) and safe (dedicated) format for storing tensors safely.

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.

This package provides a Plain C/C++ implementation based on ggml, working in the same way as llama.cpp. It supports diffusion models such as SD, Flux, Wan, Qwen and Image,Z-Image.

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

Pomegranate is a graphical models library for Python, implemented in Cython for speed.

GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. GPy implements a range of machine learning algorithms based on GPs.

Low-precision, high-performance matrix-matrix multiplications and convolution library for server-side inference.

This package implements a variety of persistent homology algorithms. It provides an interface for

• computing persistence cohomology of sparse and dense data sets

• visualizing persistence diagrams

• computing lowerstar filtrations on images

• computing representative cochains

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility

• Reduces boilerplate

• Automatic accumulation over batches

• Metrics optimized for distributed-training

• Automatic synchronization between multiple devices

Vowpal Wabbit is a machine learning system with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

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.

Apache Burr makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple Python building blocks. Apache Burr works well for any application that uses LLMs, and can integrate with any of your favorite frameworks. Burr includes a UI that can track/monitor/trace your system in real time, along with pluggable persisters (e.g. for memory) to save and load application state.

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

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

git-ai is an extension for git that tracks which lines of code were created by an LLM along with metadata about how and why.

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 command line interface for Lightning AI services.

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 tool provides ordinary differential equation solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost.

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