lxqt-powermanagement is providing tools to monitor power management events and optionally trigger actions like e. g. shut down a system when laptop batteries are low on power.

lxqt-notificationd is LXQt's implementation of a daemon according to the Desktop Notifications Specification.

lxqt-sudo is a graphical front-end of commands sudo and su respectively. As such it enables regular users to launch applications with permissions of other users including root.

QTerminal is a lightweight Qt terminal emulator based on QTermWidget.

This package provides a Qt graphical interface to archiving programs like tar and zip.

lxqt-config is providing several tools involved in the configuration of both LXQt and the underlying operating system.

libsysstat is a library to query system information like CPU and memory usage or network traffic.

GNU M4 is an implementation of the M4 macro language, which features some extensions over other implementations, some of which are required by GNU Autoconf. It is used as a macro processor, which means it processes text, expanding macros as it encounters them. It also has some built-in functions, for example to run shell commands or to do arithmetic.

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

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.

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.

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.

This package provides a generic API for dispatch to Pyro backends.

PyNNDescent provides a Python implementation of Nearest Neighbor Descent for k-neighbor-graph construction and approximate nearest neighbor search.

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.

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.

Kaldi is an extensible toolkit for speech recognition written in C++.

This package enables you to deserialize Lua torch-serialized objects from Python.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

This package provides OCaml bindings for the MCL graph clustering algorithm.

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 C++ and Python library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

Not all possible optimizations can be directly implemented on ONNX graphs--- some will need additional backend-specific information---but many can, and the aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.