Reverse Dependencies of py-cpuinfo

The following projects have a declared dependency on py-cpuinfo:

• Accuinsight — Model life cycle and monitoring library in Accuinsight+
• afipcaeqrdecode — Package to decode and extract invoice metadata from an AFIP CAE qr code link
• ai-z — GPU usage graph in the terminal for AMD and NVIDIA GPUs
• airosentris — A sentiment analysis platform with AI runner and trainer components
• amifast — amifast: simple powerful benchmarking with Python
• andromeda-torch — Andromeda - Pytorch
• ApiTools-for-aiyoloAPI — api tools
• ApiTools-fsdjfoi — Description of your library
• arline-benchmarks — Automated benchmarking platform for quantum compilers
• artifi — A Automation Tool Made By Noob
• auto-round — Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs
• auto-round-lib — Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs
• autobiasdetector — tools for detecting bias patterns of LLMs
• autonomi-nos — Nitrous oxide system (NOS) for computer-vision.
• autovariate — A package made for streamlining Variational Autoencoders
• azureml-automl-common-tools — Internal metapackage used for Azure machine learning.
• badlands-doe-toolset — badlands_doe_toolset is a set of tools to help build and analyse Design of Experiment configurations for badlands modelling
• bcipy — Python Software for Brain-Computer Interface.
• beta-rec — Beta-RecSys: Build, Evaluate and Tune Automated Recommender Systems
• bfas — Brute Force Architecture Search
• bigdl-llm — Large Language Model Develop Toolkit
• bigdl-nano — High-performance scalable acceleration components for intel.
• binpan — Binance API wrapper with backtesting tools.
• blosc2 — A fast & compressed ndarray library with a flexible compute engine.
• bluemist — Bluemist AI is a low code machine learning library written in Python to develop, evaluate and deploy automated ML pipleines.
• booltest — Booltest: Polynomial randomness tester
• bpm-ai-inference — Inference and server for local AI implementations of bpm-ai-core abstractions.
• Brian2 — A clock-driven simulator for spiking neural networks
• cabrnet — Generic library for prototype-based classifiers
• calabash-experimenter — A software energy experiment orchestrator.
• capsula — A Python package to capture command/function execution context for reproducibility.
• causalbench-asu — Spatio Temporal Causal Benchmarking Platform
• cengal — General purpose library
• cengal-light — General purpose library
• CengalPolyBuild — A Comprehensive and Hackable Build System for Multilingual Python Packages: Cython (including automatic conversion from Python to Cython), C/C++, Objective-C, Go, and Nim, with ongoing expansions to include additional languages
• chai-sacred — Facilitates automated and reproducible experimental research
• cheesechaser — Swiftly get tons of images from indexed tars on Huggingface.
• chessboard — CLI to solve combinatoric chess puzzles.
• chia-tea — A library dedicated to chia-blockchain farmer.
• chronogram — Chrono-gram, the diachronic word embedding model based on Word2vec Skip-gram with Chebyshev approximation
• codearth — Calculate your carbon emission !
• codecarbon — no summary
• codeproject-ai-sdk — Python SDK for writing Modules for CodeProject AI Server
• codetrack — Calculate your carbon emission !
• composer — Composer is a PyTorch library that enables you to train neural networks faster, at lower cost, and to higher accuracy.
• conbench — Continuous Benchmarking (CB) Framework
• cpu-monitor — Cpu monitoring and burning tool
• cpumodel — Get info about your CPU
• crop-yolo — Crop people from jpg files via yolo v10
• danila — This is the module for detecting and classifying text on rama pictures
• danila-lib — This is the module for detecting and classifying text on rama pictures
• daskperiment — A lightweight tool to perform reproducible machine learning experiment using Dask.
• DDFacet — Facet-based radio astronomy continuum imager
• deep-sort-reid — A re-mastered version of the original Deep Sort implementation, with added functionalities such as re-identification.
• deeplabcut-live — Class to load exported DeepLabCut networks and perform pose estimation on single frames (from a camera feed)
• devito — Finite Difference DSL for symbolic computation.
• dghs-imgutils — A convenient and user-friendly anime-style image data processing library that integrates various advanced anime-style image processing models.
• dghs-realutils — A convenient and user-friendly image data processing library that integrates various advanced image processing models.
• doclayout-yolo — DocLayout-YOLO: an effecient and robust document layout analysis method.
• DPA — The Density Peak Advanced packages.
• drcell — GUI to generate, cluster and optimize dimensionality reduction output
• ds-boost — Package for Practical & efficient Data Science in Python. Initially written for data-science-keras repo
• dvinfo — A package for getting system information on Windows and Linux
• EA2P — EA2P (Energy-Aware Application Profiler): A multi-platform profiling tool that offers precise and detailed energy usage measurements for applications, with the ability to adapt to different needs.
• eco2ai — emission tracking library
• edgesoftware — A CLI wrapper for management of Intel® Edge Software Hub packages.
• encpng — A steganographic library to encrypt files and text in PNG images
• enova — enova
• esrally — Macrobenchmarking framework for Elasticsearch
• face-rhythm — A pipeline for analysis of facial behavior using optical flow
• fastcdc — FastCDC (content defined chunking) in pure Python.
• fastestimator — Deep learning framework
• fastestimator-nightly — Deep learning framework
• fastllama-python-test — no summary
• feloopy — FelooPy: Efficient and feature-rich integrated decision environment
• fishsound-finder — Python software to automatically detect fish sounds in passive acoustic recordings
• flowcept — Capture and query workflow provenance data using data observability
• FranKGraphBench — pip install package for frankgraphbench.
• gaitmap_challenges — A set of benchmark challenges for IMU based human gait analysis
• galadriel-node — no summary
• gaussian-step — A SEAMM plugin for A SEAMM plug-in for Gaussian
• gdp-time-series — no summary
• geekbench-browser-python — Simple package for getting data from browser.geekbench.com
• geowatch — no summary
• gordo — Train and build models for Argo / Kubernetes
• graphiq — GraphiQ is a Python library for the simulation, design, and optimization of quantum photonic circuits.
• guro — A Simple System Monitoring & Benchmarking Toolkit
• HardwareProvider — A package used to get hardware info and specs.
• hfutils — Useful utilities for huggingface
• hivemind-bus-client — Hivemind Websocket Client
• hmxlabs.hplx — This project is a collection of tools to help use the HPL benchmark. See https://github.com/hmxlabs/hplx/blob/main/README.md for more information.
• hmxlabs.sysinfo — Package to get basic system information including CPU count, HT/SMT status, RAM and disk. Not doing anything special. Just uses psutil and py-cpuinfo
• hyperglass — hyperglass is the modern network looking glass that tries to make the internet better.
• i8kgui — A Dell thermal management GUI to control fan speeds and monitor temperatures.
• icland — Recreating Google DeepMind's XLand RL environment in JAX
• icontract — Provide design-by-contract with informative violation messages.
• ilit — Repository of Intel® Low Precision Optimization Tool
• inex-library — A comprehensive Python library for AI integration, web development with FastAPI/Flask, advanced encryption, secure database management, file operations, library development, text styling, system management, translation services, video creation, web automation, and cryptocurrency token analysis. Features include JWT handling, password management, rate limiting, input sanitization, and more.
• inference — With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.
• inference-cli — With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference CLI.