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
• 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 high-performance 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
• 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.
• 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.
• HardwareProvider — A package used to get hardware info and specs.
• hfutils — Useful utilities for huggingface
• 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.
• icontract — Provide design-by-contract with informative violation messages.
• ilit — Repository of Intel® Low Precision Optimization Tool
• 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.
• inference-core — 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-cpu — 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-gpu — 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-sdk — 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.
• instructlab-training — Training Library
• InterProcessPyObjects — This high-performance package delivers blazing-fast inter-process communication through shared memory, enabling Python objects to be shared across processes with exceptional efficiency
• invoke-docker-flow — A small set of tools to make using Docker with the Invoke task runner easier. Also incorporates a Flow system for use with git-flow.