Reverse Dependencies of py-cpuinfo

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

• Accuinsight — Model life cycle and monitoring library in Accuinsight+
• ai-z — GPU usage graph in the terminal for AMD and NVIDIA GPUs
• 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
• 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.
• blendr-cli — Blendr CLI tool for GPU Lending
• blosc2 — Python wrapper for the C-Blosc2 library
• 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
• capsula — A Python package to capture and reproduce command execution context
• cengal — General purpose library
• cengal-light — General purpose library
• chai-sacred — Facilitates automated and reproducible experimental research
• ChaProEV — ChaProEV: Charging Profiles of Electric Vehicles
• 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
• 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.
• DPA — The Density Peak Advanced packages.
• 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 : A flexible and accurate multi-plateforms profiling tool for fine-grained energy measurement of applications
• 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
• esrally — Macrobenchmarking framework for Elasticsearch
• ETS-CookBook — The ETS (TNO) CookBook of useful Python Scripts
• 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
• flowcept — FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
• gaitmap_challenges — A set of benchmark challenges for IMU based human gait analysis
• 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
• HardwareProvider — A package used to get hardware info and specs.
• hfutils — Useful utilities for huggingface
• 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.
• 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.
• invoke-tools — A set of tools to use the Invoke task runner easier in a work-flow.
• inxi — A Python template project
• ipex-llm — Large Language Model Develop Toolkit
• ivystar — python tools package of ivystar
• janus-dtnaas — Janus DTNaaS Controller
• jutge-monitor — Monitor for worker machines of Jutge.org
• kk-sacred — Facilitates automated and reproducible experimental research
• lbmpy — Code Generation for Lattice Boltzmann Methods
• lcreg — Efficient 3D rigid and affine image registration
• LightSim2Grid — LightSim2Grid implements a c++ backend targeting the Grid2Op platform.
• LightZero — A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkits.
• llama-index-embeddings-ipex-llm — llama-index embeddings ipex-llm integration
• llcv — A Modular and Extensible Framework for Computer Vision
• LongTermBiosignals — Python library for easy managing and processing of large Long-Term Biosignals.
• lpot — Repository of Intel® Low Precision Optimization Tool
• m3-learning — Tutorials, Projects, and datasets from the M3-learning research group
• maestror — no summary