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Showing 110 open source projects for "core gnu/linux"
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Computer Science Flash Cards
Mini website for testing both general CS knowledge and enforce coding
This repository collects concise flash cards that cover the core ideas of a traditional computer science curriculum with a focus on interview readiness. The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. Many cards point at canonical... -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
RLax (pronounced “relax”) is a JAX-based library developed by Google DeepMind that provides reusable mathematical building blocks for constructing reinforcement learning (RL) agents. Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as... -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP... -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used... -
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NoneBot
Asynchronous multi-platform robot framework written in Python
Use NB-CLI to quickly build your own robot. Plug-in development, modular management. Supports multiple platforms and multiple incident response methods. Asynchronous priority development to improve operational efficiency. Simple and clear dependency injection system, built-in dependency functions reduce user code. NoneBot2 is a modern, cross-platform, and extensible Python chatbot framework. It is based on Python's type annotations and asynchronous features, and can provide convenient and... -
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Standard Webhooks
The Standard Webhooks specification
Standard Webhooks is a community-driven specification and set of open-source tools designed to make webhooks consistent, secure, and interoperable across providers. The project defines strict guidelines covering aspects like signature formats, headers, timestamps, replay protection, and forward compatibility. It includes reference implementations for signature verification and signing across multiple languages such as Python, JavaScript/TypeScript, Go, Rust, Ruby, PHP, C#, Java, and Elixir,... -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,... -
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aws-devops-zero-to-hero
AWS zero to hero repo for devops engineers to learn AWS in 30 Days
aws-devops-zero-to-hero is a 30-day AWS learning roadmap aimed squarely at DevOps engineers who want both conceptual understanding and hands-on projects. The README is structured as a day-by-day syllabus, starting with “Day 1: Introduction to AWS” and moving through IAM, EC2, VPC networking, security, DNS (Route 53), storage (S3), and many other core services. Each day mixes explanation with at least one concrete project or lab, such as deploying applications on EC2, designing secure VPCs,... -
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Pyro
Deep universal probabilistic programming with Python and PyTorch
Pyro is a flexible, universal probabilistic programming language (PPL) built on PyTorch. It allows for expressive deep probabilistic modeling, combining the best of modern deep learning and Bayesian modeling. Pyro is centered on four main principles: Universal, Scalable, Minimal and Flexible. Pyro is universal in that it can represent any computable probability distribution. It scales easily to large datasets with minimal overhead, and has a small yet powerful core of composable... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ... -
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Cortex AI Infrastructure
Open-source multi-chain data routing & low-latency scanning framework.
Cortex AI Infrastructure is an institutional-grade open-source crypto arbitrage bot framework for low-latency multi-chain data routing across TON, Solana, and EVM layers. The engine optimizes high-frequency distributed pipelines to eliminate execution lag and network jitter. Core Technical Modules: 1. Cross-Exchange Telemetry Scanner: Monitors order book depth and spreads across 18 venues concurrently (Binance, ByBit, OKX, HTX) with zero thread-locking. 2. Polymarket Analytics Engine:... -
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Dshell
Dshell is a network forensic analysis framework
An extensible network forensic analysis framework. Enables rapid development of plugins to support the dissection of network packet captures. This is a major framework update to Dshell. Plugins written for the previous version are not compatible with this version, and vice versa. By extension, dpkt and pypcap have been replaced with Python3-friendly pypacker and pcapy (respectively). Enables development of external plugin packs, allowing the sharing and installation of new,... -
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Errbot
Chatbot daemon that connects to your favorite chat services
Errbot is a chatbot, a daemon that connects to your favorite chat service and brings your tools into the conversation. The goal of the project is to make it easy for you to write your own plugins so you can make it do whatever you want, a deployment, retrieving some information online, trigger a tool via an API, troll a co-worker, etc. Errbot is being used in a lot of different contexts, chatops (tools for devops), online gaming chatrooms like EVE, video streaming chatrooms like... -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce... -
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Name-That-Hash
Identify MD5, SHA256 and 300+ other hashes
Name-That-Hash is a modern hash identification system that tells you what type of hash you are looking at, supporting MD5, SHA-256, and more than 300 other hash types. It is designed as a successor and improvement to older tools like HashID and Hash-Identifier, focusing on up-to-date hash databases and better usability. One of its core ideas is popularity-aware ranking: when you feed in a hash, it prioritizes likely real-world types such as NTLM over obscure ones like Skype hashes, instead... -
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CommandlineConfig
A library for users to write configurations in Python
CommandlineConfig is a lightweight Python library designed to simplify managing configuration parameters for experiments and applications, especially in research workflows that require frequent tweaking of hyperparameters. It lets you define configuration in familiar Python dictionaries or JSON files and then access nested parameters via dot notation in code, improving readability and reducing boilerplate. One of its core strengths is the ability to override configuration values directly... -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time... -
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tom_core
tom_core - a tool for automating events on a computer
tom_core is a software tool used for the automation of everything that happens on your computer. By using this application, you can easily record your activity on your computer, starting the recording at any moment that you choose. The application repeats all your clicks or drags, keystrokes, hotkeys, etc. All in exactly the timing and number of repetitions you need. The toolbox such as the optical recognition and voice control enables to branch out the recordings into complex forms, with... -
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pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,... -
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...This formalism is an extension of the Shlaer–Mellor method and executable UML, facilitating deployment of domains across multiple hosts, using networked bridges to process and pass events between them. Core technologies: XML, Python, Qt/PySide. Current release solely targets Linux, but has previously run on macOS. Currently released components are: - eda-model: XML schema and schematron that define the storage of a valid design. - eda-model-interface: python3 library for loading, editing, diffing and validating an instance of an EDA design...
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Thesa
It is a Platform to connect to tryton (json-rpc) and is based on qt
Thesa It is a Platform to connect to tryton (json-rpc) and is based on qt/qml libraries. Requires designing the interface of each Tab without having to touch the core. Tabs are created with qml files and can be loaded locally from a folder or from trytond using thesamodule (https://github.com/numaelis/thesamodule). Thesa's goal is to be able to combine tryton with Qt / Qml, for special cases such as using the opengl performance of qml2 Requirements: pyside2 5.12 or... -
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TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to... -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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AWS IoT Greengrass Core SDK
SDK to use with functions running on Greengrass Core using Python
The AWS IoT Greengrass Core SDK is meant to be used by AWS Lambda functions running on an AWS IoT Greengrass Core. It will enable Lambda functions to invoke other Lambda functions deployed to the Greengrass Core, publish messages to the Greengrass Core and work with the local Shadow service. To use the AWS IoT Greengrass Core SDK, you must first import the AWS IoT Greengrass Core SDK in your Lambda function as you would with any other external libraries. You then need to create a client for...