Search Results for "written in python"
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Showing 122 open source projects for "written in python"
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Diagnose and Resolve IT Issues in Real TimeScreenMeet’s unique combination of video calling, screen share, and remote desktop functionality lets you quickly diagnose hardware and software issues with no frustration.
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Raima Database Manager is an embedded in-memory database for IoT and Edge devicesRaima Database Manager (RDM) is an embedded relational database optimized to run on resource-constrained IoT edge devices that require real-time response. RDM enables intelligent decisions to be made at the device level within microseconds.
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Groq Python
The official Python Library for the Groq API
Groq Python is the official Python SDK for the Groq REST API, giving Python developers straightforward access to Groq’s LLM, chat, audio, and other AI services. Through this library, you can call Groq’s models from Python code — for example to request chat completions, code generation, transcription, or any supported endpoint — using idiomatic Python syntax. -
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AIOGram
Framework for Telegram Bot API written in Python 3.7 with asyncio
aiogram is modern and fully asynchronous framework for Telegram Bot API written in Python with asyncio and aiohttp. It helps you to make your bots faster and simpler. Is a pretty simple and fully asynchronous framework for Telegram Bot API written in Python 3.7 with asyncio and aiohttp. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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Monty
A minimal, secure Python interpreter written in Rust for use by AI
...It prioritizes guardrails like resource limits and restricted capabilities, which is especially useful for agentic workflows that need to execute small pieces of Python for data transforms, validation, or tool-like computations. Because it’s written in Rust, it’s positioned to deliver a compact, portable runtime that can be embedded into larger systems that need dependable isolation. -
World class QA, 100% done-for-youMuukTest is a test automation service that combines our own proprietary, AI-powered software with expert QA services to help you achieve world class test automation at a fraction of the in-house costs.
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NoneBot
Asynchronous multi-platform robot framework written in Python
...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 flexible support for your needs. NoneBot2 is written based on Python asyncio , and has a certain degree of synchronous function compatibility based on the asynchronous mechanism. NoneBot2 provides an easy-to-use, interactive command-line tool -- nb-cli, making it easier to get started with NoneBot2 for the first time. ... -
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labelme Image Polygonal Annotation
Image polygonal annotation with Python
Labelme is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Image annotation for polygon, rectangle, circle, line and point. Image flag annotation for classification and cleaning. Video annotation. (video annotation). GUI customization (predefined labels / flags, auto-saving, label validation, etc). Exporting VOC-format dataset for semantic/instance segmentation. -
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pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
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Sunfish
Sunfish: a Python Chess Engine in 111 lines of code
sunfish is a minimalist yet surprisingly strong chess engine written in Python, designed to demonstrate how powerful algorithms can be implemented in a highly compact codebase. Despite being only around a hundred lines of core logic, the engine achieves competitive performance, reaching ratings above 2000 on online platforms. It implements classic chess engine techniques such as alpha-beta pruning and efficient board representation while maintaining readability and simplicity. ... -
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scikit-image
Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. scikit-image builds on scipy.ndimage to provide a versatile set of image processing routines in Python. This library is developed by its community, and contributions are most welcome! Read about our mission, vision, and values and how we govern the project. Major proposals to the project are documented in SKIPs. ... -
Easy management of simple and complex projectsPlan more projects with Worksection. Use Gantt chart and Kanban boards to organize your projects, get your team onboard and assign tasks and due dates.
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. ... -
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Freqtrade
Free, open source crypto trading bot
Freqtrade is a free and open-source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or WebUI. It contains backtesting, plotting, and money management tools as well as strategy optimization by machine learning. Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect. -
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nanoGPT
The simplest, fastest repository for training/finetuning models
NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare or custom corpora. It emphasizes readability and clarity: the training loop is cleanly written, and the code avoids heavy abstractions, letting students follow the architecture step by step. ... -
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Atomic Agents
Building AI agents, atomically
...The framework provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization. All logic and control flows are written in Python, enabling developers to apply familiar best practices and workflows from traditional software development without compromising flexibility or clarity. -
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DeepSeek-OCR
Contexts Optical Compression
...It supports local deployment, enabling organizations concerned about privacy or latency to run the pipeline on-premises rather than send sensitive documents to third-party cloud services. The codebase is written in Python with a focus on modularity: you can swap preprocessing, recognition, and post-processing components as needed for custom workflows. -
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The Hundred-Page Machine Learning Book
The Python code to reproduce illustrations from Machine Learning Book
The Hundred-Page Machine Learning Book is the official companion repository for The Hundred-Page Machine Learning Book written by machine learning researcher Andriy Burkov. The repository contains Python code used to generate the figures, visualizations, and illustrative examples presented in the book. Its purpose is to help readers better understand the concepts explained in the text by allowing them to run and experiment with the underlying code themselves. -
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gTTS
Python library and CLI tool to interface with Google Translate
gTTS (Google Text-to-Speech) is a Python library and command-line tool that wraps the speech functionality of Google Translate. It lets you send text to the Google Translate TTS endpoint and receive spoken audio back as MP3 data, either written to a file, a file-like object, or standard output. The library is designed to handle long texts, using a speech-specific sentence tokenizer that keeps intonation and punctuation natural while splitting requests into acceptable chunks. ... -
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TorchIO
Medical imaging toolkit for deep learning
...TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity. -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
Nano-vLLM is a lightweight implementation of the vLLM inference engine designed to run large language models efficiently while maintaining a minimal and readable codebase. The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. ... -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can... -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
...Each lesson includes a short video, a written guide, runnable samples for Azure OpenAI, the GitHub Marketplace Model Catalog, and the OpenAI API, plus a “Keep Learning” section for deeper study. -
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Coursera-ML-AndrewNg-Notes
Personal notes from Wu Enda's machine learning course
Coursera-ML-AndrewNg-Notes is an open-source repository that provides detailed study notes and explanations for Andrew Ng’s well-known machine learning course. The project aims to help students understand the mathematical concepts, algorithms, and intuition behind fundamental machine learning techniques taught in the course. It organizes the material into clear written summaries that accompany each lecture topic, including supervised learning, regression methods, neural networks, and... -
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Lingua-Py
The most accurate natural language detection library for Python
Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing... -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry... -
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Telegram Drive
Telegram Drive
Telegram Drive is a powerful utility that enables you to organize your telegram files and much more. Teldrive stands out among similar tools, thanks to its implementation in Go, a language known for its efficiency. Its performance surpasses alternatives written in Python and other languages, with the exception of Rust. Teldrive not only excels in speed but also offers an intuitive user interface for efficient file interaction which other tool lacks. Its compatibility with Rclone further enhances file management. -
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GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts,...
