Open Source Python Software - Page 43
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. -
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Pyrogram
Elegant, modern and asynchronous Telegram MTProto API framework
Pyrogram is a modern, elegant and asynchronous MTProto API framework. It enables you to easily interact with the main Telegram API through a user account (custom client) or a bot identity (bot API alternative) using Python. Ready: Install Pyrogram with pip and start building your applications right away. Easy: Makes the Telegram API simple and intuitive, while still allowing advanced usages. Elegant: Low-level details are abstracted and re-presented in a more convenient way. Fast: Boosted up by TgCrypto, a high-performance cryptography library written in C. Type-hinted: Types and methods are all type-hinted, enabling excellent editor support. Async: Fully asynchronous (also usable synchronously if wanted, for convenience). Powerful: Full access to Telegram's API to execute any official client action and more. -
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Python Crypto Bot
Python Crypto Bot (PyCryptoBot)
Python Crypto Bot (PyCryptoBot). -
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Python LSP Server
Fork of the python-language-server project
Fork of the python-language-server project, maintained by the Spyder IDE team and the community. A Python 3.7+ implementation of the Language Server Protocol. The base language server requires Jedi to provide Completions, Definitions, Hover, References, Signature Help, and Symbols. Like all language servers, the configuration can be passed from the client that talks to this server (i.e. your editor/IDE or another tool that has the same purpose). The details of how this is done depend on the editor or plugin that you are using to communicate with python-lsp-server. The configuration options are available at that level are documented in CONFIGURATION.md. Overall configuration is computed first from user configuration (in home directory), overridden by configuration passed in by the language client, and then overridden by configuration discovered in the workspace. -
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QuTiP
QuTiP: Quantum Toolbox in Python
QuTiP is open-source software for simulating the dynamics of open quantum systems. The QuTiP library depends on the excellent Numpy, Scipy, and Cython numerical packages. In addition, graphical output is provided by Matplotlib. QuTiP aims to provide user-friendly and efficient numerical simulations of a wide variety of Hamiltonians, including those with arbitrary time-dependence, commonly found in a wide range of physics applications such as quantum optics, trapped ions, superconducting circuits, and quantum nanomechanical resonators. QuTiP is freely available for use and/or modification on all major platforms such as Linux, Mac OSX, and Windows*. Being free of any licensing fees, QuTiP is ideal for exploring quantum mechanics and dynamics in the classroom. -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a versatile tool for developers looking to integrate advanced AI functionalities into their applications. -
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Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
Qwen2.5 is a series of large language models developed by the Qwen team at Alibaba Cloud, designed to enhance natural language understanding and generation across multiple languages. The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. -
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Qwen-Agent
Agent framework and applications built upon Qwen>=3.0
Qwen-Agent is a framework for building applications / agents using Qwen models (version 3.0+). It provides components for instruction following, tool usage (function calling), planning, memory, RAG (retrieval augmented generation), code interpreter, etc. It ships with example applications (Browser Assistant, Code Interpreter, Custom Assistant), supports GUI front-ends, backends, server setups. Agent workflow can maintain context / memory to perform multi-turn or more complex logic over time. It acts as the backend for Qwen Chat among other use cases. Built-in Code Interpreter tool that can execute code (locally) as part of agent workflows. -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. Code & examples provided with Hugging Face transformers, and usage via AutoProcessor, model classes etc. High performance on many standard benchmarks: ASR, speech-emotion recognition, vocal sound classification, speech translation etc. -
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Qwen3-Omni
Qwen3-omni is a natively end-to-end, omni-modal LLM
Qwen3-Omni is a natively end-to-end multilingual omni-modal foundation model that processes text, images, audio, and video and delivers real-time streaming responses in text and natural speech. It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and 10 speech output languages. It achieves state-of-the-art results: across 36 audio and audio-visual benchmarks, it hits open-source SOTA on 32 and overall SOTA on 22, outperforming or matching strong closed-source models such as Gemini-2.5 Pro and GPT-4o. To reduce latency, especially in audio/video streaming, Talker predicts discrete speech codecs via a multi-codebook scheme and replaces heavier diffusion approaches. -
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REST APIs with Flask and Python
Projects and e-book for our course, REST APIs with Flask and Python
A full course to teach you how to use Flask and Python to make REST APIs using multiple Flask extensions and PostgreSQL. Learn Flask, Docker, PostgreSQL, and more. Build professional-grade REST APIs with Python. No more outdated tutorials. Use Python 3.10+ and the latest versions of every Flask extension and library. Run your apps in Docker, host your code with Git, write documentation with Swagger, and test your APIs while developing. Learn how to perform user authentication using JWTs and the Flask-JWT-Extended library. Here we talk about access token JWTs, as well as refresh tokens, JWT claims, blocklists, password hashing, and more. -
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RPA for Python
Python package for doing RPA
Python package for doing RPA. RPA for Python's simple and powerful API makes robotic process automation fun! You can use it to quickly automate away repetitive time-consuming tasks on websites, desktop applications, or the command line. See sample Python script, the RPA Challenge solution, and RedMart groceries example. To send a Telegram app notification, simply look up @rpapybot to allow receiving messages. To automate Chrome browser invisibly, use headless mode. To run 10X faster instead of normal human speed, use turbo mode (read the caveats!). Some CAPTCHAs can be solved using services like 2Captcha or directly by replicating user actions. TagUI is a leading open-source RPA software with tens of thousands of users. It was created in 2016-2017 when I left DBS Bank as a test automation engineer, for a one-year sabbatical to Eastern Europe. Most of its code base was written in Novi Sad Serbia. In 2018, I joined AI Singapore to continue development of TagUI. -
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Raven Python
Raven is the legacy Python client for Sentry
Raven is the official legacy Python client for Sentry, officially supports Python 2.6–2.7 & 3.3–3.7, and runs on PyPy and Google App Engine. It tracks errors and exceptions that happen during the execution of your application and provides instant notification with detailed information needed to prioritize, identify, reproduce, and fix each issue. It provides full out-of-the-box support for many of the popular Python frameworks, including Django, and Flask. Raven also includes drop-in support for any WSGI-compatible web application. -
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Recap
Recap tracks and transform schemas across your whole application
Recap is a schema language and multi-language toolkit to track and transform schemas across your whole application. Your data passes through web services, databases, message brokers, and object stores. Recap describes these schemas in a single language, regardless of which system your data passes through. Recap schemas can be defined in YAML, TOML, JSON, XML, or any other compatible language. -
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RecoverPy
Interactively find and recover deleted or overwritten files
RecoverPy is a powerful tool that leverages your system capabilities to recover lost files. Unlike others, you can not only recover deleted files but also overwritten data. Every block of your partition will be scanned. You can even find a string in binary files. -
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Reformer PyTorch
Reformer, the efficient Transformer, in Pytorch
This is a Pytorch implementation of Reformer. It includes LSH attention, reversible network, and chunking. It has been validated with an auto-regressive task (enwik8). -
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Responder
A familiar HTTP Service Framework for Python
Responder is a web service framework, written for human beings. This gets you a ASGI app, with a production static files server (WhiteNoise) pre-installed, jinja2 templating (without additional imports), and a production web server based on uvloop, serving up requests with gzip compression automatically. A pleasant API, with a single import statement. Class-based views without inheritance. ASGI framework, the future of Python web services. WebSocket support! The ability to mount any ASGI / WSGI app at a subroute. f-string syntax route declaration. Mutable response object passed into each view. No need to return anything. Background tasks spawned off in a ThreadPoolExecutor. GraphQL (with GraphiQL) support! OpenAPI schema generation, with interactive documentation! Single-page webapp support! -
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Rhino
On-device Speech-to-Intent engine powered by deep learning
Rhino is Picovoice's Speech-to-Intent engine. It directly infers intent from spoken commands within a given context of interest, in real-time. The end-to-end platform for embedding private voice AI into any software in a few lines of code. Design with no limits on top of a modular platform. Create use-case-specific voice AI models in seconds. Develop voice features with a few lines of code using intuitive and cross-platform SDKs. Deliver voice AI everywhere: on-device, mobile, web browsers, on-premise, or cloud. Measure adoption, learn, and iterate. Continuously re-design and re-train to optimize engagement. Building accurate, responsive, and private voice technology is difficult. We learned the hard way, so you don’t have to. Picovoice heavily invests in R&D to offer superior voice AI that surpasses even Big Tech in accuracy and efficiency. Picovoice researchers do not follow recent frameworks and techniques but build them. -
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RoboSats
A simple and private bitcoin exchange
RoboSats is a privacy-preserving, peer-to-peer Bitcoin exchange that allows users to buy and sell Bitcoin using Lightning Network payments without requiring identity verification. It uses Tor by default and leverages a simple robot-based user interface to facilitate one-time trades between anonymous users. Each trade session is isolated, and no long-term accounts or KYC are needed, making RoboSats one of the most private Bitcoin P2P tools available. Designed with a strong focus on usability and anonymity, it's especially popular among privacy advocates and Bitcoin maximalists. -
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Robust Video Matting (RVM)
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX
We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications. RVM is specifically designed for robust human video matting. -
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Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
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SQL Explorer
Easily share data across your company via SQL queries
SQL Explorer aims to make the flow of data between people fast, simple, and confusion-free. It is a Django-based application that you can add to an existing Django site, or use as a standalone business intelligence tool. Quickly write and share SQL queries in a simple, usable SQL editor, preview the results in the browser, share links, download CSV, JSON, or Excel files (and even expose queries as API endpoints, if desired), and keep the information flowing! Comes with support for multiple connections, to many different SQL database types, a schema explorer, query history (e.g. lightweight version control), a basic security model, in-browser pivot tables, and more. SQL Explorer values simplicity, intuitive use, unobtrusiveness, stability, and the principle of least surprise. SQL Explorer is inspired by any number of great query and reporting tools out there. -
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SQLModel
SQL databases in Python, designed for simplicity, compatibility
SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python-type annotations, and powered by Pydantic and SQLAlchemy. -
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SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
SWE-agent turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI). -
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Saleor Commerce
A modular, high performance, headless e-commerce platform
An open-source, GraphQL-first e-commerce platform delivering ultra-fast, dynamic and personalized shopping experiences. A headless, GraphQL commerce platform delivering ultra-fast, dynamic, personalized shopping experiences. Beautiful online stores, anywhere, on any device. Saleor is a rapidly-growing open source e-commerce platform that has served high-volume companies from branches like publishing and apparel since 2012. Based on Python and Django, the latest major update introduces a modular front end powered by a GraphQL API and written with React and TypeScript. A comprehensive system for orders, dispatch, and refunds. Advanced payment and tax options, with full control over discounts and promotions. Packed with features that get stores to a wider audience.