Open Source Python Software - Page 64
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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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Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library. -
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Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Fast3R is Meta AI’s official CVPR 2025 release for “Towards 3D Reconstruction of 1000+ Images in One Forward Pass.” It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. The repository includes pretrained models, Gradio-based demos, and modular APIs for direct integration into research or production workflows. -
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FastAPI CRUD Router
A dynamic FastAPI router that automatically creates CRUD routes
Tired of rewriting generic CRUD routes? Need to rapidly prototype a feature for a presentation or a hackathon? Thankfully, fastapi-crudrouter has your back. As an extension to the APIRouter included with FastAPI, the FastAPI CRUDRouter will automatically generate and document your CRUD routes for you, all you have to do is pass your model and maybe your database connection. fastapi-crudrouter provides a number of features that allow you to get the most out of your automatically generated CRUD routes. The CRUDRouter is able to dynamically generate detailed documentation based on the models given to it. By default, all routes generated by the CRUDRouter will be documented according to OpenAPI spec. The CRUDRouter is able to dynamically generate detailed documentation based on the models given to it. -
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FastAgency
The fastest way to bring multi-agent workflows to production
FastAgency is a framework that simplifies the creation and deployment of AI-driven automation agents. It provides a structured environment for developing AI assistants capable of handling various business and technical tasks. -
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FastEdit
Editing large language models within 10 seconds
FastEdit focuses on rapid “model editing,” letting you surgically update facts or behaviors in an LLM without full fine-tuning. It implements practical editing algorithms that insert or revise knowledge with targeted parameter updates, aiming to preserve model quality outside the edited scope. This approach is valuable when you need urgent corrections—think product names, APIs, or fast-changing facts—without retraining on large corpora. The repository provides evaluation harnesses so you can measure locality (does the change stay contained?) and generalization (does the change apply where it should?). It’s structured for repeatable experiments, making side-by-side comparisons of editing methods and hyperparameters straightforward. For applied teams, FastEdit offers a toolbox to keep models current and compliant while minimizing collateral damage to overall performance. -
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Finetune Transformer LM
Code for "Improving Language Understanding by Generative Pre-Training"
finetune-transformer-lm is a research codebase that accompanies the paper “Improving Language Understanding by Generative Pre-Training,” providing a minimal implementation focused on fine-tuning a transformer language model for evaluation tasks. The repository centers on reproducing the ROCStories Cloze Test result and includes a single-command training workflow to run the experiment end to end. It documents that runs are non-deterministic due to certain GPU operations and reports a median accuracy over multiple trials that is slightly below the single-run result in the paper, reflecting expected variance in practice. The project ships lightweight training, data, and analysis scripts, keeping the footprint small while making the experimental pipeline transparent. It is provided as archived, research-grade code intended for replication and study rather than continuous development. -
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FlashInfer
FlashInfer: Kernel Library for LLM Serving
FlashInfer is a kernel library designed to enhance the serving of Large Language Models (LLMs) by optimizing inference performance. It provides a high-performance framework that integrates seamlessly with existing systems, aiming to reduce latency and improve efficiency in LLM deployments. FlashInfer supports various hardware architectures and is built to scale with the demands of production environments. -
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Flask-MongoEngine
MongoEngine flask extension with WTF model forms support
Flask-MongoEngine is a Flask extension that provides integration with MongoEngine, WtfForms and FlaskDebugToolbar. By default, Flask-MongoEngine will install integration only between Flask and MongoEngine. Integration with WTFForms and FlaskDebugToolbar are optional and should be selected as extra option, if required. This is done by users request, to limit amount of external dependencies in different production setup environments. All methods end extras described below are compatible between each other and can be used together. We still maintain special case for Flask = 1.1.4 support (the latest version in 1.x.x branch). To install flask-mongoengine with required dependencies use legacy extra option. Flask-mongoengine can be installed with Flask-WTF and WTFForms support. This will extend project dependencies with Flask-WTF, WTFForms and related packages. -
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FullTClash
General proxy performance testing tool based on Clash using Telegram
Back end part useClash project(It can also be called nowmihomo)The relevant code is used as the outing agent. The front end part uses Telegram API as the interactive interface, which needs to be used in conjunction with Telegram, that is, a Telegram robot (bot), FullTClash bot is a Telegram robot (hereinafter referred to as bot) carrying its test tasks. -
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GIXY
Nginx configuration static analyzer
Gixy is a tool to analyze Nginx configuration. The main goal of Gixy is to prevent security misconfiguration and automate flaw detection. Currently supported Python versions are 2.7, 3.5, 3.6 and 3.7. Gixy is well tested only on GNU/Linux, other OSs may have some issues. You can find things that Gixy is learning to detect at Issues labeled with "new plugin". By default Gixy will try to analyze Nginx configuration placed in /etc/nginx/nginx.conf. Or something else, you can find all other gixy arguments with the help command: gixy --help. Gixy is available as a Docker image from the Docker hub. To use it, mount the configuration that you want to analyse as a volume and provide the path to the configuration file when running the Gixy image. If you have an image that already contains your nginx configuration, you can share the configuration with the Gixy container as a volume. -
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GPT Researcher
LLM based autonomous agent that does online comprehensive research
Say Hello to GPT Researcher, your AI agent for rapid insights and comprehensive research. GPT Researcher is the leading autonomous agent that takes care of everything from accurate source gathering to organization of research results. -
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GPT-2
Code for the paper Language Models are Unsupervised Multitask Learners
This repository contains the code and model weights for GPT-2, a large-scale unsupervised language model described in the OpenAI paper “Language Models are Unsupervised Multitask Learners.” The intent is to provide a starting point for researchers and engineers to experiment with GPT-2: generate text, fine‐tune on custom datasets, explore model behavior, or study its internal phenomena. The repository includes scripts for sampling, training, downloading pre-trained models, and utilities for tokenization and model handling. Support for memory-saving gradient techniques/optimizations during training. Sampling/generation scripts (conditional, unconditional, interactive). -
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GPT2 for Multiple Languages
GPT2 for Multiple Languages, including pretrained models
With just 2 clicks (not including Colab auth process), the 1.5B pretrained Chinese model demo is ready to go. The contents in this repository are for academic research purpose, and we do not provide any conclusive remarks. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC) Simplifed GPT2 train scripts（based on Grover, supporting TPUs). Ported bert tokenizer, multilingual corpus compatible. 1.5B GPT2 pretrained Chinese model (~15G corpus, 10w steps). Batteries-included Colab demo. 1.5B GPT2 pretrained Chinese model (~30G corpus, 22w steps). -
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GRR
GRR Rapid Response, remote live forensics for incident response
GRR Rapid Response is an incident response framework focused on remote live forensics. It consists of a python client (agent) that is installed on target systems, and python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR client is deployed on systems that one might want to investigate. On every such system, once deployed, GRR client periodically polls GRR frontend servers for work. “Work” means running a specific action, downloading file, listing a directory, etc. GRR server infrastructure consists of several components (frontends, workers, UI servers, fleetspeak) and provides a web-based graphical user interface and an API endpoint that allows analysts to schedule actions on clients and view and process collected data. -
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Gigi
Framework for rapid prototyping and development of real-time rendering
Gigi is software designed for rapid prototyping and rapid development of real-time rendering techniques. It is meant for use by professionals, researchers, students, and hobbyists. The goal is to allow work at the speed of thought, and then easily use what was created in real applications using various APIs or engines. Gigi is being actively used and developed but is young software. You may hit bugs or missing features. Please report these so we can improve Gigi and push forward in the most useful directions. Pull requests are also appreciated. Currently, only dx12 code generation is available in this public version of Gigi, but we are hoping to support other APIs, and engines as well, in the future. -
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GitPitch
Markdown Presentations for Tech Conferences, Training, Development
GitPitch 4.0 is the perfect slide deck solution for tech conferences, training, developer advocates, and educators. Available on MacOS, Linux, and Windows 10. Work and present offline. Export to PDF, PPTX, and HTML. Or git-push to share public, private and password-protected slide decks online. GitPitch is a markdown presentation tool for MacOS, Linux, and Windows 10. GitPitch Desktop lets you develop, preview, and present markdown presentations offline. Using modular markdown to deliver modular decks. Export your markdown presentations to PDF, PPTX, and HTML. And publish and share your markdown presentations online. To publish any deck just git-push to any repo on GitHub, GitLab, or Bitbucket. And share it as a public, private, or password-protected slide deck. -
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GoAgent
GoAgent will regularly scan the available google gae ip
GoAgent, which is always available, will regularly scan the available google gae ip, and provide a version that can automatically obtain the ip to run. GoAgent, which has always been available, will regularly scan the available google gae ip, goagent is the source code around May 2015. You can download the googleip.txt file. It is a list of available Google ip addresses. There are about 2w ips. The source is obtained by scanning all Google address domains with my vpn. The reliability is guaranteed. You can download it Come and scan with GoGoTest and GScan yourself! Directly download the source code, unzip it and enter the local folder, open GoAgent.exe directly on Windows, run proxy.py directly on Mac os or Linux, and start fully automatic wall-over, enjoys. -
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Google Fonts
Font files available from Google Fonts, and a public issue tracker
This is the central GitHub repository for Google Fonts, containing font binaries, metadata, and tools for uploading new typeface families. It serves as the staging area for fonts and follows stringent licensing structures. The top-level directories indicate the license of all files found within them. Subdirectories are named according to the family name of the fonts within. The /catalog subdirectory contains additional metadata, such as profile texts and portrait/avatar images of font designers, and this is open for contributions and corrections from anyone via GitHub. Since all the fonts available here are licensed with permission to redistribute, subject to the license terms, you can self-host using a variety of third-party projects. -
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Graphene
GraphQL in Python Made Easy
Graphene is a Python library for building GraphQL APIs fast and easily, using a code-first approach. Instead of writing GraphQL Schema Definition Langauge (SDL), Python code is written to describe the data provided by your server. Graphene helps you use GraphQL effortlessly in Python, but what is GraphQL? GraphQL is a data query language developed internally by Facebook as an alternative to REST and ad-hoc webservice architectures. With Graphene you have all the tools you need to implement a GraphQL API in Python, with multiple integrations with different frameworks including Django, SQLAlchemy and Google App Engine. -
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Guardrails
Adding guardrails to large language models
Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). At the heart of Guardrails is the rail spec. rail is intended to be a language-agnostic, human-readable format for specifying structure and type information, validators and corrective actions over LLM outputs. We create a RAIL spec to describe the expected structure and types of the LLM output, the quality criteria for the output to be considered valid, and corrective actions to be taken if the output is invalid. -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). -
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HTTPie
A CLI, cURL-like tool for humans
HTTPie is a modern command-line HTTP client that makes CLI interaction with web services as human-friendly as possible. It offers a plethora of friendly features that make it an excellent curl alternative. It is equipped with an intuitive UI, JSON support, syntax highlighting and so much more. HTTPie gives a single http command for sending arbitrary HTTP requests with a simple, natural syntax, and displayed in a formatted, colorized terminal output. HTTPie can be installed on macOS, Windows and Linux. It can be used for painless debugging, testing, and general interactions with HTTP servers. -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
This repo contains the code for the O'Reilly Media, Inc. book "Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" by Ankur A. Patel. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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Hera
Hera is an Argo Python SDK
Hera is an Argo Python SDK. Hera aims to make the construction and submission of various Argo Project resources easy and accessible to everyone! Hera abstracts away low-level setup details while still maintaining a consistent vocabulary with Argo. -
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized network. Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond. Decentralized parameter averaging: iteratively aggregate updates from multiple workers without the need to synchronize across the entire network. Train neural networks of arbitrary size: parts of their layers are distributed across the participants with the Decentralized Mixture-of-Experts. If you have succesfully trained a model or created a downstream repository with the help of our library, feel free to submit a pull request that adds your project to the list.