Open Source Python Software - Page 93
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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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TorchRec
Pytorch domain library for recommendation systems
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding. The TorchRec planner can automatically generate optimized sharding plans for models. Pipelined training overlaps dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance. Optimized kernels for RecSys powered by FBGEMM. Quantization support for reduced precision training and inference. Common modules for RecSys. -
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Trape
OSINT tool for tracking users and analyzing browser data online
Trape is an open source OSINT analysis and research tool designed to track and analyze users on the internet in real time. The project focuses on demonstrating how web browsers can reveal sensitive information about users while interacting with websites and online services. It provides researchers, security professionals, and organizations with a platform for studying how attackers could gather intelligence through social engineering techniques. The tool can clone websites and monitor interactions in order to collect data from visitors, allowing investigators to observe user behavior and session activity. Trape was originally created to educate the public about how large internet services may obtain confidential information such as session status or browser details without users realizing it. Over time, it has evolved into a research platform that helps analysts track cybercriminal activity and study online tracking mechanisms. -
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Tree
tree is a library for working with nested data structures
Tree (dm-tree) is a lightweight Python library developed by Google DeepMind for manipulating nested data structures (also called pytrees). It generalizes Python’s built-in map function to operate over arbitrarily nested collections — including lists, tuples, dicts, and custom container types — while preserving their structure. This makes it particularly useful in machine learning pipelines and JAX-based workflows, where complex parameter trees or hierarchical state representations are common. The library provides efficient operations such as flatten, unflatten, and map_structure, enabling users to apply functions to all leaves of a nested structure seamlessly. Backed by a high-performance C++ core, tree is optimized for large-scale, performance-critical applications. -
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UFO³
Weaving the Digital Agent Galaxy
UFO is an open-source framework developed by Microsoft for building intelligent agents that automate interactions with graphical user interfaces on the Windows operating system. The system allows users to issue natural language instructions that are translated into automated actions across multiple desktop applications. Using a dual-agent architecture, the framework analyzes both visual interface elements and system control structures in order to understand how applications should be manipulated. This enables the agent to navigate complex software environments and perform tasks that normally require manual interaction. UFO integrates mechanisms for task decomposition, planning, and execution so that high-level user requests can be broken down into smaller steps performed by specialized agents. The framework can operate across multiple applications simultaneously, allowing workflows that span several programs to be automated seamlessly. -
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Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. Its architecture emphasizes performance optimization, balancing speed and accuracy to support real-time applications across industries. Ultralytics also provides pretrained models and flexible configuration options, allowing users to adapt the system to different datasets and use cases with minimal effort. -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. Build confidence in the quality of your data by defining schemas for complex data objects. -
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Unofficial TikTok API in Python
The Unofficial TikTok API Wrapper In Python
This is an unofficial API wrapper for TikTok in python. With this API you are able to call most trending and fetch specific user information as well as much more. If you run into an issue please check the closed issues on GitHub, although feel free to re-open a new issue if you find an issue that's been closed for a few months. The codebase can and does run into similar issues as it has before because TikTok changes things up. To run the example scripts from the repository root, make sure you use them an option on python. You can access the dictionary type of an object using .as_dict. On a video, this may look like this, although TikTok changes its structure from time to time so it's worth investigating the structure of the dictionary when you use this package. You'll probably need to use this beyond just for legacy support since not all attributes are parsed out and attached to the different objects. -
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VCR.py
Automatically mock your HTTP interactions to simplify testing
Automatically mock your HTTP interactions to simplify and speed up testing. VCR.py simplifies and speeds up tests that make HTTP requests. The first time you run code that is inside a VCR.py context manager or decorated function, VCR.py records all HTTP interactions that take place through the libraries it supports and serializes and writes them to a flat file (in yaml format by default). This flat file is called a cassette. When the relevant piece of code is executed again, VCR.py will read the serialized requests and responses from the aforementioned cassette file, and intercept any HTTP requests that it recognizes from the original test run and return the responses that corresponded to those requests. This means that the requests will not actually result in HTTP traffic, which confers several benefits including: -
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VGGSfM
VGGSfM: Visual Geometry Grounded Deep Structure From Motion
VGGSfM is an advanced structure-from-motion (SfM) framework jointly developed by Meta AI Research (GenAI) and the University of Oxford’s Visual Geometry Group (VGG). It reconstructs 3D geometry, dense depth, and camera poses directly from unordered or sequential images and videos. The system combines learned feature matching and geometric optimization to generate high-quality camera calibrations, sparse/dense point clouds, and depth maps in standard COLMAP format. Version 2.0 adds support for dynamic scene handling, dense point cloud export, video-based reconstruction (1000+ frames), and integration with Gaussian Splatting pipelines. It leverages tools like PyCOLMAP, poselib, LightGlue, and PyTorch3D for feature matching, pose estimation, and visualization. With minimal configuration, users can process single scenes or full video sequences, apply motion masks to exclude moving objects, and train neural radiance or splatting models directly from reconstructed outputs. -
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VSCode-LaTeX-Inkscape
A way to integrate LaTeX, VS Code, and Inkscape in macOS
A way to integrate LaTeX, VS Code, and Inkscape in macOS. I use LaTeX heavily for both academic work and professional work, and I think I'm quite proficient in terms of typing things out in LaTeX. But when I see the mind-blowing blog posts from Gilles Castel (RIP)-How I'm able to take notes in mathematics lectures using LaTeX and Vim and also How I draw figures for my mathematical lecture notes using Inkscape, I realize that I'm still far from fast, so I decided to adapt the whole setup from Linux-Vim to macOS-VS Code. -
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Vector AI
A platform for building vector based applications
Vector AI is a framework designed to make the process of building production-grade vector-based applications as quick and easily as possible. Create, store, manipulate, search and analyze vectors alongside json documents to power applications such as neural search, semantic search, personalized recommendations etc. Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning). Store your vectors alongside documents without having to do a db lookup for metadata about the vectors. Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalization, etc. There are scenarios where vector search is not as effective as traditional search, e.g. searching for skus. Vector AI lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, and keyword matching to create an even more powerful search. -
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Wikipedia2Vec
A tool for learning vector representations of words and entities
Wikipedia2Vec is an embedding learning tool that creates word and entity vector representations from Wikipedia, enabling NLP models to leverage structured and contextual knowledge. -
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Xfl
An Efficient and Easy-to-use Federated Learning Framework
XFL is a lightweight, high-performance federated learning framework supporting both horizontal and vertical FL. It integrates homomorphic encryption, DP, secure MPC, and optimizes network resilience. Compatible with major ML libraries and deployable via Docker or Conda. -
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Youtu-GraphRAG
Vertically Unified Agents for Graph Retrieval-Augmented Reasoning
Youtu-GraphRAG is a research framework developed by Tencent for performing complex reasoning using graph-based retrieval-augmented generation. The system combines knowledge graphs, retrieval mechanisms, and agent-based reasoning into a unified architecture designed to handle knowledge-intensive tasks. Instead of relying solely on text retrieval, the framework organizes information into structured graph schemas that represent entities, relationships, and attributes. These structures allow the system to perform multi-hop reasoning by decomposing complex questions into smaller queries that can be executed across different parts of the graph. The framework also incorporates hierarchical community detection algorithms that organize knowledge into clusters, improving both retrieval efficiency and reasoning performance. In addition to graph construction and retrieval, the system integrates iterative reasoning techniques that refine answers through multiple retrieval and reasoning cycles. -
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bidict
The bidirectional mapping library for Python
Depended on by Google, Venmo, CERN, Baidu, Tencent, and teams across the world since 2009. Familiar, Pythonic APIs that are carefully designed for safety, simplicity, flexibility, and ergonomics. Lightweight, with no runtime dependencies outside Python's standard library. Implemented in concise, well-factored, fully type-hinted Python code that is optimized for running efficiently as well as for long-term maintenance and stability. Extensively documented. 100% test coverage running continuously across all supported Python versions. Enterprise-level support for bidict can be obtained via the Tidelift subscription. One of the best things about bidict is that it touches a surprising number of interesting Python corners, especially given its small size and scope. Choose a tier and GitHub handles everything else. Your GitHub sponsorship will automatically go on the same bill you already have set up with GitHub, so after the one-click signup, there’s nothing else to do. -
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blockfrost-python
Python 3 SDK for the Blockfrost.io API
API for Cardano decentralized blockchain. Accessing and processing information stored on the blockchain is not trivial. We provide abstraction between you and blockchain data, taking away the burden of complexity, so you can focus on what really matters - developing your applications. Our basic tier is and always will be free of charge. We nurture development and the Cardano ecosystem. However, if you want to support us, please consider upgrading. -
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cloud_enum
Multi-cloud OSINT tool for discovering public cloud resources
cloud_enum is an open source reconnaissance and OSINT tool designed to discover publicly accessible cloud resources across major cloud providers. It focuses on enumerating assets in Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform using keyword-based discovery techniques. It works by taking user-provided keywords and generating variations through mutation wordlists, then testing these combinations against common cloud service naming patterns. cloud_enum performs both HTTP probing and DNS lookups to identify resources such as storage buckets, cloud applications, and databases that may be exposed or accessible. cloud_enum uses concurrent processing to speed up scanning, enabling efficient enumeration of large numbers of possible resource names. It can identify both publicly accessible and protected resources, helping security researchers understand the external cloud footprint of an organization. -
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django-admin-interface
Django's default admin interface made customizable
django-admin-interface is a modern responsive flat admin interface customizable by the admin itself. Themes management and customization (you can customize admin title, logo and colors) You can add a theme you've created through the admin to this repository by sending us a PR. Themes management and customization (you can customize admin title, logo and colors). Responsive, related modal (instead of the old popup window), environment name/marker, language chooser, list filter dropdown. -
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docext
An on-premises, OCR-free unstructured data extraction
docext is a document intelligence toolkit that uses vision-language models to extract structured information from documents such as PDFs, forms, and scanned images. The system is designed to operate entirely on-premises, allowing organizations to process sensitive documents without relying on external cloud services. Unlike traditional document processing pipelines that rely heavily on optical character recognition, docext leverages multimodal AI models capable of understanding both visual and textual information directly from document images. This allows the system to detect and extract structured elements such as tables, signatures, key fields, and layout information while maintaining semantic understanding of the document content. The toolkit can also convert complex documents into structured markdown representations that preserve formatting and contextual relationships. -
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jupyter-themer
Apply custom CSS styling to your jupyter notebooks
Apply custom CSS styling to your jupyter notebooks. Contributions are welcome! -
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llama2.c
Inference Llama 2 in one file of pure C
llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). While it can technically load Meta’s official Llama 2 models, current support is limited to fp32 precision, meaning practical use is capped at models up to around 7B parameters. The goal of llama2.c is to demonstrate how a compact and transparent implementation can perform meaningful inference even with small models, emphasizing simplicity, clarity, and accessibility. The project builds upon lessons from nanoGPT and takes inspiration from llama.cpp, focusing instead on minimalism and educational value over large-scale performance. -
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loonflow
A workflow engine base on django python
a workflow engine base on django The django-based workflow engine system (called through the http interface, can be used as a unified workflow engine within the enterprise, providing all workflows such as permission application, resource application, release application, leave, reimbursement, it service, etc. Scenario services), if there is a certain development capability, it is recommended to use only the back-end engine function, and the front-end customized development according to the scenario can be dispersed in various internal background management systems (such as personnel, operation and maintenance, monitoring, cmdb, etc.). Since version 1.1.x, loonflow comes with a front-end interface for creating and processing work orders, which can be used directly. The official version is shown in the release . It is recommended to use the latest version. -
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macOS Security Compliance
macOS Security Compliance Project
The macOS Security Compliance Project is an open source effort to provide a programmatic approach to generating security guidance. The configuration settings in this document were derived from National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53, Security and Privacy Controls for Information Systems and Organizations, Revision 5. This is a joint project of federal operational IT Security staff from the National Institute of Standards and Technology (NIST), National Aeronautics and Space Administration (NASA), Defense Information Systems Agency (DISA), and Los Alamos National Laboratory (LANL). -
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nbmake
Pytest plugin for testing notebooks
Pytest plugin for testing and releasing notebook documentation. To raise the quality of scientific material through better automation. Research/Machine Learning Software Engineers who maintain packages/teaching materials with documentation written in notebooks. -
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nesa
Run AI models end-to-end encrypted
nesa is an open-source initiative focused on building decentralized AI infrastructure that enables secure, verifiable, and privacy-preserving machine learning and inference across distributed environments. The project aims to address key challenges in modern AI systems, such as data privacy, trust, and centralization, by leveraging cryptographic techniques and decentralized architectures. NESA allows developers to run AI computations in a way that ensures data integrity and confidentiality, making it particularly relevant for applications involving sensitive or regulated data. It integrates mechanisms for verifiable computation, enabling users to confirm that AI outputs were generated correctly without exposing underlying data or models. The platform is designed to be modular and extensible, supporting integration with various machine learning frameworks and deployment environments.
