Open Source Python Libraries - Page 13
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Python Libraries
View 73 business solutionsBrowse free open source Python Libraries and projects below. Use the toggles on the left to filter open source Python Libraries by OS, license, language, programming language, and project status.
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Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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A collection of python libraries used by MARIMORE Inc. http://www.marimore.co.jp THIS PROJECT HAS MOVED TO https://github.com/marimore/marimorepy
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Malicious PDF Generator
Generate a bunch of malicious pdf files with phone-home functionality
Generate ten different malicious PDF files with phone-home functionality. Can be used with Burp Collaborator or Interact.sh. Used for penetration testing and/or red-teaming etc. I created this tool because I needed a third-party tool to generate a bunch of PDF files with various links. -
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MetaNet
Free portable library for meta neural network research
MetaNet provides free library for meta neural network research. MetaNet library contain feed-forward neural net realisation and several integrated dataset (MNIST). -
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The purpose of the Metabrain library is to give developers a way to extract this information from the Internet without resorting to natural language parsing or other complex techniques, using instead statistical methods and patterns/trends analysis.
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Assembled is the only unified platform for staffing and managing your human and AI support team.Assembled is the only platform that unifies AI agents and intelligent workforce management to power fast and flexible support operations. Built for scale, we help teams automate over 50% of customer interactions, forecast with 90%+ accuracy, and optimize staffing across in-house and BPO teams. Orchestrate every chat, email, or call, balancing workloads between human and AI agents in real time — without sacrificing quality or control. Trusted by Stripe, Canva, and Robinhood, Assembled transforms support from a cost center into a strategic advantage. Our Workforce and Vendor Management tools connect forecasting, scheduling, and performance for smarter staffing decisions. AI Agents automate conversations across channels with your workflows and brand voice. AI Copilot empowers agents with real-time guidance, suggested replies, and one-click actions for faster, higher-quality resolutions.
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. Such parameter-space sparsity used for model compression compresses networks that operate on dense tensors and all intermediate activations of these networks are also dense tensors. -
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Addons to the Django Framework for mobile clients. MoGo was originally built to handle JP specific issues, but code to handle other locales are welcome as well. Developed and maintained by MARIMORE Inc http://www.marimore.co.jp THIS PROJECT HAS MOVED TO https://github.com/marimore/mobiledjango
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Multi-language library to deal with multimethod dispatch, disambiguation and type-checking using dispatch tables. This approach yields fast dispatch in constant-time and greatly helps resolving ambiguities.
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Multimodal
TorchMultimodal is a PyTorch library
This project, also known as TorchMultimodal, is a PyTorch library for building, training, and experimenting with multimodal, multi-task models at scale. The library provides modular building blocks such as encoders, fusion modules, loss functions, and transformations that support combining modalities (vision, text, audio, etc.) in unified architectures. It includes a collection of ready model classes—like ALBEF, CLIP, BLIP-2, COCA, FLAVA, MDETR, and Omnivore—that serve as reference implementations you can adopt or adapt. The design emphasizes composability: you can mix and match encoder, fusion, and decoder components rather than starting from monolithic models. The repository also includes example scripts and datasets for common multimodal tasks (e.g. retrieval, visual question answering, grounding) so you can test and compare models end to end. Installation supports both CPU and CUDA, and the codebase is versioned, tested, and maintained. -
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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 model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU-related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-processing, build model, train, validate, infer, save or load a model. -
Sell your products and services as gift cards, vouchers and tickets with powerful automationVouchers, gift cards and tickets are the most profitable items your business can sell today. On average, only 85% of business vouchers are redeemed. Since VoucherCart delivers payment in full and in advance, your business can sell confidently, safely, and without hassle.
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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 of treating them equally. The tool provides concise “hash summaries” that explain where a given hash format is commonly used, helping users decide how to proceed with cracking or further analysis. Name-That-Hash is accessible via a Python CLI (nth) and also exposes an API and JSON output, making it easy to plug into other tools or workflows, and there is also a web app that requires no local installation. -
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Nerfies
This is the code for Deformable Neural Radiance Fields
Nerfies demonstrates deformation-aware neural radiance fields that reconstruct and render dynamic, real-world scenes from casual video. Instead of assuming a static world, the method learns a canonical space plus a deformation field that maps changing poses or expressions back to that space during training. This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles imperfect captures by modeling camera poses, exposure variations, and background segmentation, producing stable geometry and appearance. A set of utilities manages dataset preparation, pose estimation, and checkpoints so researchers can reproduce results on their own footage. The work sits at the intersection of graphics and vision, showing how learned volumetric rendering can handle human motion without dense markers or studio rigs. -
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NeuMan
Neural Human Radiance Field from a Single Video (ECCV 2022)
NeuMan is a reference implementation that reconstructs both an animatable human and its background scene from a single monocular video using neural radiance fields. It supports novel view and novel pose synthesis, enabling compositional results like transferring reconstructed humans into new scenes. The pipeline separates human/body and environment, learning consistent geometry and appearance to support animation. Demos showcase sequences such as dance and handshake, and the code provides guidance for running evaluations and rendering. As a research release, it serves both as a baseline and as a starting point for work on human-centric NeRFs. The emphasis is on practical reconstruction quality from minimal capture setups. Compositional outputs to blend humans and backgrounds. Novel view and novel pose synthesis from learned radiance fields. -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is great for customization and teaching purposes. -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels, perform exact GP inference, and study training dynamics analytically for infinitely wide networks. The library closely mirrors JAX’s stax API while extending it to return a kernel_fn alongside init_fn and apply_fn, enabling drop-in workflows for kernel computation. Kernel evaluation is highly optimized for speed and memory, and computations can be automatically distributed across accelerators with near-linear scaling. -
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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. Nevergrad supports parallelization, budget scheduling, and multiple cost/resource constraints, allowing it to scale to nontrivial optimization problems. It includes visualization tools and diagnostic metrics to compare strategy performance, track parameter evolution, and detect stagnation. -
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Nimporter
Compile Nim Extensions for Python On Import
Nimporter allows the seamless import of Nim code into Python projects, enabling the use of Nim's performance and syntax within Python applications. -
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Ollama Python
Ollama Python library
ollama-python is an open-source Python SDK that wraps the Ollama CLI, allowing seamless interaction with local large language models (LLMs) managed by Ollama. Developers use it to load models, send prompts, manage sessions, and stream responses directly from Python code. It simplifies integration of Ollama-based models into applications, supporting synchronous and streaming modes. This tool is ideal for those building AI-driven apps with local model deployment. -
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Opacus
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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That project aims at delivering a reference implementation of a library, estimating and serving average prices paid for air travel products. It is not intended for use by an actual airline, but rather by simulators or other airline-related modules of
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Optimized Storage for temporal Data
open Optimized Storage of time series data
Beta version. Base class for optimized storage of time series data. Uses any kind of relational database. Cross plateform with multiple languages (C++, C#, Java). Conditional storage based on value variation : DeltaValue and DeltaTime params. Get back data without losts. -
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PROTON
High-level python framework that facilitates rapid server-side develop
PROTON is a high-level Python framework that facilitates rapid server-side development with clean & pragmatic design. Thanks for checking it out! PROTON aims at easing server-side development for all Python enthusiasts. Essentially, by running a shell command, developer will auto generate necessary Model, Controller and APIs! All of this with connectivity to Transactional Databases (PROTON supports Postgresql, MySQL & SQL Server),caching (Redis middleware), Auto generated OpenAPI specs & descriptive logging! One command, to get a production ready server-side stack! -
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Papis
Powerful and highly extensible command-line based document
Papis is a powerful and highly extensible CLI document and bibliography manager. With Papis, you can search your library for books and papers, add documents and notes, import and export to and from other formats, and much much more. Papis uses a human-readable and easily hackable .yaml file to store each entry's bibliographical data. It strives to be easy to use while providing a wide range of features. And for those who still want more, Papis makes it easy to write scripts that extend its features even further. -
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Parsera
Lightweight library for scraping web-sites with LLMs
Scrape data from any website with only a link and column descriptions. Parsera is a tool designed to scrape web content, specifically handling poorly structured or messy websites. -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. Its modular design includes tools for tree manipulation, named axes, and declarative neural network construction. The library integrates tightly with Treescope, an advanced pretty-printer for visualizing deeply nested JAX pytrees and NDArray structures. Penzai’s penzai.nn module provides a compositional, combinator-based API for building neural networks. -
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C Library to manage a pool of event/task in a persistent way to assure that your events/tasks won't be deleted because of a failure. Events/tasks are saved on a FS. if FS is NFS, NFS availability checks are made. (comes with a python binding)
