Open Source Python Software Development Software - Page 79
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Python Software Development Software
View 6291 business solutionsBrowse free open source Python Software Development Software and projects below. Use the toggles on the left to filter open source Python Software Development Software by OS, license, language, programming language, and project status.
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Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Nebula reporter
The optional reporter container which reads nebula reports from Kafka
Nebula is an open source-distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking effect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe. Ever wandered how your going to push an update to that smart fridge your company is working on as it's thousands of devices around the globe? wish you could have the assurance that your service will always use the latest code\envvars\etc in all of its edge locations? want the ability to stop\start a globally distributed service with a single command? Nebula was designed from the ground up to answer all of these needs and much more, refer to the documentation if your interested in seeing how to use it. -
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Nebula worker
The worker node manager container which manages nebula nodes
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are IoT devices, appliances\virtual appliances located at clients data centers, and edge computing. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes. -
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Nebula-Python-SDK
A python SDK for managing Nebula container orchestrator
A python SDK for managing Nebula container orchestrator. First, get NebulaPythonSDK onto your machine, now use it in your code. Nebula container orchestrator aims to help devs and ops treat IoT devices just like distributed Dockerized apps. It aim is to act as Docker orchestrator for IoT devices as well as for distributed services such as CDN or edge computing that can span thousands (possibly even millions) of devices worldwide and it does it all while being open-source and completely free. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes. -
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Needle
The iOS Security Testing Framework
Needle is an open-source, modular framework to streamline the process of conducting security assessments of iOS apps. Needle was originally made to work with iOS 9 and iOS 10. Since then, Frida was released and become the defacto tool to use with mobile security assessments. Some common Frida mobile security scripts were later implemented within Needle, as some of these scripts worked better or addressed some issues that were present in Needle's custom tooling. Assessing the security of an iOS application typically requires a plethora of tools, each developed for a specific need and all with different modes of operation and syntax. The Android ecosystem has tools like "drozer" that have solved this problem and aim to be a ‘one-stop shop’ for the majority of use cases, however, iOS does not have an equivalent. Needle is the MWR's iOS Security Testing Framework, released at Black Hat USA in August 2016. It is an open-source modular framework. -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
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Android Developers Team around the world. #BuildTheFuture
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Neoformat
A (Neo)vim plugin for formatting code
Neoformat uses a variety of formatters for many filetypes. Currently, Neoformat will run a formatter using the current buffer data, and on success, it will update the current buffer with the formatted text. On a formatter failure, Neoformat will try the next formatter defined for the filetype. By using getbufline() to read from the current buffer instead of file, Neoformat is able to format your buffer without you having to :w your file first. Also, by using setline(), marks, jumps, etc. are all maintained after formatting. Neoformat supports both sending buffer data to formatters via stdin, and also writing buffer data to /tmp/ for formatters to read that do not support input via stdin. If a formatter is either not configured to use stdin, or is not able to read from stdin, then buffer data will be written to a file in /tmp/neoformat/, where the formatter will then read from. -
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Neomake
Asynchronous linting and make framework for Neovim/Vim
Neomake is an asynchronous linting and build framework for Vim and Neovim that predates and inspires newer tooling in this space. It runs “makers” (linters, compilers, format checkers, test commands) in the background and surfaces results as signs, virtual text, or via quickfix/location lists. The system is highly configurable: you can define per-filetype makers, chain multiple tools, and control when they run (on save, on write, on cursor hold, or manually). Run several makers concurrently and aggregate results. Because it uses job control and timers, it scales well on large codebases and keeps the editor responsive. It’s useful both as a general “make” front end and as a diagnostic engine for code quality. Async execution of linters, compilers, and test commands via “makers”. Teams often adopt it to standardize checks across languages while keeping developers inside Vim. -
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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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Nested Editor
Specialized editor for structured documents.
Nested is a specialized editor focused on creating structured documents such as reports, publications, presentations, books, etc. It is designed to help the user concentrate on writing content without been distracted by format or markup. It offers a rich WYSIWYM interface where the user writes plain text with a lightweight markup language. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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 Networks Collection
Neural Networks Collection
This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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Neural Networks and Deep Learning
Code samples for my book "Neural Networks and Deep Learning"
Neural Networks and Deep Learning is an open-source collection of Python code examples accompanying Michael Nielsen's educational book of the same name. It helps students, researchers, and developers understand how neural networks function through practical implementations. The project demonstrates fundamental concepts such as feedforward networks, stochastic gradient descent, backpropagation, and neural network training. Its examples include recognizing handwritten digits using the MNIST dataset. The repository also explores more advanced neural network architectures through implementations using NumPy and Theano. The code encourages experimentation with network parameters, optimization techniques, and learning algorithms. Originally developed for Python 2, the project serves as an educational reference rather than a modern production framework. -
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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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Neuro SAN Studio
A playground for neuro-san
Neuro SAN Studio is a development environment and playground for building, testing, and deploying multi-agent AI systems using the Neuro SAN framework. It provides a hands-on interface where users can design agent networks, run experiments, and observe how multiple agents collaborate to solve complex tasks. The platform is built around a data-driven approach, where entire agent systems can be defined using configuration files rather than extensive code, making it accessible to both developers and domain experts. It supports advanced orchestration through decentralized communication protocols, allowing agents to dynamically delegate tasks and adapt to changing requirements. The system also includes mechanisms for secure data handling, ensuring sensitive information is not exposed directly to language models. Neuro SAN Studio offers built-in examples, tutorials, and debugging tools, which help users quickly prototype and refine multi-agent workflows. -
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NeuroKiwi is the application of the Associative Model (AM, previously dubbed AMDroid), being a model storing relational change-detections. The application tries to emulate ground-up knowledge accumulation with limited or no prior knowledge of shapes or patterns.
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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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Nextcord
A Python wrapper for the Discord API forked from discord.py
A modern, easy-to-use, feature-rich, and async-ready API wrapper for Discord written in Python. -
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NiL is a modular multi-purpose gaming engine including a multiplayer worm game a little like quake, except it's 2d and a little like worms except it's realtime.
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NiceGUI
Create web-based user interfaces with Python
NiceGUI is a Python-based UI framework that enables developers to create interactive web applications using only Python code. It abstracts away the complexities of HTML, CSS, and JavaScript, allowing for rapid development of web interfaces directly from Python scripts. NiceGUI is suitable for building dashboards, control panels, and other web-based tools, especially in contexts like robotics and data visualization. -
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NickelsWebtranslator is a simple web-based application for internationalization of java-based projects. Properties files can easily be translated by a remote and widespread group of translaters (utf8-encoding). Python web application with mysql backend.
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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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"No comment!" is a command-line tool that generates various comment-related metrics for source code files (e. g. comment density, blank lines per file)
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Graphical debugger for programs that don't just follow one stream (e.g. threaded, web applications). Also allows debugging in headless computers, in distributed environments and for crashing applications.
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NoneBot
Asynchronous multi-platform robot framework written in Python
Use NB-CLI to quickly build your own robot. Plug-in development, modular management. Supports multiple platforms and multiple incident response methods. 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. The plug-in system is the core of NoneBot2, through which the modularization and function expansion of the robot can be realized, which is convenient for maintenance and management.
