Open Source Python Software Development Software - Page 18
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Python Software Development Software
View 6111 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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FuseSoC
Package manager and build abstraction tool for FPGA/ASIC development
FuseSoC is a package manager and build abstraction tool for hardware description language (HDL) code, aimed at simplifying the development and reuse of IP cores. It provides a standardized way to describe, manage, and build hardware projects, facilitating collaboration and reducing duplication of effort in FPGA and ASIC development. -
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GPT All Star
AI-powered code generation tool for scratch development of web apps
AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research project, and its primary value is to explore the possibility of autonomous AI agents. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
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GitHub520
Community-maintained approach to improving access to GitHub services
GitHub520 is a community-maintained approach to improving access to GitHub services from regions with network friction by leveraging host mappings. The repository provides a regularly updated list of domain-to-IP entries meant to be appended to a system’s hosts file so certain GitHub endpoints resolve faster or more reliably. It includes scripts or guidance to automate updates, reducing the need for manual lookups when IPs change. The project’s goal is pragmatic: improve developer productivity by mitigating timeouts and slow asset retrieval during cloning, package installs, or browsing. It is intended for users who understand the implications of hosts modifications and want a reversible, client-side tweak. While simple in concept, it has become a widely referenced workaround for network constraints affecting developer workflows. -
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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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Google Open Source Project Style Guide
Chinese version of Google open source project style guide
Each larger open source project has its own style guide, a series of conventions on how to write code for the project (sometimes more arbitrary). When all the code maintains a consistent style, it is more important when understanding large code bases. easy. The meaning of "style" covers a wide range, from "variables use camelCase" to "never use global variables" to "never use exceptions". The English version of the project maintains the programming style guidelines used in Google. If the project you are modifying originates from Google, you may be directed to the English version of the project page to understand the style used by the project. The Chinese version of the project uses reStructuredText plain text markup syntax, and uses Sphinx to generate document formats such as HTML / CHM / PDF. -
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Higher
higher is a pytorch library
higher is a specialized library designed to extend PyTorch’s capabilities by enabling higher-order differentiation and meta-learning through differentiable optimization loops. It allows developers and researchers to compute gradients through entire optimization processes, which is essential for tasks like meta-learning, hyperparameter optimization, and model adaptation. The library introduces utilities that convert standard torch.nn.Module instances into “stateless” functional forms, so parameter updates can be treated as differentiable operations. It also provides differentiable implementations of common optimizers like SGD and Adam, making it possible to backpropagate through an arbitrary number of inner-loop optimization steps. By offering a clear and flexible interface, higher simplifies building complex learning algorithms that require gradient tracking across multiple update levels. Its design ensures compatibility with existing PyTorch models. -
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Hypothesis
The property-based testing library for Python
Hypothesis is a powerful library for property-based testing in Python. Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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Impacket
A collection of Python classes for working with network protocols
Impacket is a collection of Python classes designed for working with network protocols. It was primarily created in the hopes of alleviating some of the hindrances associated with the implementation of networking protocols and stacks, and aims to speed up research and educational activities. It provides low-level programmatic access to packets, and the protocol implementation itself for some of the protocols, like SMB1-3 and MSRPC. It features several protocols, including Ethernet, IP, TCP, UDP, ICMP, IGMP, ARP, NMB and SMB1, SMB2 and SMB3 and more. Impacket's object oriented API makes it easy to work with deep hierarchies of protocols. It can construct packets from scratch, as well as parse them from raw data. -
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KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations. -
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Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems together. Makes a seamless transition from development to production, as you can write quick, throw-away exploratory code and transition to maintainable, easy-to-share, code experiments quickly. Puts the "engineering" back into data science because it borrows concepts from software engineering and applies them to machine-learning code. It is the foundation for clean, data science code. -
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Kubespray
Deploy a Production Ready Kubernetes Cluster
Can be deployed on AWS, GCE, Azure, OpenStack, vSphere, Equinix Metal (bare metal), Oracle Cloud Infrastructure (Experimental), or Baremetal. Highly available cluster. Composable (Choice of the network plugin for instance). Supports most popular Linux distributions. Continuous integration tests. The list of available docker versions is 18.09, 19.03, and 20.10. The recommended docker version is 20.10. The kubelet might break on docker's non-standard version numbering (it no longer uses semantic versioning). To ensure auto-updates don't break your cluster look into e.g. yum version lock plugin or apt pin). The target servers must have access to the Internet in order to pull docker images. Otherwise, additional configuration is required. The target servers are configured to allow IPv4 forwarding. If using IPv6 for pods and services, the target servers are configured to allow IPv6 forwarding. -
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LBRY SDK
The LBRY SDK for building decentralized content apps
Join top creators and more than 10,000,000 people on LBRY, an open, free, and fair network for digital content. LBRY is a decentralized peer-to-peer protocol for publishing and accessing digital content. It utilizes the LBRY blockchain as a global namespace and database of digital content. Blockchain entries contain searchable content metadata, identities, rights and access rules. LBRY also provides a data network that consists of peers (seeders) uploading and downloading data from other peers, possibly in exchange for payments, as well as a distributed hash table used by peers to discover other peers. LBRY SDK for Python is currently the most fully featured implementation of the LBRY Network protocols and includes many useful components and tools for building decentralized applications. -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises. -
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Loggifly
Get Alerts from your Docker Container Logs
LoggiFly is a lightweight, open-source monitoring tool designed to watch Docker container logs in real time and trigger alerts, notifications, or automated actions based on predefined keywords or regular expression patterns. Instead of manually scanning logs for issues or relying solely on centralized monitoring stacks, LoggiFly proactively inspects streams of container output and notifies users through services like Ntfy, Slack, Discord, Telegram, or webhooks when significant events occur. It supports plain text, regex, and multi-line pattern matching, and its flexible alert templating lets operators tailor messages for clarity and context, including attaching relevant log excerpts. Beyond notifications, LoggiFly can take automated actions such as restarting or stopping containers when specific critical patterns are detected, which is especially useful for preventing damage from misbehaving services. -
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Maya
Datetimes for Humans
Maya is a Python library that simplifies working with datetime objects. It provides a human-friendly API for parsing, formatting, and manipulating dates and times, addressing common frustrations with Python's built-in datetime module. -
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MegaLinter
Mega-Linter analyzes 50 languages, 22 formats, 21 tooling formats etc.
Verify your code consistency with an open-source tool. MegaLinter is an Open-Source tool for CI/CD workflows that analyzes the consistency of your code, IAC, configuration, and scripts in your repository sources, to ensure all your projects sources are clean and formatted whatever IDE/toolbox is used by their developers, powered by OX Security. Supporting 54 languages, 24 formats, 22 tooling formats and ready to use out of the box, as a GitHub action or any CI system highly configurable and free for all uses. Projects need to contain clean code, in order to avoid technical debt, which makes evolutive maintenance harder and time-consuming. By using code formatters and code linters, you ensure that your code base is easier to read and respects best practices, from the kick-off to each step of the project lifecycle. Not all developers have the good habit to use linters in their IDEs, making code reviews harder and longer to process. -
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Mentat
Mentat - The AI Coding Assistant
Mentat is the AI tool that assists you with any coding task, right from your command line. Unlike Copilot, Mentat coordinates edits across multiple locations and files. And unlike ChatGPT, Mentat already has the context of your project, no copy and pasting is required. Run Mentat from within your project directory. Mentat uses Git, so if your project doesn't already have Git set up, run git init. List the files you would like Mentat to read and edit as arguments. Mentat will add each of them to context, so be careful not to exceed the GPT-4 token context limit. -
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Mimesis
High-performance fake data generator for Python
Mimesis is an open source high-performance fake data generator for Python, able to provide data for various purposes in various languages. It's currently the fastest fake data generator for Python, and supports many different data providers that can produce data related to people, food, transportation, internet and many more. Mimesis is really easy to use, with everything you need just an import away. Simply import an object, called a Provider, which represents the type of data you need. Mimesis currently supports 34 different locales, the specification of which when creating providers will return data that is appropriate for the language or country associated with that locale. -
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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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MkDocs
Project documentation with Markdown
MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file. Start by reading the introductory tutorial, then check the User Guide for more information. There's a stack of good-looking themes available for MkDocs. Choose between the built in themes: mkdocs and readthedocs, select one of the third-party themes listed on the MkDocs Themes wiki page, or build your own. Get your project documentation looking just the way you want it by customizing your theme and/or installing some plugins. Modify Markdown's behavior with Markdown extensions. Many configuration options are available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes. -
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ModernGL
Modern OpenGL binding for Python
ModernGL is a Python wrapper over OpenGL, designed to simplify the creation of high-performance, modern graphics applications. It provides an intuitive API for rendering 2D and 3D graphics, making it accessible to both beginners and experienced developers. ModernGL is suitable for applications such as games, simulations, and data visualizations. -
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Mopidy
Mopidy is an extensible music server written in Python
Mopidy plays music from local disk, Spotify, SoundCloud, TuneIn, and more. You can edit the playlist from any phone, tablet, or computer using a variety of MPD and web clients. Vanilla Mopidy only plays music from files and radio streams. Through extensions, Mopidy can play music from cloud services like Spotify, SoundCloud, and TuneIn. With Mopidy's extension support, you can easily add backends for new music sources. Mopidy is a Python application that runs in a terminal or in the background on Linux computers or Macs that have network connectivity and audio output. Out of the box, Mopidy is an HTTP server. If you install the Mopidy-MPD extension, it becomes an MPD server too. Many additional frontends for controlling Mopidy are available as extensions. You and the people around you can all connect their favorite MPD or web client to the Mopidy server to search for music and manage the playlist together. -
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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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NSync
nsync is a C library that exports various synchronization primitives
nsync is a portable C library that provides a collection of advanced synchronization primitives designed to facilitate safe and efficient multithreaded programming. It offers reader-writer locks, condition variables, run-once initialization, waitable counters, and waitable bits for coordination and cancellation between threads. Unlike traditional pthreads-based synchronization, nsync introduces conditional critical sections, allowing developers to wait for arbitrary conditions without explicit signaling or complex loop-based logic. This approach simplifies concurrency management and often improves readability and maintainability of multithreaded code. The library emphasizes efficiency, with locks and condition variables occupying minimal memory and supporting cancellation mechanisms through nsync_note objects rather than thread-level cancellation. Designed with portability and performance in mind, nsync can be compiled on Unix-like systems and Windows using a C90 compiler.
