Open Source Python Software Development Software - Page 8
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Python Software Development Software
View 5728 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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PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research
Scale your models, not your boilerplate with PyTorch Lightning! PyTorch Lightning is the ultimate PyTorch research framework that allows you to focus on the research while it takes care of everything else. It's designed to decouple the science from the engineering in your PyTorch code, simplifying complex network coding and giving you maximum flexibility. PyTorch Lightning can be used for just about any type of research, and was built for the fast inference needed in AI research and production. When you need to scale up things like BERT and self-supervised learning, Lightning responds accordingly by automatically exporting to ONNX or TorchScript. PyTorch Lightning can easily be applied for any use case. With just a quick refactor you can run your code on any hardware, run distributed training, perform logging, metrics, visualization and so much more! -
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Pydantic-Core
Core validation logic for pydantic written in rust
pydantic-core is the Rust-based core validation logic for Pydantic, a widely used data validation library in Python. It offers significant performance improvements over its predecessor, enabling faster and more efficient data parsing and validation. -
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Pylint
It's not just a linter that annoys you!
Pylint is a static code analyzer for Python 2 or 3. The latest version supports Python 3.7.2 and above. Pylint analyses your code without actually running it. It checks for errors, enforces a coding standard, looks for code smells, and can make suggestions about how the code could be refactored. Projects that you might want to use alongside pylint include flake8 (faster and simpler checks with very few false positives), mypy, pyright or pyre (typing checks), bandit (security-oriented checks), black and isort (auto-formatting), autoflake (automated removal of unused import or variable), pyupgrade (automated upgrade to newer python syntax) and pydocstringformatter (automated pep257). Pylint isn't smarter than you: it may warn you about things that you have conscientiously done or checks for some things that you don't care about. During adoption, especially in a legacy project where pylint was never enforced. -
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Rasa
Open source machine learning framework to automate text conversations
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed. Rasa enables you to build assistants that can do this in a scalable way. Rasa uses Poetry for packaging and dependency management. If you want to build it from the source, you have to install Poetry first. By default, Poetry will try to use the currently activated Python version to create the virtual environment for the current project automatically. -
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Reflex Dev
Web apps in pure Python
Reflex is a Python framework for building full-stack web apps entirely in Python—without writing JavaScript for the frontend. It provides fast live reloads, built-in state management, deployment tooling, and optional AI-powered scaffolding to accelerate development. -
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Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
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SMSBoom
SMS bombing / SMS pressure measurement, a robust and free python
SMS bombing / SMS pressure measurement / | A robust and free python SMS bombing program, specializing in blowing up bad guys, with millions of interfaces, multi-threaded automatic addition of effective interfaces, support for millions of concurrent asynchronous coroutines, a completely free SMS bombing too! Hongkonger develops the whole network for the first time! api.jsonDefine the interface in a custom way. Support keyword replacement. Timestamp [timestamp] phone number [phone] Multithreaded/asynchronous requests. Provide web page testing/adding interface through Flask. Friendly command line argument support. Adopt convenient pipenv package management. Call SMS interface through proxy, support http, socks4, socks5 proxy. Use a random User-Agent. The number of bombings and the interval between bombings can be specified. -
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Slither
Static Analyzer for Solidity
Slither is a Solidity static analysis framework written in Python 3. It runs a suite of vulnerability detectors, prints visual information about contract details, and provides an API to easily write custom analyses. Slither enables developers to find vulnerabilities, enhance their code comprehension, and quickly prototype custom analyses. Slither is the first open-source static analysis framework for Solidity. Slither is fast and precise; it can find real vulnerabilities in a few seconds without user intervention. It is highly customizable and provides a set of APIs to inspect and analyze Solidity code easily. We use it in all of our security reviews. Now you can integrate it into your code-review process. We are open sourcing the core analysis engine of Slither. This core provides advanced static-analysis features, including an intermediate representation (SlithIR) with taint tracking capabilities on top of which complex analyses (“detectors”) can be built. -
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Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. The kit’s design goal is to shorten the “data prep” bottleneck by turning dataset creation into a repeatable pipeline rather than ad-hoc notebooks. It supports generation of rationales/chain-of-thought variants, configurable sampling, and guardrails so outputs meet format constraints and quality checks. Examples and guides show how to target task-specific behaviors like tool use or step-by-step reasoning, then save directly into training-ready files. -
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The Falcon Web Framework
The no-nonsense REST API and microservices framework
Falcon is a minimalist WSGI library for building speedy web APIs and app backends. We like to think of Falcon as the Dieter Rams of web frameworks. When it comes to building HTTP APIs, other frameworks weigh you down with tons of dependencies and unnecessary abstractions. Falcon cuts to the chase with a clean design that embraces HTTP and the REST architectural style. Highly optimized, extensible code base. Easy access to headers and bodies through request and response objects. DRY request processing via middleware components and hooks. Strict adherence to RFCs. Idiomatic HTTP error responses. Straightforward exception handling. Snappy testing with WSGI/ASGI helpers and mocks. CPython 3.5+ and PyPy 3.5+ support. No reliance on magic globals for routing and state management. Stable interfaces with an emphasis on backward compatibility. Simple API modeling through centralized RESTful routing. Highly-optimized, extensible code base. -
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Wally
Distributed Stream Processing
Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API. -
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WiFi-Pumpkin
WiFi-Pumpkin - Framework for Rogue Wi-Fi Access Point Attack
The WiFi-Pumpkin is a rogue AP framework to easily create these fake networks, all while forwarding legitimate traffic to and from the unsuspecting target. It comes stuffed with features, including rogue Wi-Fi access points, deauth attacks on client APs, a probe request and credentials monitor, transparent proxy, Windows update attack, phishing manager, ARP Poisoning, DNS Spoofing, Pumpkin-Proxy, and image capture on the fly. moreover, the WiFi-Pumpkin is a very complete framework for auditing Wi-Fi security check the list of features is quite broad. -
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django-push-notifications
Send push notifications to mobile devices through GCM or APNS
A minimal Django app that implements Device models that can send messages through APNS, FCM/GCM, WNS and WebPush. The app also implements an admin panel, through which you can test single and bulk notifications. Select one or more FCM/GCM, APNS, WNS or WebPush devices and in the action dropdown, select "Send test message" or "Send test message in bulk", accordingly. Note that sending a non-bulk test message to more than one device will just iterate over the devices and send multiple single messages. UPDATE_ON_DUPLICATE_REG_ID: Transform create of an existing Device (based on registration id) into an update. Sending messages in bulk makes use of the bulk mechanics offered by GCM and APNS. It is almost always preferable to send bulk notifications instead of single ones. -
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fvcore
Collection of common code shared among different research projects
fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. Because it is small, stable, and well-tested, fvcore is frequently imported by projects like Detectron2 and PyTorchVideo to avoid duplicating infrastructure and to keep research repos. -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. The repository provides training recipes, data pipelines, and evaluation code that clarify which masking patterns and architectural choices matter most. -
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macOS Simple KVM
Tools to set up a quick macOS VM in QEMU, accelerated by KVM
macOS-Simple-KVM is a project that provides scripts and configuration files to easily set up and run macOS in a virtual machine using QEMU and KVM. It simplifies what is typically a complex process by offering a straightforward approach to creating a macOS VM on Linux systems with hardware virtualization support. The repository includes tools for preparing installation media, configuring virtual hardware, and managing VM launch scripts. By using KVM acceleration, the virtual machine runs with near-native performance, making it useful for testing, development, or personal experimentation. The project also supports GPU passthrough and other advanced configurations for users who want a more optimized macOS VM environment. While primarily intended for educational and testing purposes, it demonstrates how macOS can be virtualized outside of Apple hardware. -
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pyfpdf
Simple PDF generation for Python (FPDF PHP port)
PyFPDF is a library for PDF document generation under Python, ported from PHP (see FPDF: "Free"-PDF, a well-known PDFlib-extension replacement with many examples, scripts, and derivatives). Compared with other PDF libraries, PyFPDF is simple, small, and versatile, with advanced capabilities, and is easy to learn, extend and maintain. -
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pyglet
pyglet is a cross-platform windowing and multimedia library for Python
Pyglet is a cross-platform windowing and multimedia library for Python, intended for developing games and other visually rich applications. It supports windowing, input event handling, OpenGL graphics, loading images and videos, and playing sounds and music. -
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GDAL wheels for linux
GDAL wheels for python and C/C++ projects (Linux only)
To use precompiled wheels: 1) go to releases (Files) and download tarball needed; 2) install it with command: python3 -m pip install /path/to/wheel.whl Or simply use URL in pip: python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download URL may be found under "View details" button (i) To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages) To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux Usually this is done via command `make wheels` GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal Built with PROJ (proj.db is included), GEOS, EXPAT. See Dockerfile.wheels for additional components. -
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QuickFIX is the worlds first Open Source C++ FIX (Financial Information eXchange) engine, helping financial institutions easily integrate with each other. The SVN repository is now locked. Latest code is hosted at github. https://github.com/quickfix/quickfix
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AICodeBot
AI-powered tool for developers, simplifying coding tasks
AICodeBot is a terminal-based coding assistant designed to make your coding life easier. Think of it as your AI version of a pair programmer. Perform code reviews, create helpful commit messages, debug problems, and help you think through building new features. A team member that accelerates the pace of development and helps you write better code. We've planned to build out multiple different interfaces for interacting with AICodeBot. To start, it's a command-line tool that you can install and run in your terminal and a GitHub Action for Code Reviews. This project was built before AI Coding Assistants were cool. As such, much of the functionality has been replicated in various IDEs. Where AICodeBot shines is a) it's in the terminal, not GUI, and b) it can be used in processes like GitHub actions. We're using AICodeBot to build AICodeBot, and it's upward spiraling all the time.️ We're looking for contributors to help us build it out. -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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Anymail
Django email backends and webhooks for Amazon SES, Mailgun, Mailjet
Anymail lets you send and receive email in Django using your choice of transactional email service providers (ESPs). It extends the standard django.core.mail with many common ESP-added features, providing a consistent API that avoids locking your code to one specific ESP (and making it easier to change ESPs later if needed). Integration of each ESP’s sending APIs into Django’s built-in email package, including support for HTML, attachments, extra headers, and other standard email features. Extensions to expose common ESP-added functionality, like tags, metadata, and tracking, with code that’s portable between ESPs. Inbound message support, to receive email through your ESP’s webhooks, with simplified, portable access to attachments and other inbound content. Anymail maintains compatibility with all Django versions that are in mainstream or extended support, plus (usually) a few older Django versions, and is extensively tested on all Python versions supported by Django. -
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs.
