Open Source Python Software Development Software - Page 16
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Python Software Development Software
View 6283 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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Crazy Eddies GUI System (CEGUI)
A fast, powerful and adaptable GUI solution
Crazy Eddie's GUI (CEGUI) system is a graphical user interface C++ library. It was designed particularly for the needs of videogames, but the library is usable for non-game tasks, such as any other type of applications (rendering/visualisation/virtual reality) and tools. It is designed for user flexibility in look-and-feel, as well as being adaptable to the user's choice in tools and operating systems. Established in 2003, CEGUI sees continual, active development and remains one of the most powerful and most popular options for developers requiring an adaptable and efficient GUI solution. CEGUI is fully supported by the developers that created - and continue to develop - the library, and who are accessible via the project's dedicated internet forums and IRC channel. For more information, more screenshots and for news, check out out our homepage: http://cegui.org.uk The repository is now at bitbucket (including our issue/bug tracker): https://bitbucket.org/cegui -
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pythondialog
Python module to build dialogs for terminal-based applications
This is a Python module for doing terminal-based user interaction. It wraps the dialog/Xdialog program, and provides a nice, object-oriented programming model. -
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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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AWS X-Ray SDK for Python
AWS X-Ray SDK for the Python programming language
AWS X-Ray SDK for the Python programming language. The AWS X-Ray SDK for Python is compatible with Python 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, and 3.9. X-Ray Python SDK will by default generate no-op trace and entity id for unsampled requests and secure random trace and entity id for sampled requests. If customer wants to enable generating secure random trace and entity id for all the (sampled/unsampled) requests (this is applicable for trace id injection into logs use case) then they should set the AWS_XRAY_NOOP_ID environment variable as False. Oftentimes, it may be useful to be able to disable X-Ray for specific use cases, whether to stop X-Ray from sending traces at any moment or to test code functionality that originally depended on X-Ray instrumented packages to begin segments prior to the code call. For example, if your application relied on an XRayMiddleware to instrument incoming web requests, and you have a method that begins subsegments based on the segment generated. -
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Amazon Braket Python Schemas
A library that contains schemas for Amazon Braket
Amazon Braket Python Schemas is an open source library that contains the schemas for Braket, including intermediate representations (IR) for Amazon Braket quantum tasks and offers serialization and deserialization of those IR payloads. Think of the IR as the contract between the Amazon Braket SDK and Amazon Braket API for quantum programs. Schemas for the S3 results of each quantum task. Schemas for the device capabilities of each device. The preferred way to get Amazon Braket Python Schemas is by installing the Amazon Braket Python SDK, which will pull in the schemas. You can install from source by cloning this repository and running a pip install command in the root directory of the repository. There are currently two types of IR, including jaqcd (JsonAwsQuantumCircuitDescription) and annealing. -
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Ansible Examples
A few starter examples of ansible playbooks, to show features
This repository collects practical, real-world examples of using Ansible to automate infrastructure, deployments, and configurations. Each directory demonstrates a specific use case—ranging from setting up web servers, load balancers, and databases to orchestrating multi-tier applications in cloud environments. The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be adapted directly into your own infrastructure or to serve as reference blueprints when learning how to structure automation projects. Whether you’re managing a handful of servers or deploying at scale, this repo provides starting points that illustrate how Ansible can streamline repetitive operational tasks. -
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Attendedsysupgrade Server
An image on demand server for OpenWrt based distributions
The Attended Sysupgrade (ASU) is a service developed by OpenWrt to streamline the firmware upgrade process for devices running OpenWrt or its derivatives. It allows users to generate custom firmware images with a selected set of pre-installed packages, eliminating the need to manually reinstall packages after an upgrade. This service simplifies maintaining up-to-date and customized firmware across various devices. -
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Azure SDK for Python
Active development of the Azure SDK for Python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs or our versioned developer docs. For your convenience, each service has a separate set of libraries that you can choose to use instead of one, large Azure package. To get started with a specific library, see the README.md (or README.rst) file located in the library's project folder. Last stable versions of packages that have been provided for usage with Azure and are production-ready. These libraries provide you with similar functionalities to the Preview ones as they allow you to use and consume existing resources and interact with them, for example: upload a blob. They might not implement the guidelines or have the same feature set as the November releases. They do however offer wider coverage of services. A new set of management libraries that follow the Azure SDK Design Guidelines for Python are now available. -
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Bandit
Bandit is a tool designed to find common security issues in Python
Bandit is a tool designed to find common security issues in Python code. To do this, Bandit processes each file, builds an AST from it, and runs appropriate plugins against the AST nodes. Once Bandit has finished scanning all the files, it generates a report. Bandit was originally developed within the OpenStack Security Project and later rehomed to PyCQA. -
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment. -
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Black
The uncompromising Python code formatter
Black is the uncompromising Python code formatter that takes total control over your Python code formatting in the best way. Black delivers speed and certainty, while freeing you from constant pycodestyle nagging. Your code review becomes a lot faster and easier, so you can spend more time and mental energy on more important things. Blackened code makes the smallest diffs possible and looks the same no matter the project. Its formatting eventually becomes transparent, so you can simply forget about it and focus on your task at hand. Black has been successfully used in many projects, and has gained stellar user reviews as an exceptional, uncompromising PEP 8 compliant opinionated formatter. -
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CARTOframes
CARTO Python package for data scientists
A Python package for integrating CARTO maps, analysis, and data services into data science workflows. Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO's services like hosted, dynamic or static maps and Data Observatory augmentation. -
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CCXT
JavaScript/TypeScript/Python/C#/PHP cryptocurrency trading API
The ccxt library is a collection of available crypto exchanges or exchange classes. Each class implements the public and private API for a particular crypto exchange. All exchanges are derived from the base Exchange class and share a set of common methods. To access a particular exchange from ccxt library you need to create an instance of corresponding exchange class. Supported exchanges are updated frequently and new exchanges are added regularly. -
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CTFd
CTFs as you need them
CTFd is a Capture The Flag framework focusing on ease of use and customizability. It comes with everything you need to run a CTF and it's easy to customize with plugins and themes. Create your own challenges, categories, hints, and flags from the Admin Interface. Dynamic Scoring Challenges. Unlockable challenge support. Challenge plugin architecture to create your own custom challenges. Static & Regex-based flags. Custom flag plugins. Unlockable hints. File uploads to the server or an Amazon S3-compatible backend. Limit challenge attempts & hide challenges. Automatic bruteforce protection. Individual and Team-based competitions. Have users play on their own or form teams to play together. Scoreboard with automatic tie resolution. Hide Scores from the public. Freeze Scores at a specific time. Scoregraphs comparing the top 10 teams and team progress graphs. Markdown content management system. SMTP + Mailgun email support. Email confirmation support. Forgot password support. -
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Click
Python composable command line interface toolkit
Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary. It’s the “Command Line Interface Creation Kit”. It’s highly configurable but comes with sensible defaults out of the box. It aims to make the process of writing command line tools quick and fun while also preventing any frustration caused by the inability to implement an intended CLI API. Click in three points, arbitrary nesting of commands, automatic help page generation, supports lazy loading of subcommands at runtime. Comes with useful common helpers (getting terminal dimensions, ANSI colors, fetching direct keyboard input, screen clearing, finding config paths, launching apps and editors, etc.). Click actually implements its own parsing of arguments and does not use optparse or argparse following the optparse parsing behavior. Click is designed to be fun and customizable but not overly flexible. -
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ComfyUI IPAdapter plus
ComfyUI reference implementation for IPAdapter models
ComfyUI_IPAdapter_plus is a ComfyUI extension that adds a reference implementation for IPAdapter models. It focuses on image-to-image conditioning, letting a reference image guide the subject, style, or composition of a new generation. The project treats IPAdapter like a one-image LoRA, making it useful when users want visual influence without full model training. It includes example workflows that cover the main IPAdapter functions and help users build practical ComfyUI graphs. The extension supports unified loaders, model loaders, advanced apply nodes, attention masks, reference image weighting, and different embedding strategies. It is now in maintenance-only mode, so it is best used by ComfyUI users who need established IPAdapter workflows rather than a rapidly evolving plugin. -
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Django Celery
Old Celery integration project for Django
Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling. Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list. Celery is Open Source and licensed under the BSD License. A task queue’s input is a unit of work called a task. Dedicated worker processes constantly monitor task queues for new work to perform. Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task the client adds a message to the queue, the broker then delivers that message to a worker. Celery requires a message transport to send and receive messages. -
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DrissionPage
Python based web automation tool. Powerful and elegant
DrissionPage is a Python-based automation framework that blends the capabilities of Selenium for browser automation with Requests-HTML for fast, headless web data extraction. It enables seamless switching between browser-controlled and headless HTTP sessions within the same interface. Ideal for web scraping, testing, and automation, DrissionPage is lightweight and highly efficient, offering more flexibility than standard Selenium or Requests usage alone. -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints. -
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Flask-Caching
A caching extension for Flask
Flask-Caching is an extension to Flask that adds caching support for various backends to any Flask application. By running on top of cachelib it supports all of werkzeug’s original caching backends through a uniformed API. It is also possible to develop your own caching backend by subclassing flask_caching.backends.base.BaseCache class. Flask’s pluggable view classes are also supported. To cache them, use the same cached() decorator on the dispatch_request method. Using the same @cached decorator you are able to cache the result of other non-view related functions. The only stipulation is that you replace the key_prefix, otherwise it will use the request.path cache_key. Keys control what should be fetched from the cache. If, for example, a key does not exist in the cache, a new key-value entry will be created in the cache. Otherwise, the value (i.e. the cached result) of the key will be returned. -
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Flask-WTF
Simple integration of Flask and WTForms, including CSRF
Simple integration of Flask and WTForms, including CSRF, file upload, and reCAPTCHA. Integration with WTForms. Secure Form with CSRF token. Global CSRF protection. reCAPTCHA support. File upload that works with Flask-Uploads. Internationalization using Flask-Babel. -
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FlaskBB
A classic Forum Software in Python using Flask
A classic Forum Software in Python with a Modern and Fresh look. FlaskBB is open source and the whole source code is available on GitHub. It is being developed under the BSD 3-Clause license. Due to the plugin system, it is possible to extend FlaskBB without touching a single line of code. You just drop the plugin into the plugin's directory and activate it in the management panel. Are you sick of the standard theme or you simply want to create a customized style for your site? No problem! You can customize every template in FlaskBB. Besides that, you can also install community-made themes. Our goal is to develop a forum system with modern technologies and sticking to already well-tested and approved layouts. FlaskBB is a simple and extensible forum software that should be seen as the foundation for building communities. It is written in Python using the web framework Flask. FlaskBB is being distributed under the BSD 3-Clause License.
