Open Source Python Software Development Software - Page 27
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Python Software Development Software
View 5723 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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Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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AALib/PyAALib - Asynchronous Action Library Project - The project is a Framework based on Pure Object Oriented Concepts to develop asynchronous communication between threads (thread safety) and external process. Platforms: Windows, MacOSX, Linux,Sola
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ACDK - Artefaktur Component Development Kit - is a platform independent C++-framework similar to Java or C#/.NET for generating distributed and scriptable components and applications.
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ADOMA stands for: Alternative Display Of Multiple Alignment. ADOMA can create four different displays of a multiple sequence alignment: a ClustalW alignment in HTML format, a simplified ClustalW alignment in HTML and/or txt format and a colored ClustalW alignment in HTML format. For examples of these outputfiles check the screenshots. ADOMA uses ClustalW to create the multiple alignment from DNA or protein sequences and displays them slightly different than the normal output of ClustalW. ADOMA is a commandline program that can easily be used in pipelines. For more information check the README.md in the Files section. How to cite ADOMA: Zaal, D. and Nota, B. (2016), ADOMA: A Command Line Tool to Modify ClustalW Multiple Alignment Output. Mol. Inf., 35: 42–44. doi: 10.1002/minf.201500083 http://onlinelibrary.wiley.com/doi/10.1002/minf.201500083/abstract
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information. -
Photo and Video Editing APIs and SDKsUnlock Picsart's full editing suite by embedding our Editor SDK directly into your platform. Offer your users the power of a full design suite without leaving your site.
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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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AKIRA aims to create a C++ development framework to build cognitive architectures and complex artificial intelligent agents.Features:KQML,Fuzzy Logic,Neural Net,Fuzzy Cognitive Maps and DIPRA (a distributed BDI - Belief Desire Intention goals model)
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Data server for user intelligent agents. Written in Python, using XML-RPC as client/server communication and RDBMS as storage.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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Data Collection and Labeling for AI Innovation.OORT DataHub is a blockchain-powered platform where people across the world can collect and pre-process data including images, audios, or videos to improve AI and machine learning models.
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Python script to get the last 5 minutes of accepted traffic logs via the trendmicro email security API and send them to a syslog server Script Python para obtener los ultimos 5 minutos de logs de trafico aceptado por medio de la API de trendmicro email security y enviarlos a un servidor de syslog Crontab */5 * * * * /usr/local/bin/python3.9 /home/user/tmes-syslog_0.02.py 2>&1 > /home/user/errores.txt ------------ # API documentation # Getting Started with Trend Micro Email Security APIs # https://docs.trendmicro.com/en-us/enterprise/trend-micro-email-security-rest-api-online-help/getting-started-with.aspx # Obtaining the API Key # https://docs.trendmicro.com/en-us/enterprise/trend-micro-email-security-rest-api-online-help/getting-started-with/obtaining-the-api-ke.aspx # List Mail Tracking Logs # https://docs.trendmicro.com/en-us/enterprise/trend-micro-email-security-rest-api-online-help/supported-apis/logs/list-mail-tracking-l.aspx
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Simplified interface to the Augmented Reality library ARToolKit for Python and other programming languages.
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ARREA
Collaborative editor, requirements management tool and much more
ARREA (A Reasonable Requirements Engineering Application) is a simple yet powerful requirements management tool that uses arbitrary files to store information and Subversion to allow team collaboration. Its simple conventions make it suitable for much other purposes. In fact, for any document-oriented tasks focused on collaborative work and exhaustive change and traceability management. At its heart, ARREA provides a thin layer on top of SVN that allows it to be used as a powerful time-aware NoSQL database, accessible through an easy REST API. -
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ART ASCII Library
ASCII art library for Python
ASCII art is also known as "computer text art". It involves the smart placement of typed special characters or letters to make a visual shape that is spread over multiple lines of text. ART is a Python lib for text converting to ASCII art fancy. -
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ASDRAD is a source code generator which reads a configuration file and generates *.c, *.h, makefiles etc. It's written in Python and uses templates. Output files are for AmigaOS and derivates like AROS and MorphOS.
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AT Managed File is a project originally developed by Enfold Systems company for Plone 2.5. This is product version patched to work in Plone version 3.x. AT Managed File product stores files in local path insted of ZOBD while maintains a Plone instance.
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Automated Test Framework for Embedded Software: A system to perform test scripts 'in system' on embedded software. It uses Python as a high level description language and interfaces with GDB to communicate with the embedded platform.
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ATX
Smart phone automation tool. Support iOS, Android, WebApp and game
ATX (AutomatorX) is an open source automated testing tool that supports testing native applications, games, and Web applications on iOS and Android platforms. Use Python to write test cases, and use image recognition and control positioning technology to complete the automation of the game. Attach a dedicated IDE to complete scripting quickly. The libraries for Android and iOS native application testing have been separated and can be used alone (it is strongly recommended to use it alone, firstly, it has less dependencies and high stability, and secondly, it can be automatically completed when writing code) -
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AWS IoT Device SDK for Python
SDK for connecting to AWS IoT from a device using Python
The AWS IoT Device SDK for Python allows developers to write Python script to use their devices to access the AWS IoT platform through MQTT or MQTT over the WebSocket protocol. By connecting their devices to AWS IoT, users can securely work with the message broker, rules, and the device shadow (sometimes referred to as a thing shadow) provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, and more. It is a complete rework, built to improve reliability, performance, and security. We invite your feedback! The SDK is built on top of a modified Paho MQTT Python client library. Developers can choose from two types of connections to connect to AWS IoT. For MQTT over TLS (port 8883 and port 443), a valid certificate and a private key are required for authentication. For MQTT over the WebSocket protocol (port 443), a valid AWS Identity and Access Management (IAM) access key ID and secret access key pair are required for authentication. -
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AWS IoT Greengrass Core SDK
SDK to use with functions running on Greengrass Core using Python
The AWS IoT Greengrass Core SDK is meant to be used by AWS Lambda functions running on an AWS IoT Greengrass Core. It will enable Lambda functions to invoke other Lambda functions deployed to the Greengrass Core, publish messages to the Greengrass Core and work with the local Shadow service. To use the AWS IoT Greengrass Core SDK, you must first import the AWS IoT Greengrass Core SDK in your Lambda function as you would with any other external libraries. You then need to create a client for ‘iot-data’ or ‘lambda’. Use ‘iot-data’ if you wish to publish messages to the local Greengrass Core and interact with the local Shadow service. Use ‘lambda’ if you wish to invoke other Lambda functions deployed to the same Greengrass Core. As new features are added to AWS IoT Greengrass, newer versions of the AWS IoT Greengrass SDK may be incompatible with older versions of the AWS IoT Greengrass core. -
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AWS Jupyter Proxy
Jupyter server extension to proxy requests with AWS SigV4 authentican
A Jupyter server extension to proxy requests with AWS SigV4 authentication. This server extension enables the usage of the AWS JavaScript/TypeScript SDK to write Jupyter frontend extensions without having to export AWS credentials to the browser. A single /awsproxy endpoint is added on the Jupyter server which receives incoming requests from the browser, uses the credentials on the server to add SigV4 authentication to the request, and then proxies the request to the actual AWS service endpoint. All requests are proxied back-and-forth as-is, e.g., a 4xx status code from the AWS service will be relayed back as-is to the browser. Using this requries no additional dependencies in the client-side code. Just use the regular AWS JavaScript/TypeScript SDK methods and add any dummy credentials and change the endpoint to the /awsproxy endpoint. -
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AWS Lambda Python Runtime Interf Client
Seamlessly extend your preferred base images to be Lambda compatible
We have open-sourced a set of software packages, Runtime Interface Clients (RIC), that implement the Lambda Runtime API, allowing you to seamlessly extend your preferred base images to be Lambda compatible. The Lambda Runtime Interface Client is a lightweight interface that allows your runtime to receive requests from and send requests to the Lambda service. The Lambda Python Runtime Interface Client is vended through pip. You can include this package in your preferred base image to make that base image Lambda compatible. To make it easy to locally test Lambda functions packaged as container images we open-sourced a lightweight web-server, Lambda Runtime Interface Emulator (RIE), which allows your function packaged as a container image to accept HTTP requests. You can install the AWS Lambda Runtime Interface Emulator on your local machine to test your function. Then when you run the image function, you set the entry point to be the emulator. -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can continue to use the same ML frameworks you use today and migrate your software onto Inf1 instances with minimal code changes and without tie-in to vendor-specific solutions. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization. -
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AWS ParallelCluster Node
Python package installed on the Amazon EC2 instances
aws-parallelcluster-node is the python package installed on the Amazon EC2 instances launched as part of AWS ParallelCluster. AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. You can build higher-level workflows, such as a Genomics portal that automates the entire DNA sequencing workflow, on top of AWS ParallelCluster. -
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AWS SAM CLI
CLI tool to build, test, debug, and deploy Serverless applications
The AWS Serverless Application Model (SAM) CLI is an open-source CLI tool that helps you develop serverless applications containing Lambda functions, Step Functions, API Gateway, EventBridge, SQS, SNS and more. The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. To get started with building SAM-based applications, use the SAM CLI. SAM CLI provides a Lambda-like execution environment that lets you locally build, test, debug, and deploy AWS serverless applications. -
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AWS Serverless Application Model
An open-source framework for building serverless applications
The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. To get started with building SAM-based applications, use the AWS SAM CLI. SAM CLI provides a Lambda-like execution environment that lets you locally build, test, and debug applications defined by SAM templates or through the AWS Cloud Development Kit (CDK). You can also use the SAM CLI to deploy your applications to AWS. SAM and SAM CLI are open-sourced under the Apache 2.0 license. You can contribute new features and enhancements to SAM on GitHub or SAM CLI on GitHub.
