Open Source Python Software Development Software - Page 75

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform.

MOCOP is a server based, rich-client, platform independent application development platform. It uses Python and comes with batteries included for developing multi-tanent server based applications.

Mondo: Shared Library Monitor

A Python-based macro processing tool to extend C-like languages with DSLs. MP reduces the amount of work necessary to extend C by extracting the relevant blocks to process and calling user-supplied Python functions to handle the macro.

Distributed Parallel Programming for Python! This package builds on traditional Python by enabling users to write distributed, parallel programs based on MPI message passing primitives. General python objects can be messaged between processors. Ru

A message passing library addressing the special needs/capabilities of Qt applications. The basic building block for classic 3-tier applications where both client and server are Qt applications.

MRV is a python framework based on wrapping the Maya API to speed up development and ease of use within pipelines using Autodesk Maya.

This code is another way to create SOA applications through HTTP. Can execute Database SPs, MSMQ, Assemblies, IronPython script and generates RSS, JSON, Web Service, Text or Excel file through HTTP. The system has authentication and logging support.

MSCViewer is a tool intended for debugging of control flows in concurrent, distributed systems. The tool loads logs generated by various entities in the system and visualize a sequence diagram chart for events and interactions. The diagram is fully interactive: entity can be added/removed from the diagram and shuffled; events can be filtered, searched, highlighted and annotated with comments. MSCViewer features integration with a Python interpreter which allows writing Python scripts interacting with the model. This powerful feature can be used to automate validatation of distributed control flows, integrate with graphing infrastructure, etc.

Automatically Get Official Product Updates on munki Server. This MUNKI SERVER Command line Tool (Munki Updater) will check for available MUNKI Product Updates on official Servers (example Mozilla) and will allow to Download and Update older Product Versions on MUNKI Repository and distribute them on clients having older versions. munki updater readme: http://svn.code.sourceforge.net/p/munkiupdater/code-0/trunk/readme.txt ############# managed software centre installer including public repo with freeware: /bin/bash -c "$(curl -fsSL http://svn.code.sourceforge.net/p/munkiupdater/code-0/trunk/online/install-munki.sh)"

MVCPython is a framework which automatically generates a MVC architecture (MVC= Model View Controller) just from Python Classes and Object defintions. MVCPython achieves this through a strong use of Python's introspection capability.

This project is redundant. All files have been copied to MaMo Py: https://sourceforge.net/projects/marimorepy/

A collection of python libraries used by MARIMORE Inc. http://www.marimore.co.jp THIS PROJECT HAS MOVED TO https://github.com/marimore/marimorepy

The project moved here: https://github.com/vbatoufflet/machette

You assemble *blocks* in your django template, with each block wired to a tastypie resource, which in turn is mapped to a django model. Each *block* will reside in a dijit ContentPane which you must define in your template. You have a choice of 2 layouts for each *block*, Form or Grid based. Form displays a single record in either a 1, 2 or 3 column layout while Grid displays many rows in a spreadsheet like grid. You can perform edits in place on fields in both layouts. You can define the master-detail relationship between the *blocks* and enblok will automatically display the related detail *block* rows as the user navigates the master *block*'s rows. You get for free the default *CRUD* functionality for each block, together with Search. You are not limited to just a 2 level master-detail relationship, in fact enblok can support n-th level relationships but we do not advise you to go beyond 3. Read the distribution's enblok/readme.txt for further instructions.

Maestro Framework is a Python framework for orchestrating AI subagents across complex tasks. It breaks a user objective into smaller subtasks, assigns those subtasks to worker models, and refines the results into a final output. The original workflow used Claude Opus and Haiku, while newer variants support Claude 3.5 Sonnet, GPT models, Gemini, Cohere, Groq, LM Studio, and Ollama through different scripts and LiteLLM support. It can maintain context between subtasks so later steps can build on earlier work. The project can also generate exchange logs and save them as Markdown for review. Overall, it is useful for experimenting with multi-agent task decomposition, AI-assisted planning, and model orchestration workflows.

This script will automatically decode an obfuscated JavaScript or VBScript in HTML and scan for suspicious/malicious codes in the page.

Mallard is a graphical game design package, akin to many applications for Windows such as The Games Factory, RPG Maker, Game Maker, etc. It is written in Python with wxWidgets and PyGame, and therefore should support any OS which supports Python and SDL.

Adobe Malware Classifier is a command-line tool that lets antivirus analysts, IT administrators, and security researchers quickly and easily determine if a binary file contains malware, so they can develop malware detection signatures faster, reducing the time in which users' systems are vulnerable. Malware Classifier uses machine learning algorithms to classify Win32 binaries – EXEs and DLLs – into three classes: 0 for “clean,” 1 for “malicious,” or “UNKNOWN.” The tool was developed using models resultant from running the J48, J48 Graft, PART, and Ridor machine-learning algorithms on a dataset of approximately 100,000 malicious programs and 16,000 clean programs. The tool extracts seven key features from an unknown binary, feeds them to one of the four classifiers or all of them, and presents its classification of the unknown binary.

Mangum is an adapter for running ASGI applications in AWS Lambda to handle Function URL, API Gateway, ALB, and Lambda@Edge events. Event handlers for API Gateway HTTP and REST APIs, Application Load Balancer, Function URLs, and CloudFront Lambda@Edge. Compatibility with ASGI application frameworks, such as Starlette, FastAPI, Quart and Django. Support for binary media types and payload compression in API Gateway using GZip or Brotli. Works with existing deployment and configuration tools, including Serverless Framework and AWS SAM. The heart of Mangum is the adapter class. It is a configurable wrapper that allows any ASGI application (or framework) to run in an AWS Lambda deployment. The adapter accepts a number of keyword arguments to configure settings related to logging, HTTP responses, ASGI lifespan, and API Gateway configuration.

Manticore helps us quickly take advantage of symbolic execution, taint analysis, and instrumentation to analyze binaries. Parts of Manticore underpinned our symbolic execution capabilities in the Cyber Grand Challenge. As an open-source tool, we hope that others can take advantage of these capabilities in their own projects. We prioritized simplicity and usability while building Manticore. We used minimal external dependencies and our API should look familiar to anyone with exploitation or reversing background. If you have never used such a tool before, give Manticore a try. Manticore comes with an easy-to-use command line tool that quickly generates new program “test cases” (or sample inputs) with symbolic execution. Each test case results in a unique outcome when running the program, like a normal process exit or crash (e.g., invalid program counter, invalid memory read/write).

Mantikhor is an *information representation* language. Like RDF, it models information as directed-edge arc-node graphs, although Mantikhor's use of such constructs is more structured and constrained.

En una pequena guia para la los estudiantes de programacion para iniciarlos en el mundo de C/C++

The market assumes an environment in which farmers sell their produce through brokers and traders locate produce to buy through brokers. The major aim of the simulator is to experiment with various reputation mechanisms to manage bottlenecks and to model various adversarial scenarios. The market is aimed to simulate agricultural trade in developing countries. It is written in python and mysql database on Linux.

The Markovski project implements an interpreter/compiler for a language named Markovski, used to write Markov algorithms (see http://en.wikipedia.org/wiki/Markov_algorithm).