Open Source Python Software Development Software - Page 51
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Python Software Development Software
View 6114 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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Database-backed Periodic Tasks
Celery Periodic Tasks backed by the Django ORM
This extension enables you to store the periodic task schedule in the database. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run. Usage and installation instructions for this extension are available from the Celery documentation. If you change the Django TIME_ZONE setting your periodic task schedule will still be based on the old timezone. To create a periodic task executing at an interval you must first create the interval object. If you have multiple periodic tasks executing every 10 seconds, then they should all point to the same schedule object. -
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DebuGui - GDB UI
Easy to use GUI for GDB
This project attempts to solve a long aching problem of a lack of a simple, yet powerful GUI for GDB. One that handles STL data types and allows easy extensibility. Requires: Python 2.7.x PySide (Qt python bindings: e.g. apt-get install python-pyside) -
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Decision Analysis is an easily-extensible expert system to help users make decisions of all types. Written entirely in Python, Decision Analysis, at this time, contains a general decsion module, which uses a weighted average technique to evaluate use
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A program to apply a link map to a Mac OS X crash log that came from a build without traceback tables. The output contains at least as much information as would the same crash log from a corresponding build with traceback tables.
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
DeepLabv3 Plus is a PyTorch implementation of DeepLabv3+ for semantic segmentation. It implements the encoder-decoder architecture with atrous separable convolution and provides a practical workflow for training, prediction, and mIoU evaluation. The repository supports VOC-style segmentation datasets and includes utilities for annotation generation, JSON dataset conversion, model summary inspection, prediction, and metric calculation. It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. The codebase is primarily Jupyter Notebooks and Python, reflecting an emphasis on experimentation and pedagogy rather than production packaging. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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DeepSpec
A full-stack codebase for training and evaluating speculative decoding
DeepSpec is a full-stack codebase for training and evaluating draft models used in speculative decoding. It provides the components needed to prepare data, train draft models, and measure acceptance behavior against target models. The workflow starts with data preparation, including prompt download, target answer regeneration, and target cache construction. It then trains a draft model using configuration files for different algorithms and target model setups. The evaluation pipeline measures speculative decoding performance across benchmark tasks such as math, coding, instruction-following, and chat-style datasets. Overall, it is useful for researchers and engineers studying faster language model inference through speculative decoding methods. -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran. -
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Defox text to speech and downloader
Written or imported text offline read or online download.
This software design to convert text to speech and download the converted speech. Description : • Installation setup with two languages (English, French) • Two areas called text reading and speech downloading • Many languages supported to download center Note 1: I'm a student yet and I'm not in the software designing industry. Therefore maybe I haven't software making skills. I'm worried about that. ! Note 2 : When you double click on the software maybe it will get some seconds to open. That's not my fault. I used Python language to make this software and Python was not supported speedy to modern computers. -
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The DVS Deployable Versioning System is for tracking the development, QA and installation of deployables. It is intended to be used in in-house project environments and uses a CVS style command line interface.
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The DevTools project is an open-source (BSD-licensed) set of development tools, including a set of shared gmake-style utility makefiles, the TLM version-control wrapper, and various other little utility C++ programs and Perl and Python scripts.
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Diazy is an application framework for the generation of source code from Dia UML models and the degeneration (reverse engineering) of source code to Dia UML models. This will allow developers to rapidly deploy object oriented design schemes.
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A Graphic User Interface with texteditor and category browser for interaction with the free encyclopedia Wikipedia
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Diems - CodeShine
Web application for posting, submitting, and evaluating assignments
CodeShine (a part of Di website) - is an application for posting, submitting, and evaluating assignments. As a whole, 'Di' is a shorthand for Deogiri Institute of Engineering and Management Studies, Aurangabad 431001, Maharashtra, India. -
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Diet Python is a flavor of Python w/ allegro, multiarray, umath, calldll, npstruct and curses builtin, all else nonessential to language ripped out. Size < 3MB, 1% of PSF Python w/ full graphics development suite. Diet Python helps keep clients thin :)
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Diffr compares two source files and graphically displays the differences side-by-side. Sections of differences are highlighted and can be selected from either file and saved to a merged source file.
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DigestIt! is a tool to browse text files with full interactive cross reference. It is written for digesting source code. So there comes the name: DigestIt! I use it on several huge projects for more than 7 years. Latest: Feb. 04, 2008
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Digital Preservation Software Platform (DPSP) consists of a number of open source products such as Xena and Digital Preservation Recorder. This installs and configures the complete platform for preserving digital records - just install and use! NO LONGER MAINTAINED, NO LONGER SUPPORTED
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Dimenso is a library of numeric types for .Net and Java, that obey dimensional analysis and permit calculations with physical quantities. Due to the lack of C++ - like templates with numeric arguments, code generation is employed using Python.
