Open Source Python Software Development Software - Page 69
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Python Software Development Software
View 6289 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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Earn up to 16% annual interest with Nexo.Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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JPE is a seamless, complete, and efficient integration of Java and standard Python (C Python). JPE provides Java access to Python's native extension, and Java's Swing to Python developpers.
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JavaScript Lint is a development tool that validates JavaScript code for best coding practices, finding common mistakes without actually running the script or opening the web page.
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Javelina is an advanced code coverage tool that uses dynamic instrumentation to minimize the overhead for collecting coverage data. Additionally, Javelina allows the application of logical operations to code coverage data.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Jeffy
Serverless Application Framework for Python AWS Lambda
Jeffy is a Serverless "Application" Framework for Python consisting of a suite of Utilities for Lambda functions that make developing serverless applications easy. There are three main things that Jeffy is focused on: 1. Logging - Jeffy provides easy to see JSON format logging. All events, responses and errors are captured and you can make configurations for any additional attributes you'd like to see on the logs. 2. Decorators - Jeffy has an extensive range of decorators/ event handlers for implementing common things for Lambda functions. 3. Tracing - Events are traceable within related functions and AWS services with generating and passing correlation_id. -
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Jianying Headless
Native Jianying drafts, isolated editing/export
Jianying Headless is a local automation toolkit for creating and editing Jianying Pro video projects on macOS. Structured editing plans can generate editable drafts with multiple video, audio, text, and overlay tracks. Existing projects can be modified inside isolated copies so the original draft is not overwritten. The system supports local media, fonts, keyframes, selected masks, transitions, and native MP4 export through an installed Jianying engine. A Python command-line interface and Agent Skill make it suitable for batch processing and AI-assisted editing. Environment checks verify compatible application versions, tools, media, and project state before running. It is an independent integration rather than an official Jianying SDK. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS. Intuitive design pattern for high-performance microservices. Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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An UI editor for j2me developers. You can draw images with this tool and then generate j2me codes which draws the same image by drawLine(), drawRect(), etc.
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Joe SDK
Web. Desktop. Best of both worlds.
Joe SDK is an easy to learn and adapt python software development kit for excellent GUI desktop applications. You can create a desktop application using python, html, css, javascript or jquery in minutes! The SDK allows you to: - Run python code, on any javascript event. - Render result of python function dynamically - Create HTML User Interfaces, using standard HTML. The SDK is powered by: - Google Chrome apps - Python anywhere - Cherrypy Web Framework Though it is a desktop application sdk, it is purely web hosted locally. This makes it extremely easy to deploy these applications to web. -
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Jostraca is a general purpose code generation toolkit for software developers, using a syntax similar to JSP. Jostraca can be extended to generate code in any language, using any language, while placing no restrictions on software design.
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. Jraph also comes with a model zoo—a collection of forkable reference implementations of common message-passing GNN architectures, such as Graph Networks, Graph Convolutional Networks, and Graph Attention Networks. -
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This is currently a bunch of scripst used to manage translations of a large number of Java Server Pages (JSP). It is not integrated into any IDE, but can be used as a general way to manage these files from anywhere.
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Jupyter Dash
Dash v2.11+ has Jupyter support built in
Dash 2.11 and later supports running Dash apps in classic Jupyter Notebooks and in JupyterLab without the need to update the code or use the additional JupyterDash library. If you are using an earlier version of Dash, you can run Dash apps in a notebook using JupyterDash. This page documents additional options available when running Dash apps in notebooks as well as troubleshooting information. -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run consistently across machines. Users can pull a particular stack image and launch a Jupyter server without worrying about installing Python, R, or complex dependencies themselves — everything needed is baked into the container. This makes the stacks especially useful for education, demos, collaborative coding, and CI/CD workflows where consistent environments are crucial, and it integrates smoothly with cloud platforms, JupyterHub deployments, and Binder for interactive sharing. -
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Jupyter Notebook Tools for Sphinx
Sphinx source parser for Jupyter notebooks
nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluated notebooks – i.e. notebooks without stored output cells – will be automatically executed during the Sphinx build process. -
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Jupyter Themes
Custom Jupyter Notebook Themes
jupyter-themes brings theme management to classic Jupyter Notebooks with a command-line tool that can restyle the interface, code cells, and UI chrome in seconds. It ships a catalog of popular dark and light themes and lets you customize fonts, font sizes, cell widths, and toolbar visibility so the notebook matches your preferred reading and coding ergonomics. The theming system adjusts CodeMirror syntax highlighting to keep code legible against chosen backgrounds and provides options to harmonize matplotlib/plotly colors for cohesive visuals. Installation and usage are streamlined through a single jt command, with flags to set or preview themes and a built-in restore option to return to defaults. Because it modifies the Notebook’s CSS, the project focuses on lightweight overrides rather than invasive changes, minimizing breakage across notebook versions. It’s especially valued by power users who spend long hours in notebooks and want a consistent, eye-friendly environment. -
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JyConsole is an advanced Java graphic console for Jython. JyConsole provides an object-oriented completion on Java and Python objects and allows the direct manipulation of objects. JyConsole includes command history, script loading and GUI preference.
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JyNV
Jupyter Notebook Frontend
Lean Webkit (QtWebKit) based Browser for Jupyter/IPython notebooks. -
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Jython Simple Dialogs
Simple UI Dialog boxes much like 'zenity' project for jython
I have wanted very simple dialog box implementation for asking user questions, such as what is intended by the 'zenity' (or previous XDialog) type of interfaces. After looking at options I settled on using swing based UI components and it is based on the information available from: https://wiki.python.org/jython/SwingExamples The specific use of this code is targeted to user inputs for simple activities and it appears as if there isn't any single 'aggregator' and I tried to provide this functionality. Hope you enjoy! -
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K Means using PyTorch
kmeans using PyTorch
PyTorch implementation of kmeans for utilizing GPU. -
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This program is a installer for KDE sourcefiles. It compiles the code for you so as you need it. It's for UNIX like operating systems. programmed with python.
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