Open Source Julia Software Development Software
Sort By:
Julia Software Development Software
View 6086 business solutionsBrowse free open source Julia Software Development Software and projects below. Use the toggles on the left to filter open source Julia Software Development Software by OS, license, language, programming language, and project status.
-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Atera - an All-in-one platform for IT managementYour IT essentials, integrated & elevated. Take your IT management from automated to autonomous, download Atera's agent to start your free trial!
-
1
AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
2
HTTP.jl
HTTP for Julia
HTTP.jl is a pure Julia implementation of the HTTP protocol, providing tools for building HTTP clients and servers. It enables users to send requests, handle responses, and construct REST APIs or web services entirely in Julia. HTTP.jl supports TLS, cookies, headers, streaming, and middleware, making it suitable for both simple scripting and full-scale web service development. -
3
QML
Build Qt6 QML interfaces for Julia programs
This package provides an interface to Qt6 QML (and to Qt5 for older versions). It uses the CxxWrap package to expose C++ classes. Current functionality allows interaction between QML and Julia using Observables, JuliaItemModels and function calling. There is also a generic Julia display, as well as specialized integration for image drawing, GR plots and Makie. -
4
AppleAccelerate.jl
Julia interface to the macOS Accelerate framework
Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions. The performance is significantly better than using standard libm functions in some cases, though there does appear to be some reduced accuracy. -
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.
-
5
Julia.jl
Curated decibans of Julia programming language
Julia.jl is a curated collection of knowledge resources for the Julia programming language, designed to support high-performance numerical analysis and computational science. The repository aggregates diverse content across domains such as mathematics, physics, data science, optimization, machine learning, and supercomputing. It functions as a structured index, helping developers, researchers, and learners easily find materials to deepen their understanding of Julia’s ecosystem. The project emphasizes community contributions, encouraging users to expand and refine the resource pool. With a wide range of topic-focused documents, it provides both academic and practical references for applied research and development. By centralizing these resources, Julia.jl supports continuous learning and growth for users at all experience levels in the Julia community. -
6
Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates. -
7
ConcurrentSim.jl
Discrete event process oriented simulation framework written in Julia
A discrete event process-oriented simulation framework written in Julia inspired by the Python library SimPy. One of the longest-lived Julia packages (originally under the name SimJulia). -
8
Evolutionary.jl
Evolutionary & genetic algorithms for Julia
A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager. -
9
GLM.jl
Generalized linear models in Julia
GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control. Under the hood, GLM.jl separates the linear predictor and response objects, allowing flexible combinations of link functions, variance structures, and fitting methods. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
10
Genie.jl
The highly productive Julia web framework
Genie Framework includes all you need to quickly build production-ready web applications with Julia. Develop Julia backends, create beautiful web UIs, build data applications and dashboards, integrate with databases and set up high-performance web services and APIs. Genie Builder is a free VSCode plugin for quickly building Julia apps without writing frontend code. Drag and drop UI components such as text, sliders, plots, and data tables onto a canvas, and connect them to the variables in the backend code. Genie.jl is the backbone of Genie Framework: the complete solution for developing modern full-stack web applications in Julia. Genie.jl includes key features like the webserver, the flexible templating engine with support for HTML, JSON, Markdown, and Julia views, caching, (encrypted) cookies and sessions, forms handling, and the powerful router. Genie.jl uses the familiar MVC architecture, follows industry best practices, and comes with lots of useful code generators. -
11
GeoStats.jl
An extensible framework for geospatial data science
GeoStats.jl is a Julia framework for geospatial data science and geostatistical modeling. It’s fully implemented in Julia and designed to provide an extensible, high-performance stack that handles spatial domains, interpolation, simulation, learning, and visualization. The package is modular: it breaks out geometry, spatial domains, transforms, variograms, covariance models, and modeling into subpackages (e.g., GeoStatsBase, GeoStatsModels, GeoStatsTransforms). Users can represent georeferenced tables (points + attributes), define domains (grids, meshes, structured/unstructured), and then apply geostatistical operations such as kriging, interpolation, simulation, variogram estimation, and learning-based prediction. Visualization is supported via integration with Makie.jl to produce spatial renderings, mesh visualizations, and variable overlays. -
12
ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
The ModelingToolkit Standard Library is a standard library of components to model the world and beyond. -
13
PkgTemplates.jl
Create new Julia packages, the easy way
PkgTemplates.jl is a Julia package that automates the creation of new Julia packages by generating a project scaffold with common best practices. It helps users quickly set up reproducible project structures with Git integration, CI configuration, testing frameworks, documentation, and more. By using customizable templates, PkgTemplates streamlines package development and enforces consistency across Julia projects. -
14
Documenter.jl
A documentation generator for Julia
A documentation generator for Julia. A package for building documentation from docstrings and markdown files. -
15
DynamicalSystems.jl
Award winning software library for nonlinear dynamics timeseries
DynamicalSystems.jl is an award-winning Julia software library for nonlinear dynamics and nonlinear time series analysis. To install DynamicalSystems.jl, run import Pkg; Pkg.add("DynamicalSystems"). To learn how to use it and see its contents visit the documentation, which you can either find online or build locally by running the docs/make.jl file. DynamicalSystems.jl is part of JuliaDynamics, an organization dedicated to creating high-quality scientific software. All implemented algorithms provide a high-level scientific description of their functionality in their documentation string as well as references to scientific papers. The documentation features hundreds of tutorials and examples ranging from introductory to expert usage. -
16
FFTW.jl
Julia bindings to the FFTW library for fast Fourier transforms
This package provides Julia bindings to the FFTW library for fast Fourier transforms (FFTs), as well as functionality useful for signal processing. These functions were formerly a part of Base Julia. Users with a build of Julia based on Intel's Math Kernel Library (MKL) can use MKL for FFTs by setting a preference in their top-level project by either using the FFTW.set_provider!() method, or by directly setting the preference using Preferences.jl. Note that this choice will be recorded for the current project, and other projects that wish to use MKL for FFTs should also set that same preference. Note further that MKL provides only a subset of the functionality provided by FFTW. -
17
Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
18
IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
IntervalArithmetic.jl is a Julia package for validated numerics in Julia. All calculations are carried out using interval arithmetic where quantities are treated as intervals. The final result is a rigorous enclosure of the true value. We are working towards having the IntervalArithmetic library be conformant with the IEEE 1788-2015 Standard for Interval Arithmetic. To do so, we have incorporated tests from the ITF1788 test suite. -
19
OhMyREPL.jl
Syntax highlighting and other enhancements for the Julia REPL
OhMyREPL.jl is a Julia package that enhances the Julia REPL (Read-Eval-Print Loop) experience with syntax highlighting, bracket matching, prompt customization, and automatic indentation. It is designed to make the command-line interface more visually appealing and user-friendly, especially during interactive development and debugging. It runs entirely in the terminal and does not require external dependencies or GUI. -
20
PETSc.jl
Julia wrappers for the PETSc library
This package provides a low level interface for PETSc and allows combining julia features (such as automatic differentiation) with the PETSc infrastructure and nonlinear solvers. -
21
PromptingTools.jl
Streamline your life using PromptingTools.jl
PromptingTools.jl is a Julia-based toolkit designed to simplify prompt engineering and unify interactions with multiple large language model providers through a consistent interface. It focuses on reducing the complexity of prompt creation by introducing templating systems, macros, and reusable functions that standardize how prompts are constructed and executed. The library provides a family of ai* functions that handle tasks such as generation, embeddings, classification, and data extraction, all following a consistent structure. It supports multiple backends, including OpenAI-compatible APIs and local models such as those served through Ollama, allowing users to switch providers without rewriting prompts. The toolkit also includes advanced capabilities such as asynchronous execution, routing, OCR, and experimental agent workflows, making it suitable for both simple and complex AI applications. -
22
ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for running only part of the test suite of a package. For example, if you made a change related to addition, and included "addition" in the description of the corresponding testsets, you can easily run only these tests. -
23
The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
24
todo-comments.nvim
Highlight, list and search todo comments in your projects
todo-comments.nvim is a Neovim plugin that highlights and searches for comment annotations such as TODO, FIX, HACK, and others. It helps developers keep track of tasks, warnings, or issues left in code by providing colorful highlights and integration with search tools like Telescope. The plugin is written in Lua and is highly configurable to match different workflows. -
25
Catlab.jl
A framework for applied category theory in the Julia language
Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory. First and foremost, Catlab provides data structures, algorithms, and serialization for applied category theory. Macros offer a convenient syntax for specifying categorical doctrines and type-safe symbolic manipulation systems. Wiring diagrams (aka string diagrams) are supported through specialized data structures and can be serialized to and from GraphML (an XML-based format) and JSON.
