Open Source Julia Software Development Software
Sort By:
Julia Software Development Software
View 5887 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.
-
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.
-
Activate Self-Service Analytics from Your Data Warehouse | KubitKubit delivers enterprise-grade customer journey analytics—directly from your existing data warehouse. Enable every team with self-service insights, driving faster, data-driven decisions with minimal engineering overhead or data movement.
-
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
PackageCompiler
Compile your Julia Package
Julia is, in general, a "just-barely-ahead-of-time" compiled language. When you call a function for the first time, Julia compiles it for precisely the types of arguments given. This can take some time. All subsequent calls within that same session use this fast compiled function, but if you restart Julia you lose all the compiled work. PackageCompiler allows you to do this work upfront — further ahead of time — and store the results for a lower latency startup. You can save loaded packages and compiled functions into a file (called a sysimage) that you pass to Julia upon startup. Typically the goal is to reduce latency on your machine; for example, you could load the packages and compile the functions used in common plotting workflows using that saved image by default. In general, sysimages are not relocatable to other machines; they'll only work on the machine they were created on. -
4
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. -
Runn is a modern resource and capacity planning platform that gets remote teams on the same page.Runn has a modern and easy-to-use interface that provides your team with a shared view of all the people and projects in your organization. Plan new work alongside existing projects and instantly see how changes to your plans and resourcing affect your company’s bottom line. Runn is intuitive to use and lets you quickly schedule work using simple drag and drop functionality. Runn also allows you to collaborate with your co-workers in real-time, seeing updates live without having to refresh your browser. Runn combines resource and capacity planning with integrated actual tracking and powerful forecasting to deliver meaningful insights and a full picture of your organization.
-
5
DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
6
Clapeyron
Framework for the development and use of fluid-thermodynamic models
Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC. -
7
Combinatorics.jl
A combinatorics library for Julia
A combinatorics library for Julia, focusing mostly (as of now) on enumerative combinatorics and permutations. As overflows are expected even for low values, most of the functions always return BigInt, and are marked as such. -
8
ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units). Given certain structural properties, PCs enable different range of tractable exact probabilistic queries such as computing marginals, conditionals, maximum a posteriori (MAP), and more advanced probabilistic queries. -
9
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. -
World class QA, 100% done-for-youMuukTest is a test automation service that combines our own proprietary, AI-powered software with expert QA services to help you achieve world class test automation at a fraction of the in-house costs.
-
10
Agents.jl
Agent-based modeling framework in Julia
Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. The simplicity of Agents.jl is due to the intuitive space-agnostic modeling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. Overall this leads to ultra-fast model prototyping where even changing the space the agents live in is a matter of only a couple of lines of code. -
11
Images.jl
An image library for Julia
JuliaImages (source code) hosts the major Julia packages for image processing. Julia is well-suited to image processing because it is a modern and elegant high-level language that is a pleasure to use, while also allowing you to write "inner loops" that compile to efficient machine code (i.e., it is as fast as C). Julia supports multithreading and, through add-on packages, GPU processing. JuliaImages is a collection of packages specifically focused on image processing. It is not yet as complete as some toolkits for other programming languages, but it has many useful algorithms. It is focused on clean architecture and is designed to unify "machine vision" and "biomedical 3d image processing" communities. -
12
Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
This package implements a variety of interpolation schemes for the Julia language. It has the goals of ease of use, broad algorithmic support, and exceptional performance. Currently, this package supports B-splines and irregular grids. The API has been designed with the intent to support more options. Initial support for Lanczos interpolation was recently added. Pull requests are more than welcome! It should be noted that the API may continue to evolve over time. -
13
InvertibleNetworks.jl
A Julia framework for invertible neural networks
Building blocks for invertible neural networks in the Julia programming language. -
14
JuliaFEM.jl
The JuliaFEM software library is a framework
The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The basic design principle is: that everything is nonlinear. All physics models are nonlinear from which the linearization are made as special cases. -
15
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. -
16
QuantumOptics.jl
Library for the numerical simulation of closed as well as open quantum
QuantumOptics.jl is a numerical framework written in the Julia programming language that makes it easy to simulate various kinds of open quantum systems. It is inspired by the Quantum Optics Toolbox for MATLAB and the Python framework QuTiP. QuantumOptics.jl optimizes processor usage and memory consumption by relying on different ways to store and work with operators. The framework comes with a plethora of pre-defined systems and interactions making it very easy to focus on the physics, not on the numerics. Every function in the framework has been severely tested with all tests and their code coverage presented on the framework's GitHub page. -
17
The NLopt module for Julia
Package to call the NLopt nonlinear-optimization library from Julia
This module provides a Julia-language interface to the free/open-source NLopt library for nonlinear optimization. NLopt provides a common interface for many different optimization algorithms. -
18
MLJ
A Julia machine learning framework
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization. -
19
Measurements.jl
Error propagation calculator and library for physical measurements
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have an attached error to quantify the confidence about its accuracy. Measurements.jl relieves you from the hassle of propagating uncertainties coming from physical measurements, when performing mathematical operations involving them. The linear error propagation theory is employed to propagate the errors. -
20
Memento.jl
A flexible logging library for Julia
Memento is a flexible hierarchical logging library for Julia. -
21
Metatheory.jl
General purpose algebraic metaprogramming
Metatheory.jl is a general purpose term rewriting, metaprogramming and algebraic computation library for the Julia programming language, designed to take advantage of the powerful reflection capabilities to bridge the gap between symbolic mathematics, abstract interpretation, equational reasoning, optimization, composable compiler transforms, and advanced homoiconic pattern matching features. The core features of Metatheory.jl are a powerful rewrite rule definition language, a vast library of functional combinators for classical term rewriting and an e-graph rewriting, a fresh approach to term rewriting achieved through an equality saturation algorithm. Metatheory.jl can manipulate any kind of Julia symbolic expression type, as long as it satisfies the TermInterface.jl. -
22
ProgressMeter.jl
Progress meter for long-running computations
ProgressMeter.jl is a lightweight Julia package that provides customizable progress bars for long-running loops and computations. It allows developers to track the progress of tasks with real-time visual feedback in the terminal, making it easier to monitor performance, debug slow operations, or report computational progress in user-facing applications. -
23
StaticArrays.jl
Statically sized arrays for Julia
StaticArrays.jl is a Julia package that provides statically sized arrays with fast, stack-allocated memory storage and optimized performance for small array computations. It is particularly useful in numerical computing where small fixed-size matrices or vectors are used frequently, such as in robotics, physics simulations, or linear algebra. StaticArrays eliminate dynamic memory allocation overhead and enable compile-time optimizations for performance close to hand-written loops. -
24
BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package implemented an execution strategy for collecting and summarizing individual benchmark results, while BenchmarkTrackers implemented a framework for organizing, running, and determining regressions of groups of benchmarks. Under the hood, BenchmarkTrackers relied on Benchmarks for actual benchmark execution. -
25
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).