Open Source Julia Software Development Software - Page 3
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Julia Software Development Software
View 6241 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.
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JuliaFormatter.jl
An opinionated code formatter for Julia
Width-sensitive formatter for Julia code. Inspired by gofmt, refmt, black, and prettier. Built with CSTParser. Sane defaults out of the box with options to customize. Supports YAS, Blue and SciML style guides. JuliaFormatter.toml configuration file to store options. JuliaFormatter exports format, format_file, format_text, and format_md. format_md has the same API as format_text but differ in that format_md expects the text content to be a Markdown document. See format_text docstring for formatting options at the text level and format_file docstring for formatting options at the file level. JuliaFormatter should work on any valid Julia and Markdown files. If JuliaFormatter cannot parse the code for any reason, it will throw an error pointing to the line that could not be parsed. If running format on multiple files, you may want to set verbose = true to print information about which file is being formatted. -
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Knet
Koç University deep learning framework
Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, and it has been reported to work on OSX and Windows. If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS or Using Microsoft Azure. If you find a bug, please open a GitHub issue. If you don't have access to a GPU machine, but would like to experiment with one, Amazon Web Services is a possible solution. I have prepared a machine image (AMI) with everything you need to run Knet. Here are step-by-step instructions for launching a GPU instance with a Knet image (the screens may have changed slightly since this writing). -
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LoopVectorization.jl
Macro(s) for vectorizing loops
LoopVectorization.jl is a Julia package for accelerating numerical loops by automatically applying SIMD (Single Instruction, Multiple Data) vectorization and other low-level optimizations. It analyzes loops and generates highly efficient code that leverages CPU vector instructions, making it ideal for performance-critical computing in fields such as scientific computing, signal processing, and machine learning. -
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Manifolds.jl
Manifolds.jl provides a library of manifolds
Package Manifolds.jl aims to provide both a unified interface to define and use manifolds as well as a library of manifolds to use for your projects. This package is mostly stable, see #438 for planned upcoming changes. The implemented manifolds are accompanied by their mathematical formulae. The manifolds are implemented using the interface for manifolds given in ManifoldsBase.jl. You can use that interface to implement your own software on manifolds, such that all manifolds based on that interface can be used within your code. -
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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. -
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Memento.jl
A flexible logging library for Julia
Memento is a flexible hierarchical logging library for Julia. -
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. Reworking Mocha with new technologies requires some non-trivial efforts, and new exciting solutions already exist nowadays, it is a good time for the retirement of Mocha.jl. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. -
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NFFT
The official NFFT library repository
NFFT is a software library, written in C, for computing non-equispaced fast Fourier transforms and related variations. -
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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. -
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Plotly.jl
A Julia interface to the plot.ly plotting library and cloud services
A Julia interface to the plot.ly plotting library and cloud services. Plotting functions provided by this package are identical to PlotlyJS. Please consult its documentation. In fact, the package depends on PlotlyJS.jl and reexports all the methods. -
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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. -
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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. -
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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. -
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Pythonidae
Curated decibans of scientific programming resources in Python
Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities, offering a structured reference for both academic and applied work. While the primary focus is on Python, some entries also highlight resources implemented in other languages like Julia, R, Go, and Java. The project emphasizes open contribution, allowing the community to continuously expand and refine the index. By gathering these resources in one place, Pythonidae acts as a central hub for scientific and data-driven programming with Python. -
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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. -
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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. -
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Revise.jl
Automatically update function definitions in a running Julia session
Revise.jl is a Julia package that automatically updates functions, types, and modules in a running Julia session when their source code changes. It significantly improves the development workflow by removing the need to restart the REPL or re-include files after edits. Revise is ideal for iterative coding, package development, and interactive exploration, enabling a fast and fluid programming experience. -
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Rocket.jl
Functional reactive programming extensions library for Julia
Rocket.jl is a Julia package for reactive programming using Observables, to make it easier to work with asynchronous data. Rocket.jl has been designed with a focus on performance and modularity. -
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SLM-Topo
Topology optimisation designed for laser based additive manufacturing
In the selective laser melting process (SLM), components are built up layer by layer by incremental melting of metal powder with a laser beam. This process leads to locally inhomogeneous material properties of the manufactured components. By integrating these specific material properties of the SLM-process into a topology optimization, product developers can be supported in the design process by simulation. For this purpose, a topology optimization method is being developed which takes into account the unique material properties of parts manufactured with the SLM-process. In this homepage, the generation of a material database as well as the development of the topology optimization method is introduced and the impact on the component design is presented. SLM-Topo is a joint research project of the Institute of Product Engineering (IPEK) and the Institute for Applied Materials (IAM) of the Karlsruhe Institute for Technology (KIT) funded by the Deutsche Forschungsgemeinschaft (DFG). -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation. You can choose to mix loss functions natively found in this package and those you provide. In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge). This strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mixed analytical and automatic differentiation. You may consider using this package if you need extensibility and/or speed, and if you want to extend SEM. -
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SymbolicUtils.jl
Symbolic expressions, rewriting and simplification
SymbolicUtils is a practical symbolic programming utility in Julia. It lets you create, rewrite and simplify symbolic expressions, and generate Julia code from them. SymbolicUtils.jl provides various utilities for symbolic computing. SymbolicUtils.jl is what one would use to build a Computer Algebra System (CAS). If you're looking for a complete CAS, similar to SymPy or Mathematica, see Symbolics.jl. If you want to build a crazy CAS for your weird Octonian algebras, you've come to the right place. Symbols in SymbolicUtils carry type information. Operations on them propagate this information. A rule-based rewriting language can be used to find subexpressions that satisfy arbitrary conditions and apply arbitrary transformations on the matches. The library also contains a set of useful simplification rules for expressions of numeric symbols and numbers. These can be remixed and extended for special purposes. -
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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. -
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TimerOutputs.jl
Formatted output of timed sections in Julia
TimerOutputs.jl is a lightweight Julia package that provides a structured way to measure and report the execution time of different parts of code. It is particularly useful for performance profiling in scientific computing, allowing developers to annotate sections of code and generate readable timing summaries. TimerOutputs.jl supports nested timers and formatted output to both terminal and files, helping users easily identify bottlenecks in their programs. -
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Tokenize.jl
Tokenization for Julia source code
Tokenize is a Julia package that serves a similar purpose and API as the tokenize module in Python but for Julia. This is to take a string or buffer containing Julia code, perform lexical analysis and return a stream of tokens.
