Julia Julia

Open-source Julia projects categorized as Julia

Top 23 Julia Julium Projects

  • julia

    The Julia Programming Language

    Project mention: Top Paying Programming Technologies 2024 | dev.to | 2024-03-06

    34. Julia - $74,963

  • DifferentialEquations.jl

    Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • Makie.jl

    Interactive data visualizations and plotting in Julia

    Project mention: Julia and Mojo (Modular) Mandelbrot Benchmark | news.ycombinator.com | 2023-09-08
  • Genie.jl

    🧞The highly productive Julia web framework

    Project mention: Tidyverse 2.0.0 | news.ycombinator.com | 2023-04-09

    Julia seems to be doing a better job catching up to R in this space than Python. I haven't used it personally, but the demos of Genie Framework are impressive: https://github.com/GenieFramework/Genie.jl / https://genieframework.com/

  • JuMP.jl

    Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

  • Gadfly.jl

    Crafty statistical graphics for Julia.

  • Plots.jl

    Powerful convenience for Julia visualizations and data analysis

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • MLJ.jl

    A Julia machine learning framework

  • DataFrames.jl

    In-memory tabular data in Julia

    Project mention: Julia's latency: Past, present and future | news.ycombinator.com | 2023-04-01

    I don't think we've seen the final state of it though. v1.9 really gives people the proper tools for solving latency problems. Before, invalidations hurt a little, but it was also kind of a wash because LLVM code didn't precompile, so you could spend time setting up a Snoopprecompile and fix some invalidations and end up LLVM bound saving 1 second out of 20. But with package images it's almost always better to fix precompilation. The only thing hampering time much now is the `using` time went up, but as mentioned in the Reddit post there's a lot of ideas for what to do there. The other thing is package extensions, which cut down the amount of code to load. There's tons of PRs floating around the ecosystem turning things into extensions, and thus cutting down the overall code that is actually ran and loaded.

    This means that in a few months, people will start to see some major tangible benefits from following the compilation improvement practices laid out here and https://sciml.ai/news/2022/09/21/compile_time/. I think then you'll have a lot more people start to take all of these new tools seriously and it will be standard to incorporate them into packages. Right now they are still kind of niche things for packages with known TTFX problems, but I think come v1.9 you'll see every major package use all of these methods.

    > Different packages are affected differently

    I think this is one of the pieces that's effected by this. I don't think "Julia has become optimised for running Plots.jl" is quite correct. Julia's compilation and runtime is much more optimized for well-inferred code, which Plots.jl is not. However, the compiler developers have been using Plots.jl as a test case for all of these new tools, and therefore its SnoopPrecompile and invalidations have gotten some dramatic improvements because those required ecosystem changes I mentioned are being done by the compiler team for this specific package. Plots.jl had things like precompilation snooping way back before there was even a package for it, the earliest I know of was around v1.0. Meanwhile, DataFrames.jl only setup its precompilation snooping 7 months ago (https://github.com/JuliaData/DataFrames.jl/pull/3182), which I would presume was just in time for the v1.8 mark on your plot and is one of the big reasons for having a sudden drop (which continues into v1.9 because of package images).

    What I mean to say then is that, I think all packages will get the improvements we've seen from Plots.jl, but package authors will need to update their packages in order for that to happen. Some packages have already done this, many have not.

  • PyCall.jl

    Package to call Python functions from the Julia language

    Project mention: I just started into Julia for ML | /r/Julia | 2023-06-06

    For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).

  • Zygote.jl

    21st century AD

  • UnicodePlots.jl

    Unicode-based scientific plotting for working in the terminal

    Project mention: UnicodePlots | /r/patient_hackernews | 2023-04-09
  • PackageCompiler.jl

    Compile your Julia Package

    Project mention: Potential of the Julia programming language for high energy physics computing | news.ycombinator.com | 2023-12-04

    Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.

    With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.

    Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)

    [1] https://docs.julialang.org/en/v1/manual/embedding/

    [2] https://pypi.org/project/juliacall/

    [3] https://www.rdocumentation.org/packages/JuliaCall/

    [4] https://github.com/JuliaLang/PackageCompiler.jl

    [5] https://github.com/tshort/StaticCompiler.jl

  • ModelingToolkit.jl

    An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

  • BeautifulAlgorithms.jl

    Concise and beautiful algorithms written in Julia

  • AlphaZero.jl

    A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

  • CUDA.jl

    CUDA programming in Julia.

    Project mention: Ask HN: Best way to learn GPU programming? | news.ycombinator.com | 2024-01-01

    It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.

    https://juliagpu.org/

    I've written CUDA kernels and I knew nothing about it going in.

  • Distributions.jl

    A Julia package for probability distributions and associated functions.

  • StatsWithJuliaBook

    Project mention: An Introduction to Statistical Learning with Applications in Python | news.ycombinator.com | 2023-07-09

    I actually like this book by Yoni Nazarathy

    https://statisticswithjulia.org/

    They have a book on Mathematics of DL too which is a natural progression from the concepts covered here.

    (I am slightly biased towards this since I've known the author by online interactions)

  • TensorFlow.jl

    A Julia wrapper for TensorFlow

  • model-zoo

    Please do not feed the models

  • Oceananigans.jl

    🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

    Project mention: Julia 1.10 Released | news.ycombinator.com | 2023-12-27

    I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those.

    The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone.

    For instance a known Julia project that simulates diff equations seems to have implemented their own solver

    https://github.com/CliMA/Oceananigans.jl

  • ForwardDiff.jl

    Forward Mode Automatic Differentiation for Julia

    Project mention: The Elements of Differentiable Programming | news.ycombinator.com | 2024-03-22

    You seem somewhat obsessed with the idea that reverse-mode autodiff is not the same technique as forward-mode autodiff. It makes you,,, angry? Seems like such a trivial thing to act a complete fool over.

    What's up with that?

    Anyway, here's a forward differentiation package with a file that might interest you

    https://github.com/JuliaDiff/ForwardDiff.jl/blob/master/src/...

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020). The latest post mention was on 2024-03-22.

Julia Julia related posts

Index

What are some of the best open-source Julium projects in Julia? This list will help you:

Project Stars
1 julia 44,317
2 DifferentialEquations.jl 2,729
3 Makie.jl 2,253
4 Genie.jl 2,168
5 JuMP.jl 2,119
6 Gadfly.jl 1,890
7 Plots.jl 1,790
8 MLJ.jl 1,710
9 DataFrames.jl 1,679
10 PyCall.jl 1,435
11 Zygote.jl 1,431
12 UnicodePlots.jl 1,384
13 PackageCompiler.jl 1,364
14 ModelingToolkit.jl 1,321
15 BeautifulAlgorithms.jl 1,297
16 AlphaZero.jl 1,207
17 CUDA.jl 1,118
18 Distributions.jl 1,062
19 StatsWithJuliaBook 1,059
20 TensorFlow.jl 880
21 model-zoo 880
22 Oceananigans.jl 869
23 ForwardDiff.jl 849
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