Training Example: The Julia Programming Language – Review the Data, Give Your Score & Compare to the Real AI Evaluation

[H2] Ecosystem

Visualization

General Purpose

Data Science

Machine Learning

Scientific Domains

Parallel Computing

[H3] General Computing

[IMG: minesweeper gameover]

[H4] Build, Deploy or Embed Your Code

Julia makes it possible to build complete applications. Write web UIs with Dash.jl and Genie.jl or native UIs with Gtk4.jl. Pull data from a variety of databases. Build shared libraries and executables with PackageCompiler. Deploy on a webserver with HTTP.jl or embedded devices. Powerful shell integration makes it easy to manage other processes.

Julia has foreign function interfaces for
C, Fortran,
C++,
Python,
R,
Java,
Mathematica,
Matlab,
and many other languages. Julia can also be embedded in other programs through its embedding API. Julia's PackageCompiler makes it possible to build binaries from Julia programs that can be integrated into larger projects. Python programs can call Julia using juliacall. R programs can do the same with R's JuliaCall, which is demonstrated by calling MixedModels.jl from R. Mathematica supports calling Julia through its External Evaluation System.

[H3] Parallel Computing

[IMG: parallel prefix graphical result]

[H4]
Parallel and Heterogeneous Computing
Julia is designed for parallelism, and provides built-in primitives for parallel computing at every level:
instruction level parallelism,
multi-threading,
GPU computing, and
distributed computing.
Oceananigans.jl achieved breakthrough resolution and energy efficiency in global ocean simulations running on 768 A100 GPUs.

The Julia compiler can also generate native code for GPUs. Packages such as DistributedArrays.jl and Dagger.jl provide higher levels of abstraction for parallelism. MPI style parallelism is also available through MPI.jl.

[H3] Machine Learning

[IMG: cartpole reinforcement learning problem visualization]

[H4]
Scalable Machine Learning
The MLJ.jl package provides a unified interface to common machine learning algorithms, which include
generalized linear models, decision trees, and clustering.
Flux.jl and Lux.jl are powerful packages for Deep Learning.
Packages such as Metalhead.jl and TextAnalysis.jl provide ready to use pre-trained models for common tasks.
AlphaZero.jl provides a high performance implementation of the reinforcement learning algorithms from AlphaZero. Turing.jl is a best in class package for probabilistic programming.

[H3] Scientific Computing

[IMG: Lorenz Attractor visualization]

[H4]
Rich Ecosystem for Scientific Computing
Julia is designed from the ground up to be very good at numerical and scientific computing.
This can be seen in the abundance of scientific tooling written in Julia, such as the state-of-the-art differential equations ecosystem (DifferentialEquations.jl), optimization tools (JuMP.jl and Optimization.jl), iterative linear solvers (Krylov.jl, LinearSolve.jl), Fast Fourier transforms (AbstractFFTs.jl), and much more. General purpose simulation frameworks are available for Scientific Machine Learning, Quantum computing and much more.
Julia also offers a number of domain-specific ecosystems, such as in biology (BioJulia), operations research (JuMP Dev), image processing (JuliaImages), quantum physics (QuantumBFS), nonlinear dynamics (JuliaDynamics), quantitative economics (QuantEcon), astronomy (JuliaAstro) and ecology (EcoJulia). With a set of highly enthusiastic developers and maintainers, the scientific ecosystem in Julia continues to grow rapidly.

[H3] Data Science

[IMG: Visualization of weighted data changing as more data is plotted]

[H4] Interact with your Data
The Julia data ecosystem provides DataFrames.jl to work with datasets, and perform common data manipulations. CSV.jl is a fast multi-threaded package to read CSV files and integration with the Arrow ecosystem is in the works with Arrow.jl. Online computations on streaming data can be performed with OnlineStats.jl.
The Queryverse provides query, file IO and visualization functionality. In addition to working with tabular data, the JuliaGraphs packages make it easy to work with combinatorial data.
Julia can work with almost all databases using ODBC.jl drivers.

[H3] Visualization

[IMG: Visualization of waves in 3D, as a heatmap, and on the x y axis]

[H4] Data Visualization and Plotting
Data visualization has a complicated history. Plotting software makes trade-offs between features and simplicity, speed and beauty, and a static and dynamic interface. Some packages make a display and never change it, while others make updates in real-time.
Makie.jl is a sophisticated package for complex graphics and animations. Plots.jl is a visualization interface and toolset. It provides a common API across various backends, like GR.jl and PlotlyJS.jl. Users who are used to "grammar of graphics" plotting APIs should take a look at Gadfly.jl. For those who do not wish to leave the comfort of the terminal, there is also UnicodePlots.jl.

[H2]
Community Standards

Please take a moment to read the Julia Community Standards. We expect that your participation in any Julia related forum, online or offline, respects these standards. The Julia Community is committed to continuing to foster an inclusive and diverse culture. Read more about how we are doing this on our diversity page.

[H2]
Community Channels

[H3] GitHub
We use GitHub for the development of Julia itself. There, we host our
source code, track
issues, and accept
pull requests. For support and questions,
please use Discourse.

[H3] Questions?
The primary online discussion venue for Julia is the Discourse forum. Learn more about our Discourse site and what it is best used for here.

[H3] Chat
For casual conversations, we have Zulip, Slack and Discord.

See other Julia Channels

[H2]
Local Communities

The Julia Community has several local communities organizing meetups and other activities. If your community is missing from the map, add it here.

[H2]
Events

The Julia Community has a shared calendar for all upcoming global events. If you are an event organizer, please email us
with the details so it can be added to the calendar. The Julia community also has local meetups around the world.

[H2]
Julia User and Developer Survey

We conduct the Julia User & Developer Survey right before JuliaCon every year and present the findings at JuliaCon. The survey has now been conducted several years in a row:
2025: Slides, and JuliaCon 2025 talk
2024: Slides, and JuliaCon 2024 talk
2023: Slides, and JuliaCon 2023 talk
2022: Slides, and JuliaCon 2022 talk
2021: Slides, and JuliaCon 2021 talk
2020: Slides, JuliaCon 2020 talk, and blog post
2019: Slides, JuliaCon 2019 talk, and blog post

[H2]
Julia GitHub Organizations

The Julia Language is proud to have fostered a diverse assortment of GitHub Organizations over the years. Find out more about our Ecosystem's GitHub Organizations.
[IMG: All Julia Org Logos]

[H2]
Working Groups

A working group is a group of people that are interested in working on a common topic. Working groups are informal and have no official authority. Find out more about Working Groups.

[H2]
Sponsors

In addition to the thousands of individuals who contribute to the Julia project in their personal capacities, we are grateful to a large number of companies and organizations that are supporting the continued growth of the Julia project and ecosystem. We maintain a list of these on the sponsors page.

[H2] The Julia Language Blog

The Julia blog discusses issues of numerical, technical, distributed and parallel computing, as well as programming language design, and how these issues touch upon the design and implementation of the Julia programming language.
Also see the JuliaHub company blog for another source of information regarding ongoing Julia development.
Blogs from the broader Julia community can be found at Julia Bloggers.

[H2] 2026

This Month in Julia World (February 2026) 2026-03-01
This Month in Julia World (January 2026) 2026-02-01
This Month in Julia World (December 2025) 2026-01-01

[H2] 2025

This Month in Julia World (November 2025) 2025-12-01
Launching the Julia Security Working Group 2025-11-25
This Month in Julia World (October 2025) 2025-11-01
Julia 1.12 Highlights 2025-10-08
This Month in Julia World (September 2025) 2025-10-01
This Month in Julia World (August 2025) 2025-09-01
This Month in Julia World (June and July 2025) 2025-08-01
Announcing Google Summer of Code 2025 selected projects 2025-07-17
This Month in Julia World (May 2025) 2025-06-01
This Month in Julia World (April 2025) 2025-05-01
This Month in Julia World (March 2025) 2025-04-01
This Month in Julia World (February 2025) 2025-03-01
So, You Want to Start a Julia Workgroup? 2025-02-10
This Month in Julia World (January 2025) 2025-02-01
The SatelliteToolbox.jl Ecosystem 2025-01-03
This Month in Julia World (December 2024) 2025-01-01

[H2] 2024

This Month in Julia World (November 2024) 2024-12-12
This Month in Julia World (October 2024) 2024-11-07
Julia 1.11 Highlights 2024-10-08
This Month in Julia World (September 2024) 2024-10-05
JuliaCon Global 2025 and JuliaCon 2024 Wrap-Up 2024-09-18
2024 GSoC and JSoC Fellows Announced 2024-05-03

[H2] 2023

Julia 1.10 Highlights 2023-12-27
PSA: Thread-local state is no longer recommended 2023-07-06
Julia 1.9 Highlights 2023-05-09

[H2] 2022

Julia 1.8 Highlights 2022-08-18
JuliaCon 2022 Highlights 2022-08-10
Doing small network scientific machine learning in Julia 5x faster than PyTorch 2022-04-14
Why We Use Julia, 10 Years Later 2022-02-14

[H2] 2021

DTable – an early performance assessment of a new distributed table implementation 2021-12-08
Julia 1.7 Highlights 2021-11-30
Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs 2021-10-21
Julia User & Developer Survey 2021 2021-08-24
JuliaCon 2021 Highlights 2021-08-20
Simulation of a swimming dogfish shark 2021-08-12
Code, docs, and tests: what's in the General registry? 2021-08-02
JuliaCon 2021, the largest Julia Programming event in history 2021-07-26
Google Season of Docs 2020-2021 Wrap-Up 2021-04-04
Julia 1.6 Highlights 2021-03-24
Apache Arrow Support in Julia 2021-01-28
Profiling type-inference 2021-01-28
The 2020 Industry Julia Users Contributhon 2021-01-20
Tutorial on precompilation 2021-01-05

[H2] 2020

Using time travel to remotely debug faulty DRAM 2020-09-24
Transitioning Code From Closed To Open: A JuliaCon 2020 Discussion Between Julia Users In Industry 2020-09-16
GSoC 2020 Wrap-Up 2020-09-08
Analyzing sources of compiler latency in Julia: method invalidations 2020-08-26
2020 Julia User and Developer Survey 2020-08-24
JuliaCon 2020 Wrap-up 2020-08-11
Julia 1.5 Highlights 2020-08-03
GSoC and JSoC 2020 Project List 2020-05-05
Julia 1.5 Feature Preview: Time Traveling (Linux) Bug Reporting 2020-05-02
Google's Code-In Contest Wrap up 2020-02-10

[H2] 2019

Yao.jl - Differentiable Quantum Programming In Julia 2019-12-28
为 Julia 包设计的可靠、可复现的二进制工件系统 2019-12-18
Pkg + BinaryBuilder -- The Next Generation 2019-11-19
Profiling tool wins and woes 2019-09-16
Julia夏季会议@Beijing - 本地化奖 2019-09-07
Julia Workshop@Beijing and the Julia Localization Prize 2019-09-07
Julia的版本发布流程 2019-09-07
Julia’s Release Process 2019-08-28
Julia User - Developer Survey 2019 2019-08-06
Julia将支持可组合的多线程并行机制 2019-07-30
Announcing composable multi-threaded parallelism in Julia 2019-07-23
Hello @DiffEqBot 2019-06-18
A Summer of Julia 2019 2019-05-31
Beyond machine learning pipelines with MLJ 2019-05-02
DiffEqFlux.jl – Julia 的神經微分方程套件 2019-04-04
A Julia interpreter and debugger 2019-03-19
The Julia Project and Its Entities 2019-02-12
GSoC 2018 - Parallel Implementations of Graph Analysis Algorithms 2019-02-03
DiffEqFlux.jl – A Julia Library for Neural Differential Equations 2019-01-18

[H2] 2018

Building a Language and Compiler for Machine Learning 2018-12-03
How to get started with Julia 1.0's package manager 2018-09-12
A portrait of JuliaCon 2018 2018-09-11
The Julia Community Prizes, 2018 2018-09-04
GSoC 2018 and Speech Recognition for the Flux Model Zoo: The Conclusion 2018-08-14
GSoC 2018: Adding Newer Features and Speeding up Convolutions in Flux 2018-08-13
Union-splitting: what it is, and why you should care 2018-08-09
Julia 1.0 Anunciando el release de Julia 1.0 (Spanish) 2018-08-08
Julia 1.0 正式发布 (Simplified Chinese) 2018-08-08
Julia 1.0 正式發佈 (Traditional Chinese) 2018-08-08
Announcing the release of Julia 1.0 2018-08-08
GSoC 2018: Reinforcement Learning and Generative models using Flux 2018-08-06
Writing Iterators in Julia 0.7 2018-07-08
First-Class Statistical Missing Values Support in Julia 0.7 2018-06-19
Extensible broadcast fusion 2018-05-11
Tetris coming to Julia language for v1.0 2018-04-01
Some π-ography 2018-03-14
Julia joins NumFOCUS in Google Summer of Code 2018 2018-02-21

[H2] 2017

機器學習以及程式語言(Traditional Chinese) 2017-12-25
机器学习与编程语言 (Simplified Chinese) 2017-12-20
On Machine Learning and Programming Languages 2017-12-06
GSoC 2017: Native Julia second order ODE and BVP solvers 2017-11-01
NeuralNetDiffEq.jl: A Neural Network solver for ODEs 2017-10-13
Command interpolation for dummies 2017-10-05
GSoC 2017 Project: Hamiltonian Indirect Inference 2017-09-19
GSoC 2017: Parallelism in BioJulia 2017-09-07
GSoC 2017: Efficient Discretizations of PDE Operators 2017-09-06
GSoC 2017 Project: MCMC with flexible numbers of parameters 2017-09-03
GSoC 2017 : A Wrapper for the FEniCS Finite Element Toolbox 2017-09-01
GSoC 2017: Documentation Browser for Juno 2017-08-28
GSoC 2017: Implementing iterative solvers for numerical linear algebra 2017-08-23
JuliaCon 2017 on the West Coast 2017-08-15
Creating domain-specific languages in Julia using macros 2017-08-09
Julia 0.6 Release Announcement 2017-06-27
Julia available in Raspbian on the Raspberry Pi 2017-05-03
Upgrades to the REPL in Julia 0.6 2017-04-25
Knowing where you are: custom array indices in Julia 2017-04-18
Paper in SIAM Review: Julia - A Fresh Approach to Numerical Computing 2017-03-18
Technical preview: Native GPU programming with CUDAnative.jl 2017-03-14
Some fun with Π in Julia 2017-03-14
More Dots: Syntactic Loop Fusion in Julia 2017-01-21

[H2] 2016

Julia 0.5 Highlights 2016-10-11
Julia 0.5 Release Announcement 2016-10-10
StructuredQueries.jl - A generic data manipulation framework 2016-10-03
A Personal Perspective On JuliaCon 2016 2016-09-21
BioJulia 2016 - online sequence search, sequence demultiplexing, new readers and much more! 2016-09-10
Vertex and Edge Metadata 2016-08-22
Announcing support for complex-domain linear programs in Convex.jl 2016-08-17
An invitation to JuliaCon 2016 2016-05-08
BioJulia Project in 2016 2016-04-30
Google Summer of Code 2016 2016-04-14
Generalizing AbstractArrays: opportunities and challenges 2016-03-27
An introduction to ParallelAccelerator.jl 2016-03-01
Multidimensional algorithms and iteration 2016-02-01
Julia IDE work in Atom 2016-01-07

[H2] 2015

JSoC 2015 project: DataStreams.jl 2015-10-25
JSoC 2015 project: Automatic Differentiation in Julia with ForwardDiff.jl 2015-10-23
JSoC 2015 project: Interactive Visualizations in Julia with GLVisualize.jl 2015-10-22
JSoC 2015 project: Efficient data structures and algorithms for sequence analysis in BioJulia 2015-10-21
JSoC 2015 project: Interactive 3D Graphics in the Browser with Compose3D 2015-10-20
JSoC 2015 project: NullableArrays.jl 2015-10-16
Julia 0.4 Release Announcement 2015-10-09
JuliaCon 2015 Preview - Deep Learning, 3D Printing, Parallel Computing, and so much more 2015-05-30
Julia Summer of Code 2015 2015-05-23

[H2] 2014

Julia 0.3 Release Announcement 2014-08-20
JuliaCon 2014 Opening Session Presentations 2014-08-09
JuliaCon 2014 Optimization Presentations 2014-08-09

[H2] 2013

Fast Numeric Computation in Julia 2013-09-04
Building GUIs with Julia, Tk, and Cairo, Part I 2013-05-23
Building GUIs with Julia, Tk, and Cairo, Part II 2013-05-23
Passing Julia Callback Functions to C 2013-05-10
Put This In Your Pipe 2013-04-08
Distributed Numerical Optimization 2013-04-05
Videos from the Julia tutorial at MIT 2013-03-30
Efficient Aggregates in Julia 2013-03-05

[H2] 2012

Design and implementation of Julia 2012-08-16
New York Open Stats Meetup 2012-04-18
Lang.NEXT Announcement 2012-03-24
Shelling Out Sucks 2012-03-11
Stanford Talk Announcement 2012-02-27
为什么我们要创造Julia (Simplified Chinese) 2012-02-14
Why We Created Julia 2012-02-14

[H2] Install Julia

It appears that you are using Windows. Install Julia using the MSIX App Installer. Alternatively, if you have access to the Microsoft Store, you can install Julia by running the following in the command prompt. In case you are not using Windows, please follow the Linux and macOS instructions.
winget install --name Julia --id 9NJNWW8PVKMN -e -s msstore

It appears that you are using macOS or Linux. Install Julia by running the following in your terminal. In case you are using Windows, please follow these instructions for Windows.
curl -fsSL https://install.julialang.org | sh

This will install the latest stable version of Julia, as well as the juliaup tool. Start Julia from the command-line by typing julia. See juliaup --help for how to configure installed versions.
If you prefer to use manual installation using a GUI-based installer, see the Manual Downloads page.

[H2] Next Steps

Join the community.
Check out the learning resources.
Set up an editor.
Do star us on GitHub.
If you use Julia in your research, please cite us.
Do consider sponsoring us.

[H2] Support Tiers

Julia supports all the major operating systems, but specific versions and architectures have different tiers of support.

[H2] Official Domains

The following domains are official and used by open source Julia infrastructure for serving content and resources:
julialang.org and all subdomains
julialang.net and all subdomains
If you are using Julia behind a firewall that blocks access to these, you may have trouble downloading and installing Julia packages. If this is the case, please ask your sysadmin to add these domains to the firewall allow list. Traffic can be limited to HTTPS (TCP port 443).

[H2] IP Address Retention Policy

Julia comes with a built-in package manager which downloads and installs packages from the Internet. In doing so, it necessarily reveals your public IP address to any server you connect to, and service providers may log your IP address. In Julia versions 1.5 and higher, by default the package manager connects to https://pkg.julialang.org, a free public service operated by the Julia project to serve open source package resources to Julia users. This service retains IP address logs for up to 31 days.