Scientific computing

(for the rest of us)

Installing Julia

In this module, we will see how we can install Julia, setup a default version, and go through some of the usual tools involved in setting up a good Julia development environment. We will not deal with the installation of packages quite yet, as this will be done with its own module.

The easiest way to install Julia is to rely on the juliaup utility, which can be downloaded for most platforms from its GitHub page.

Once the juliaup program is executed, installing the current released version of Julia can be done by starting your command prompt, and typing

juliaup add release

This will take care of downloading and installing the correct latest release for your computer, but also make it available on your path (which is to say, if you type julia in a command prompt, Julia will start).

Another function of juliaup is to serve a version multiplexer: it allows to have different versions of the Julia installed on the same computer. We will not use this functionality here, but for the sake of being epxlicit, we will specify a default version:

juliaup default release

This will ensure that the command julia will start the currently released version. This is important to keep in mind, because this website is generated using the current Julia release, and so you might not get the exact same result if you use a very old version of the language.

The idea behind this material is to type as much of it as possible in the Julia REPL (i.e. what happens when you start julia). In practice, most (all?) coding is done in a text editor. For Julia, the one with the best support is VSCode, which is free, and has a dedicated Julia plugin.

The Julia VSCode plugin website has a full documentation of what can be done – it is, essentially, working under the same logic as e.g. RStudio, only for any language.

We like Julia in VSCode because there are a lot of user-friendly additions that make it easy to track bug, identify bottlenecks, and understand what is loaded in your environment. That being said, there are a number of other solutions to develop Julia programs, including IJulia, Pluto, Quarto, (neo)vim and lsp, etc… The examples in this class have been built using a mix of these tools!

One characteristic of Julia is that it has amazing support for unicode characters, enabling to, for example, use mathematical symbols to reproduce mathematical notation. This assumes that your font will have good support for these characters. The project Beautiful Algorithms showcases various algorithms written to use Julia’s support for unicode.

One such font is JuliaMono. Other very popular alternatives, all free, are Recursive, JetBrains Mono, Plex Mono, Victor Mono, Fira Code, Noto Sans Mono, Hack, Cascadia Code, and Iosevka (the code in this website is set in Iosevka). There are many others, but these fonts have good symbol coverage, and tend to be very legible on all screens. Feel free to experiment with one that suits you – setting up a good environment is also about your own user experience, and having a font that does not strain your eyes or make differentiating between symbols difficult is definitely a part of it.

Ideally, the font you pick should let you differentiate between these characters:


When you are all set with your installation of Julia (and any additional packages), it is time to conclude this section, and start with the fundamentals.