JuliaRun is Julia’s latest cloud-based version. In my book, Julia for Data Science, I’ve mentioned that there is an online version of the language, called JuliaBox. This version uses Jupyter as its front-end and runs on the cloud. JuliaRun is the next version of JuliaBox, still using Jupyter, but also offering various scalability options. JuliaRun is powered by the Microsoft cloud, aka Azure. However, there is an option of running it on your own cluster (ask the Julia Computing people for details).
Signing in JuliaRun is a fairly simple process. You just need to use either your GitHub credentials or your Google account. It’s not clear why someone has to be tied to an external party instead of having a Julia Computing account, but since creating a Google account is free, it's not a big issue! Also, it is a bit peculiar that JuliaRun doesn’t support Microsoft credentials, but then again, a MS account is not as popular as these other two sign-in options.
After you sign in, you need to accept the Terms of Service, a fairly straight-forward document, considering that it is a legal one. The most useful take-away from it is that if you leave your account inactive for about 4 months, it’s gone, so this is not for people who are not committed to using it.
Once you accept the ToS, you are taken to an IJulia directory, on Jupyter. This is where all your code notebooks are stored. The file system has a few things there already, the most noteworthy of which being a few tutorials. These are very helpful to get you started and also to demonstrate how Julia works in this platform. If you’ve never used IJulia before, there are also a good guide for that. Note that IJulia can run on Jupyter natively too, once you install the IJulia package and the Jupyter platform, on your machine.
Kernel and Functionality
The Julia version being used on JuliaRun is the latest stable release, which at the time of this writing is 0.6. However, the kernel version may differ for certain notebooks (e.g. for the JuliaTutorial one, it’s 0.5.2). Still, the differences between the last couple of versions are minute, for the most part. I’d recommend you go through the tutorials and also create some of your own test notebooks, before starting on a serious project, unless of course you use IJulia already on your computer.
Adding packages is fairly straight-forward, though it can be time-consuming as a process, especially if you have a lot of packages to install. Also, you have the option of installing a package in either one of the two latest versions of the language, or both, if you prefer. If you are more adventurous, you can even installed an unregistered package, by providing the corresponding URL.
You can also add code to JuliaRun through a Git repository (not necessarily GitHub). You just need to specify the URL of the repository, the branch, and which folder on JuliaBox you want to clone it in.
JuliaRun also offers a brief, but useful, help option. It mainly consists of a few FAQs, as well as an email address for more specialized questions. This is probably better than the long help pages in some other platforms that are next to impossible to navigate and are written by people who are terribly at writing. The help on this platform is brief, but comprehensive and with the user in mind.
For those who are closer to the metal and prefer the direct interaction with the Julia kernel, rather than the IJulia notebook interface, there is also the option to start a terminal. You can access that via the New button at the directory page.
From what I’ve seen of JuliaRun, both through a demo from the Julia team, and through my own experience, it is fairly easy to use. What I found very useful is that it doesn’t require any low-level data engineering expertise, though if you are good at working the processes of a cloud platform through the terminal or via Jupyter, that’s definitely useful. However, if you are someone more geared towards the high-level aspects of the craft, you can still do what you need to do, without spending too much time on the configurations.
I’d love to write more about this great platform that takes Julia to the next level, but this post is already too long. So, whenever you have a chance, give it a try and draw your own conclusions about this immensely useful tool.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.