Welcome to Learn With Me: Julia. A series where you can follow me along my journey of learning Julia, Data Science and Machine Learning. This series is heavily inspired by Learn With Me: Elixir, a series by Kevin Peter / The Inquisitive Developer and the format of this post will follow his introductory post for Elixir.
I realised that while there are plenty of resources about Julia already out there, it would be interesting to document my journey in picking up the language and some fundamental data science and machine learning with it.
The Julia community, to a large degree, consists of academics. The level of discourse on the Julia Slack / Zulip is often too advanced or me to understand. Researchers from all kinds of fields, space engineering, bio engineering, mathematics all come together to practice Julia. The 2020 Community Survey nicely shows this:
I'm going to be writing this series for someone who has some programming knowledge and like me wants to learn about Julia. Some familiarity with computer science and algorithms will be useful and you should also have an interest in mathematics as I like to lean into the mathematics of machine learning when I'm ready to explore it with Julia.
For the rest of this section I'll quote Kevin Peter, since it also applies here:
So while this series is not meant for beginning programmers, you don't have to be a master programmer to follow along either. I very much doubt I will be delving into any advanced theoretical concepts or heavy mathematics. I'm aiming for practical stuff that a typical experienced software developer will be able to read and understand. I aim to be easily readable and informative.
If you need a resource on how to get started with Julia and a quick overview of why I chose this language you can read my Getting started with Julia post.
About Me
I left academia six years ago and have since been working with Ruby, JavaScript and Python in a professional setting almost exclusively. In my job I build web application backends and ETL pipelines and work closely with data scientists.
My personal interest in Machine Learning is what's driving me to Julia. Its expressivity over other languages like Python intrigues me and makes me think that it's only going to grow going forward.
Recently I've challenged myself to practice Julia 45 minutes every (week)day as part of a #100daysofcode challenge. I'm 25 days into this challenge and have explored the popular libraries such as Pluto, Plots, Revise and Javis.
You can find more information about who I am on the About page
Welcome to Learn With Me: Julia. A series where you can follow me along my journey of learning Julia, Data Science and Machine Learning. This series is heavily inspired by Learn With Me: Elixir, a series by Kevin Peter / The Inquisitive Developer and the format of this post will follow his introductory post for Elixir.
I realised that while there are plenty of resources about Julia already out there, it would be interesting to document my journey in picking up the language and some fundamental data science and machine learning with it.
The Julia community, to a large degree, consists of academics. The level of discourse on the Julia Slack / Zulip is often too advanced for me to understand. Researchers from all kinds of fields, space engineering, bio engineering, mathematics all come together to practice Julia. The 2020 Community Survey nicely shows this:
I'm going to be writing this series for someone who has some programming knowledge and like me wants to learn about Julia. Some familiarity with computer science and algorithms will be useful and you should also have an interest in mathematics as I like to lean into the mathematics of machine learning when I'm ready to explore it with Julia.
For the rest of this section I'll quote Kevin Peter, since it also applies here:
So while this series is not meant for beginning programmers, you don't have to be a master programmer to follow along either. I very much doubt I will be delving into any advanced theoretical concepts or heavy mathematics. I'm aiming for practical stuff that a typical experienced software developer will be able to read and understand. I aim to be easily readable and informative.
If you need a resource on how to get started with Julia and a quick overview of why I chose this language you can read my Getting started with Julia post.
About Me
I left academia six years ago and have since been working with Ruby, JavaScript and Python in a professional setting almost exclusively. In my job I build web application backends and ETL pipelines and work closely with data scientists.
My personal interest in Machine Learning is what's driving me to Julia. Its expressivity over other languages like Python intrigues me and makes me think that it's only going to grow going forward.
Recently I've challenged myself to practice Julia 45 minutes every (week)day as part of a #100daysofcode challenge. I'm 25 days into this challenge and have explored the popular libraries such as Pluto, Plots, Revise and Javis.
You can find more information about who I am on the About page
I use Windows for gaming. It's been a long time since I've last done any serious development work on my Windows machine and yet I still spent a good chunk of money on building out a beefy machine for my efforts to learn machine learning. It has taken me a few months to finally sit down and get this machine ready for anything other than gaming.
With the recent announcement of GUI support for WSLg I got really excited to try out WSL and see how good the GPU support actually is, but that's not the main reason. I've been shying away from developing on Windows because I'm used to a *NIX environment. WSL gives you that, but up until recently you wouldn't have been able to interact with any GPU – and this all changed with this announcement!
You can watch the video below to see what's coming for WSL2.
The first half of this article will show you how to get everything set up and in the second half we'll set up CUDA.jl in Julia. Since the latter part is about CUDA I'll assume that you have a compatible nVidia GPU.
Installation
Here's a summary of what we need to go through to prepare our environment:
Update Windows 10 to latest release on the dev channel
Install nVidia CUDA drivers
Install Ubuntu 20.20 in WSL2
Install Linux CUDA packages
🎉
Windows 10 Insider Preview
At the time of writing all of the features are only available through the Windows Insider Program. The Windows Insider Program allows you to receive new Windows features before the hit the main update line. The program is split into three channels: Dev, Beta and Release Preview.
To receive the update with WSLg and GPU support we will need to switch to the dev channel.
Note: The dev channel comes with some rough edges and potential for system instability. Be mindful of this when you switch and make sure you have backups!
After installing all downloaded updates you should end up with OS Build 21364 or higher. You can check your OS Build by running winver in PowerShell/cmd.
Run winver using the Windows Run command
With this all set we can hop on to install the latest WSL2 compatible CUDA drivers.
CUDA drivers
NVIDIA are providing special CUDA drivers for Windows 10 WSL. The link below will take you to the download page. It's required to sign up for the NVIDIA Developer Program, which is free.
Follow the setup wizard (I chose the express installation which keeps existing settings in place).
Note: There's also a documentation page provided by NVIDIA around setting up your GPU for WSL. I found the docs to be outdated and not working on my machine.
Installing Ubuntu 20.04 LTS with WSL2
Before we can proceed with installing Ubuntu I advise to update the WSL kernel by running:
wsl --update
In case you're like me and don't enjoy the default terminal Windows comes with I suggest you install Windows Terminal from the Microsoft Store. This terminal comes is a lot more pleasant to use than either cmd or the PowerShell terminal ever were.
It's also a good idea to set WSL to default to version 2:
wsl --set-default-version 2
Finally let's install Ubuntu with:
wsl --install --distribution Ubuntu-20.04
In case you were wondering what other distributions are available you can simply run: wsl --list --online.
Ubuntu 20.04 LTS
With Ubuntu installed we have a couple of final steps. First we will add an upstream repo to apt for getting the latest CUDA builds directly from NVIDIA:
You may pick a different target directory for your installation of Julia or use a version manager like asdf-vm.
Install Cuda.jl
At this point simply run julia in your terminal and you should be dropped into the Julia REPL. I assume you've worked with Julia before and know how to operate it's package manager.
Hit ] to enter pkg mode and install CUDA with:
activate --temp
add CUDA
From here hit backspace and import CUDA. CUDA.jl provides a useful function called functional which will confirm that we've done everything right (well, that's the hope at least, right?).
using CUDA
CUDA.functional()
Ensuring Julia CUDA.jl is functional inside WSL2
You can additionally run CUDA.versioninfo() to get a more detailed breakdown of the supported features on your GPU.
At this point you should have a working installation with WSL2, Ubuntu 20.04, Julia and CUDA.jl. In case you're new to CUDA.jl I suggest you follow the excellent introduction to GPU programming by JuliaGPU or jump in at the deep end with FluxML's GPU support.
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