As a data scientist and data engineer, I get a lot of comments from peers on the fact that I prefer to develop on a Windows machine compared to Mac or Linux. I have always really liked my Windows machines, and for the longest time I stuck with them even when specific machine learning libraries weren’t supported on Windows. However, about a year ago I finally gave in and started using Linux distribution for about a half of my data science work because it simply became too difficult to avoid unsupported libraries. Additionally, I was deploying a lot of Python code using Docker, which ended up running on a Linux distribution.
While it was time to start using Linux, I wanted to keep using Windows for my day to day work, therefore, I decided to create a Hyper-V VM. To be completely honest there are a ton of resources on the internet that walk you through setting up a Linux Hyper-V VM on Windows (and probably better than this one), but I am writing a demo of a popular Auto ML library, MLBox, and it is not yet supported on Windows, therefore, this will serve as the first step of the demo.
Creating a Linux VM
- My favorite way to develop on Linux is to create a Hyper-V VM on my local desktop. To enable Hyper-V on your Windows 10 machine, search for “Turn Windows features on or off” by opening your start menu.
- Now scroll down to find “Hyper-V” and check the box next to it to enable.
Now that Hyper-V is enable, we can create a virtual machine on our computer. First we will need to download a Linux distribution, I prefer Ubuntu. Note that this is download (~2GB) so downloading can take some time, and varies depending on your network speed.
Once you have the `.iso` file we can create a virtual machine. In your start menu search for “Hyper-V Manager”
- In the Hyper-V manager navigate “New” > “Virtual Machine…”. This will launch the start up wizard.
- For the most part in Wizard defaults will be acceptable. The first menu will have you provide the name of your virtual machine.
- The second menu will have you select the generation of the VM. We will want to use “Generation 1”.
- Third we will need to allocate memory for your machine. The default of 1024 MB of Memory is fine, and we will also check the box to use “Dynamic Memory”.
- Next we will need configure the network access for the virtual machine. Simply select “Default Switch”.
- Next we have the option to specify where we want to store our virtual machine hard disk. It is easiest to simply use the default locations. Note that the name of the Hard Disk will be determined by what you name your VM in the previous step.
- Now we simply need to select our Ubuntu `.iso` file we downloaded, and click Finish.
This will launch the Ubuntu setup menu, and simply follow the instructions to create the virtual machine with your username and password. Now you have a Ubuntu machine to develop your data science solutions on! I would recommend downloading and installing the Anaconda distribution of Python.