How to Create a Linux Virtual Machine For Machine Learning Development With Python 3
Benefits of a Linux Virtual Machine
There are a number of reasons that you may want to use a Linux virtual machine for Python machine learning development.
For example, below is a list of 5 top benefits for using a virtual machine:
- To use tools not available on your system (if you’re on Windows).
- To install and use machine learning tools without impacting your local environment (e.g. use Python 3 tools).
- To have highly customized environments for different projects (Python2 and Python3).
- To save the state of the machine and pick up exactly where you left off (jump from machine to machine).
- To share development environment with other developers (set-up once and reuse many times).
Perhaps the most beneficial point is the first, being able to easily use machine learning tools not supported on your environment.
I’m an OS X user, and even though machine learning tools can be installed using brew and macports, I still find it easier to setup and use Linux virtual machines for machine learning development.
This tutorial is broken down into 3 parts:
- Download and Install VirtualBox.
- Download and Install Fedora Linux in a Virtual Machine.
- Install Python Machine Learning Environment.