Jan 03, 2020

7 min read

Machine Learning Setup – Part 1: Python Virtual Environments

Written by

Vippy The VPS


Before you get started with your journey of Machine Learning, it is important to understand the underlying technology that you will be using. In most cases, this will involve using TensorFlow with Python. ML workloads are quite compute intensive, this is why TensorFlow is written in C++. The Python interface is meant for you to use TensorFlow more effectively and easily without getting bogged down by C++ implementations details. Similar interfaces exist for JavaScript and a few other languages. We will be using Python for our setup. Also, TensorFlow comes with a GPU specific implementation meant to take advantage of Nvidia GPUs, to keep things simple we will ignore that as well.

SSDNodes TensorFlow Template!

If you are already comfortable with it, or want to jump straight into running Python snippets, try out one of our TensorFlow templates at SSDNodes which will get you started with a Ubuntu 18.04 environment preconfigured with Jupyter Labs and TensorFlow. There are a lot of advantages in offloading your ML workload to our cloud, a few include:  
  1. Blazing fast internet connection to download huge sets of training data.
  2. Tonnes of memory and compute at low low prices! It will burn through the data before you are done with your coffee break.
  3. Freedom to break things. If you screwup, just click reinstall and you have a clean slate to start over again!

Python and its packaging problem

Python is one of the easiest languages to pickup, with readable syntax and projects like TensorFlow that have real world use cases you would be hard pressed to find a better language to begin your journey into computing. However, some of its success has resulted in a lot of similar projects and confusion surrounding them. Some unintended
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