Using Google Colab for Machine Learning Development

Due to work restrictions, I have been using Tensorflow CPU version for all of my development. However, with model training taking several hours, I have been fininding the development cycle a bit slow. I was therefore on the lookout for some GPU machines that I could try and see what speedup I got. Google Colab is a great free resource for development and even model training on GPUs (and TPUs). I have been using Tensorflow 2.0 and that is going to be the default enviroment on Colab soon. In the mean time you can make sure it picks that by using jupyter magic command:

%tensorflow_version 2.x

Then go ahead and import the libraries you want. As for data for training and model outputs e.g. checkpoints, you can save them in Google Drive. You just have to mount your drive first:

from google.colab import drive

I have to say, it is great to see Google providing such resources for free. It is making coding and machine learning development that much more accessible.

Python Development with Kite

While discussing tools, I also want to mention another python development tool, Kite. It is a plugin for most of the major code editors (no Emacs though!) and useful autocomplete and documentation functionality.

Omar Jamil

Exeter, England
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I am a Senior Scientist at the UK Met Office, using machine learning to develop and improve the physics in the weather and climate model. I specialise in radiative transfer with remote sensing applications. In short, I create mathematical and physics models to make sense of data.