5 Amazing Google Colab Hacks You Should Try Today!

Introduction

Google Colab is an amazing gift to the data science community from the fine folks at Google. Colab gives us the ability to build complex and heavy machine learning and deep learning models without having to expend our machine’s limited resources.

I can certainly appreciate this given how much I used to struggle on my machine! The ‘out of memory’ error is now quite infamous in the data science community – Google Colab provides us with the workaround and adds several cherries on top!

I love the free GPU and TPU support – it’s simply unparalleled and unrivalled in any other coding IDE. If you’re new to Google Colab, I highly recommend checking out my tutorial for beginners here.

In this article, I have listed five awesome Google Colab hacks that I’ve personally found really useful in my data science journey. These hacks will not just save you a lot of time but will broaden your understanding of Colab as a whole.

So, let’s make you a Google Colab expert!

The 5 Google Colab Hacks We’ll Cover:

• Increase Google Colab RAM
• Stop Google Colab From Disconnecting
• Snippets in Google Colab
• Top Keyboard Shortcuts for Google Colab
• Modes in Colab

1. Increase Google Colab RAM

Update: Recently, I have noticed that this hack is not working for some users. So keep this in mind before using this.

Google Colab already gives us about 13 GB of RAM for free. That’s quite impressive, but there are times when even that isn’t enough when we’re building heavy deep learning models. Here’s how you can quickly increase the RAM in your Colab notebook.

It’s a very simple trick and it almost doubles your RAM capacity. For getting higher RAM, you need to run the following code in your Colab code cell and wait for Google Colab to crash:

i = []
while(True):
    i.append('a')

Yes, you read it right – wait for Google Colab to crash. As soon as it crashes due to a lack of RAM, it asks if you want more RAM:

Now, click on Get more RAM  and you will see another popup:

Just click on the YES button:

And voila! You now have about 25 GB of RAM at your disposal!

2. Stop Google Colab From Disconnecting

Here’s a story I’m sure a lot of you will be familiar with. I was working on Colab the other day and left my laptop up and running for about an hour. When I came back, I saw that Google Colab disconnected my notebook and the model training had stopped. Imagine my immense disappointment!

Of course, I found out later that Google Colab disconnects our notebook if we leave it idle for more than 30 minutes. But here’s the good news -from today, you will never face this issue.

Just open your Chrome DevTools by pressing F12 or Ctrl+Shift+I on Linux and run the following JavaScript code in your console:

function KeepClicking(){
   console.log("Clicking");
   document.querySelector("colab-toolbar-button#connect").click()
}setInterval(KeepClicking,60000)

Here, the value 60,000 represents the milliseconds(equivalent to one minute). This small piece of code makes a click on the Colab screen after every 60 seconds. Thus, Colab thinks that the notebook is not idle and you don’t have to worry about being disconnected!

3. Snippets in Google Colab

4. Top Keyboard Shortcuts in Google Colab

Switching between your keyboard and mouse is a tedious and time taking task. Therefore, to speed things up, Google Colab provides us with certain really useful keyboard shortcuts. Honestly, you can’t master the Colab experience until you’ve used and become acquainted with these keyboard shortcuts.

Honestly, I feel these keyboard shortcuts increase our productivity as data scientists. I know it takes some time to learn these, but once you do get a hang of them, you can complete your work much faster.

I have created a list of top keyboard shortcuts that you should know when working with Google Colab. These key combinations are what I use, but you can modify them according to whatever is more comfortable:

5. Modes in Colab

Abhishek Sharma 18 May 2020

He is a data science aficionado, who loves diving into data and generating insights from it. He is always ready for making machines to learn through code and writing technical blogs. His areas of interest include Machine Learning and Natural Language Processing still open for something new and exciting.