In this post, we take a look at How to Use Google Colab for Python. If you are a data scientist or a python developer, you may come across various online python project environments out there. Codeanywhere, VS Code Server and repl are some of the popular ones out there.
But what about the python projects that require the data science libraries? Google already thought about this and managed to connect their Google Cloud with this solution. And that is where the Google Colab was born.
Colab is a cloud-based Jupyter notebook environment that makes use of the server power from the Google Cloud. It allows you usage of all the necessary data science libraries for your instance.
Why You Should Use Google Colab?
Google colab offers you variety of use cases for your data science work. And coding in cloud has a bonus as it can save you from all those computing requirements that you have to do on your native computer with high specs.
So here are some of the reasons why you should use the Google Colab.
- Free Account for Limited Usage
- Allows usage of GPUs/TPUs
- All data science libraries supported.
- Share your notebooks with team/Public
These are some of the good reasons for making use of the Google Colab. If you are still not convinced I'd recommend giving it a shot. I am sure you would love using it for everyday data science work.
I have created a video to give you an overview on how to use the Google Colab for the data science work. You should give this one a try.
In the video I have covered some of the simple basics. I have covered how you can use it for most of the simple usage of the code, visualization and also making use of the CPU/TPU. Which is kind of tour like video for anyone who wants to learn about the Google Colab.
What about the Visualization?
I think one of the coolest use of the Jupyter notebook is it's ability to do the visualization on the web page. And the notebook can allow you to have the comments, code share and other feature on the Google Colab.
So if you have plans to do the Visualization for the data science work. You are making use of the right set of the libraries. And that means you should be making use of the Google Colab for this type of the feature. And the share feature which makes sharing as simple as sharing some spreadsheet on Google's workspace.
What about CPU/TPU support in the Google Colab?
I think this is another good feature which would be something I'd vouch for. Let's say you have those low RAM laptops on which you can't run the high speed computations and the big libraries of the python for processing.
You get to choose when to add and remove those changes and you can have the Notebook getting what you want. Like for low level calculations you can disable things you don't want and then enable as required.
In such case just making few changes in the settings you could easily get the power you need from the notebook. You can run all those high speed calculations that you seek from the Google Colab.
Google Colab can be a good tool for learning the data science concepts. You can easily use it for the online courses that you take for learning the data science. Like say Pytorch, Tensorflow and other libraries. Do note that it becomes a bit difficult otherwise to use libraries and you have to setup a lot of things in the due process.
You can also import the data from the google drive and so that storing those notebooks wont be much of an issue. I'd say there are plenty of use cases. So my approach is from next tutorial onwards you can see some of the libraries that I wish to share with you one by one. I hope you find those useful for your future use case.
If you happen to like this content, do give me feedback over there and that would help me improve my efforts in near future.