If you are new to data analytics, chances are you might have never heard about Google Colab before. In our Python exercises, we have introduced you to Jupyter notebook which is a popular open-source application where you can create and run live codes, visualizations and perform mathematical operations. However, we have discovered something better which will enhance your learning experience in Python. We have added Google colab as a tool for you to create and share Jupyter notebooks easier without installing anything. This free research tool has a lot of nice features that will interest both students and teachers, especially for sharing works related to machine learning and research.
Why Use Google Colab?
Google Colab is a cloud service created for research and machine learning education. It now comes with a GPU which is totally free. We have moved our Python exercises to Google Colab for convenience on both the students and the teacher. Since you can easily share and collaborate your work with others, you can improve your Python skills and easily use the available libraries for your applications. If you are used to working with Jupyter notebooks, you can easily upload and share them without any set-up required.
Google Colab can be used with common browsers, such as Chrome and Firefox. Since you don’t have to install any software to work on your codes, you can simply run your notebooks using the browsers of your choice.
All your notebooks will be saved in your Google Drive and can be shared like your Google Sheets or Docs. Your shared notebooks will include its full contents (code, text, etc.) except for the virtual machine and custom libraries you’ve used. If you need to share this as well, it is advised that you include the cells that automatically install and load the custom files needed for your application.
Another thing that’s great about Google Colab is that it supports both Python 2.7 and Python 3.6. So if you have previously worked on your code using any of these versions, you should be able to upload and run them without any problems. Most of our Python exercises support either of these two versions.
What’s the Difference Between Jupyter and Colaboratory?
We have already talked about the main features of Jupyter and Google Colab, but you may have asked what is the difference between the two. First of all, the creation of Google Colab was actually inspired from the open-source Jupyter notebook. Unlike Jupyter, Google Colab allows you to immediately run your codes without any installations required. It works like how you would upload and share your Google Docs or Sheets. Since it allows you to use the cloud service for free, you can take advantage of its GPU and computation capabilities for your projects, especially for machine learning. However, you cannot use it for applications that require long-running computations like cryptocurrency mining. This will automatically result in service unavailability. Google Colab is intended for interactive usage and if you need to continuously run your code for your project, you need to use a local runtime through Colaboratory’s UI.
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