“In general, I usually write and share machine learning and data science-related code on Google Colab. I thought of writing a detailed article about Colab so that everyone can benefit from it.
Colab is an online code editor for writing and running Python code in the browser. It is more than just a code editor. It has features that are often not available on our desktop computers. For example, if you need to run code that requires a GPU but your own computer doesn’t have one, you can use Colab to run that code. And the best part is that it is completely free. There is no need for any configuration to write and run code in Colab.
For machine learning or data analysis tasks, we often need various libraries or frameworks that need to be installed and configured on our own computers, which can be time-consuming for beginners. In Colab, all the necessary libraries or frameworks are pre-installed. You can simply import and use them without any hassle.
Colab is based on Jupyter Notebook. If you are already familiar with Jupyter Notebook, you will see that you can write both code and text in the same file, much like a word processor. This allows you to include details about why a particular code is written. Colab notebooks are saved on Google Drive, so you can easily share them or collaborate with others.
Another great feature of Colab is that you can view, write, and run code from your mobile or tablet devices. Even if you don’t have a computer, you can learn Python programming, machine learning, or anything related to Python using Colab.
If you visit the homepage of Colab at https://colab.research.google.com/, you will see the ‘Welcome to Colaboratory’ notebook, where you can learn more about Colab in detail. Additionally, you will find options to create a new notebook like the one mentioned below:
Collab notebooks can also be created from Google Drive. For that, click on New > More > Google Collaboratory.
After creating a Colab file, you will initially see an empty code cell where you can write any code to run. There is a “Play” or “Run” button right beside any code block. When you click on it, the code will be executed.
After running a cell, you will see the output below each cell in Colab. The name of the Colab file is usually “Untitled.ipynb” and it is saved by default. To change the name, you can click on “Untitled0.ipynb” at the top left corner and rename it as per your preference.
To add a new code cell, you can click on “+ Code” on the top toolbar. Similarly, you can add a text cell by clicking on “+ Text”. In a text cell, you can write text using Markdown formatting. You can learn more about Markdown in this guide.
By default, Colab does not allocate a GPU. If a program requires GPU acceleration, you can select GPU by going to “Runtime” > “Change Runtime type” and selecting GPU. There, you will also see another option called TPU, which stands for Tensor Processing Unit. TPUs are primarily used for accelerating model training in machine learning programs, especially those using TensorFlow.
One of the greatest advantages of Colab is sharing. You can share any notebook file with someone else and even grant them editing access if desired. This allows you to seek help or collaborate with others by sharing your work. The sharing option is available in the top right corner.
These are the basic ideas about Colab. You can visit the links provided in the “Welcome to Colaboratory” notebook for more detailed information. If you have specific topics or concepts you need to learn, it’s worth searching for specific tutorials. This blog covers a lot of topics related to machine learning and data science that you might find helpful.