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Google Colab Tutorial For Running Python Notebooks

Leo Migdal
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google colab tutorial for running python notebooks

Google Colab is a free Jupyter notebook that allows to run Python in the browser without the need for complex configuration. It comes with Python installed and has all the main Python libraries installed. It also comes integrated with free GPUs. In this tutorial, we will cover everything that you need to get started using Python with Google Colab. Google Colab is truly the fastest way to start using Python on any computer. Google Colab is a browser-based product created by Google Research that allows to write and execute Python code without specific configuration.

Python comes pre-installed in Google Colab. You can start using Python in Google Colab straight away. Google Colab, short for Google Colaboratory, is a free cloud - based Jupyter notebook environment that allows you to write and execute Python code in your web browser. It provides access to powerful computing resources such as GPUs and TPUs, making it an excellent choice for data analysis, machine learning, and scientific computing. This blog post will guide you through the process of running Python code in Colab, covering fundamental concepts, usage methods, common practices, and best practices. Google Colab is a hosted Jupyter notebook service that enables users to write and execute Python code without the need to install anything on their local machines.

It offers several advantages: In Colab, notebooks are organized into cells. There are two main types of cells: code cells and text cells. To create a new code cell, click on the "+ Code" button in the toolbar or use the keyboard shortcut Ctrl + M B (Windows/Linux) or Cmd + M B (Mac). To run the code in a code cell, you can: Google Colab lets you run Python notebooks in the browser with zero setup, free GPUs on the entry tier, and easy sharing.

If you're new to notebooks, session limits, or bringing in your own data, this guide gives you a clean, repeatable setup that works on both the Free and Pro plans. Go to Colab, click New Notebook. You’ll see a code cell and a text cell (Markdown). Click the code cell and press Shift+Enter to run it. Add a text cell for notes with Ctrl+M then B (Mac: Cmd+M, then B). Open Runtime → Change runtime type and choose Hardware accelerator: None (CPU), GPU, or TPU.

Free users get variable availability; Pro tiers offer higher priority. After switching, the environment restarts. Pro tip: If you need CUDA, verify it with !nvidia-smi right after selecting GPU. For TPU, use TensorFlow or JAX builds that support it. Each notebook is a linear list of cells. Code cells run top‑to‑bottom and share the same Python kernel state.

Use Shift+Enter to run a cell and move, or click the play icon. Restart the runtime anytime via Runtime → Restart session to clear memory and variables. Running Python code in the cloud without worrying about local setup is a game-changer. With the rise of collaborative and remote data science, Google Colab, often referred to as Google Notebook, has emerged as a top tool for coders, data scientists, and researchers. Google Colab offers a powerful browser-based notebook interface, making it easy to write and execute Python code from any device, anywhere. Backed by Google’s cloud infrastructure and integrated with Google Drive, it empowers you to develop and share notebooks seamlessly.

Google Colab (short for Colaboratory) is a free Jupyter notebook environment that runs in the cloud and requires no setup. It supports Python and offers access to GPUs and TPUs, making it a great platform for AI, data analysis, and education. Unlike traditional Jupyter Notebooks that require local setup via Anaconda or pip, Google Notebook is ready-to-use directly in the browser and comes pre-installed with major Python libraries like NumPy, Pandas, TensorFlow, Keras, and OpenCV. Using Google Colab over local environments offers several advantages: In the world of data analysis, machine learning, and Python programming, Google Python Notebooks have emerged as a powerful and convenient tool. Google Python Notebooks, often associated with Google Colab (Colaboratory), provide an interactive environment where users can write, run, and share Python code.

This blog aims to provide a detailed exploration of Google Python Notebooks, covering fundamental concepts, usage methods, common practices, and best practices. Whether you're a beginner or an experienced Python developer, this guide will help you make the most out of this platform. A Google Python Notebook is a web-based interactive computing environment that allows you to combine live code, narrative text, equations, and visualizations in a single document. It uses Python as the programming language, enabling users to perform various tasks such as data analysis, scientific computing, and machine learning. Google Colab is a free cloud-based service that provides access to Google Python Notebooks. It is integrated with Google Drive, allowing you to easily save, share, and collaborate on notebooks.

Colab also offers GPU and TPU acceleration for computationally intensive tasks, making it a popular choice among data scientists and machine learning engineers. A Google Python Notebook consists of cells, which can be of two types: code cells and text cells. Code cells contain Python code that can be executed, while text cells are used for writing explanations, documentation, and markdown-formatted text. Notebooks can be organized into sections, making it easier to navigate and understand the code. This will mount your Google Drive to the Colab environment, allowing you to access your files. Prerequisite: How to use Google Colab Google provides Jupyter Notebook like interface to run Python code on online Virtual Machines.

In this article, we will see how to run simple Python code on Google Colab. Step #1: Open https://colab.research.google.com/ Step #2: Select New Python3 Notebook Step #3: Start Typing code into the code cells. Import all necessary libraries. Step #4: To add new cell, click on Insert->Code Cell Step #5: To run a particular cell, select the cell and press Ctrl + ENTER keys. We might need to install some external modules while working on some projects in Google Colab. By default not all the required modules are being pre-installed in Google Colab.

So how can we install them using pip command ? Step - 2 : Try to import that module and check if it is pre-installed or not. Here I am trying to import the Streamlit module which doesn't come pre-installed in collab. So we need to install it externally using pip. Step - 3 : Now we will install our required missing module/package using the following syntax. Google Colab is a product from Google Research that allows anyone to write and execute arbitrary Python code through the browser.

It's especially well-suited for machine learning, data analysis, and educational purposes. With Colab, you can leverage the power of GPUs and TPUs for free, making it a popular choice for resource-intensive tasks. When running deep-learning scripts, you will need to change from CPU to GPU. To do this, click Runtime → Change runtime type, then select T4 GPU. Then click Save. Note: You are encouraged to sign up to Google Colab with an existing Google account.

If you create a new Google account, your GPU usage may be cut off. You may find yourself doing a tutorial where the creator provides you with a link to a Google Colab link. Just like in our Fine-Tuning Embedding Models Course, we provided a link to this Google Colab book. To run external Colab notebooks, you run them as you would any notebook you’ve built yourself. Just click the run icon that’s located to the left of the cell and it will execute the code. It’s important to run each cell in succession.

Python is a versatile programming language widely used in various fields such as web development, data analysis, artificial intelligence, scientific computing, and more. This article explains how to perform multiple programming tasks using Google Colab, a popular cloud-based platform for coding. Python is a high-level programming language. It was first released in 1991. It became popular due to clear syntax and simpler coding processes compared to C++ and Java. It is preferred over other coding applications because of:

There are several libraries and environments available for Python development, each catering to different needs. Google provides a Jupyter Notebook-type interface for running Python code on an online virtual machine such as Google Colab. Google Colab comes with many popular Python libraries pre-installed, such as numpy, pandas, matplotlib, and tensorflow. Colab allows for seamless collaboration, much like Google Docs. You can share your notebook with others, giving them access to view, comment on, or edit the notebook. This feature is particularly useful for team projects, code reviews, and peer learning.

Colab autosaves work progress in Google Drive. You can also download notebooks in several formats to work offline or integrate them with other tools. Colab allows you to mount your Google Drive, making it easy to access, read, and write files directly. This feature is beneficial for loading datasets, saving processed data, or storing outputs and models. Now you can start writing your code and saving it to your Google Drive. Colab is a product from Google Research that allows anybody with an internet connection to write and execute python code through a browser.

Colab is free to use, and there is no setup on your computer needed. It is especially useful if you have a slow computer since Google hosts your Jupyter notebook and thus uses their GPU free of charge. Or if you want to work together on the same code, since you can collaborate on the same cloud file with multiple editors. It is time to create your own Colab notebook! Once you are in your Colab notebook you can use it just like any offline Jupyter notebook. Type your code in the darker gray box and press the run arrow to run the code.

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