Google Colab: Your Jupyter Notebook on the Cloud

In previous posts, we mentioned that Google Colab is a great alternative to Python programming using Anaconda and Jupyter Notebook. Essentially, Colab is Jupyter Notebook in the cloud. Their interfaces are very similar, both can be used to write and run Python code, and both are very popular in the data science community.

The main difference is the fact that Anaconda is operated using your computer’s own hardware and computing resources. This means that its capacity and speed is dependent on your computer’s CPU, RAM and GPU. Colab, on the other hand, runs within Google’s data centers. Users can perform operations on it remotely using an internet connection, but all the computing processes happen within Google’s hardware.

Basically, Colab is to Anaconda as Google Drive is to Microsoft Office. They’re very similar platforms but one runs locally and the other one resides in the cloud. At first glance, they’ll look very similar, but once you dive deeper, you’ll notice some differences here and there. If you need a guide to get you started on Colab, read on because this post is fo you.

The Advantages of Google Colab

Google Colab extends beyond the features of traditional coding environments with its cloud-based platform, offering distinct advantages tailored to enhance the productivity and capabilities of data scientists and researchers. Here are the main reasons why some Python coders and data scientists prefer using it:

  • Effortless Collaboration. Much like other tools in the Google Suite, Colab allows for real-time collaboration. Teams can work concurrently on the same notebook, providing comments, suggestions, and edits. This transforms the solitary act of coding into a team effort, enabling a dynamic exchange of ideas and fostering a more collective approach to problem-solving.
  • No Installation Required. Jumping straight into data analysis without the overhead of setup is one of Colab’s hallmarks. It liberates users from the often tedious task of configuring a local environment, which can be particularly daunting for those new to the field.
  • Complimentary GPU and TPU Use. Colab grants users complimentary access to Google’s GPU and TPU resources, which can drastically reduce computation times. This is not just a convenience; it’s a game-changer for those working on complex models and simulations who otherwise lack access to such computing power.
  • Google Drive Integration. With Google Drive integration, Colab ensures that datasets, notebooks, and outputs are effortlessly synced. This means less time spent on file management and more on analysis. Your work can be as mobile as you are, with all changes saved and accessible across devices.
  • Ready-to-Use ML Libraries. A rich array of pre-installed libraries removes the barrier of managing dependencies, allowing users to focus on the machine learning workflow. This feature is not only a time saver but also reduces the complexity of starting new projects.
  • Interactive Visualizations in the Browser. Colab supports a variety of libraries for creating interactive visualizations, which can be manipulated within the browser for immediate insights. This interactive capability enriches the exploratory data analysis process.
  • A Hub for Learning and Sharing. With an extensive collection of publicly shared notebooks, Colab serves as a vast learning repository. From introductory tutorials to advanced research, it’s a resource for growth and learning, showcasing the practical application of theoretical concepts.

By eliminating the barriers associated with traditional programming setups, Google Colab makes advanced computing power and collaborative workspaces accessible to everyone, everywhere.

Disadvantages of Google Colab

While Google Colab offers numerous benefits, there are some limitations to consider when integrating it into your data science workflow. Here are some reasons why some data professionals may not jump on to the Colab bandwagon:

  • Limited Computational Resources. Colab provides free access to GPUs and TPUs, but these resources are shared among many users and come with certain limitations. The available computational time is capped, and there may be restrictions on the continuous runtime. For long, intensive tasks, this can pose a challenge as the environment may reset after a period of inactivity or after hitting the usage limits.
  • Internet Dependency. Colab’s cloud-based nature means that a stable internet connection is a must. This can be a significant drawback for those with limited or unreliable internet access. Without connectivity, the interactive features and the ability to save progress or access stored notebooks are unavailable.
  • Data Privacy Concerns. When working with sensitive data, one must consider the privacy implications of using a cloud service. Google’s servers host all operations and stored files, which may not be suitable for projects requiring high levels of confidentiality.
  • File Management. While Google Drive integration is an advantage, it can also be a hindrance. Navigating large datasets or multiple files can be cumbersome, and the interface may not be as intuitive for file operations as a local file system.
  • Limited Customization. Colab environments are pre-configured, which means there’s less flexibility for customization compared to a local setup. While you can install additional libraries, some deeper system-level changes are not possible.
  • Version Control Challenges. Although Colab notebooks can be stored in Google Drive and shared via GitHub, they don’t integrate as seamlessly with version control systems as locally run notebooks. The JSON format used to save notebooks can be verbose and may lead to complex merge conflicts.
  • Feature Parity with Jupyter Notebooks. Colab often lags behind Jupyter in terms of feature updates and plugin support. Some Jupyter extensions that can enhance productivity are not available or have limited functionality in Colab.

Understanding these limitations is crucial for data scientists and researchers to determine when and how to effectively use Google Colab for their specific needs. It’s often a balance between the convenience of cloud-based features and the requirements of individual projects.

How to Get Started on Google Colab

Embarking on your Google Colab journey is a straightforward process. Here’s a guide to help you get up and running:

Step 1: Access Colab

  • Google Account: Ensure you have a Google account, as Colab is part of the Google suite of tools.
  • Navigate to Colab: Open your web browser and go to the Colab website. You can also access Colab via your Google Drive by clicking ‘New’ > ‘More’ > ‘Google Colaboratory’.

Step 2: Open a New Notebook

  • Create a New Notebook: Once on the Colab landing page, you can start a new notebook by clicking on ‘New Notebook’, which will open a new tab with a fresh, untitled notebook.
  • Use a Template: Alternatively, explore the ‘Examples’ or ‘Template’ tabs to start with a pre-made notebook that suits your project’s theme or demonstrates a specific functionality.

Step 3: Familiarize Yourself with the Interface

  • Explore the Interface: Take a moment to get accustomed to the Colab interface. You’ll find it similar to Jupyter Notebook if you have previous experience with it.
  • Menu Options: Review the menu bar at the top for options like File, Edit, View, Insert, Runtime, Tools, and Help for various functionalities.

Step 4: Save Your Notebook

  • Google Drive Integration: Your notebook will be automatically saved to your Google Drive. You can also click on the ‘File’ menu and select ‘Save’ or use the ‘Ctrl+S’ (Cmd+S on Mac) shortcut to save manually.
  • Rename Your Notebook: Click on the notebook name at the top-left corner to rename it for easy identification later.

Step 5: Start Coding

  • Writing Code: Click in a code cell and start typing your Python code. Execute it by pressing ‘Shift+Enter’.
  • Adding Text: Add text cells to document your work by clicking on ‘+ Text’ in the toolbar, and use the markdown format to style your text.

Step 6: Using Advanced Features

  • Accessing GPUs/TPUs: For machine learning tasks, access the GPU/TPU accelerators by going to ‘Runtime’ > ‘Change runtime type’ and select either GPU or TPU from the hardware accelerator dropdown.
  • Mounting Google Drive: To access files in your Google Drive, use the command from google.colab import drive followed by drive.mount(‘/content/drive’) in a code cell.

Step 7: Share and Collaborate

  • Sharing: Click the ‘Share’ button in the upper right to add collaborators via email or to generate a shareable link.
  • Version History: Use ‘File’ > ‘Version history’ to track changes and revert to previous versions if needed.

Google Colab removes the barrier to entry for many aspects of data science and machine learning, offering a robust platform that is both flexible and user-friendly. By following these steps, you’ll be well on your way to performing sophisticated data analysis and model training in the cloud.

Basic Google Colab Operations

Getting comfortable with Google Colab involves learning a few basic operations that are fundamental to performing data analysis in this environment. Here’s how you can master these tasks:

Adding Cells

  • To add a new cell in Colab, you can click on the + Code or + Text buttons in the toolbar for code and markdown cells, respectively.
  • Alternatively, you can use the keyboard shortcuts Ctrl+M B to insert a cell below or Ctrl+M A to insert above the currently selected cell.

Changing Cell Types

  • You can change a cell’s type by clicking on the cell to select it and then using the dropdown menu in the toolbar to switch between ‘Code’ and ‘Text’.
  • For keyboard aficionados, Ctrl+M Y will change the selected cell to a code cell, and Ctrl+M M will change it to a markdown cell.

Deleting Cells

  • To delete a cell, select it and click on the scissors icon in the toolbar, or use the shortcut Ctrl+M D.
  • If you delete a cell by accident, you can use Ctrl+M Z to undo the last action.

Moving Cells

  • Moving cells around is as simple as selecting them and then clicking on the cell movement arrows in the toolbar to shift them up or down.
  • There are no direct keyboard shortcuts for this, but you can cut and paste cells to move them, using Ctrl+X and Ctrl+V.

Loading Libraries

  • Libraries can be loaded into a Colab notebook using the standard Python import statement, for example: import pandas as pd.
  • If a library is not available, you can install it using !pip install library-name, and it will be available for the duration of your Colab session.

Loading Datasets

  • Colab facilitates data import through its integration with Google Drive. Use from google.colab import drive to mount your drive and access your datasets.
  • You can also upload data directly from your local file system using the file upload feature in the left sidebar, or by using the files.upload() method from the google.colab module.
  • For larger datasets, you may want to leverage the ability to read directly from public URLs or use integration with Google Cloud Storage.

By mastering these basic operations, you’ll have a solid foundation for conducting data analysis and developing machine learning models in Google Colab. These operations are the building blocks for creating a seamless and efficient workflow in the cloud.

 

Overall, Google Colab is an amazing coding platform for people doing Python and data science projects in a team setting. It democratizes Python programming by breaking the hardware barrier and allowing even the most basic of devices to get in on the action. If you’re starting out your data science and analytics journey, you should strongly consider Colab to be your primary coding environment.

About Glen Dimaandal

Glen Dimaandal
Glen Dimaandal is a data scientist from the Philippines. He has a post-graduate degree in Data Science and Business Analytics from the prestigious McCombs School of Business in the University of Texas, Austin. He has nearly 20 years of experience in the field as he worked with major brands from the US, UK, Australia and the Asia-Pacific. Glen is also the CEO of SearchWorks.PH, the Philippines’ most respected SEO agency.
Glen Dimaandal
Glen Dimaandal is a data scientist from the Philippines. He has a post-graduate degree in Data Science and Business Analytics from the prestigious McCombs School of Business in the University of Texas, Austin. He has nearly 20 years of experience in the field as he worked with major brands from the US, UK, Australia and the Asia-Pacific. Glen is also the CEO of SearchWorks.PH, the Philippines’ most respected SEO agency.
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