---
title: Google Colab - Everything you Need to Know - Scaler Topics
description: Learn everything about Google Colab in this article. Scaler Topics explains why Google Colab is better than Jupyter Notebook. Click here to know more.
author: Prabanjan Raja
Category: Miscellaneous

---
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Google Colab is a free, cloud-based platform provided by Google that allows users to write and execute Python code in a collaborative environment. It provides access to GPU and TPU resources, facilitating machine learning and data analysis. Colab notebooks are stored in Google Drive and can be easily shared with others for real-time collaboration.

Google developed Google Colab to provide free access to GPU’s and TPU’s to anyone who needs them to build a machine learning or deep learning model. Google Colab can be defined as an improved version of Jupyter Notebook.

Jupyter Notebook is an application that allows editing and running Notebook documents through a web browser or an Integrated Development Environment (IDE). Instead of files, you will work with Notebooks.

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## Why Should You Use Google Colab?
Google Colab provides many exciting features that any modern IDE offers, and much more. Some of the most exciting features are listed below.

* **Pre-installed libraries:**

Google Colab comes pre-installed with the most popular machine-learning libraries. Colab comes pre-installed with Keras, PyTorch, and TensorFlow, which saves you the time and hassle of setting up a local environment.

* **Saved on the cloud:**

Every Notebook you create in the Google Google Colab is saved on the cloud. This lets you access and work with those Notebooks from any machine. You only need a browser and a reliable network connection, and you can work from anywhere and anytime.

* **Collaboration :**

Collaboration is another fantastic reason to choose Google Google Colab when you are working on a project with a team of developers. You can share your Notebook with your teammates and assign them roles so they can only perform operations that fit their roles. The various options available for each role is shown below:

– Editors can change permissions and share
– Viewers and commenters can see the option to download, print, and copy

* **Free GPU and TPU use:**

Google Colab provides free access to GPUs and TPUs developed by Google Research. So you can work on your projects with powerful GPUs irrespective of your local machine.

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## What can you do with Google Colab?
In the dynamic landscape of coding, data science, and machine learning, Google Colab emerges as a versatile and powerful tool, offering many capabilities to individuals and teams. From running Python code to collaborative data analysis and machine learning experimentation, here's a comprehensive look at what you can accomplish with Google Colab.

* **Running Python Code with Ease:**
Google Colab provides a user-friendly Jupyter notebook-style environment, allowing you to seamlessly write and execute Python code. The built-in code cells facilitate quick testing and iteration, making it an ideal platform for both beginners and experienced developers.

* **Accelerated Machine Learning and Deep Learning:**
Take advantage of Colab's free access to Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) for accelerated machine learning tasks. Train and deploy models using popular libraries such as TensorFlow and PyTorch, and benefit from pre-installed machine learning tools that streamline the process.

* **Data Analysis and Visualization Made Simple:**
Perform robust data analysis using libraries like pandas and NumPy. Create interactive visualizations with Matplotlib, Seaborn, and Plotly to gain insights from your data. The collaborative environment enhances the exploration and understanding of datasets.

* **Seamless Collaboration for Teams:**
Collaborate effortlessly with team members in real-time within the same Colab notebook. Share your work easily via links or leverage Google Drive integration for streamlined collaboration and version control. Commenting on code cells fosters communication within the notebook.

* **Access to Cloud Storage and Datasets:**
Integrate Colab seamlessly with Google Drive, facilitating easy storage, sharing, and version control of your notebooks—access datasets stored in Google Drive or other cloud storage platforms, providing flexibility in managing your data.

* **Educational Excellence:**
Google Colab is an invaluable resource for educators and students. It provides an interactive and collaborative environment for teaching and learning Python, data science, and machine learning. Conduct coding exercises, projects, and experiments easily, accommodating varying computing resources.

* **Rapid Prototyping and Experimentation:**
Accelerate your prototyping and experimentation processes using Colab's free access to GPU and TPU resources. Test and refine ideas without the need for local hardware, making it an ideal platform for researchers and developers pushing the boundaries of their work.

* **Integration with External Libraries:**
Customize your Colab environment by installing and using external libraries and dependencies as needed. This flexibility allows you to tailor the platform to suit the specific requirements of your projects.

* **Markdown Documentation for Clarity:**
Document your code and analyse it effectively using Markdown cells. Combine code, text, and visualizations to create comprehensive and well-documented notebooks, enhancing the clarity of your work.

* **Command Line Integration for Advanced Users:**
Execute shell commands directly within Colab notebooks, giving advanced users greater control. Install and manage additional packages and dependencies seamlessly from the command line.

In conclusion, Google Colab emerges as a go-to platform for a wide range of tasks, catering to beginners, educators, and experienced professionals. Its blend of collaborative features, cloud-based accessibility, and integrated tools positions it as a valuable asset in the toolkit of anyone involved in coding, data science, or machine learning. Whether you're a learner, a researcher, or a team player, Google Colab empowers you to explore, create, and innovate with efficiency and ease.
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## Google Colab vs. Jupyter Notebook
As coding enthusiasts, data scientists, and machine learning practitioners, choosing the right coding environment can significantly impact productivity and collaboration. Google Colab and Jupyter Notebook are two widely used platforms with unique features and advantages. Let's compare these two coding companions in a tabular format to help you make an informed decision.

| Feature | Google Colab | Jupyter Notebook |
| -------- | -------- | -------- |
| Accessibility and Setup | Cloud-based, requires no local setup. | Requires local installation and setup. |
| Collaboration | Real-time collaboration with team members. | Collaboration requires external services or plugins. |
| Hardware Resources | Free access to GPU and TPU resources. | Relies on local hardware resources for computation. |
| Integration with Cloud | Seamless integration with Google Drive. | Limited native integration with cloud services. |
| Library Management | Pre-installed libraries for machine learning. | Requires manual installation of libraries. |
| Visualization Tools | Supports Matplotlib, Seaborn, Plotly, etc. | Similar support for visualization libraries. |
| Educational Use | Ideal for teaching and learning with ease. | Widely used in educational settings. |
| Offline Usage | Limited offline functionality. | Full functionality available offline. |
| Command Line Integration | Supports shell commands within notebooks. | Limited support; requires external plugins. |
| Ease of Sharing | Shareable via links or Google Drive integration. | Sharing involves file transfer or external services. |
| Community Support | Strong community support and resources. | Well-established community with extensive resources. |
| Customization | Limited customization options. | Highly customizable based on local environment. |

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## Conclusion

* Easy access through a web browser. No local setup is required.
* Provides free GPU resources for computationally intensive tasks.
* Seamless integration for saving, sharing, and collaborating on notebooks.
* Supports popular Python libraries and frameworks (e.g., TensorFlow, PyTorch).
* Widely used in educational settings for teaching and collaborative projects.
* Comes with many pre-installed libraries, reducing setup time.
* Benefits from an active user community and ample documentation.
* GPU usage Limitations may not be suitable for long-running or resource-intensive tasks.
* Sessions have time limits, and users may be disconnected if exceeded.
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