---
title: "Google Colab Tutorial"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Google Colab Tutorial

[Watch Google Colab Tutorial for Beginners](https://www.youtube.com/watch?v=rsBiVxzmhG0)

[Google Colab Tutorial](https://colab.research.google.com/drive/16pBJQePbqkz3QFV54L4NIkOn1kwpuRrj)


### Google Colab Tutorial: A Detailed Guide for Beginners

Google Colab, or Colaboratory, is a free, cloud-based environment that allows you to write and execute Python code in your browser. It’s particularly popular for machine learning and data science projects because it provides free access to GPUs and TPUs, making it a powerful tool for prototyping and experimenting with code. This tutorial will walk you through the basics of using Google Colab, from setting up your environment to executing code and sharing your work.

### Table of Contents
1. Introduction to Google Colab
2. Getting Started
   - Creating a New Notebook
   - Interface Overview
3. Writing and Executing Code
   - Code Cells
   - Text Cells
   - Markdown Basics
4. Managing Your Environment
   - Installing Libraries
   - Using GPUs and TPUs
   - Uploading and Downloading Files
5. Advanced Features
   - Mounting Google Drive
   - Using External Data Sources
   - Sharing and Collaborating
6. Best Practices and Tips
7. Conclusion

### 1. Introduction to Google Colab

Google Colab is an online Jupyter notebook environment that runs in the cloud. It is part of the Google Research Project and is especially useful for machine learning, data analysis, and artificial intelligence projects. Colab offers the following advantages:

- **Free access to GPUs and TPUs**: Ideal for running compute-intensive tasks.
- **No setup required**: You can start coding immediately.
- **Easy sharing and collaboration**: Share your notebooks with others just like you would with Google Docs.
- **Integration with Google Drive**: Store and access your notebooks from your Google Drive.

### 2. Getting Started

#### Creating a New Notebook

1. **Access Google Colab**:
   - Go to [Google Colab](https://colab.research.google.com/) and sign in with your Google account.

2. **Create a New Notebook**:
   - Click on **File > New notebook** to create a new notebook.
   - Alternatively, you can create a notebook directly from your Google Drive by clicking **New > More > Google Colaboratory**.

#### Interface Overview

Once you’ve created a new notebook, you’ll see a user interface with the following components:

- **Menu Bar**: Provides access to various functions such as saving, editing, and running your notebook.
- **Toolbar**: Contains buttons for adding new cells, running cells, and stopping code execution.
- **Notebook Cells**: Where you write and execute your code or text.

### 3. Writing and Executing Code

#### Code Cells

Code cells are where you write and run Python code.

1. **Adding a Code Cell**:
   - Click on the `+ Code` button in the toolbar or press `Ctrl + M + B` to add a new code cell.

2. **Writing and Running Code**:
   - Type your Python code into the cell.
   - To execute the code, press `Shift + Enter` or click the play button next to the cell.

**Example**:

```python
# Simple Python code
print("Hello, Google Colab!")
```

#### Text Cells

Text cells are used to add explanations, documentation, or any other text content to your notebook. These cells support Markdown, a lightweight markup language.

1. **Adding a Text Cell**:
   - Click on the `+ Text` button in the toolbar or press `Ctrl + M + A` to add a new text cell.

2. **Writing Text**:
   - Enter your text, using Markdown for formatting (e.g., headers, lists, bold, italics).

**Example**:

```markdown
# This is a Header
This is some **bold text** and this is *italicized text*.
```

#### Markdown Basics

Markdown is easy to learn and provides basic formatting for your text:

- **Headers**: Use `#` for headers. The number of `#` symbols indicates the header level (e.g., `#` for H1, `##` for H2).
- **Bold**: Wrap text in `**` for bold (e.g., `**bold text**`).
- **Italic**: Wrap text in `*` for italics (e.g., `*italicized text*`).
- **Lists**: Use `-` for bullet points or `1.` for numbered lists.

### 4. Managing Your Environment

#### Installing Libraries

Google Colab comes pre-installed with many popular Python libraries, but you can install additional libraries using `pip`.

```python
!pip install pandas matplotlib
```

#### Using GPUs and TPUs

One of the powerful features of Google Colab is access to free GPUs and TPUs, which can significantly speed up computation.

1. **Enable GPU/TPU**:
   - Go to **Runtime > Change runtime type**.
   - Select **GPU** or **TPU** from the Hardware accelerator drop-down menu.
   - Click **Save**.

2. **Check GPU Availability**:

```python
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
```

#### Uploading and Downloading Files

1. **Upload Files**:
   - Click on the folder icon in the left sidebar to open the file browser.
   - Use the **Upload** button to upload files from your local machine.

2. **Download Files**:

```python
from google.colab import files
files.download('your_file.csv')
```

### 5. Advanced Features

#### Mounting Google Drive

To easily access files stored in your Google Drive, you can mount your Drive directly in Colab.

```python
from google.colab import drive
drive.mount('/content/drive')
```

After running this code, you’ll be prompted to authenticate your Google account. Once authenticated, your Drive files will be accessible under `/content/drive/My Drive/`.

#### Using External Data Sources

You can access data from external sources such as GitHub, Google Sheets, or public datasets.

**Example: Cloning a GitHub Repository**:

```python
!git clone https://github.com/username/repository.git
```

**Example: Accessing a Google Sheet**:

```python
import pandas as pd
url = 'your_google_sheet_url'
sheet = pd.read_csv(url)
```

#### Sharing and Collaborating

Google Colab makes it easy to share your notebooks with others:

1. **Share**:
   - Click the **Share** button in the top-right corner.
   - Set permissions (e.g., view-only, edit access) and share the link with your collaborators.

2. **Collaborate in Real-Time**:
   - Multiple people can work on the same notebook simultaneously, similar to Google Docs.

### 6. Best Practices and Tips

- **Use Comments**: Add comments to your code to explain complex logic.
- **Save Regularly**: Your notebook saves automatically, but you can also manually save it to your Google Drive.
- **Clear Output**: To keep your notebook tidy, you can clear the output from all cells by going to **Edit > Clear all outputs**.
- **Use Version Control**: If you’re working on a project, consider using GitHub for version control and collaboration.

### 7. Conclusion

Google Colab is a versatile and powerful tool for anyone interested in data science, machine learning, or Python programming. It allows you to start coding without any setup, provides powerful computational resources, and makes sharing your work with others straightforward. Whether you're a beginner or an experienced programmer, Colab offers a seamless environment for prototyping and experimenting with your projects.

## Google Colab Alternatives

Google Colab is a popular choice for AI prototyping due to its accessibility, ease of use, and the fact that it provides free access to GPUs. However, depending on your specific needs, there are other environments that might offer better features or performance for AI prototyping:

### 1. **Kaggle Notebooks**
   - **Pros:**
     - Free access to GPUs and TPUs, similar to Colab.
     - Built-in datasets and a strong community for sharing notebooks and code.
     - Easy access to competitions and datasets, which is great for learning and experimentation.
   - **Cons:**
     - Limited to 9 hours of continuous runtime.
     - Slightly less flexible than Colab in terms of installing custom libraries or dependencies.

### 2. **Paperspace Gradient**
   - **Pros:**
     - Offers a more powerful and flexible environment with access to high-performance GPUs (including A100s) for a fee.
     - Integrated with Jupyter Notebooks and offers pre-configured environments for deep learning.
     - Provides a "Notebooks" service similar to Colab but with more robust hardware options.
   - **Cons:**
     - Free tier is limited in terms of resources and usage.
     - Paid options can get expensive depending on the hardware selected.

### 3. **Azure Machine Learning Studio**
   - **Pros:**
     - Scalable cloud resources with access to a wide range of GPUs.
     - Integrated environment for developing, training, and deploying machine learning models.
     - Supports drag-and-drop ML model building as well as code-based prototyping.
     - Strong integration with other Azure services for end-to-end ML workflows.
   - **Cons:**
     - Requires an Azure subscription, and costs can add up depending on resource usage.
     - Slightly steeper learning curve compared to Colab for those new to Azure.

### 4. **AWS SageMaker Studio**
   - **Pros:**
     - Provides a full-fledged environment for developing, training, and deploying machine learning models.
     - Access to a wide range of instance types, including GPUs and TPUs.
     - Seamless integration with other AWS services for scalable AI/ML workflows.
     - Includes advanced features like AutoML, model tuning, and debugging.
   - **Cons:**
     - More complex setup compared to Colab.
     - Costs can be high, especially if using large GPU instances.

### 5. **Jupyter Notebooks on Local/Cloud Environments**
   - **Pros:**
     - Complete control over the environment, libraries, and configurations.
     - Can be run locally or on any cloud provider, giving flexibility in terms of resources and cost management.
     - Great for custom setups, including using Docker containers or Kubernetes for scalable AI development.
   - **Cons:**
     - Requires more setup and maintenance compared to managed services like Colab.
     - Lack of built-in collaboration features unless you use additional tools (e.g., JupyterHub, Git).

### 6. **Run.ai**
   - **Pros:**
     - Focused on providing scalable GPU resources for deep learning workflows.
     - Offers dynamic resource allocation and sharing, which can be very efficient for large teams.
     - Integrated with Kubernetes, making it suitable for production-grade AI workloads.
   - **Cons:**
     - Geared towards enterprise users, which might be overkill for individual prototyping.
     - Requires integration with existing infrastructure, making it less accessible for beginners.

### 7. **Deepnote**
   - **Pros:**
     - Designed for collaboration, similar to Google Docs but for data science and machine learning.
     - Supports Jupyter-compatible notebooks with additional features for team collaboration.
     - Offers free and paid tiers with access to different levels of compute resources.
   - **Cons:**
     - Limited GPU support compared to Colab or other cloud services.
     - Might not be as feature-rich for AI-specific tasks compared to specialized environments like SageMaker.

### 8. **Hugging Face Spaces (with Gradio or Streamlit)**
   - **Pros:**
     - Ideal for quickly prototyping and sharing AI/ML models with interactive UIs.
     - Provides free access to hosted environments with the ability to use GPUs.
     - Integrated with the Hugging Face ecosystem, which is great for NLP tasks.
   - **Cons:**
     - More focused on deploying and sharing models rather than extensive training.
     - Less suited for heavy computation compared to Colab or SageMaker.

### Conclusion

If you're looking for ease of use, cost-effectiveness, and decent GPU access, Google Colab remains one of the top choices. However, if you need more powerful hardware, better collaboration features, or a more integrated environment for end-to-end machine learning workflows, alternatives like AWS SageMaker, Azure Machine Learning Studio, or Paperspace Gradient might be better suited to your needs. The best environment depends on the specific requirements of your project, including the complexity of your models, the scale of your data, and your budget.
