Google Colab: Everything You Need to Know

Google Colab is a free cloud-based Jupyter notebook environment that makes it easy to get started with data science, machine learning and AI without configuring complex environments. With Colab you get free access to GPUs and TPUs to train neural networks along with many other benefits.

In this comprehensive guide, we‘ll cover everything you need to know about Google Colab including:

  • What is Colab and how it works
  • Key features and benefits
  • Differences from Jupyter Notebooks
  • Getting started guide
  • Common use cases and examples
  • Advanced features
  • Limitations to be aware of

And much more! Let‘s dive in.

What is Google Colab and How Does it Work?

Google Colab, short for Google Colaboratory, allows you to write and execute Python code through your browser. It is a hosted Jupyter notebook service that requires no setup to use.

With Colab you get free access to:

  • GPUs and TPUs for hardware acceleration
  • A fully featured Python environment
  • Popular machine learning and data science libraries like TensorFlow, PyTorch, pandas, matplotlib etc.

Since Colab runs entirely on Google‘s cloud, you don‘t need to install any software locally. You can access it through any modern web browser like Chrome.

Under the hood Colab uses Docker containers to provide isolated environments for each user. The hardware resources are managed by the Google Cloud Platform.

Overall if you have used Jupyter notebooks before, the workflow is very similar with Colab.

Key Features and Benefits of Google Colab

Some of the standout features of Google Colab include:

Free GPU and TPU Access

Colab offers free access to GPUs and TPUs which can significantly speed up machine learning workflows.

With a GPU runtime you get:

  • NVIDIA Tesla K80 GPU
  • 13GB of RAM
  • 12GB GPU memory

For TPUs you have access to cloud TPUs with upto 180 TFLOPS of compute.

The free tier allocates GPU/TPU resources for a maximum of 12 hours continuously. You may use them for longer by restarting the runtime.

Easy Sharing and Collaboration

Colab makes sharing your notebooks seamless. You can simply share a link to your notebook stored in Google Drive for others to access.

You can also invite others directly via their Google account to collaborate on a private notebook. All changes sync automatically making it great for teams.

Install Additional Libraries

While Colab comes preloaded with popular data science libraries, you may install other pip or conda packages by just running a code cell with !pip install or !conda install.

No need to create specific environments or worry about conflicting packages.

Import Data From Anywhere

Getting data into Colab notebooks is easy thanks to the built-in storage support. You can:

  • Import data from local files
  • Access files on Google Drive
  • Pull notebooks from GitHub
  • Load datasets from cloud storage like S3

Colab manages data upload/downloads behind the scene to ensure a smooth experience.

Real-time Collaboration

Colab supports real-time collaboration when working with others. You can invite collaborators via their Google account to edit a shared notebook simultaneously.

The notebook saves automatically on Google Drive storage so there‘s no need to worry about lost changes.

Version Control and Revision History

Every save and change to your Colab notebook is versioned on Google Drive storage.

You can view the complete revision history and roll back to older versions with ease. There‘s no need configure explicit version control like Git.

Integrates with GitHub

You can connect your GitHub account in Colab for easy imports from public GitHub repositories.

Notebooks can also be exported and saved to a GitHub repo making it simple to maintain versions there.

Train Machine Learning Models

Colab is designed for machine learning and data science workloads. The free GPU/TPU access makes it easy to train models at scale.

You can work with all popular frameworks like TensorFlow, PyTorch, Keras etc. right inside Colab.

It supports training on:

  • Image data
  • Text data
  • Audio data
  • Video data
  • And more

Run TensorFlow Programs

Since Colab provides hosted TensorFlow runtimes with GPU acceleration support out of the box, it is a perfect platform for testing TensorFlow workflows for free.

This removes the need to configure complex TensorFlow environments locally.

Automatic Backups on Google Drive

Notebooks created in Colaboratory are stored in your Google Drive account. This ensures your work is automatically backed up in the cloud.

You can access them from any device by simply logging into your Google account.

Key Differences Between Google Colab and Jupyter Notebooks

Since Colab is a hosted version of Jupyter, what are some of the main differences to be aware of?

Feature Google Colab Jupyter Notebooks
Environment Setup No install required. Use via browser. Need to install Jupyter and configure environment.
Sharing Notebooks Simple shareable links. Harder to share notebooks publicly.
Pre-installed Libraries Comes readily with data science libraries. Need to manually install libraries.
Hardware Access Free access to GPUs and TPUs. Limited to local machine compute.
Collaboration Real-time collaboration support. Limited options for live collaboration.
Cost Free tier available. Paid tiers for more resources. Open source and free to use.

In summary:

  • Colab removes the hassle of installing software locally and configuring development environments. Plus you get free temporary access to powerful hardware which makes it beginner friendly.

  • Jupyter Notebooks provide more customization and control over your environment. But there is a higher setup overhead and limited collaboration abilities.

So Colab simplifies a lot of infra challenges with Jupyter notebooks. But advanced users may still prefer managing custom environments locally with Jupyter.

Getting Started with Google Colab

To get started with Google Colab you need:

  • A Google account
  • The latest version of Chrome or Firefox browser

Then go ahead and open Google Colab in your browser.

Make sure to agree to their terms of service related data usage while running notebooks.

With that you‘re ready to start creating new Python notebooks in Colab!

The workflow is very similar to existing Jupyter notebooks. You can edit code cells, run them, and view outputs all inline.

Let‘s look at some common tasks now.

Executing Common Tasks in Colab Notebooks

Here are examples of frequent tasks when working with Colab notebooks:

Creating New Notebooks

To create a blank notebook:

  • Go to File > New Python 3 Notebook
  • This opens an empty notebook with the untitled default name
  • You can rename it by clicking the name on the top left

Uploading Existing Notebooks

To import an existing notebook from your local machine:

  • Go to File > Upload Notebook
  • Choose your .ipynb file to upload
  • This will open the notebook in Colab

To import notebooks from GitHub:

  • Go to File > Upload Notebook
  • Switch to the GitHub tab
  • Enter details to search and upload notebooks from public GitHub repositories

Accessing Google Drive

You can access files stored on Google Drive in multiple ways:

Open a notebook file:

  • Go to File > Open Notebook
  • Switch to Google Drive tab
  • Navigate and open .ipynb notebooks

Mount Google Drive:

  • Go to File > Mount Drive
  • This will mount your Google Drive allowing access via the /content/drive virtual path

Version Control and Revisions

Colab notebooks saved on Drive are automatically version controlled.

To access revision history:

  • Go to File > See Revision History
  • This lists all saves and changes
  • Can restore to any previous version

Importing Datasets

Some ways to import datasets:

From local machine:

  • Upload files using notebook code:
from google.colab import files 
uploaded = files.upload()

From Google Drive:

  • First mount Drive
  • Access files using virtual path

From cloud storage like S3:

!pip install bytehub  
import bytehub
data = bytehub.load.s3(‘<my-s3-path>‘) 

From Kaggle:

!pip install kaggle
import json
token_json = json.load(open(‘kaggle.json‘))
!kaggle datasets download <dataset-name> -p /tmp

This covers some of the common workflows when working with Colab notebooks!

Advanced Features and Use Cases

Let‘s explore some more advanced features of Google Colab:

Leveraging Free GPUs for ML Training

The free GPU access can speed up model training times by 5-10x easily. To enable a GPU backed runtime:

  • Go to Runtime > Change runtime type
  • Select GPU from the options

This will provision a Tesla K80 GPU for your notebook environment.

Now install libraries like TensorFlow or PyTorch and leverage the faster hardware for training:

import tensorflow as tf
print(tf.reduce_sum(tf.random.normal([1000, 1000])))

Similarly you can select TPU runtime for access to cloud TPU hardware.

Sharing Notebooks with Collaborators

To collaborate with others on a notebook:

  • Click File > Share Notebook
  • Add collaborator emails and hit Send
  • Collaborators can edit simultaneously and changes save automatically

Scheduling Notebooks

You can schedule notebooks to run on a recurring schedule using crontab.

For example to run hourly:

!pip install crontab
!crontab -l
!echo "0 * * * * /usr/bin/python /content/" | crontab -
!crontab -l 

This supports lot of powerful automation use cases with Colab.

Developing Chrome Extensions

Colab is a great platform for building Chrome extensions with its free serverless environment:

!npm install -g @cli/create-pwa
!create-pwa my-extension
%cd /my-extension
!npm install && npm run build 
# package extension code

You get free hosting for your extension‘s assets on Colab‘s servers.

Accessing More Hardware with Colab Pro

While the free tier offers 12 hours of GPU access per session, Colab Pro extends this to 24 hours with higher memory allocations. This allows you to train bigger models.

Pro also gives access to scaling up to multiple GPUs and TPUs based on workload requirements.

The paid tiers unlock more options for businesses that want to standardize on Colab.

These show some more advanced use cases with Colab beyond basics.

Limitations to Be Aware Of

While Colab brings a lot to the table, few limitations to keep in mind:

  • Session timeouts – Free notebooks timeout after 90 mins of inactivity and 12 hours of continuous run time. Pro tier has longer limits.
  • Reliability – Being cloud based means reliance on Google‘s servers being up. Rare but outages happen.
  • Performance – At heavy loads, can be delays allocating hardware resources like GPUs. Usually resolves in minutes.
  • Storage limits – Permanent storage limited to 15GB total on Drive. Data deleted after 90 days of inactivity.

These constraints make Colab great for experimentation but perhaps not ideal for critical production workloads. Tradeoffs with convenience versus performance and reliability.

Key Takeaways

Here are the major benefits of using Google Colab:

  • Quickstart ML training – Instantly leverage free hardware like GPUs and TPUs for faster training times minus configuration overheads.
  • Streamlined team collaboration – Work seamlessly with others on shared notebooks.
  • Limited in-browser coding – Be productive on the go without local environment setup.
  • Auto version control – Revision history easily managed with stored notebooks.
  • Killer docs and community – Wealth of documentation and help given its popularity.


  • Temporary environments – Work often lost post session unlike persistent servers.
  • Timeout limits – Usage capped at 12 hours max on free tier hindering some long running workloads.
  • Potential performance lags – Resource availability not consistent. Issues at scale.

So in summary – Colab is a handy tool for lightweight ML exploration and rapid prototyping. It removes fixed infra impediments common with model training and serving. Easy collaboration makes it a favorite with academics as well.

However for working on production grade solutions with larger datasets, native tooling like Jupyter or VS Code may be more appropriate.

Hopefully this detailed overview gives you a very good idea of how you can leverage Google Colab effectively!