Google Colab: The Perfect Platform for Research and Development

Google Colab


Google Colab is a free cloud-based platform for data analysis, research, and machine learning. With Colab, researchers can access powerful hardware, develop and run Python code in a Jupyter notebook environment, and share and collaborate on projects with ease.

Researchers should be aware of Colab’s limits, which include a monthly utilization cap on CPU, GPU, and TPU usage as well as a session runtime cap and memory restrictions. Colab is nonetheless a potent tool for academics despite these drawbacks, offering a user-friendly design, compatibility for widely used libraries, and simple access to resources.

In this article, we’ll look at Colab’s fundamentals, its drawbacks, and how researchers may use it to their advantage to improve workflows and accomplish their research objectives.

Is there a Difference between Colab.research and Google Colab?

“Colab.research” and “Google Colab” refer to the same platform. “Google Colab” is the official name for the service provided by Google, while “Colab.research” is a shorthand or informal way of referring to it. So, there is no inherent difference between the two terms; they both refer to the same cloud-based Python notebook environment provided by Google.

Advantages of Using Google Colab

Users can build and run Python programs on the free cloud-based Google Colaboratory platform, also known as Colab. Machine learning, data analysis, and research are its main uses. Using Colab has a number of major advantages, including:
1. Powerful hardware is available to customers through Colab, which can considerably speed up the training of machine learning models.
2. Teams can easily work together on a project using Colab notebooks, which allow for easy collaboration and sharing.

3. Google Drive integration allows Colab notebooks to be loaded from and saved to Google Drive, making it simple to access and share files.
4. Free to use: There are no setup fees or usage charges when using Colab.
5. Popular libraries are supported by Colab, making it simple to start working on machine learning projects. These libraries include TensorFlow, PyTorch, and Keras.
6. Convenient interface: The data science and machine learning community frequently uses the Jupyter notebook interface, which Colab offers.

Colab is a strong tool that can be used for a variety of activities, including research, data analysis, and machine learning. It offers access to powerful hardware, makes it simple to share and collaborate, and has an easy-to-use interface, making it especially helpful for folks who are just getting started with these kinds of projects.

How to Use Google Colaboratory (Colab) for my Research Work?

The process of using Google Colaboratory (Colab) is simple and easy. The initial steps are listed below:
If you don’t have a Google account, create one now.
Enter your Google account information to sign in at the Google Colaboratory website (
To start a new notebook, click the “New Notebook” button. Decide on a runtime type and give your notebook a name (Python 3 is the default).
Start writing code in the available code cells in the notebook. Click on a code cell and press Shift+Enter to run it.
Use the “Insert” menu at the top of the screen to add additional cells, or use “Ctrl+M, B” on your keyboard to insert cells below.

Use the “Upload” option in the “Files” menu or the “Files” button on the left sidebar to add data or other files to your notebook.
Click the “Save” button or choose “Save a Copy in Drive” from the “File” menu to save your work.
You can change the Hardware accelerator setting to GPU or TPU by choosing the runtime option from the “Runtime” menu.
Keep in mind that the runtime will reset after 90 minutes of inactivity and that data uploaded via Files will be deleted once the Colab has been closed. So you might wish to connect to your Google Drive from Colab and save your data there.

Colab can be used for many different things, including preprocessing data, creating and training models, and visualizing outcomes. Several pre-installed libraries, including Tensorflow, Keras, PyTorch, OpenCV, etc., are also included.
You can seek assistance from the community and the Colab documentation if you have any queries or problems.

Can I Import other Data Sets to Google Colab?

To use in your notebooks, you can import data sets into Google Colaboratory (Colab). There are various methods for doing this:
1. Transferring files: By selecting “Files” from the left sidebar or by selecting “Upload” from the “Files” menu, you can add files to your Colab notebook. You can use the files in your notebook by specifying their path after they have been uploaded.
2. Google Drive usage: By running the following code, you can save your data sets to your Google Drive and subsequently mount your drive in Colab:

from google.colab import drive

You will then be prompted to grant Colab access to your Google Drive, after which you may specify the route to access the data sets there.
3. URL usage: Using Colab’s wget or curl commands, you can also import data sets straight from a URL. For instance, the following command can be used to download a CSV file:


4. Using libraries: To import data sets from different file formats like CSV, Excel, etc., you can use libraries like pandas.

How to Access GitHub data Using Google Colab?

To access GitHub data using Google Colab, you can follow the steps below:

  1. Open Google Colab in your browser and create a new notebook.
  2. Import the necessary libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import requests
from io import StringIO
  1. Use the requests library to get the raw data from the GitHub repository. For example, if you want to access a CSV file named data.csv in a GitHub repository with URL,
  2. you can use the following code:
url = ''
response = requests.get(url).text
data = pd.read_csv(StringIO(response))

This code gets the raw text data from the CSV file and uses the pd.read_csv() function to convert it into a Pandas DataFrame.

  1. You can now use the data DataFrame to analyze and visualize the data as you normally would in a Jupyter notebook or Python environment.

Note: You may need to authenticate your access to the GitHub repository if it’s private. You can use pygithub or github library to do so.

Is it Possible to Draw Graphs and Charts using Google Colab?

It is possible to draw graphs and charts in Google Colab. Google Colab supports a variety of data visualization libraries like Matplotlib, Seaborn, Plotly, Bokeh, etc., which can be used to create graphs and charts.

Here’s an example of how to create a line plot using Matplotlib in Google Colab:

  1. Import the necessary libraries:
import matplotlib.pyplot as plt
import numpy as np
  1. Generate some sample data:
x = np.linspace(0, 10, 100)
y = np.sin(x)
  1. Create a line plot using Matplotlib:
plt.plot(x, y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Line Plot')

This will create a line plot with the X Label, Y Label, and Line Plot as the title.

Similarly, you can use other data visualization libraries to create different types of graphs and charts in Google Colab.

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How can I install additional packages or libraries in Google Colab?

You can install additional packages or libraries in Google Colab using the !pip command. pip is a package installer for Python, and ! is used to run shell commands in Jupyter notebooks.

Here are the steps to install a package in Google Colab:

  1. Open a new or existing notebook in Google Colab.
  2. Type !pip install package-name in a code cell and replace package-name with the name of the package you want to install.
  3. Run the code cell by clicking on the “Play” button or by pressing Shift+Enter.

For example, if you want to install the numpy package, you can use the following code:

!pip install numpy

You can install multiple packages at once by separating them with spaces, like this:

!pip install numpy pandas matplotlib

Once the installation is complete, you can use the package in your code by importing it using the import statement. For example, to import the numpy package, you can use the following code:

import numpy as np

This will allow you to use the numpy package in your code as np.

How can I save and load my Google Colab notebook?

You can save and load your Google Colab notebook in a few different ways:

  1. Save to Google Drive: You can save your Google Colab notebook to your Google Drive by clicking on “File” > “Save a copy in Drive”. This will create a copy of your notebook in your Google Drive, which you can access and open later.
  2. Download as .ipynb file: You can download your Google Colab notebook as a .ipynb file by clicking on “File” > “Download .ipynb”. This will download a copy of your notebook to your local computer, which you can save and open later using Jupyter Notebook or other compatible software.
  3. Save to GitHub: You can save your Google Colab notebook to GitHub by connecting your Google Colab account to your GitHub account. Once you’re connected, you can create a new repository on GitHub and save your notebook to it using the “Save a copy to GitHub” option under “File” > “Save a copy to GitHub”.
  4. Use Google Colab’s autosave feature: Google Colab has an autosave feature that saves your notebook every few minutes. You can also manually save your notebook by clicking on “File” > “Save” or by using the keyboard shortcut Ctrl+S (or Command+S on a Mac).

To load a saved Google Colab notebook, you can open it from your Google Drive or GitHub repository, or you can upload the .ipynb file to Google Colab by clicking on “File” > “Upload notebook”. Once you’ve opened or uploaded your notebook, you can continue working on it as usual.

How can I check the system specifications of my Google Colab runtime?

You can check the system specifications of your Google Colab runtime using the !cat /proc/cpuinfo and !cat /proc/meminfo commands in a code cell.

Here’s how you can check the system specifications of your Google Colab runtime:

  1. Open a new or existing notebook in Google Colab.
  2. Create a new code cell by clicking on the “+” icon or by pressing Ctrl+M B (or Command+M B on a Mac).
  3. Type !cat /proc/cpuinfo in the code cell and run it by clicking on the “Play” button or by pressing Shift+Enter. This will display the CPU specifications of your Google Colab runtime, including the number of CPUs, the clock speed, and the cache size.
  4. Create another code cell and type !cat /proc/meminfo in the code cell. Run the cell to display the memory specifications of your Google Colab runtime, including the total memory available and the amount of memory currently in use.

You can also check the GPU specifications of your Google Colab runtime by typing !nvidia-smi in a code cell and running it. This will display the GPU specifications, including the GPU model, the memory usage, and the driver version.

Note that the system specifications of your Google Colab runtime may change depending on the type of runtime you choose (CPU, GPU, or TPU), the amount of memory allocated, and other factors.

How can I Increase the RAM or Disk Space of my Google Colab Runtime?

You can increase the RAM or disk space of your Google Colab runtime by upgrading to a higher tier. Google Colab offers three tiers of runtimes: CPU, GPU, and TPU. Each tier has a different amount of RAM and disk space available.

Here’s how you can upgrade to a higher tier in Google Colab:

  1. Open a new or existing notebook in Google Colab.
  2. Click on the “Runtime” menu at the top of the page and select “Change runtime type”.
  3. In the “Runtime type” dropdown, select the type of runtime you want to upgrade to (CPU, GPU, or TPU).
  4. In the “Hardware accelerator” dropdown, select the type of accelerator you want to use (if applicable).
  5. In the “Runtime shape” dropdown, select the amount of RAM and disk space you want to allocate to your runtime.

Note that upgrading to a higher tier may require you to wait in a queue before your new runtime is provisioned. Additionally, upgrading to a GPU or TPU runtime may also incur additional costs.

If you need more disk space than is available in your current tier, you can also mount your Google Drive to your Google Colab runtime and use it to store data. To do this, you can run the following code in a code cell:

from google.colab import drive

This will prompt you to authenticate and then mount your Google Drive to your runtime. You can then access your Google Drive files and folders from your Google Colab notebook. Note that there may be performance implications when using Google Drive as a storage solution, especially when dealing with large files or datasets.

How can I run shell Commands in Google Colab?

You can run shell commands in Google Colab by prefixing the command with an exclamation mark ! in a code cell.

Here’s an example of how to run a shell command in Google Colab:

  1. Open a new or existing notebook in Google Colab.
  2. Create a new code cell by clicking on the “+” icon or by pressing Ctrl+M B (or Command+M B on a Mac).
  3. Type a shell command, such as ls to list the files in the current directory, and prefix it with an exclamation mark !. For example: !ls.
  4. Run the cell by clicking on the “Play” button or by pressing Shift+Enter. This will execute the shell command in the terminal and display the output in the cell.

You can run any valid shell command in Google Colab using this syntax, including commands to navigate the file system, install packages, download data, and more. Note that shell commands may have different syntax or behavior depending on the operating system and shell environment used in Google Colab.

Is Google Colab Cost-Free?

Yes, Google Colab is a free service to utilize. Using the platform is free of any setup fees or ongoing charges. As many notebooks as you desire are available for free creation, editing, and operation. The quantity of RAM and CPU that can be used, as well as the length of a GPU runtime, are some of the resources that Colab offers, although they are not without certain restrictions. All users will have equal access to the resources offered by Colab thanks to these restrictions.

Colab Pro, a commercial version of Colab that offers you additional memory, longer runtimes, and higher priority access to resources, is also an option. But unless you are working on a large-scale project or doing tasks that demand more resources than the free version can offer, it is not necessary.

Here is a comparison of the main differences between Google Colab and Colab Pro:

FeatureGoogle ColabColab Pro
Maximum runtime for a single session12 hours24 hours
Maximum number of CPU/GPU/TPU hours per month12/12/12300/300/300
Priority access to resourcesNoYes
Additional memoryNoYes
Additional StorageNoYes
Additional supportNoYes

Please note that these are general differences and the actual resources and services might change in the future.

For users managing significant projects or occupations that call for more resources than the free edition of Colab can offer, the subscription version of the platform offers more resources and priority access to resources.
As Google Colab’s pricing plans are subject to change, it is important to check the most recent pricing on the site. Please be aware that the pricing varies depending on the region, the number of users, and the particular resources needed for the project.

What Alternatives Exist to Google Colab?

Google Colaboratory (Colab) can be used for machine learning, data analysis, and research, although there are a number of alternatives. Here are a few well-liked choices:
1. Kaggle Kernels: Users of the cloud-based Kaggle Kernels platform can create and run code in a Jupyter notebook setting. It has a sizable user base and is largely used for data science and machine learning competitions.
2. Microsoft Azure Notebooks : Users can create and use Jupyter notebooks on the cloud-based platform known as Microsoft Azure Notebooks. Both Azure Machine Learning services and access to powerful hardware are incorporated.

3. Jupyter Notebook: Jupyter Notebook is an open-source web tool that enables users to create and share documents with live code, equations, visuals, and text. You have the option of running it locally on your computer or online.
4. Databricks: A cloud-based platform that enables the creation and execution of Apache Spark workloads. It offers a collaborative setting for analytics, machine learning, and data engineering.
5. IBM Watson Studio: A cloud-based tool called IBM Watson Studio enables users to create, develop, and employ machine learning models. It integrates with IBM’s existing AI services and offers access to potent hardware like GPUs and TPUs.
6. DataCamp: DataCamp is a cloud-based platform that offers practice problems for data science and machine learning as well as interactive coding training.

These are just a few of the numerous possibilities that are accessible. The option that is suitable for you will rely on your unique requirements and use scenario. While some platforms are more suitable for enterprise use cases, others are better suited for data science competitions.

Here is a comparison of some popular alternatives to Google Colab:

PlatformCostMaximum runtime for a single sessionMaximum number of CPU/GPU/TPU hours per monthPriority access to resourcesAdditional memoryAdditional StorageAdditional support
Google ColabFree12 hours12/12/12NoNoNoNo
Kaggle KernelsFree6 hoursNoNoNo
Microsoft Azure NotebooksFreeNoNoNoNo
Jupyter NotebookOpen-sourceDepends on the hostDepends on the hostNoNoNoNo
IBM Watson StudioPaidYesYesYesYes
Popular Alternatives for Google Colab

Please note that these are general differences and the actual resources and services might change in future. You may also want to check out the detailed documentation of each platform to have a better understanding and also to see which platform fits your use case the best.


In conclusion, Google Colaboratory (Colab) is a potent tool that has the potential to be extremely useful for academics working in the fields of data analysis, machine learning, and research. It offers an easy-to-use interface, access to strong hardware, and straightforward sharing and teamwork. Additionally, Colab gives researchers access to a variety of well-known libraries and pre-installed software, making it simple to launch a new study.

Colab does have some restrictions, though, so researchers should be aware of these. One session’s maximum runtime, memory restrictions, and a monthly usage cap on CPU, GPU, and TPU usage are a few examples. Furthermore, Colab does not grant users preferential access to resources and might not be appropriate for extensive projects that call for a lot of resources.

Despite these drawbacks, Colab is still a useful tool for researchers, and it is always getting better. Colab is being improved by Google to be more reliable, to offer additional resources, and to be better suited for large-scale projects. Future improvements include adding support for Big Query, AutoML, Cloud Storage, and other Google services, as well as new runtime options like preemptible GPUs.

Overall, Colab is a powerful tool that can assist expedite workflows and advancing research, even though it may not be the perfect option for every research project. Colab’s benefits and downsides should be considered by researchers as they choose whether Colab is the best option for their particular study and objectives.

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Dr. Vijay Rajpurohit
Author: Dr. Vijay Rajpurohit
Dr. Vijay Rajpurohit is a researcher in Computer Science. He loves to educate researchers and research scholars on Research Paper Writing, Thesis Writing, Research Grants, Patenting Research Work and the latest Research-related issues. You can reach him @ [email protected]