How to use Google Colab

If you want to create a machine learning model but say you don't have a computer that can take the workload, Google Colab is the platform for you. In this article, we'll learn how to use google colab.

What is Google Colab?

Google Colab, short for Colaboratory, is a free cloud-based platform provided by Google that allows users to write and execute Python code collaboratively in a Jupyter Notebook environment. Google Collaboratory notebook, is designed to facilitate machine learning (ML) and data science tasks by providing a virtual environment, Google colab python with access to free GPU resources.

Benefits of Google Colab

Google Colab offers several benefits that make it a popular choice among data scientists, researchers, and machine learning practitioners. Key features of Google Collaboratory notebook include:

• Free Access to GPUs: Colab offers free GPU access, which is particularly useful for training machine learning models that require significant computational power.

• No Setup Required : Colab runs in the cloud, eliminating the need for users to set up and configure their own development environment. This makes it convenient for quick coding and collaboration.
• Collaborative Editing: Multiple users can work on the same Colab notebook simultaneously, making it a useful tool for collaborative projects.
• Integration with Google Drive : Colab is integrated with Google Drive, allowing users to save their work directly to their Google Drive account. This enables easy sharing and access to notebooks from different devices.
• Support for Popular Libraries :Colab comes pre-installed with many popular Python libraries for machine learning, data analysis, and visualization, such as TensorFlow, PyTorch, Matplotlib, and more.
• Easy Sharing :Colab notebooks can be easily shared just like Google Docs or Sheets. Users can provide a link to the notebook, and others can view or edit the code in real-time.

Getting Started With Google Colab

To start working with Google Collaboratory Notebook you first need to log in to your Google account, then go to this link https://colab.research.google.com .

Open Collaboratory Notebook

On opening the website you will see a pop-up containing the following tabs -

• EXAMPLES: Contain a number of Jupyter notebooks of various examples.
• RECENT: Jupyter notebook you have recently worked with.
• GOOGLE DRIVE: Jupyter notebook in your google drive.
• GITHUB: You can add Jupyter notebook from your GitHub but you first need to connect Colab with GitHub.
• UPLOAD: Upload from your local directory.

Create Collaboratory Notebook

Else you can create a new Jupyter Notebook by clicking New Python3 Notebook or New Python2 Notebook at the bottom right corner.

Notebook's Description

On creating a new notebook, it will create a Jupyter notebook with Untitled0.ipynb and save it to your google drive in a folder named Colab Notebooks .

Now as it is essentially a Jupyter Notebook, all commands of Jupyter Notebooks will work here. Though, you can refer to the details in Getting Started with Jupyter Notebook .

Let's talk about what is different here:

Change Runtime Environment: Click the "Runtime" dropdown menu. Select "Change runtime type" . Select python2 or 3 from the "Runtime type" dropdown menu.

Use GPU and TPU

Click the "Runtime" dropdown menu. Select "Change runtime type" . Now select anything(GPU, CPU, None) you want in the "Hardware accelerator" dropdown menu.

Verify GPU in Colab

import tensorflow as tf
tf.test.gpu_device_name()

If GPU is connected it will output the following -

'/device:GPU:0'

Otherwise, it will output following

''

Verify TPU

import os

if 'COLAB_TPU_ADDR' not in os.environ:
  print('Not connected to TPU')
else:
  print("Connected to TPU")

If GPU is connected it will output following

Connected to TPU

Otherwise, it will output following

Not connected to TPU

Install Python packages

Use can use pip to install any package. For example:

! pip install pandas

Clone GitHub repos in Google Colab

Use the git clone command. For example:

! git clone https://github.com/souvik3333/Testing-and-Debugging-Tools

Upload File on Google Colab

from google.colab import files
uploaded = files.upload()

Select "Choose file" and upload the file you want. Enable third-party cookies if they are disabled.

Then you can save it in a dataframe.

import io
df2 = pd.read_csv(io.BytesIO(uploaded['file_name.csv']))

Upload File By Mounting Google Drive

To mount your drive inside the "mntDrive" folder execute the following -

from google.colab import drive
drive.mount('/mntDrive')

Then you’ll see a link, click on the link, then allow access, copy the code that pops up, and paste it at "Enter your authorization code:". Now to see all data in your google drive you need to execute the following:

! ls '/mntDrive/My Drive&quot'

File Hierarchy In Google Colab

You can also see the file hierarchy by clicking ">" at the top left below the control buttons (CODE, TEXT, CELL).

Download Files from Google Colab

Let's say you want to download "file_name.csv". You can copy the file to your google drive (In "data" folder, you need to create the "data" folder in google drive) by executing this:

cp file_name.csv "/mntDrive/My Drive/data/renamed_file_name.csv"

The file will be saved in the "data" folder with the "renamed_file_name.csv" name. Now you can directly download from there, Or, you can just open the file hierarchy and right-clicking will give a download option. Download Jupyter Notebook: Click the "File" dropdown menu at the top left corner. Choose "download .ipynb" or "download .py"

Share Jupyter Notebook: You can share your notebook by adding others' email addresses or by creating a shareable link.

Conclusion

In conclusion, Google Colab stands out as a versatile and accessible platform for Python coding.