How to Connect Google Colab with Google Drive

Effortlessly integrate Google Colab with Google Drive to streamline your deep learning projects. Avoid data loss and maximize your productivity with this quick guide.

How to Connect Google Colab with Google Drive for Deep Learning

In this tutorial, you'll learn how to connect Google Colab with Google Drive to build deep learning models effectively. Google Colab provides 12GB of free GPU for model training, and now it also supports TPUs for even faster computation.

This is a completely free research project from Google, making it accessible to everyone.

What is Google Colaboratory?

Google Colaboratory, often called Colab, is a cloud-based service by Google. It offers support for GPUs and TPUs, making it a powerful tool for machine learning and deep learning projects.

Using Colab, you can:

Enhance your Python coding skills.
Build state-of-the-art deep learning models with popular libraries like TensorFlow, Keras, PyTorch, and OpenCV.
Avoid the hassle of managing packages and libraries, as most are pre-installed. For additional libraries, a simple command like !pip install package_name does the job.

Loading Your Data into Google Colab

One of the standout features of Colab is its seamless integration with Google Drive, allowing you to save and share Jupyter Notebooks with ease. However, a challenge arises when working with large datasets. When Colab's virtual machine disconnects, any data stored locally is lost upon reconnection.

To solve this issue, follow these steps to upload and persist your data in Google Colab:

Upload Data to Google Drive
First, upload your dataset to your Google Drive account.
Connect Google Drive to Colab
Run the following script in your Colab notebook to mount your Google Drive:
from google.colab import drive drive.mount('/content/gdrive')