Introduction to Google Colab

Duration: 5 min

This module provides an in-depth look at Google Colab, a powerful cloud computing platform that is ideal for AI and machine learning projects. We will explore its key features, understand how to leverage its capabilities, and learn why it is a valuable tool for data scientists and developers alike.

Understanding Google Colab

Google Colab is a free cloud service for machine-learning research and development. It offers a Jupyter notebook environment that allows you to write and execute Python code in the browser. Colab is particularly useful because it provides access to powerful hardware like GPUs and TPUs for free, which can significantly speed up your computations.

# Importing necessary libraries
from google.colab import drive

# Mounting Google Drive to access files
drive.mount('/content/drive')

# Verifying the mount
import os
print(os.listdir('/content/drive/My Drive/'))

Try it in Google Colab:

Mounting your Google Drive...
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth...
Enter your authorization code:
...
['SampleFolder', 'SampleFile.txt']

Using Colab's Computational Resources

Colab provides access to free GPU and TPU resources, which can be incredibly useful for training machine learning models. You can switch between GPU and TPU runtimes directly from the Colab interface, which allows you to experiment with different hardware setups without any additional cost.

# Checking if a GPU is available
import tensorflow as tf

# List all available GPUs
print("GPUs Available:", tf.config.experimental.list_physical_devices('GPU'))

# If GPU is available, switch to GPU runtime
try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
    print('Running on TPU ', tpu.master())
except ValueError:
    print("Couldn't find TPU")

GPUs Available: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'),...]
Running on TPU grpc://10.0.0.2:8470

💡 Tip: Always check the runtime type before running long computations to ensure you are using the desired hardware (GPU/TPU) to avoid unexpected costs or performance issues.