How to Use GPU in Google Colab?

Google Colab provides free access to powerful GPU resources, which can significantly speed up the execution of your machine learning and data analysis tasks. Here's a step-by-step guide on how to use GPU in Google Colab:

Step 1: Open Google Colab

Go to Google Colab and sign in with your Google account if you haven't already.

Step 2: Create a New Notebook or Open an Existing One

You can either create a new notebook by clicking on "File" > "New Notebook" or open an existing one by uploading it from your local system or opening it from Google Drive.

Step 3: Change Runtime Type

Go to "Runtime" > "Change runtime type". In the dialog that appears, select "GPU" as the hardware accelerator. Click "Save" to apply the changes.

Step 4: Verify GPU Availability

To verify that the GPU has been successfully enabled, you can run the following Python code snippet in a code cell:

import tensorflow as tf
print("GPU Available:", tf.config.list_physical_devices('GPU'))

Step 5: Utilize GPU in Your Code

Once you've confirmed that the GPU is available, you can use it to accelerate your computations. For example, if you're using TensorFlow for deep learning tasks, TensorFlow will automatically utilize the GPU for operations.

Here's an example of how to train a simple neural network using the GPU:

import tensorflow as tf
from tensorflow.keras import layers, models

# Define a simple convolutional neural network
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=10, batch_size=64)

Conclusion

By following the steps outlined above, you can leverage the power of GPU acceleration in Google Colab to speed up your machine learning and data analysis workflows. Whether you're training deep learning models or running computationally intensive tasks, using GPU resources in Colab can significantly reduce execution times and improve productivity.