Colab GPU Tutorial

This tutorial will guide you through setting up and using Google Colab with GPU support for deep learning and other computationally intensive tasks.

Prerequisites

• Google Colab
• Google Account
• Basic Python knowledge

Getting Started

1. Open Google Colab: Go to Google Colab and log in with your Google account.
2. Create a New Notebook: Click on "File" > "New Notebook" to start a new project.

Setting Up GPU

1. Open a New Cell: Click on the "+" button to create a new cell.
2. Install GPU Support: Run the following code to install the necessary libraries for GPU support.

!pip install --upgrade google-colab

3. Enable GPU: Click on "Runtime" > "Change runtime type" and select "Python 3" with GPU support.

Basic Example

Here's a simple example of using a GPU in Colab:

import tensorflow as tf

# Create a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
    tf.keras.layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Generate some random data
x = tf.random.normal([1000, 100])
y = tf.random.normal([1000, 1])

# Train the model
model.fit(x, y, epochs=10)

Further Reading

For more advanced tutorials and examples, check out our Deep Learning with Colab guide.

Images

Here's an image of a neural network in action:

If you encounter any issues or have questions, feel free to reach out to our support team.