Installing a CPU-Only Version of PyTorch

PyTorch is a popular open-source machine learning library that provides a flexible platform for developing deep learning models. While PyTorch is well-known for its GPU support, there are many scenarios where a CPU-only version is preferable, especially for users with limited hardware resources or those deploying applications on platforms without GPU support. This article will guide you through the process of installing a CPU-only version of PyTorch in Google Colab.

Why Use a CPU-Only Version of PyTorch?

Before diving into the installation process, it's essential to understand why one might opt for a CPU-only version of PyTorch:

• Resource Constraints: Not all users have access to GPUs, especially when working on personal laptops or deploying applications in environments where GPU resources are not available.
• Cost Considerations: Running models on CPUs can be more cost-effective, particularly when deploying applications in cloud environments where GPU usage incurs additional costs.
• Development and Testing: During the development phase, using a CPU can simplify the setup and debugging process, as it avoids potential issues related to GPU drivers and compatibility.

Installing the CPU-Only Version of PyTorch

To install the CPU-only version of PyTorch in Google Colab, you can follow these steps:

Step 1: Check Current PyTorch Installation

This command will list all installed PyTorch-related packages. If you see versions with +cu (e.g., torch==1.8.1+cu111), it indicates that GPU support is included.

!pip list | grep torch