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

Output:

torch                            2.4.0+cu121
torchaudio                       2.4.0+cu121
torchsummary                     1.5.1
torchvision                      0.19.0+cu121

Step 2: Uninstall Existing PyTorch Version

If a GPU-enabled version is installed, uninstall it to avoid conflicts:

!pip uninstall -y torch torchvision torchaudio

Output:

Found existing installation: torch 2.4.0+cu121
Uninstalling torch-2.4.0+cu121:
  Successfully uninstalled torch-2.4.0+cu121
Found existing installation: torchvision 0.19.0+cu121
Uninstalling torchvision-0.19.0+cu121:
  Successfully uninstalled torchvision-0.19.0+cu121
Found existing installation: torchaudio 2.4.0+cu121
Uninstalling torchaudio-2.4.0+cu121:
  Successfully uninstalled torchaudio-2.4.0+cu121

Step 3: Install CPU-Only PyTorch

Now, install the CPU-only version of PyTorch using the following command:

!pip install torch==2.1.1+cpu torchvision==0.14.1+cpu torchaudio==0.10.1+cpu --index-url https://download.pytorch.org/whl/cpu

Output:

Looking in indexes: https://download.pytorch.org/whl/cpu
Collecting torch==2.1.1+cpu
  Downloading https://download.pytorch.org/whl/cpu/torch-2.1.1%2Bcpu-cp310-cp310-linux_x86_64.whl (184.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 184.9/184.9 MB 8.5 MB/s eta 0:00:00
Collecting torchvision==0.14.1+cpu
  Downloading https://download.pytorch.org/whl/cpu/torchvision-0.14.1%2Bcpu-cp310-cp310-linux_x86_64.whl (16.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.8/16.8 MB 77.1 MB/s eta 0:00:00

This command specifies the CPU-only version of PyTorch and its associated libraries, ensuring that no CUDA dependencies are installed.

Common Issues and Troubleshooting

While installing PyTorch with CPU support is generally straightforward, you may encounter some issues:

• Compatibility Issues: Ensure that the version of PyTorch you are installing is compatible with your Python version and other dependencies in your environment.
• Index URL Errors: If you encounter errors related to the index URL, double-check the URL and ensure your internet connection is stable.
• Module Not Found: If you receive errors about missing modules after installation, verify that the installation completed successfully and that your environment is activated.

Conclusion

Installing a CPU-only version of PyTorch in Google Colab is a straightforward process that can be beneficial for specific use cases. By following the steps outlined in this guide, you can efficiently set up your environment and focus on developing and testing your machine learning models. Whether you're working on small-scale projects or simply want to conserve resources, using a CPU-only setup can be a practical choice.