Installing a CPU-Only Version of PyTorch
Last Updated :
30 Aug, 2024
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.
Python
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:
Python
!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:
Python
!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.
Similar Reads
Machine Learning Tutorial
Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.It can
5 min read
Non-linear Components
In electrical circuits, Non-linear Components are electronic devices that need an external power source to operate actively. Non-Linear Components are those that are changed with respect to the voltage and current. Elements that do not follow ohm's law are called Non-linear Components. Non-linear Co
11 min read
Linear Regression in Machine learning
Linear regression is a type of supervised machine-learning algorithm that learns from the labelled datasets and maps the data points with most optimized linear functions which can be used for prediction on new datasets. It assumes that there is a linear relationship between the input and output, mea
15+ min read
Support Vector Machine (SVM) Algorithm
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. While it can handle regression problems, SVM is particularly well-suited for classification tasks. SVM aims to find the optimal hyperplane in an N-dimensional space to separate data
10 min read
Class Diagram | Unified Modeling Language (UML)
A UML class diagram is a visual tool that represents the structure of a system by showing its classes, attributes, methods, and the relationships between them. It helps everyone involved in a project—like developers and designers—understand how the system is organized and how its components interact
12 min read
K means Clustering – Introduction
K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
4 min read
K-Nearest Neighbor(KNN) Algorithm
K-Nearest Neighbors (KNN) is a supervised machine learning algorithm generally used for classification but can also be used for regression tasks. It works by finding the "k" closest data points (neighbors) to a given input and makesa predictions based on the majority class (for classification) or th
8 min read
Logistic Regression in Machine Learning
In our previous discussion, we explored the fundamentals of machine learning and walked through a hands-on implementation of Linear Regression. Now, let's take a step forward and dive into one of the first and most widely used classification algorithms — Logistic RegressionWhat is Logistic Regressio
12 min read
Spring Boot Tutorial
Spring Boot is a Java framework that makes it easier to create and run Java applications. It simplifies the configuration and setup process, allowing developers to focus more on writing code for their applications. This Spring Boot Tutorial is a comprehensive guide that covers both basic and advance
10 min read
100+ Machine Learning Projects with Source Code [2025]
This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. Whether you're a student enhancing your resume or a professional advancing your career these projects offer practical insights into the world of Machine Learning an
5 min read