How to Install PyTorch with CUDA in

In this blog, we will learn about PyTorch, a widely used open-source machine learning framework favored by data scientists and software engineers engaged in deep learning tasks. PyTorch’s popularity stems from its robust support for NVIDIA CUDA, a parallel computing platform that facilitates GPU acceleration for deep learning models. Within this article, we’ll guide you through the installation process of PyTorch with CUDA using

If you’re a data scientist or software engineer working with deep learning, chances are you’re familiar with PyTorch. PyTorch is a popular open-source machine learning framework that’s widely used for developing deep learning models. One of the biggest advantages of PyTorch is that it provides support for NVIDIA CUDA, a parallel computing platform that enables GPU acceleration for deep learning tasks. In this article, we’ll walk you through the process of installing PyTorch with CUDA in

Table of Contents

  1. Why Install PyTorch with CUDA?
  2. Prerequisites
  3. Step-by-step installing Pytorch with CUDA in
  4. Pros and Cons of Using for Installation
  5. Common Errors and Troubleshooting
  6. Conclusion

Why Install PyTorch with CUDA?

Before we dive into the installation process, let’s first take a look at why you might want to install PyTorch with CUDA. GPUs are highly parallel devices that can perform a large number of calculations simultaneously. This makes them ideal for accelerating deep learning tasks, which often involve large amounts of matrix operations. PyTorch provides support for CUDA, which allows you to harness the power of GPUs and significantly speed up your model’s training and inference times.


Before we get started with the installation process, make sure you have the following prerequisites installed:

  • Python 3.x
  • pip
  • CUDA-enabled GPU
  • NVIDIA CUDA Toolkit (version 10.2 or later)
  • NVIDIA cuDNN library (version 7.6.5 or later)

Step-by-step installing Pytorch with CUDA in

Step 1: Create a virtual environment

It’s always a good idea to create a virtual environment before installing any new packages. This ensures that any changes made to your system won’t affect other projects that you’re working on. To create a new virtual environment, run the following command:

$ python3 -m venv myenv

This will create a new virtual environment named myenv.

Step 2: Activate the virtual environment

Next, activate the virtual environment by running the following command:

$ source myenv/bin/activate

This will activate the myenv virtual environment.

Step 3: Install PyTorch with CUDA

Now that we have our virtual environment set up, we can install PyTorch with CUDA. To do this, we’ll use pip. Run the following command to install PyTorch with CUDA:

$ pip install torch torchvision torchaudio -f

This will install the latest version of PyTorch with CUDA support.

Step 4: Update

To ensure that PyTorch with CUDA is included as a dependency when someone installs your package, you need to update your file. Open your file and add the following line to the install_requires list:


This will ensure that PyTorch with CUDA is installed when someone installs your package using pip.

Step 5: Build and install your package

Finally, you can build and install your package as you normally would. Run the following command to build your package:

$ python sdist bdist_wheel

This will create a source distribution and a wheel distribution of your package.

To install your package, run the following command:

$ pip install dist/<package-name>-<version>.tar.gz

Replace <package-name> and <version> with the name and version of your package.

Pros and Cons of Using for Installation


  • Customization: allows for fine-grained control over the installation process, enabling you to configure CUDA-specific settings.

  • Integration: It seamlessly integrates with existing Python packaging tools and workflows.


  • Complexity: Modifying may be intimidating for beginners, and incorrect configurations can lead to installation failures.

  • Dependency Management: Ensuring that users have the correct CUDA toolkit version and dependencies can be challenging.

Common Errors and Troubleshooting

CUDA Compatibility Issues

Ensure that your GPU is CUDA-compatible and that you have the correct version of the CUDA Toolkit installed.

Missing Dependencies

Check for missing dependencies such as cuDNN and ensure they are installed and configured correctly.

Verifying the Installation

After installation, verify that PyTorch is utilizing CUDA by running a simple GPU-enabled script.


In this article, we’ve walked you through the process of installing PyTorch with CUDA in By following these steps, you can ensure that your package includes PyTorch with CUDA as a dependency and takes full advantage of the power of GPUs for deep learning tasks. Happy coding!

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