How to Install PyTorch Using Pip

Key Takeaways

• PyTorch can be easily installed using pip with simple commands.
• Choose the correct installation command based on your system and GPU support.
• Always verify the installation to ensure PyTorch works as expected.

Introduction

PyTorch is a popular open‑source deep learning framework developed by Meta AI and maintained by the PyTorch Foundation . It supports tensor computations, dynamic neural networks, GPU acceleration via CUDA or ROCm, and is widely used in research and industry.

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1. Prerequisites

Before installing PyTorch via pip, make sure you have:

• Python (version 3.9 or later recommended)

• pip installed and up‑to‑date:

  python -m pip install --upgrade pip

• (Optional) A CUDA-capable NVIDIA GPU or ROCm-capable AMD GPU if you want GPU acceleration

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2. Choosing the Right pip install Command

Head to the official “Get Started” page to pick your OS, Python version, and compute platform . Here are common installation commands based on your setup:

A. CPU‑only installation (no GPU)

pip3 install torch torchvision torchaudio

This installs the latest stable CPU version. Easy and fast for systems without GPUs.

B. With CUDA support

Replace cu118 with whatever CUDA version you have (for example, cu120 or cu117):

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

The --index-url option points pip to PyTorch’s CUDA-enabled binary repository .

C. Earlier PyTorch versions (optional)

If you need a specific past version (e.g., v2.6.0):

pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118

See the “previous versions” section for more options .

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3. Common Installation Issues

• Unsupported Python version PyTorch requires Python 3.9+. If you use 3.10+ or an unsupported OS, you might see No matching distribution found . Downgrading Python or installing via Conda can help.

• Cache-related problems If installations fail, try forcing a fresh install:

  pip install torch --upgrade --force-reinstall --no-cache-dir --index-url https://download.pytorch.org/whl/cu118

  This can fix issues related to corrupted or outdated cached packages .

• Virtual environment mistakes Always activate your virtual environment before running pip. Otherwise PyTorch may install into the system Python instead .

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4. Verifying the Installation

After installation, confirm that everything works:

1. Check package info

   pip3 show torch

2. Launch Python and test:

   import torch
   x = torch.rand(5, 3)
   print(x)
   print(torch.cuda.is_available())

   • You should see a random tensor printed
   • torch.cuda.is_available() returns True if CUDA is correctly enabled

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5. Optional: Uninstalling PyTorch

To remove PyTorch at any time:

pip uninstall torch torchvision torchaudio

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6. Summary Table

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7. Conclusion

Installing PyTorch with pip is straightforward:

1. Ensure you have Python (3.9+) and pip
2. Choose the correct pip install command for CPU or CUDA
3. Activate your environment
4. Verify the installation using a simple script

With PyTorch set up, you’re ready to dive into model building, training, and GPU-accelerated research. Happy coding!

FAQs

What is the recommended way to install PyTorch?

Use pip with the official command that matches your Python and system requirements.

How do I verify PyTorch was installed correctly?

Import torch in Python and check with torch.cuda.is_available().

What should I do if pip install fails?

Try upgrading pip, using a virtual environment, or adding --no-cache-dir and the correct index URL.

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