How to Install PyTorch Using Conda

Key Takeaways

• Use Conda for a smoother PyTorch installation experience and better dependency management
• Always follow the official PyTorch site for the latest, correct installation commands
• Verify installation to ensure PyTorch and GPU support are working

Introduction

PyTorch is a popular open-source deep learning library. While pip can install it, Conda simplifies handling dependencies—ideal for those using the Anaconda distribution.

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1. Choose Your Environment

Start by creating and activating a clean Conda environment:

conda create --name pytorch_env python=3.8
conda activate pytorch_env

Using environments ensures independence from your system Python.

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2. Select Build Options on the Official Site

Visit the official PyTorch “Get Started” page and configure the options:

• Package: Conda

• Language: Python

• Compute Platform: Choose one of the following:

  • cpuonly – If you don’t need GPU support
  • CUDA x.x – For NVIDIA GPU users (e.g., CUDA 11.8 or 12.1)
  • ROCm – For AMD GPU users

The site will then provide the exact conda install command .

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3. Run the Conda Install Command

Copy the command suggested by the site. Here are typical examples for PyTorch version ≥ 2.5:

• CPU-only:

  conda install pytorch torchvision torchaudio cpuonly -c pytorch

• CUDA 11.8 GPU support:

  conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

These commands pull from the pytorch channel (and nvidia when using GPUs) .

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4. Verify Installation

After installation, test whether PyTorch and GPU support work:

import torch
print(torch.__version__)

# Check for GPU availability:
print(torch.cuda.is_available())

Expected output:

• A version like 2.5.1
• True if CUDA support is properly enabled .

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5. Troubleshooting Tips

1. Missing Packages: If you get “PackagesNotFoundError”, ensure you're using -c pytorch (and optionally -c nvidia) .
2. Use Official Commands: Avoid community-maintained channels like conda-forge for GPU builds—stick with the official site recommendations .
3. CUDA Version Mismatch: Align pytorch-cuda= with your installed CUDA toolkit; incompatible versions can cause import errors .

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6. Optional: Building from Source

If you need the absolute latest development version, build from source:

1. Install developer tools (CUDA/ROCm, C++ compilers).

2. Clone the PyTorch repo.

3. Run:

   python setup.py install

This is for advanced users; mainstream users should prefer the standard Conda install .

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7. Summary Table

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Conclusion

Using Conda to install PyTorch is reliable and convenient. By using the official selector on the PyTorch website, you can ensure the correct version and dependencies are installed—whether you're running on CPU or GPU.

FAQs

What is the best way to install PyTorch with Conda?

Use the command provided on the official PyTorch website.

How do I verify if PyTorch GPU support is enabled?

Run print(torch.cuda.is_available()) in Python after installation.

What should I do if installation fails with a package error?

Check you are using the correct channels: -c pytorch and -c nvidia for GPU.

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