Introduction: PyTorch is a popular machine-learning library that provides fast and easy-to-use tensor computation with GPU acceleration. In this tutorial, we will walk you through the steps to install PyTorch with GPU support in Python on Windows, MacOS, and Linux operating systems.
Step 1: Check GPU Compatibility Before installing PyTorch with GPU support, you should check if your system’s GPU is compatible with CUDA, a parallel computing platform and programming model developed by NVIDIA. Visit the NVIDIA website to download and install the latest CUDA Toolkit version that is compatible with your GPU.
Step 2: Install Anaconda Anaconda is an open-source distribution of Python that includes pre-built packages and tools for data science and machine learning. Download and install the latest Anaconda distribution for your operating system.
Step 3: Create a New Conda Environment After installing Anaconda, create a new conda environment by running the following command in your terminal or command prompt:
conda create --name <env_name> python=<version>
<env_name> with the name you want to give to your new environment, and
<version> with the Python version you want to use. Activate the environment using the following command:
conda activate <env_name>
Step 4: Install PyTorch with GPU Support Install PyTorch with GPU support by running the following command in your activated conda environment:
conda install pytorch torchvision torchaudio cudatoolkit=<cuda_version> -c pytorch
<cuda_version> with the version of CUDA you installed in Step 1. This command will install PyTorch, torchvision, and torchaudio packages with GPU support.
Step 5: Test the Installation To ensure that PyTorch has been successfully installed with GPU support, run the following Python code in your terminal or command prompt:
import torch print(torch.cuda.is_available())
If the output is
True, then PyTorch has been installed with GPU support and is ready to use.
Conclusion: In this tutorial, we walked you through the steps to install PyTorch with GPU support in Python on Windows, MacOS, and Linux operating systems. By following these steps, you can leverage the power of GPU acceleration for faster and more efficient machine-learning computations. Happy coding!