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Title: Installing PyTorch on Linux using Conda: A Step-by-Step Tutorial
Introduction:
PyTorch is a popular open-source machine learning library that provides a flexible and dynamic computational graph. Installing PyTorch on Linux can sometimes be challenging, but using Conda simplifies the process by managing dependencies and environments. This tutorial will guide you through the steps to install PyTorch on Linux using Conda.
Prerequisites:
Before you begin, make sure you have Conda installed on your Linux system. If you don’t have it installed, you can download and install Miniconda, a minimal version of Conda, from the official website:
Step 1: Open a Terminal
Open a terminal on your Linux machine. You can do this by pressing Ctrl + Alt + T or using your system’s application launcher.
Step 2: Create a Conda Environment (Optional but Recommended)
Creating a Conda environment is a good practice to isolate dependencies for different projects. To create a new environment named “myenv” with Python 3.8, run the following command:
Activate the environment:
Step 3: Install PyTorch using Conda
Now that you have your Conda environment set up, you can install PyTorch. Check the official PyTorch website ( for the latest installation command. As of my knowledge cutoff in January 2022, the following command installs PyTorch with CUDA support:
This command installs PyTorch, torchvision (for vision tasks), and torchaudio (for audio tasks) with the specified CUDA toolkit version. If you don’t have a compatible GPU or don’t need CUDA support, you can omit the cudatoolkit option:
Step 4: Verify the Installation
To ensure that PyTorch is installed correctly, open a Python interpreter within your Conda environment and import PyTorch:
This should print the installed PyTorch version.
Conclusion:
Congratulations! You have successfully installed PyTorch on your Linux machine using Conda. You can now start building and experimenting with machine learning models using PyTorch in your Conda environment. Remember to activate your Conda environment whenever you want to work on your PyTorch projects.
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