Installing PyTorch GPU in Conda / Anaconda Virtual Environments

PyTorch is a popular open-source machine learning library for Python that is widely used in the field of deep learning. PyTorch provides strong support for both CPU and GPU computing, making it an excellent choice for training deep learning models on powerful GPU hardware.

In this tutorial, we will guide you through the process of installing PyTorch with GPU support in a Conda virtual environment. Conda is a package manager and virtual environment management system that allows you to easily manage dependencies and package installations for your Python projects. Anaconda is a distribution that includes Conda as well as a collection of popular Python packages for data science and machine learning.

Here are the steps to install PyTorch with GPU support in a Conda virtual environment:

Step 1: Install Conda or Anaconda
If you do not already have Conda or Anaconda installed on your system, you can download and install it from the official website (https://www.anaconda.com/products/individual) for your operating system.

Step 2: Create a new Conda virtual environment
Open a terminal or command prompt and create a new Conda virtual environment by running the following command:

conda create -n myenv

Replace myenv with the name you want to give to your virtual environment. You can choose any name you like.

Activate the virtual environment by running:

conda activate myenv

Step 3: Install PyTorch with GPU support
Now that you have activated your Conda virtual environment, you can install PyTorch with GPU support using the following command:

conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch

This command will install the latest version of PyTorch, torchvision, torchaudio, and the CUDA toolkit compatible with your GPU on your system. If you have a different version of CUDA installed on your system, you can change the version number in the command accordingly.

Step 4: Verify the installation
To verify that PyTorch has been successfully installed with GPU support in your Conda virtual environment, you can run a simple Python script that checks if the GPU is available for computation. Create a new Python script (e.g., test_gpu.py) with the following code:

import torch

if torch.cuda.is_available():
    print('GPU is available!')
else:
    print('GPU is not available!')

Run the Python script in your Conda virtual environment by executing:

python test_gpu.py

If you see the message "GPU is available!" printed on the screen, it means that PyTorch has been successfully installed with GPU support in your Conda virtual environment.

In conclusion, this tutorial has provided you with detailed steps to install PyTorch with GPU support in a Conda virtual environment. By following these steps, you can leverage the power of GPU computing to train deep learning models using PyTorch.