Step 1: Install Nvidia-driver. Check Nvidia drivers from the link provided as follows:

https://www.nvidia.com/en-us/drivers/

sudo apt install nvidia-driver-566

Step 2: Create a conda environment and name it. For example, a torch environment with the name "torch" can be created as:

conda create --name torch python=3.11

Step 3: Activate the newly created conda environment.

Step 4: Install cudatoolkit and pytorch. Check compatible versions in the link below:

https://pytorch.org/get-started/locally/

conda install pytorch torchvision torchaudio cudatoolkit=11.7 -c pytorch -c nvidia

By following these steps, you'll set up a conda environment for PyTorch with the necessary GPU support in no time, enabling efficient deep learning workflows.

Step 1: Install Nvidia-driver. Check Nvidia drivers from the link provided as follows:

https://www.nvidia.com/en-us/drivers/

sudo apt install nvidia-driver-566

Step 2: Create a conda environment and name it. For example, a torch environment with the name "torch" can be created as:

conda create --name torch python=3.11

Step 3: Activate the newly created conda environment.

Step 4: Install cudatoolkit and pytorch. Check compatible versions in the link below:

https://pytorch.org/get-started/locally/

conda install pytorch torchvision torchaudio cudatoolkit=11.7 -c pytorch -c nvidia

By following these steps, you'll set up a conda environment for PyTorch with the necessary GPU support in no time, enabling efficient deep learning workflows.