Often, the latest CUDA version is better. To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Conda and the CUDA version suited to your machine. Then, run the command that is presented to you.

GPU support), in the above selector, choose OS: Linux, Package: Conda, Language: Python and Compute Platform: CPU. To install PyTorch via Anaconda, and do not have a CUDA-capable or ROCm-capable system or do not require CUDA/ROCm (i.e. Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary. If you decide to use APT, you can run the following command to install it: However, if you want to install another version, there are multiple ways: If you want to use just the command python, instead of python3, you can symlink python to the python3 binary. Tip: By default, you will have to use the command python3 to run Python. Python 3.8 or greater is generally installed by default on any of our supported Linux distributions, which meets our recommendation. The specific examples shown were run on an Ubuntu 18.04 machine. An example difference is that your distribution may support yum instead of apt.

The install instructions here will generally apply to all supported Linux distributions. PyTorch is supported on Linux distributions that use glibc >= v2.17, which include the following: Prerequisites Supported Linux Distributions

It is recommended, but not required, that your Linux system has an NVIDIA or AMD GPU in order to harness the full power of PyTorch’s CUDA support or ROCm support. Depending on your system and compute requirements, your experience with PyTorch on Linux may vary in terms of processing time. PyTorch can be installed and used on various Linux distributions.

Ubuntu nvidia driver