Get Started Locally - PyTorch

Select preferences and run the command to install PyTorch locally, or get started quickly with one of the supported cloud platforms.
Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have met the prerequisites below (e.g., numpy), depending on your package manager. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.
NOTE: Latest Stable PyTorch requires Python 3.11 or later.
Could not find the right platform for your hardware? See the PyTorch Additional Platforms page.
PyTorch can be installed and used on macOS. Depending on your system and GPU capabilities, your experience with PyTorch on macOS may vary in terms of processing time.
PyTorch is supported on macOS 10.15 (Catalina) or above.
It is recommended that you use Python 3.10 - 3.14. You can install Python either through Homebrew or the Python website.
To install the PyTorch binaries, you will need to use the supported package manager: pip.
If you installed Python via Homebrew or the Python website, pip was installed with it. If you installed Python 3.x, then you will be using the command pip3.
Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary.
To install PyTorch via pip, use the following command, depending on your Python version:
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
The output should be something similar to:
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.
You can verify the installation as described above.
PyTorch can be installed and used on various Linux distributions. Depending on your system and compute requirements, your experience with PyTorch on Linux may vary in terms of processing time. 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.
PyTorch is supported on Linux distributions that use glibc >= v2.28, which include the following:
The install instructions here will generally apply to all supported Linux distributions. An example difference is that your distribution may support yum instead of apt. The specific examples shown were run on an Ubuntu 18.04 machine.
Python 3.10-3.14 is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
Tip: By default, you will have to use the command python3 to run Python. If you want to use just the command python, instead of python3, you can symlink python to the python3 binary.
However, if you want to install another version, there are multiple ways:
If you decide to use APT, you can run the following command to install it:
To install the PyTorch binaries, you will need to use the supported package manager: pip.
While Python 3.x is installed by default on Linux, pip is not installed by default.
Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary.
To install PyTorch via pip, and do not have a CUDA-capable or ROCm-capable system or do not require CUDA/ROCm (i.e. GPU support), in the above selector, choose OS: Linux, Package: Pip, Language: Python and Compute Platform: CPU. Then, run the command that is presented to you.
To install PyTorch via pip, and do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Pip, Language: Python and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
To install PyTorch via pip, and do have a ROCm-capable system, in the above selector, choose OS: Linux, Package: Pip, Language: Python and the ROCm version supported. Then, run the command that is presented to you.
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
The output should be something similar to:
Additionally, to check if your GPU driver and CUDA/ROCm is enabled and accessible by PyTorch, run the following commands to return whether or not the GPU driver is enabled (the ROCm build of PyTorch uses the same semantics at the python API level link, so the below commands should also work for ROCm):
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.
You can verify the installation as described above.
PyTorch can be installed and used on various Windows distributions. Depending on your system and compute requirements, your experience with PyTorch on Windows may vary in terms of processing time. It is recommended, but not required, that your Windows system has an NVIDIA GPU in order to harness the full power of PyTorch’s CUDA support.
PyTorch is supported on the following Windows distributions:
The install instructions here will generally apply to all supported Windows distributions. The specific examples shown will be run on a Windows 10 Enterprise machine
Currently, PyTorch on Windows only supports Python 3.10-3.14; Python 2.x is not supported.
As it is not installed by default on Windows, there are multiple