Select preferences and run the command to install PyTorch locally, or get started quickly with one of the supported cloud platforms. Additional Platforms Start via Cloud Partners Previous PyTorch Versions PyTorch for Edge 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 3.10 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 . Package Manager 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: Building from source 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 . [Optional] Install pip Follow the steps described here: https://github.com/pytorch/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. Supported Linux Distributions PyTorch is supported on Linux distributions that use glibc >= v2.28, which include the following: Arch Linux , minimum version 2020.01.22 CentOS , minimum version 8 Debian , minimum version 10.0 Fedora , minimum version 24 Mint , minimum version 20 OpenSUSE , minimum version 15 PCLinuxOS , minimum version 2014.7 Slackware , minimum version 14.2 Ubuntu , minimum version 20.04 (please note that 20.04 reached EOL) 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: Package Manager 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): Building from source 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 . If you need to build PyTorch with GPU support a. for NVIDIA GPUs, install CUDA , if your machine has a CUDA-enabled GPU . b. for AMD GPUs, install ROCm , if your machine has a ROCm-enabled GPU Follow the steps described here: https://github.com/pytorch/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 . Supported Windows Distributions PyTorch is supported on the following Windows distributions: Windows 7 and greater; Windows 10 or greater recommended. Windows Server 2008 r2 and greater 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 ways to install Python: If you decide to use Chocolatey, and haven’t installed Chocolatey yet, ensure that you are running your command prompt as an administrator. For a Chocolatey-based install, run the following command in an administrative command prompt: Package Manager To install the PyTorch binaries, you will need to use the supported package manager: pip . If you installed Python by any of the recommended ways above , pip will have already been installed for you. To install PyTorch via pip, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Pip and CUDA: None. 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: Windows, Package: Pip 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 ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor. From the command line, type: then enter the following code: The output should be something similar to: Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled: Building from source 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 . Install CUDA , if your machine has a CUDA-enabled GPU . If you want to build on Windows, Visual Studio with MSVC toolset, and NVTX are also needed. The exact requirements of those dependencies could be found out here . Follow the steps described here: https://github.com/pytorch/pytorch#from-source You can verify the installation as described above . Access comprehensive developer documentation for PyTorch Get in-depth tutorials for beginners and advanced developers View Tutorials › Find development resources and get your questions answered View Resources › Stay in touch for updates, event info, and the latest news By submitting this form, I consent to receive marketing emails from the LF and its projects regarding their events, training, research, developments, and related announcements. I understand that I can unsubscribe at any time using the links in the footers of the emails I receive. Privacy Policy . © 2026 PyTorch. Copyright © The Linux Foundation®. All rights reserved. The Linux Foundation has registered trademarks and uses trademarks. For more information, including terms of use, privacy policy, and trademark usage, please see our Policies page. Trademark Usage . Privacy Policy . Learn Get Started Tutorials Learn the Basics PyTorch Recipes Intro to PyTorch – YouTube Series Webinars PyTorch Certification Learn the Basics PyTorch Recipes Intro to PyTorch – YouTube Series PyTorch Certification Community Landscape Join the Ecosystem Community Hub Forums Developer Resources Events Working Groups Meeting Calendar Contributor Awards Ambassadors Join the Ecosystem Developer Resources Meeting Calendar Contributor Awards Projects PyTorch Executorch vLLM DeepSpeed Ray Helion Safetensors Host Your Project PyTorch Executorch Host Your Project Docs PyTorch Domains Blog & News Blog Announcements Case Studies Newsletter About PyTorch Foundation Members Governing Board Technical Advisory Council Cloud Credit Program Staff Contact Brand Guidelines PyTorch Foundation Governing Board Technical Advisory Council Cloud Credit Program Brand Guidelines