PyTorch can be installed and used on macOS. Depending on your system and GPU capabilities, your experience with PyTorch on a Mac may vary in terms of processing time.

PyTorch is supported on macOS 10.15 (Catalina) or above.

It is recommended that you use Python 3.7 or greater, which can be installed either through the Anaconda package manager (see below), Homebrew, or the Python website.

To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.

To install Anaconda, you can download graphical installer or use the command-line installer. If you use the command-line installer, you can right-click on the installer link, select Copy Link Address, or use the following commands on Intel Mac:

or following commands on M1 Mac:

Python 3

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 Anaconda, use the following conda command:

To install PyTorch via pip, use one of the following two commands, 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鈥檚 CUDA support or ROCm support.

PyTorch is supported on Linux distributions that use glibc >= v2.17, 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 3.7 or greater 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:

It is recommended that you use Python 3.6, 3.7 or 3.8, which can be installed via any of the mechanisms above .

If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.

To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.

To install Anaconda, you will use the command-line installer. Right-click on the 64-bit installer link, select Copy Link Location, and then use the following commands:

You may have to open a new terminal or re-source your ~/.bashrc to get access to the conda command.

Python 3

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 Anaconda, 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: Conda, Language: Python and Compute Platform: CPU. Then, run the command that is presented to you.

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. Often, the latest CUDA version is better. Then, run the command that is presented to you.

PyTorch via Anaconda is not supported on ROCm currently. Please use pip instead.

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 (https://github.com/pytorch/pytorch/blob/master/docs/source/notes/hip.rst#hip-interfaces-reuse-the-cuda-interfaces), 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鈥檚 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 3.7-3.9; Python 2.x is not supported.

As it is not installed by default on Windows, there are multiple ways to install Python:

If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.

If you decide to use Chocolatey, and haven鈥檛 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:

To install the PyTorch binaries, you will need to use at least one of two supported package managers: Anaconda and pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python and pip.

To install Anaconda, you will use the 64-bit graphical installer for PyTorch 3.x. Click on the installer link and select Run. Anaconda will download and the installer prompt will be presented to you. The default options are generally sane.

If you installed Python by any of the recommended ways above, pip will have already been installed for you.

To install PyTorch with Anaconda, you will need to open an Anaconda prompt via Start | Anaconda3 | Anaconda Prompt.

To install PyTorch via Anaconda, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Conda and CUDA: None. Then, run the command that is presented to you.

To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Conda 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 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:

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.