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. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.
NOTE: Latest PyTorch requires Python 3.8 or later. For more details, see Python section below.
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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 3.8 - 3.11. You can install Python either through the Anaconda package manager (see below), Homebrew, or the Python website.
In one of the upcoming PyTorch releases, support for Python 3.8 will be deprecated.
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:
# The version of Anaconda may be different depending on when you are installing`
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
sh Miniconda3-latest-MacOSX-x86_64.sh
# and follow the prompts. The defaults are generally good.`
or following commands on M1 Mac:
# The version of Anaconda may be different depending on when you are installing`
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
sh Miniconda3-latest-MacOSX-arm64.sh
# and follow the prompts. The defaults are generally good.`
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 ofpip3, you can symlinkpipto thepip3binary.
To install PyTorch via Anaconda, use the following conda command:
conda install pytorch torchvision -c pytorch
To install PyTorch via pip, use one of the following two commands, depending on your Python version:
# Python 3.x
pip3 install torch torchvision
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.
import torch
x = torch.rand(5, 3)
print(x)
The output should be something similar to:
tensor([[0.3380, 0.3845, 0.3217],
[0.8337, 0.9050, 0.2650],
[0.2979, 0.7141, 0.9069],
[0.1449, 0.1132, 0.1375],
[0.4675, 0.3947, 0.1426]])
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 >= v2.17, 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
yuminstead ofapt. The specific examples shown were run on an Ubuntu 18.04 machine.
Python 3.8-3.11 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
python3to run Python. If you want to use just the commandpython, instead ofpython3, you can symlinkpythonto thepython3binary.
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:
sudo apt 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.
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:
# The version of Anaconda may be different depending on when you are installing`
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
sh Miniconda3-latest-Linux-x86_64.sh
# and follow the prompts. The defaults are generally good.`
You may have to open a new terminal or re-source your
~/.bashrcto get access to thecondacommand.
Python 3
While Python 3.x is installed by default on Linux, pip is not installed by default.
sudo apt install python3-pip
Tip: If you want to use just the command
pip, instead ofpip3, you can symlinkpipto thepip3binary.
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.
import torch
x = torch.rand(5, 3)
print(x)
The output should be something similar to:
tensor([[0.3380, 0.3845, 0.3217],
[0.8337, 0.9050, 0.2650],
[0.2979, 0.7141, 0.9069],
[0.1449, 0.1132, 0.1375],
[0.4675, 0.3947, 0.1426]])
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):
import torch
torch.cuda.is_available()
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.
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