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data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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href=\"https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png\"\u003e\u003cimg src=\"https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png\" alt=\"PyTorch Logo\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003ePyTorch is a Python package that provides two high-level features:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTensor computation (like NumPy) with strong GPU acceleration\u003c/li\u003e\n\u003cli\u003eDeep neural networks built on a tape-based autograd system\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eYou can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOur trunk health (Continuous Integration signals) can be found at \u003ca href=\"https://hud.pytorch.org/ci/pytorch/pytorch/main\" rel=\"nofollow\"\u003ehud.pytorch.org\u003c/a\u003e.\u003c/p\u003e\n\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#more-about-pytorch\"\u003eMore About PyTorch\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#a-gpu-ready-tensor-library\"\u003eA GPU-Ready Tensor Library\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#dynamic-neural-networks-tape-based-autograd\"\u003eDynamic Neural Networks: Tape-Based Autograd\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#python-first\"\u003ePython First\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#imperative-experiences\"\u003eImperative Experiences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#fast-and-lean\"\u003eFast and Lean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#extensions-without-pain\"\u003eExtensions Without Pain\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#binaries\"\u003eBinaries\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#nvidia-jetson-platforms\"\u003eNVIDIA Jetson Platforms\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#from-source\"\u003eFrom Source\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#nvidia-cuda-support\"\u003eNVIDIA CUDA Support\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#amd-rocm-support\"\u003eAMD ROCm Support\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#intel-gpu-support\"\u003eIntel GPU Support\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#get-the-pytorch-source\"\u003eGet the PyTorch Source\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#install-dependencies\"\u003eInstall Dependencies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#install-pytorch\"\u003eInstall PyTorch\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#adjust-build-options-optional\"\u003eAdjust Build Options (Optional)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#docker-image\"\u003eDocker Image\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#using-pre-built-images\"\u003eUsing pre-built images\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#building-the-image-yourself\"\u003eBuilding the image yourself\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#building-the-documentation\"\u003eBuilding the Documentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#previous-versions\"\u003ePrevious Versions\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#communication\"\u003eCommunication\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#releases-and-contributing\"\u003eReleases and Contributing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#the-team\"\u003eThe Team\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMore About PyTorch\u003c/h2\u003e\u003ca id=\"user-content-more-about-pytorch\" class=\"anchor\" aria-label=\"Permalink: More About PyTorch\" href=\"#more-about-pytorch\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://pytorch.org/tutorials/beginner/basics/intro.html\" rel=\"nofollow\"\u003eLearn the basics of PyTorch\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAt a granular level, PyTorch is a library that consists of the following components:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/torch.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA Tensor library like NumPy, with strong GPU support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/autograd.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch.autograd\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA tape-based automatic differentiation library that supports all differentiable Tensor operations in torch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/jit.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch.jit\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/nn.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch.nn\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eA neural networks library deeply integrated with autograd designed for maximum flexibility\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/multiprocessing.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch.multiprocessing\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003ePython multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://pytorch.org/docs/stable/data.html\" rel=\"nofollow\"\u003e\u003cstrong\u003etorch.utils\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eDataLoader and other utility functions for convenience\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eUsually, PyTorch is used either as:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eA replacement for NumPy to use the power of GPUs.\u003c/li\u003e\n\u003cli\u003eA deep learning research platform that provides maximum flexibility and speed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eElaborating Further:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eA GPU-Ready Tensor Library\u003c/h3\u003e\u003ca id=\"user-content-a-gpu-ready-tensor-library\" class=\"anchor\" aria-label=\"Permalink: A GPU-Ready Tensor Library\" href=\"#a-gpu-ready-tensor-library\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use NumPy, then you have used Tensors (a.k.a. ndarray).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/pytorch/pytorch/blob/main/docs/source/_static/img/tensor_illustration.png\"\u003e\u003cimg src=\"/pytorch/pytorch/raw/main/docs/source/_static/img/tensor_illustration.png\" alt=\"Tensor illustration\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the\ncomputation by a huge amount.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe provide a wide variety of tensor routines to accelerate and fit your scientific computation needs\nsuch as slicing, indexing, mathematical operations, linear algebra, reductions.\nAnd they are fast!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDynamic Neural Networks: Tape-Based Autograd\u003c/h3\u003e\u003ca id=\"user-content-dynamic-neural-networks-tape-based-autograd\" class=\"anchor\" aria-label=\"Permalink: Dynamic Neural Networks: Tape-Based Autograd\" href=\"#dynamic-neural-networks-tape-based-autograd\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch has a unique way of building neural networks: using and replaying a tape recorder.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMost frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world.\nOne has to build a neural network and reuse the same structure again and again.\nChanging the way the network behaves means that one has to start from scratch.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWith PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to\nchange the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes\nfrom several research papers on this topic, as well as current and past work such as\n\u003ca href=\"https://github.com/twitter/torch-autograd\"\u003etorch-autograd\u003c/a\u003e,\n\u003ca href=\"https://github.com/HIPS/autograd\"\u003eautograd\u003c/a\u003e,\n\u003ca href=\"https://chainer.org\" rel=\"nofollow\"\u003eChainer\u003c/a\u003e, etc.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhile this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.\nYou get the best of speed and flexibility for your crazy research.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif\"\u003e\u003cimg src=\"https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif\" alt=\"Dynamic graph\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePython First\u003c/h3\u003e\u003ca id=\"user-content-python-first\" class=\"anchor\" aria-label=\"Permalink: Python First\" href=\"#python-first\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch is not a Python binding into a monolithic C++ framework.\nIt is built to be deeply integrated into Python.\nYou can use it naturally like you would use \u003ca href=\"https://www.numpy.org/\" rel=\"nofollow\"\u003eNumPy\u003c/a\u003e / \u003ca href=\"https://www.scipy.org/\" rel=\"nofollow\"\u003eSciPy\u003c/a\u003e / \u003ca href=\"https://scikit-learn.org\" rel=\"nofollow\"\u003escikit-learn\u003c/a\u003e etc.\nYou can write your new neural network layers in Python itself, using your favorite libraries\nand use packages such as \u003ca href=\"https://cython.org/\" rel=\"nofollow\"\u003eCython\u003c/a\u003e and \u003ca href=\"http://numba.pydata.org/\" rel=\"nofollow\"\u003eNumba\u003c/a\u003e.\nOur goal is to not reinvent the wheel where appropriate.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImperative Experiences\u003c/h3\u003e\u003ca id=\"user-content-imperative-experiences\" class=\"anchor\" aria-label=\"Permalink: Imperative Experiences\" href=\"#imperative-experiences\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch is designed to be intuitive, linear in thought, and easy to use.\nWhen you execute a line of code, it gets executed. There isn't an asynchronous view of the world.\nWhen you drop into a debugger or receive error messages and stack traces, understanding them is straightforward.\nThe stack trace points to exactly where your code was defined.\nWe hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFast and Lean\u003c/h3\u003e\u003ca id=\"user-content-fast-and-lean\" class=\"anchor\" aria-label=\"Permalink: Fast and Lean\" href=\"#fast-and-lean\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch has minimal framework overhead. We integrate acceleration libraries\nsuch as \u003ca href=\"https://software.intel.com/mkl\" rel=\"nofollow\"\u003eIntel MKL\u003c/a\u003e and NVIDIA (\u003ca href=\"https://developer.nvidia.com/cudnn\" rel=\"nofollow\"\u003ecuDNN\u003c/a\u003e, \u003ca href=\"https://developer.nvidia.com/nccl\" rel=\"nofollow\"\u003eNCCL\u003c/a\u003e) to maximize speed.\nAt the core, its CPU and GPU Tensor and neural network backends\nare mature and have been tested for years.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHence, PyTorch is quite fast — whether you run small or large neural networks.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives.\nWe've written custom memory allocators for the GPU to make sure that\nyour deep learning models are maximally memory efficient.\nThis enables you to train bigger deep learning models than before.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExtensions Without Pain\u003c/h3\u003e\u003ca id=\"user-content-extensions-without-pain\" class=\"anchor\" aria-label=\"Permalink: Extensions Without Pain\" href=\"#extensions-without-pain\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWriting new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward\nand with minimal abstractions.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can write new neural network layers in Python using the torch API\n\u003ca href=\"https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html\" rel=\"nofollow\"\u003eor your favorite NumPy-based libraries such as SciPy\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate.\nNo wrapper code needs to be written. You can see \u003ca href=\"https://pytorch.org/tutorials/advanced/cpp_extension.html\" rel=\"nofollow\"\u003ea tutorial here\u003c/a\u003e and \u003ca href=\"https://github.com/pytorch/extension-cpp\"\u003ean example here\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBinaries\u003c/h3\u003e\u003ca id=\"user-content-binaries\" class=\"anchor\" aria-label=\"Permalink: Binaries\" href=\"#binaries\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCommands to install binaries via Conda or pip wheels are on our website: \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003ehttps://pytorch.org/get-started/locally/\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNVIDIA Jetson Platforms\u003c/h4\u003e\u003ca id=\"user-content-nvidia-jetson-platforms\" class=\"anchor\" aria-label=\"Permalink: NVIDIA Jetson Platforms\" href=\"#nvidia-jetson-platforms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePython wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided \u003ca href=\"https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048\" rel=\"nofollow\"\u003ehere\u003c/a\u003e and the L4T container is published \u003ca href=\"https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch\" rel=\"nofollow\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThey require JetPack 4.2 and above, and \u003ca href=\"https://github.com/dusty-nv\"\u003e@dusty-nv\u003c/a\u003e and \u003ca href=\"https://github.com/ptrblck\"\u003e@ptrblck\u003c/a\u003e are maintaining them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFrom Source\u003c/h3\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrerequisites\u003c/h4\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you are installing from source, you will need:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython 3.9 or later\u003c/li\u003e\n\u003cli\u003eA compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required, on Linux)\u003c/li\u003e\n\u003cli\u003eVisual Studio or Visual Studio Build Tool (Windows only)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e* PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise,\nProfessional, or Community Editions. You can also install the build tools from\n\u003ca href=\"https://visualstudio.microsoft.com/visual-cpp-build-tools/\" rel=\"nofollow\"\u003ehttps://visualstudio.microsoft.com/visual-cpp-build-tools/\u003c/a\u003e. The build tools \u003cem\u003edo not\u003c/em\u003e\ncome with Visual Studio Code by default.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e* We highly recommend installing an \u003ca href=\"https://www.anaconda.com/download\" rel=\"nofollow\"\u003eAnaconda\u003c/a\u003e environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAn example of environment setup is shown below:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLinux:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ source \u0026lt;CONDA_INSTALL_DIR\u0026gt;/bin/activate\n$ conda create -y -n \u0026lt;CONDA_NAME\u0026gt;\n$ conda activate \u0026lt;CONDA_NAME\u0026gt;\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_INSTALL_DIR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e/bin/activate\n$ conda create -y -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_NAME\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n$ conda activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_NAME\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWindows:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ source \u0026lt;CONDA_INSTALL_DIR\u0026gt;\\Scripts\\activate.bat\n$ conda create -y -n \u0026lt;CONDA_NAME\u0026gt;\n$ conda activate \u0026lt;CONDA_NAME\u0026gt;\n$ call \u0026quot;C:\\Program Files\\Microsoft Visual Studio\\\u0026lt;VERSION\u0026gt;\\Community\\VC\\Auxiliary\\Build\\vcvarsall.bat\u0026quot; x64\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_INSTALL_DIR\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-cce\"\u003e\\S\u003c/span\u003ecripts\u003cspan class=\"pl-cce\"\u003e\\a\u003c/span\u003ectivate.bat\n$ conda create -y -n \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_NAME\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n$ conda activate \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eCONDA_NAME\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\n$ call \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eC:\\Program Files\\Microsoft Visual Studio\\\u0026lt;VERSION\u0026gt;\\Community\\VC\\Auxiliary\\Build\\vcvarsall.bat\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e x64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNVIDIA CUDA Support\u003c/h5\u003e\u003ca id=\"user-content-nvidia-cuda-support\" class=\"anchor\" aria-label=\"Permalink: NVIDIA CUDA Support\" href=\"#nvidia-cuda-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to compile with CUDA support, \u003ca href=\"https://pytorch.org/get-started/locally/\" rel=\"nofollow\"\u003eselect a supported version of CUDA from our support matrix\u003c/a\u003e, then install the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cuda-downloads\" rel=\"nofollow\"\u003eNVIDIA CUDA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://developer.nvidia.com/cudnn\" rel=\"nofollow\"\u003eNVIDIA cuDNN\u003c/a\u003e v8.5 or above\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/ax3l/9489132\"\u003eCompiler\u003c/a\u003e compatible with CUDA\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eNote: You could refer to the \u003ca href=\"https://docs.nvidia.com/deeplearning/cudnn/reference/support-matrix.html\" rel=\"nofollow\"\u003ecuDNN Support Matrix\u003c/a\u003e for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to disable CUDA support, export the environment variable \u003ccode\u003eUSE_CUDA=0\u003c/code\u003e.\nOther potentially useful environment variables may be found in \u003ccode\u003esetup.py\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are \u003ca href=\"https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/\" rel=\"nofollow\"\u003eavailable here\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAMD ROCm Support\u003c/h5\u003e\u003ca id=\"user-content-amd-rocm-support\" class=\"anchor\" aria-label=\"Permalink: AMD ROCm Support\" href=\"#amd-rocm-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to compile with ROCm support, install\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html\" rel=\"nofollow\"\u003eAMD ROCm\u003c/a\u003e 4.0 and above installation\u003c/li\u003e\n\u003cli\u003eROCm is currently supported only for Linux systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eBy default the build system expects ROCm to be installed in \u003ccode\u003e/opt/rocm\u003c/code\u003e. If ROCm is installed in a different directory, the \u003ccode\u003eROCM_PATH\u003c/code\u003e environment variable must be set to the ROCm installation directory. The build system automatically detects the AMD GPU architecture. Optionally, the AMD GPU architecture can be explicitly set with the \u003ccode\u003ePYTORCH_ROCM_ARCH\u003c/code\u003e environment variable \u003ca href=\"https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html#supported-gpus\" rel=\"nofollow\"\u003eAMD GPU architecture\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to disable ROCm support, export the environment variable \u003ccode\u003eUSE_ROCM=0\u003c/code\u003e.\nOther potentially useful environment variables may be found in \u003ccode\u003esetup.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntel GPU Support\u003c/h5\u003e\u003ca id=\"user-content-intel-gpu-support\" class=\"anchor\" aria-label=\"Permalink: Intel GPU Support\" href=\"#intel-gpu-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to compile with Intel GPU support, follow these\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html\" rel=\"nofollow\"\u003ePyTorch Prerequisites for Intel GPUs\u003c/a\u003e instructions.\u003c/li\u003e\n\u003cli\u003eIntel GPU is supported for Linux and Windows.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIf you want to disable Intel GPU support, export the environment variable \u003ccode\u003eUSE_XPU=0\u003c/code\u003e.\nOther potentially useful environment variables may be found in \u003ccode\u003esetup.py\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet the PyTorch Source\u003c/h4\u003e\u003ca id=\"user-content-get-the-pytorch-source\" class=\"anchor\" aria-label=\"Permalink: Get the PyTorch Source\" href=\"#get-the-pytorch-source\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone --recursive https://github.com/pytorch/pytorch\ncd pytorch\n# if you are updating an existing checkout\ngit submodule sync\ngit submodule update --init --recursive\"\u003e\u003cpre\u003egit clone --recursive https://github.com/pytorch/pytorch\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e pytorch\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if you are updating an existing checkout\u003c/span\u003e\ngit submodule sync\ngit submodule update --init --recursive\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall Dependencies\u003c/h4\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install Dependencies\" href=\"#install-dependencies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCommon\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda install cmake ninja\n# Run this command on native Windows\nconda install rust\n# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below\npip install -r requirements.txt\"\u003e\u003cpre\u003econda install cmake ninja\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run this command on native Windows\u003c/span\u003e\nconda install rust\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below\u003c/span\u003e\npip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Linux\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install mkl-static mkl-include\n# CUDA only: Add LAPACK support for the GPU if needed\nconda install -c pytorch magma-cuda121 # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo\n\n# (optional) If using torch.compile with inductor/triton, install the matching version of triton\n# Run from the pytorch directory after cloning\n# For Intel GPU support, please explicitly `export USE_XPU=1` before running command.\nmake triton\"\u003e\u003cpre\u003epip install mkl-static mkl-include\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e CUDA only: Add LAPACK support for the GPU if needed\u003c/span\u003e\nconda install -c pytorch magma-cuda121 \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e (optional) If using torch.compile with inductor/triton, install the matching version of triton\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Run from the pytorch directory after cloning\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e For Intel GPU support, please explicitly `export USE_XPU=1` before running command.\u003c/span\u003e\nmake triton\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn MacOS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Add this package on intel x86 processor machines only\npip install mkl-static mkl-include\n# Add these packages if torch.distributed is needed\nconda install pkg-config libuv\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add this package on intel x86 processor machines only\u003c/span\u003e\npip install mkl-static mkl-include\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add these packages if torch.distributed is needed\u003c/span\u003e\nconda install pkg-config libuv\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Windows\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install mkl-static mkl-include\n# Add these packages if torch.distributed is needed.\n# Distributed package support on Windows is a prototype feature and is subject to changes.\nconda install -c conda-forge libuv=1.39\"\u003e\u003cpre\u003epip install mkl-static mkl-include\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Add these packages if torch.distributed is needed.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Distributed package support on Windows is a prototype feature and is subject to changes.\u003c/span\u003e\nconda install -c conda-forge libuv=1.39\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall PyTorch\u003c/h4\u003e\u003ca id=\"user-content-install-pytorch\" class=\"anchor\" aria-label=\"Permalink: Install PyTorch\" href=\"#install-pytorch\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Linux\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you would like to compile PyTorch with \u003ca href=\"https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html\" rel=\"nofollow\"\u003enew C++ ABI\u003c/a\u003e enabled, then first run this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export _GLIBCXX_USE_CXX11_ABI=1\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e _GLIBCXX_USE_CXX11_ABI=1\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease \u003cstrong\u003enote\u003c/strong\u003e that starting from PyTorch 2.5, the PyTorch build with XPU supports both new and old C++ ABIs. Previously, XPU only supported the new C++ ABI. If you want to compile with Intel GPU support, please follow \u003ca href=\"#intel-gpu-support\"\u003eIntel GPU Support\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you're compiling for AMD ROCm then first run this command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Only run this if you're compiling for ROCm\npython tools/amd_build/build_amd.py\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Only run this if you're compiling for ROCm\u003c/span\u003e\npython tools/amd_build/build_amd.py\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInstall PyTorch\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export CMAKE_PREFIX_PATH=\u0026quot;${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}\u0026quot;\npython setup.py develop\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_PREFIX_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${CONDA_PREFIX\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e$(dirname $(which conda))/../\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${CMAKE_PREFIX_PATH}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npython setup.py develop\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn macOS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 setup.py develop\"\u003e\u003cpre\u003epython3 setup.py develop\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eOn Windows\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to build legacy python code, please refer to \u003ca href=\"https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#building-on-legacy-code-and-cuda\"\u003eBuilding on legacy code and CUDA\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCPU-only builds\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn this mode PyTorch computations will run on your CPU, not your GPU\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-batchfile notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python setup.py develop\"\u003e\u003cpre\u003epython setup.py develop\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking \u003ccode\u003eCMAKE_INCLUDE_PATH\u003c/code\u003e and \u003ccode\u003eLIB\u003c/code\u003e. The instruction \u003ca href=\"https://github.com/pytorch/pytorch/blob/main/docs/source/notes/windows.rst#building-from-source\"\u003ehere\u003c/a\u003e is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCUDA based build\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm\" rel=\"nofollow\"\u003eNVTX\u003c/a\u003e is needed to build Pytorch with CUDA.\nNVTX is a part of CUDA distributive, where it is called \"Nsight Compute\". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox.\nMake sure that CUDA with Nsight Compute is installed after Visual Studio.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If \u003ccode\u003eninja.exe\u003c/code\u003e is detected in \u003ccode\u003ePATH\u003c/code\u003e, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.\n\u003cbr\u003e If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdditional libraries such as\n\u003ca href=\"https://developer.nvidia.com/magma\" rel=\"nofollow\"\u003eMagma\u003c/a\u003e, \u003ca href=\"https://github.com/oneapi-src/oneDNN\"\u003eoneDNN, a.k.a. MKLDNN or DNNL\u003c/a\u003e, and \u003ca href=\"https://github.com/mozilla/sccache\"\u003eSccache\u003c/a\u003e are often needed. Please refer to the \u003ca href=\"https://github.com/pytorch/pytorch/tree/main/.ci/pytorch/win-test-helpers/installation-helpers\"\u003einstallation-helper\u003c/a\u003e to install them.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can refer to the \u003ca href=\"https://github.com/pytorch/pytorch/blob/main/.ci/pytorch/win-test-helpers/build_pytorch.bat\"\u003ebuild_pytorch.bat\u003c/a\u003e script for some other environment variables configurations\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-batchfile notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cmd\n\n:: Set the environment variables after you have downloaded and unzipped the mkl package,\n:: else CMake would throw an error as `Could NOT find OpenMP`.\nset CMAKE_INCLUDE_PATH={Your directory}\\mkl\\include\nset LIB={Your directory}\\mkl\\lib;%LIB%\n\n:: Read the content in the previous section carefully before you proceed.\n:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.\n:: \u0026quot;Visual Studio 2019 Developer Command Prompt\u0026quot; will be run automatically.\n:: Make sure you have CMake \u0026gt;= 3.12 before you do this when you use the Visual Studio generator.\nset CMAKE_GENERATOR_TOOLSET_VERSION=14.27\nset DISTUTILS_USE_SDK=1\nfor /f \u0026quot;usebackq tokens=*\u0026quot; %i in (`\u0026quot;%ProgramFiles(x86)%\\Microsoft Visual Studio\\Installer\\vswhere.exe\u0026quot; -version [15^,17^) -products * -latest -property installationPath`) do call \u0026quot;%i\\VC\\Auxiliary\\Build\\vcvarsall.bat\u0026quot; x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%\n\n:: [Optional] If you want to override the CUDA host compiler\nset CUDAHOSTCXX=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community\\VC\\Tools\\MSVC\\14.27.29110\\bin\\HostX64\\x64\\cl.exe\n\npython setup.py develop\n\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003ecmd\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e Set the environment variables after you have downloaded and unzipped the mkl package,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e else CMake would throw an error as `Could NOT find OpenMP`.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eCMAKE_INCLUDE_PATH\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e{Your directory}\\mkl\\include\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eLIB\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e{Your directory}\\mkl\\lib;\u003cspan class=\"pl-smi\"\u003e%LIB%\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e Read the content in the previous section carefully before you proceed.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e \"Visual Studio 2019 Developer Command Prompt\" will be run automatically.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e Make sure you have CMake \u0026gt;= 3.12 before you do this when you use the Visual Studio generator.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eCMAKE_GENERATOR_TOOLSET_VERSION\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e14.27\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eDISTUTILS_USE_SDK\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e /f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eusebackq tokens=*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e%i in (`\"%\u003c/span\u003eProgramFiles(x86)\u003cspan class=\"pl-smi\"\u003e%\\Microsoft Visual Studio\\Installer\\vswhere.exe\" -version [15^,17^) -products * -latest -property installationPath`) do call \"%\u003c/span\u003ei\\VC\\Auxiliary\\Build\\vcvarsall.bat\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e x64 -vcvars_ver=\u003cspan class=\"pl-smi\"\u003e%CMAKE_GENERATOR_TOOLSET_VERSION%\u003c/span\u003e\u003cspan class=\"pl-ii\"\u003e\u003c/span\u003e\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e::\u003c/span\u003e [Optional] If you want to override the CUDA host compiler\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eset\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eCUDAHOSTCXX\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003eC:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community\\VC\\Tools\\MSVC\\14.27.29110\\bin\\HostX64\\x64\\cl.exe\n\npython setup.py develop\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdjust Build Options (Optional)\u003c/h5\u003e\u003ca id=\"user-content-adjust-build-options-optional\" class=\"anchor\" aria-label=\"Permalink: Adjust Build Options (Optional)\" href=\"#adjust-build-options-optional\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can adjust the configuration of cmake variables optionally (without building first), by doing\nthe following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done\nwith such a step.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOn Linux\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export CMAKE_PREFIX_PATH=\u0026quot;${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}\u0026quot;\npython setup.py build --cmake-only\nccmake build # or cmake-gui build\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_PREFIX_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${CONDA_PREFIX\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e$(dirname $(which conda))/../\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${CMAKE_PREFIX_PATH}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\npython setup.py build --cmake-only\nccmake build \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or cmake-gui build\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOn macOS\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export CMAKE_PREFIX_PATH=\u0026quot;${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}\u0026quot;\nMACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only\nccmake build # or cmake-gui build\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CMAKE_PREFIX_PATH=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${CONDA_PREFIX\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e$(dirname $(which conda))/../\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e:\u003cspan class=\"pl-smi\"\u003e${CMAKE_PREFIX_PATH}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nMACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only\nccmake build \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or cmake-gui build\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocker Image\u003c/h3\u003e\u003ca id=\"user-content-docker-image\" class=\"anchor\" aria-label=\"Permalink: Docker Image\" href=\"#docker-image\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing pre-built images\u003c/h4\u003e\u003ca id=\"user-content-using-pre-built-images\" class=\"anchor\" aria-label=\"Permalink: Using pre-built images\" href=\"#using-pre-built-images\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also pull a pre-built docker image from Docker Hub and run with docker v19.03+\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest\"\u003e\u003cpre\u003edocker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.\nfor multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you\nshould increase shared memory size either with \u003ccode\u003e--ipc=host\u003c/code\u003e or \u003ccode\u003e--shm-size\u003c/code\u003e command line options to \u003ccode\u003envidia-docker run\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding the image yourself\u003c/h4\u003e\u003ca id=\"user-content-building-the-image-yourself\" class=\"anchor\" aria-label=\"Permalink: Building the image yourself\" href=\"#building-the-image-yourself\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE:\u003c/strong\u003e Must be built with a docker version \u0026gt; 18.06\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003eDockerfile\u003c/code\u003e is supplied to build images with CUDA 11.1 support and cuDNN v8.\nYou can pass \u003ccode\u003ePYTHON_VERSION=x.y\u003c/code\u003e make variable to specify which Python version is to be used by Miniconda, or leave it\nunset to use the default.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"make -f docker.Makefile\n# images are tagged as docker.io/${your_docker_username}/pytorch\"\u003e\u003cpre\u003emake -f docker.Makefile\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e images are tagged as docker.io/${your_docker_username}/pytorch\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also pass the \u003ccode\u003eCMAKE_VARS=\"...\"\u003c/code\u003e environment variable to specify additional CMake variables to be passed to CMake during the build.\nSee \u003ca href=\"/pytorch/pytorch/blob/main/setup.py\"\u003esetup.py\u003c/a\u003e for the list of available variables.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"make -f docker.Makefile\"\u003e\u003cpre\u003emake -f docker.Makefile\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding the Documentation\u003c/h3\u003e\u003ca id=\"user-content-building-the-documentation\" class=\"anchor\" aria-label=\"Permalink: Building the Documentation\" href=\"#building-the-documentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo build documentation in various formats, you will need \u003ca href=\"http://www.sphinx-doc.org\" rel=\"nofollow\"\u003eSphinx\u003c/a\u003e and the\nreadthedocs theme.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd docs/\npip install -r requirements.txt\nmake html\nmake serve\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e docs/\npip install -r requirements.txt\nmake html\nmake serve\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun \u003ccode\u003emake\u003c/code\u003e to get a list of all available output formats.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you get a katex error run \u003ccode\u003enpm install katex\u003c/code\u003e. If it persists, try\n\u003ccode\u003enpm install -g katex\u003c/code\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eNote: if you installed \u003ccode\u003enodejs\u003c/code\u003e with a different package manager (e.g.,\n\u003ccode\u003econda\u003c/code\u003e) then \u003ccode\u003enpm\u003c/code\u003e will probably install a version of \u003ccode\u003ekatex\u003c/code\u003e that is not\ncompatible with your version of \u003ccode\u003enodejs\u003c/code\u003e and doc builds will fail.\nA combination of versions that is known to work is \u003ccode\u003enode@6.13.1\u003c/code\u003e and\n\u003ccode\u003ekatex@0.13.18\u003c/code\u003e. To install the latter with \u003ccode\u003enpm\u003c/code\u003e you can run\n\u003ccode\u003enpm install -g katex@0.13.18\u003c/code\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrevious Versions\u003c/h3\u003e\u003ca id=\"user-content-previous-versions\" class=\"anchor\" aria-label=\"Permalink: Previous Versions\" href=\"#previous-versions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInstallation instructions and binaries for previous PyTorch versions may be found\non \u003ca href=\"https://pytorch.org/previous-versions\" rel=\"nofollow\"\u003eour website\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThree-pointers to get you started:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/tutorials/\" rel=\"nofollow\"\u003eTutorials: get you started with understanding and using PyTorch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pytorch/examples\"\u003eExamples: easy to understand PyTorch code across all domains\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/docs/\" rel=\"nofollow\"\u003eThe API Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pytorch/pytorch/blob/main/GLOSSARY.md\"\u003eGlossary\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResources\u003c/h2\u003e\u003ca id=\"user-content-resources\" class=\"anchor\" aria-label=\"Permalink: Resources\" href=\"#resources\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch.org\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/tutorials/\" rel=\"nofollow\"\u003ePyTorch Tutorials\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pytorch/examples\"\u003ePyTorch Examples\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/hub/\" rel=\"nofollow\"\u003ePyTorch Models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.udacity.com/course/deep-learning-pytorch--ud188\" rel=\"nofollow\"\u003eIntro to Deep Learning with PyTorch from Udacity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.udacity.com/course/intro-to-machine-learning-nanodegree--nd229\" rel=\"nofollow\"\u003eIntro to Machine Learning with PyTorch from Udacity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.coursera.org/learn/deep-neural-networks-with-pytorch\" rel=\"nofollow\"\u003eDeep Neural Networks with PyTorch from Coursera\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://twitter.com/PyTorch\" rel=\"nofollow\"\u003ePyTorch Twitter\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/blog/\" rel=\"nofollow\"\u003ePyTorch Blog\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw\" rel=\"nofollow\"\u003ePyTorch YouTube\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommunication\u003c/h2\u003e\u003ca id=\"user-content-communication\" 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rel=\"nofollow\"\u003ehttps://discuss.pytorch.org\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eGitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.\u003c/li\u003e\n\u003cli\u003eSlack: The \u003ca href=\"https://pytorch.slack.com/\" rel=\"nofollow\"\u003ePyTorch Slack\u003c/a\u003e hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is \u003ca href=\"https://discuss.pytorch.org\" rel=\"nofollow\"\u003ePyTorch Forums\u003c/a\u003e. If you need a slack invite, please fill this form: \u003ca href=\"https://goo.gl/forms/PP1AGvNHpSaJP8to1\" rel=\"nofollow\"\u003ehttps://goo.gl/forms/PP1AGvNHpSaJP8to1\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eNewsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: \u003ca href=\"https://eepurl.com/cbG0rv\" rel=\"nofollow\"\u003ehttps://eepurl.com/cbG0rv\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eFacebook Page: Important announcements about PyTorch. \u003ca href=\"https://www.facebook.com/pytorch\" rel=\"nofollow\"\u003ehttps://www.facebook.com/pytorch\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eFor brand guidelines, please visit our website at \u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003epytorch.org\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReleases and Contributing\u003c/h2\u003e\u003ca id=\"user-content-releases-and-contributing\" class=\"anchor\" aria-label=\"Permalink: Releases and Contributing\" href=\"#releases-and-contributing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTypically, PyTorch has three minor releases a year. Please let us know if you encounter a bug by \u003ca href=\"https://github.com/pytorch/pytorch/issues\"\u003efiling an issue\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us.\nSending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo learn more about making a contribution to Pytorch, please see our \u003ca href=\"/pytorch/pytorch/blob/main/CONTRIBUTING.md\"\u003eContribution page\u003c/a\u003e. For more information about PyTorch releases, see \u003ca href=\"/pytorch/pytorch/blob/main/RELEASE.md\"\u003eRelease page\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eThe Team\u003c/h2\u003e\u003ca id=\"user-content-the-team\" class=\"anchor\" aria-label=\"Permalink: The Team\" href=\"#the-team\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch is a community-driven project with several skillful engineers and researchers contributing to it.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePyTorch is currently maintained by \u003ca href=\"http://soumith.ch\" rel=\"nofollow\"\u003eSoumith Chintala\u003c/a\u003e, \u003ca href=\"https://github.com/gchanan\"\u003eGregory Chanan\u003c/a\u003e, \u003ca href=\"https://github.com/dzhulgakov\"\u003eDmytro Dzhulgakov\u003c/a\u003e, \u003ca href=\"https://github.com/ezyang\"\u003eEdward Yang\u003c/a\u003e, and \u003ca href=\"https://github.com/malfet\"\u003eNikita Shulga\u003c/a\u003e with major contributions coming from hundreds of talented individuals in various forms and means.\nA non-exhaustive but growing list needs to mention: \u003ca href=\"https://github.com/killeent\"\u003eTrevor Killeen\u003c/a\u003e, \u003ca href=\"https://github.com/chsasank\"\u003eSasank Chilamkurthy\u003c/a\u003e, \u003ca href=\"https://github.com/szagoruyko\"\u003eSergey Zagoruyko\u003c/a\u003e, \u003ca href=\"https://github.com/adamlerer\"\u003eAdam Lerer\u003c/a\u003e, \u003ca href=\"https://github.com/fmassa\"\u003eFrancisco Massa\u003c/a\u003e, \u003ca href=\"https://github.com/alykhantejani\"\u003eAlykhan Tejani\u003c/a\u003e, \u003ca href=\"https://github.com/lantiga\"\u003eLuca Antiga\u003c/a\u003e, \u003ca href=\"https://github.com/albanD\"\u003eAlban Desmaison\u003c/a\u003e, \u003ca href=\"https://github.com/andreaskoepf\"\u003eAndreas Koepf\u003c/a\u003e, \u003ca href=\"https://github.com/jamesb93\"\u003eJames Bradbury\u003c/a\u003e, \u003ca href=\"https://github.com/ebetica\"\u003eZeming Lin\u003c/a\u003e, \u003ca href=\"https://github.com/yuandong-tian\"\u003eYuandong Tian\u003c/a\u003e, \u003ca href=\"https://github.com/glample\"\u003eGuillaume Lample\u003c/a\u003e, \u003ca href=\"https://github.com/Maratyszcza\"\u003eMarat Dukhan\u003c/a\u003e, \u003ca href=\"https://github.com/ngimel\"\u003eNatalia Gimelshein\u003c/a\u003e, \u003ca href=\"https://github.com/csarofeen\"\u003eChristian Sarofeen\u003c/a\u003e, \u003ca href=\"https://github.com/martinraison\"\u003eMartin Raison\u003c/a\u003e, \u003ca href=\"https://github.com/ezyang\"\u003eEdward Yang\u003c/a\u003e, \u003ca href=\"https://github.com/zdevito\"\u003eZachary Devito\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: This project is unrelated to \u003ca href=\"https://github.com/hughperkins/pytorch\"\u003ehughperkins/pytorch\u003c/a\u003e with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePyTorch has a BSD-style license, as found in the \u003ca href=\"/pytorch/pytorch/blob/main/LICENSE\"\u003eLICENSE\u003c/a\u003e file.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"More About PyTorch","anchor":"more-about-pytorch","htmlText":"More About PyTorch"},{"level":3,"text":"A GPU-Ready Tensor Library","anchor":"a-gpu-ready-tensor-library","htmlText":"A GPU-Ready Tensor Library"},{"level":3,"text":"Dynamic Neural Networks: Tape-Based Autograd","anchor":"dynamic-neural-networks-tape-based-autograd","htmlText":"Dynamic Neural Networks: Tape-Based Autograd"},{"level":3,"text":"Python First","anchor":"python-first","htmlText":"Python First"},{"level":3,"text":"Imperative Experiences","anchor":"imperative-experiences","htmlText":"Imperative Experiences"},{"level":3,"text":"Fast and Lean","anchor":"fast-and-lean","htmlText":"Fast and Lean"},{"level":3,"text":"Extensions Without Pain","anchor":"extensions-without-pain","htmlText":"Extensions Without Pain"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Binaries","anchor":"binaries","htmlText":"Binaries"},{"level":4,"text":"NVIDIA Jetson Platforms","anchor":"nvidia-jetson-platforms","htmlText":"NVIDIA Jetson Platforms"},{"level":3,"text":"From Source","anchor":"from-source","htmlText":"From Source"},{"level":4,"text":"Prerequisites","anchor":"prerequisites","htmlText":"Prerequisites"},{"level":5,"text":"NVIDIA CUDA Support","anchor":"nvidia-cuda-support","htmlText":"NVIDIA CUDA Support"},{"level":5,"text":"AMD ROCm Support","anchor":"amd-rocm-support","htmlText":"AMD ROCm Support"},{"level":5,"text":"Intel GPU Support","anchor":"intel-gpu-support","htmlText":"Intel GPU Support"},{"level":4,"text":"Get the PyTorch Source","anchor":"get-the-pytorch-source","htmlText":"Get the PyTorch Source"},{"level":4,"text":"Install Dependencies","anchor":"install-dependencies","htmlText":"Install Dependencies"},{"level":4,"text":"Install PyTorch","anchor":"install-pytorch","htmlText":"Install PyTorch"},{"level":5,"text":"Adjust Build Options (Optional)","anchor":"adjust-build-options-optional","htmlText":"Adjust Build Options (Optional)"},{"level":3,"text":"Docker Image","anchor":"docker-image","htmlText":"Docker Image"},{"level":4,"text":"Using pre-built images","anchor":"using-pre-built-images","htmlText":"Using pre-built images"},{"level":4,"text":"Building the image yourself","anchor":"building-the-image-yourself","htmlText":"Building the image yourself"},{"level":3,"text":"Building the Documentation","anchor":"building-the-documentation","htmlText":"Building the Documentation"},{"level":3,"text":"Previous Versions","anchor":"previous-versions","htmlText":"Previous Versions"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":2,"text":"Resources","anchor":"resources","htmlText":"Resources"},{"level":2,"text":"Communication","anchor":"communication","htmlText":"Communication"},{"level":2,"text":"Releases and Contributing","anchor":"releases-and-contributing","htmlText":"Releases and Contributing"},{"level":2,"text":"The Team","anchor":"the-team","htmlText":"The Team"},{"level":2,"text":"License","anchor":"license","htmlText":"License"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fpytorch%2Fpytorch"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"pytorch","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of 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href="https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png"><img src="https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png" alt="PyTorch Logo" style="max-width: 100%;"></a></p> <hr> <p dir="auto">PyTorch is a Python package that provides two high-level features:</p> <ul dir="auto"> <li>Tensor computation (like NumPy) with strong GPU acceleration</li> <li>Deep neural networks built on a tape-based autograd system</li> </ul> <p dir="auto">You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.</p> <p dir="auto">Our trunk health (Continuous Integration signals) can be found at <a href="https://hud.pytorch.org/ci/pytorch/pytorch/main" rel="nofollow">hud.pytorch.org</a>.</p> <ul dir="auto"> <li><a href="#more-about-pytorch">More About PyTorch</a> <ul dir="auto"> <li><a href="#a-gpu-ready-tensor-library">A GPU-Ready Tensor Library</a></li> <li><a href="#dynamic-neural-networks-tape-based-autograd">Dynamic Neural Networks: Tape-Based Autograd</a></li> <li><a href="#python-first">Python First</a></li> <li><a href="#imperative-experiences">Imperative Experiences</a></li> <li><a href="#fast-and-lean">Fast and Lean</a></li> <li><a href="#extensions-without-pain">Extensions Without Pain</a></li> </ul> </li> <li><a href="#installation">Installation</a> <ul dir="auto"> <li><a href="#binaries">Binaries</a> <ul dir="auto"> <li><a href="#nvidia-jetson-platforms">NVIDIA Jetson Platforms</a></li> </ul> </li> <li><a href="#from-source">From Source</a> <ul dir="auto"> <li><a href="#prerequisites">Prerequisites</a> <ul dir="auto"> <li><a href="#nvidia-cuda-support">NVIDIA CUDA Support</a></li> <li><a href="#amd-rocm-support">AMD ROCm Support</a></li> <li><a href="#intel-gpu-support">Intel GPU Support</a></li> </ul> </li> <li><a href="#get-the-pytorch-source">Get the PyTorch Source</a></li> <li><a href="#install-dependencies">Install Dependencies</a></li> <li><a href="#install-pytorch">Install PyTorch</a> <ul dir="auto"> <li><a href="#adjust-build-options-optional">Adjust Build Options (Optional)</a></li> </ul> </li> </ul> </li> <li><a href="#docker-image">Docker Image</a> <ul dir="auto"> <li><a href="#using-pre-built-images">Using pre-built images</a></li> <li><a href="#building-the-image-yourself">Building the image yourself</a></li> </ul> </li> <li><a href="#building-the-documentation">Building the Documentation</a></li> <li><a href="#previous-versions">Previous Versions</a></li> </ul> </li> <li><a href="#getting-started">Getting Started</a></li> <li><a href="#resources">Resources</a></li> <li><a href="#communication">Communication</a></li> <li><a href="#releases-and-contributing">Releases and Contributing</a></li> <li><a href="#the-team">The Team</a></li> <li><a href="#license">License</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">More About PyTorch</h2><a id="user-content-more-about-pytorch" class="anchor" aria-label="Permalink: More About PyTorch" href="#more-about-pytorch"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://pytorch.org/tutorials/beginner/basics/intro.html" rel="nofollow">Learn the basics of PyTorch</a></p> <p dir="auto">At a granular level, PyTorch is a library that consists of the following components:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Component</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://pytorch.org/docs/stable/torch.html" rel="nofollow"><strong>torch</strong></a></td> <td>A Tensor library like NumPy, with strong GPU support</td> </tr> <tr> <td><a href="https://pytorch.org/docs/stable/autograd.html" rel="nofollow"><strong>torch.autograd</strong></a></td> <td>A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch</td> </tr> <tr> <td><a href="https://pytorch.org/docs/stable/jit.html" rel="nofollow"><strong>torch.jit</strong></a></td> <td>A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code</td> </tr> <tr> <td><a href="https://pytorch.org/docs/stable/nn.html" rel="nofollow"><strong>torch.nn</strong></a></td> <td>A neural networks library deeply integrated with autograd designed for maximum flexibility</td> </tr> <tr> <td><a href="https://pytorch.org/docs/stable/multiprocessing.html" rel="nofollow"><strong>torch.multiprocessing</strong></a></td> <td>Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training</td> </tr> <tr> <td><a href="https://pytorch.org/docs/stable/data.html" rel="nofollow"><strong>torch.utils</strong></a></td> <td>DataLoader and other utility functions for convenience</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Usually, PyTorch is used either as:</p> <ul dir="auto"> <li>A replacement for NumPy to use the power of GPUs.</li> <li>A deep learning research platform that provides maximum flexibility and speed.</li> </ul> <p dir="auto">Elaborating Further:</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">A GPU-Ready Tensor Library</h3><a id="user-content-a-gpu-ready-tensor-library" class="anchor" aria-label="Permalink: A GPU-Ready Tensor Library" href="#a-gpu-ready-tensor-library"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use NumPy, then you have used Tensors (a.k.a. ndarray).</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/pytorch/pytorch/blob/main/docs/source/_static/img/tensor_illustration.png"><img src="/pytorch/pytorch/raw/main/docs/source/_static/img/tensor_illustration.png" alt="Tensor illustration" style="max-width: 100%;"></a></p> <p dir="auto">PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount.</p> <p dir="auto">We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Dynamic Neural Networks: Tape-Based Autograd</h3><a id="user-content-dynamic-neural-networks-tape-based-autograd" class="anchor" aria-label="Permalink: Dynamic Neural Networks: Tape-Based Autograd" href="#dynamic-neural-networks-tape-based-autograd"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch has a unique way of building neural networks: using and replaying a tape recorder.</p> <p dir="auto">Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.</p> <p dir="auto">With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as <a href="https://github.com/twitter/torch-autograd">torch-autograd</a>, <a href="https://github.com/HIPS/autograd">autograd</a>, <a href="https://chainer.org" rel="nofollow">Chainer</a>, etc.</p> <p dir="auto">While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif"><img src="https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif" alt="Dynamic graph" data-animated-image="" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Python First</h3><a id="user-content-python-first" class="anchor" aria-label="Permalink: Python First" href="#python-first"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use <a href="https://www.numpy.org/" rel="nofollow">NumPy</a> / <a href="https://www.scipy.org/" rel="nofollow">SciPy</a> / <a href="https://scikit-learn.org" rel="nofollow">scikit-learn</a> etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as <a href="https://cython.org/" rel="nofollow">Cython</a> and <a href="http://numba.pydata.org/" rel="nofollow">Numba</a>. Our goal is to not reinvent the wheel where appropriate.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Imperative Experiences</h3><a id="user-content-imperative-experiences" class="anchor" aria-label="Permalink: Imperative Experiences" href="#imperative-experiences"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch is designed to be intuitive, linear in thought, and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Fast and Lean</h3><a id="user-content-fast-and-lean" class="anchor" aria-label="Permalink: Fast and Lean" href="#fast-and-lean"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch has minimal framework overhead. We integrate acceleration libraries such as <a href="https://software.intel.com/mkl" rel="nofollow">Intel MKL</a> and NVIDIA (<a href="https://developer.nvidia.com/cudnn" rel="nofollow">cuDNN</a>, <a href="https://developer.nvidia.com/nccl" rel="nofollow">NCCL</a>) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends are mature and have been tested for years.</p> <p dir="auto">Hence, PyTorch is quite fast — whether you run small or large neural networks.</p> <p dir="auto">The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Extensions Without Pain</h3><a id="user-content-extensions-without-pain" class="anchor" aria-label="Permalink: Extensions Without Pain" href="#extensions-without-pain"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward and with minimal abstractions.</p> <p dir="auto">You can write new neural network layers in Python using the torch API <a href="https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html" rel="nofollow">or your favorite NumPy-based libraries such as SciPy</a>.</p> <p dir="auto">If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate. No wrapper code needs to be written. You can see <a href="https://pytorch.org/tutorials/advanced/cpp_extension.html" rel="nofollow">a tutorial here</a> and <a href="https://github.com/pytorch/extension-cpp">an example here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Binaries</h3><a id="user-content-binaries" class="anchor" aria-label="Permalink: Binaries" href="#binaries"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Commands to install binaries via Conda or pip wheels are on our website: <a href="https://pytorch.org/get-started/locally/" rel="nofollow">https://pytorch.org/get-started/locally/</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">NVIDIA Jetson Platforms</h4><a id="user-content-nvidia-jetson-platforms" class="anchor" aria-label="Permalink: NVIDIA Jetson Platforms" href="#nvidia-jetson-platforms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Python wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided <a href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048" rel="nofollow">here</a> and the L4T container is published <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch" rel="nofollow">here</a></p> <p dir="auto">They require JetPack 4.2 and above, and <a href="https://github.com/dusty-nv">@dusty-nv</a> and <a href="https://github.com/ptrblck">@ptrblck</a> are maintaining them.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">From Source</h3><a id="user-content-from-source" class="anchor" aria-label="Permalink: From Source" href="#from-source"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Prerequisites</h4><a id="user-content-prerequisites" class="anchor" aria-label="Permalink: Prerequisites" href="#prerequisites"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you are installing from source, you will need:</p> <ul dir="auto"> <li>Python 3.9 or later</li> <li>A compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required, on Linux)</li> <li>Visual Studio or Visual Studio Build Tool (Windows only)</li> </ul> <p dir="auto">* PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise, Professional, or Community Editions. You can also install the build tools from <a href="https://visualstudio.microsoft.com/visual-cpp-build-tools/" rel="nofollow">https://visualstudio.microsoft.com/visual-cpp-build-tools/</a>. The build tools <em>do not</em> come with Visual Studio Code by default.</p> <p dir="auto">* We highly recommend installing an <a href="https://www.anaconda.com/download" rel="nofollow">Anaconda</a> environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.</p> <p dir="auto">An example of environment setup is shown below:</p> <ul dir="auto"> <li>Linux:</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ source <CONDA_INSTALL_DIR>/bin/activate $ conda create -y -n <CONDA_NAME> $ conda activate <CONDA_NAME>"><pre>$ <span class="pl-c1">source</span> <span class="pl-k"><</span>CONDA_INSTALL_DIR<span class="pl-k">></span>/bin/activate $ conda create -y -n <span class="pl-k"><</span>CONDA_NAME<span class="pl-k">></span> $ conda activate <span class="pl-k"><</span>CONDA_NAME<span class="pl-k">></span></pre></div> <ul dir="auto"> <li>Windows:</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ source <CONDA_INSTALL_DIR>\Scripts\activate.bat $ conda create -y -n <CONDA_NAME> $ conda activate <CONDA_NAME> $ call "C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat" x64"><pre>$ <span class="pl-c1">source</span> <span class="pl-k"><</span>CONDA_INSTALL_DIR<span class="pl-k">></span><span class="pl-cce">\S</span>cripts<span class="pl-cce">\a</span>ctivate.bat $ conda create -y -n <span class="pl-k"><</span>CONDA_NAME<span class="pl-k">></span> $ conda activate <span class="pl-k"><</span>CONDA_NAME<span class="pl-k">></span> $ call <span class="pl-s"><span class="pl-pds">"</span>C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat<span class="pl-pds">"</span></span> x64</pre></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">NVIDIA CUDA Support</h5><a id="user-content-nvidia-cuda-support" class="anchor" aria-label="Permalink: NVIDIA CUDA Support" href="#nvidia-cuda-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you want to compile with CUDA support, <a href="https://pytorch.org/get-started/locally/" rel="nofollow">select a supported version of CUDA from our support matrix</a>, then install the following:</p> <ul dir="auto"> <li><a href="https://developer.nvidia.com/cuda-downloads" rel="nofollow">NVIDIA CUDA</a></li> <li><a href="https://developer.nvidia.com/cudnn" rel="nofollow">NVIDIA cuDNN</a> v8.5 or above</li> <li><a href="https://gist.github.com/ax3l/9489132">Compiler</a> compatible with CUDA</li> </ul> <p dir="auto">Note: You could refer to the <a href="https://docs.nvidia.com/deeplearning/cudnn/reference/support-matrix.html" rel="nofollow">cuDNN Support Matrix</a> for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware</p> <p dir="auto">If you want to disable CUDA support, export the environment variable <code>USE_CUDA=0</code>. Other potentially useful environment variables may be found in <code>setup.py</code>.</p> <p dir="auto">If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are <a href="https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/" rel="nofollow">available here</a></p> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">AMD ROCm Support</h5><a id="user-content-amd-rocm-support" class="anchor" aria-label="Permalink: AMD ROCm Support" href="#amd-rocm-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you want to compile with ROCm support, install</p> <ul dir="auto"> <li><a href="https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html" rel="nofollow">AMD ROCm</a> 4.0 and above installation</li> <li>ROCm is currently supported only for Linux systems.</li> </ul> <p dir="auto">By default the build system expects ROCm to be installed in <code>/opt/rocm</code>. If ROCm is installed in a different directory, the <code>ROCM_PATH</code> environment variable must be set to the ROCm installation directory. The build system automatically detects the AMD GPU architecture. Optionally, the AMD GPU architecture can be explicitly set with the <code>PYTORCH_ROCM_ARCH</code> environment variable <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html#supported-gpus" rel="nofollow">AMD GPU architecture</a></p> <p dir="auto">If you want to disable ROCm support, export the environment variable <code>USE_ROCM=0</code>. Other potentially useful environment variables may be found in <code>setup.py</code>.</p> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Intel GPU Support</h5><a id="user-content-intel-gpu-support" class="anchor" aria-label="Permalink: Intel GPU Support" href="#intel-gpu-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you want to compile with Intel GPU support, follow these</p> <ul dir="auto"> <li><a href="https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html" rel="nofollow">PyTorch Prerequisites for Intel GPUs</a> instructions.</li> <li>Intel GPU is supported for Linux and Windows.</li> </ul> <p dir="auto">If you want to disable Intel GPU support, export the environment variable <code>USE_XPU=0</code>. Other potentially useful environment variables may be found in <code>setup.py</code>.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Get the PyTorch Source</h4><a id="user-content-get-the-pytorch-source" class="anchor" aria-label="Permalink: Get the PyTorch Source" href="#get-the-pytorch-source"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone --recursive https://github.com/pytorch/pytorch cd pytorch # if you are updating an existing checkout git submodule sync git submodule update --init --recursive"><pre>git clone --recursive https://github.com/pytorch/pytorch <span class="pl-c1">cd</span> pytorch <span class="pl-c"><span class="pl-c">#</span> if you are updating an existing checkout</span> git submodule sync git submodule update --init --recursive</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Install Dependencies</h4><a id="user-content-install-dependencies" class="anchor" aria-label="Permalink: Install Dependencies" href="#install-dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Common</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda install cmake ninja # Run this command on native Windows conda install rust # Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below pip install -r requirements.txt"><pre>conda install cmake ninja <span class="pl-c"><span class="pl-c">#</span> Run this command on native Windows</span> conda install rust <span class="pl-c"><span class="pl-c">#</span> Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below</span> pip install -r requirements.txt</pre></div> <p dir="auto"><strong>On Linux</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install mkl-static mkl-include # CUDA only: Add LAPACK support for the GPU if needed conda install -c pytorch magma-cuda121 # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo # (optional) If using torch.compile with inductor/triton, install the matching version of triton # Run from the pytorch directory after cloning # For Intel GPU support, please explicitly `export USE_XPU=1` before running command. make triton"><pre>pip install mkl-static mkl-include <span class="pl-c"><span class="pl-c">#</span> CUDA only: Add LAPACK support for the GPU if needed</span> conda install -c pytorch magma-cuda121 <span class="pl-c"><span class="pl-c">#</span> or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo</span> <span class="pl-c"><span class="pl-c">#</span> (optional) If using torch.compile with inductor/triton, install the matching version of triton</span> <span class="pl-c"><span class="pl-c">#</span> Run from the pytorch directory after cloning</span> <span class="pl-c"><span class="pl-c">#</span> For Intel GPU support, please explicitly `export USE_XPU=1` before running command.</span> make triton</pre></div> <p dir="auto"><strong>On MacOS</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Add this package on intel x86 processor machines only pip install mkl-static mkl-include # Add these packages if torch.distributed is needed conda install pkg-config libuv"><pre><span class="pl-c"><span class="pl-c">#</span> Add this package on intel x86 processor machines only</span> pip install mkl-static mkl-include <span class="pl-c"><span class="pl-c">#</span> Add these packages if torch.distributed is needed</span> conda install pkg-config libuv</pre></div> <p dir="auto"><strong>On Windows</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install mkl-static mkl-include # Add these packages if torch.distributed is needed. # Distributed package support on Windows is a prototype feature and is subject to changes. conda install -c conda-forge libuv=1.39"><pre>pip install mkl-static mkl-include <span class="pl-c"><span class="pl-c">#</span> Add these packages if torch.distributed is needed.</span> <span class="pl-c"><span class="pl-c">#</span> Distributed package support on Windows is a prototype feature and is subject to changes.</span> conda install -c conda-forge libuv=1.39</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Install PyTorch</h4><a id="user-content-install-pytorch" class="anchor" aria-label="Permalink: Install PyTorch" href="#install-pytorch"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>On Linux</strong></p> <p dir="auto">If you would like to compile PyTorch with <a href="https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html" rel="nofollow">new C++ ABI</a> enabled, then first run this command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export _GLIBCXX_USE_CXX11_ABI=1"><pre><span class="pl-k">export</span> _GLIBCXX_USE_CXX11_ABI=1</pre></div> <p dir="auto">Please <strong>note</strong> that starting from PyTorch 2.5, the PyTorch build with XPU supports both new and old C++ ABIs. Previously, XPU only supported the new C++ ABI. If you want to compile with Intel GPU support, please follow <a href="#intel-gpu-support">Intel GPU Support</a>.</p> <p dir="auto">If you're compiling for AMD ROCm then first run this command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Only run this if you're compiling for ROCm python tools/amd_build/build_amd.py"><pre><span class="pl-c"><span class="pl-c">#</span> Only run this if you're compiling for ROCm</span> python tools/amd_build/build_amd.py</pre></div> <p dir="auto">Install PyTorch</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}" python setup.py develop"><pre><span class="pl-k">export</span> CMAKE_PREFIX_PATH=<span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${CONDA_PREFIX<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>$(dirname $(which conda))/../<span class="pl-pds">'</span></span>}</span>:<span class="pl-smi">${CMAKE_PREFIX_PATH}</span><span class="pl-pds">"</span></span> python setup.py develop</pre></div> <p dir="auto"><strong>On macOS</strong></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 setup.py develop"><pre>python3 setup.py develop</pre></div> <p dir="auto"><strong>On Windows</strong></p> <p dir="auto">If you want to build legacy python code, please refer to <a href="https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#building-on-legacy-code-and-cuda">Building on legacy code and CUDA</a></p> <p dir="auto"><strong>CPU-only builds</strong></p> <p dir="auto">In this mode PyTorch computations will run on your CPU, not your GPU</p> <div class="highlight highlight-source-batchfile notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python setup.py develop"><pre>python setup.py develop</pre></div> <p dir="auto">Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking <code>CMAKE_INCLUDE_PATH</code> and <code>LIB</code>. The instruction <a href="https://github.com/pytorch/pytorch/blob/main/docs/source/notes/windows.rst#building-from-source">here</a> is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.</p> <p dir="auto"><strong>CUDA based build</strong></p> <p dir="auto">In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching</p> <p dir="auto"><a href="https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm" rel="nofollow">NVTX</a> is needed to build Pytorch with CUDA. NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox. Make sure that CUDA with Nsight Compute is installed after Visual Studio.</p> <p dir="auto">Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If <code>ninja.exe</code> is detected in <code>PATH</code>, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019. <br> If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.</p> <p dir="auto">Additional libraries such as <a href="https://developer.nvidia.com/magma" rel="nofollow">Magma</a>, <a href="https://github.com/oneapi-src/oneDNN">oneDNN, a.k.a. MKLDNN or DNNL</a>, and <a href="https://github.com/mozilla/sccache">Sccache</a> are often needed. Please refer to the <a href="https://github.com/pytorch/pytorch/tree/main/.ci/pytorch/win-test-helpers/installation-helpers">installation-helper</a> to install them.</p> <p dir="auto">You can refer to the <a href="https://github.com/pytorch/pytorch/blob/main/.ci/pytorch/win-test-helpers/build_pytorch.bat">build_pytorch.bat</a> script for some other environment variables configurations</p> <div class="highlight highlight-source-batchfile notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cmd :: Set the environment variables after you have downloaded and unzipped the mkl package, :: else CMake would throw an error as `Could NOT find OpenMP`. set CMAKE_INCLUDE_PATH={Your directory}\mkl\include set LIB={Your directory}\mkl\lib;%LIB% :: Read the content in the previous section carefully before you proceed. :: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block. :: "Visual Studio 2019 Developer Command Prompt" will be run automatically. :: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator. set CMAKE_GENERATOR_TOOLSET_VERSION=14.27 set DISTUTILS_USE_SDK=1 for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION% :: [Optional] If you want to override the CUDA host compiler set CUDAHOSTCXX=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe python setup.py develop "><pre><span class="pl-k">cmd</span> <span class="pl-c"><span class="pl-c">::</span> Set the environment variables after you have downloaded and unzipped the mkl package,</span> <span class="pl-c"><span class="pl-c">::</span> else CMake would throw an error as `Could NOT find OpenMP`.</span> <span class="pl-k">set</span> <span class="pl-smi">CMAKE_INCLUDE_PATH</span><span class="pl-k">=</span>{Your directory}\mkl\include <span class="pl-k">set</span> <span class="pl-smi">LIB</span><span class="pl-k">=</span>{Your directory}\mkl\lib;<span class="pl-smi">%LIB%</span> <span class="pl-c"><span class="pl-c">::</span> Read the content in the previous section carefully before you proceed.</span> <span class="pl-c"><span class="pl-c">::</span> [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.</span> <span class="pl-c"><span class="pl-c">::</span> "Visual Studio 2019 Developer Command Prompt" will be run automatically.</span> <span class="pl-c"><span class="pl-c">::</span> Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.</span> <span class="pl-k">set</span> <span class="pl-smi">CMAKE_GENERATOR_TOOLSET_VERSION</span><span class="pl-k">=</span>14.27 <span class="pl-k">set</span> <span class="pl-smi">DISTUTILS_USE_SDK</span><span class="pl-k">=</span><span class="pl-c1">1</span> <span class="pl-k">for</span> /f <span class="pl-s"><span class="pl-pds">"</span>usebackq tokens=*<span class="pl-pds">"</span></span> <span class="pl-smi">%i in (`"%</span>ProgramFiles(x86)<span class="pl-smi">%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%</span>i\VC\Auxiliary\Build\vcvarsall.bat<span class="pl-s"><span class="pl-pds">"</span> x64 -vcvars_ver=<span class="pl-smi">%CMAKE_GENERATOR_TOOLSET_VERSION%</span><span class="pl-ii"></span></span> <span class="pl-c"><span class="pl-c">::</span> [Optional] If you want to override the CUDA host compiler</span> <span class="pl-k">set</span> <span class="pl-smi">CUDAHOSTCXX</span><span class="pl-k">=</span>C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe python setup.py develop </pre></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Adjust Build Options (Optional)</h5><a id="user-content-adjust-build-options-optional" class="anchor" aria-label="Permalink: Adjust Build Options (Optional)" href="#adjust-build-options-optional"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can adjust the configuration of cmake variables optionally (without building first), by doing the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done with such a step.</p> <p dir="auto">On Linux</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}" python setup.py build --cmake-only ccmake build # or cmake-gui build"><pre><span class="pl-k">export</span> CMAKE_PREFIX_PATH=<span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${CONDA_PREFIX<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>$(dirname $(which conda))/../<span class="pl-pds">'</span></span>}</span>:<span class="pl-smi">${CMAKE_PREFIX_PATH}</span><span class="pl-pds">"</span></span> python setup.py build --cmake-only ccmake build <span class="pl-c"><span class="pl-c">#</span> or cmake-gui build</span></pre></div> <p dir="auto">On macOS</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}" MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only ccmake build # or cmake-gui build"><pre><span class="pl-k">export</span> CMAKE_PREFIX_PATH=<span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${CONDA_PREFIX<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>$(dirname $(which conda))/../<span class="pl-pds">'</span></span>}</span>:<span class="pl-smi">${CMAKE_PREFIX_PATH}</span><span class="pl-pds">"</span></span> MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only ccmake build <span class="pl-c"><span class="pl-c">#</span> or cmake-gui build</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Docker Image</h3><a id="user-content-docker-image" class="anchor" aria-label="Permalink: Docker Image" href="#docker-image"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Using pre-built images</h4><a id="user-content-using-pre-built-images" class="anchor" aria-label="Permalink: Using pre-built images" href="#using-pre-built-images"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest"><pre>docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest</pre></div> <p dir="auto">Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with <code>--ipc=host</code> or <code>--shm-size</code> command line options to <code>nvidia-docker run</code>.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Building the image yourself</h4><a id="user-content-building-the-image-yourself" class="anchor" aria-label="Permalink: Building the image yourself" href="#building-the-image-yourself"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>NOTE:</strong> Must be built with a docker version > 18.06</p> <p dir="auto">The <code>Dockerfile</code> is supplied to build images with CUDA 11.1 support and cuDNN v8. You can pass <code>PYTHON_VERSION=x.y</code> make variable to specify which Python version is to be used by Miniconda, or leave it unset to use the default.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="make -f docker.Makefile # images are tagged as docker.io/${your_docker_username}/pytorch"><pre>make -f docker.Makefile <span class="pl-c"><span class="pl-c">#</span> images are tagged as docker.io/${your_docker_username}/pytorch</span></pre></div> <p dir="auto">You can also pass the <code>CMAKE_VARS="..."</code> environment variable to specify additional CMake variables to be passed to CMake during the build. See <a href="/pytorch/pytorch/blob/main/setup.py">setup.py</a> for the list of available variables.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="make -f docker.Makefile"><pre>make -f docker.Makefile</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Building the Documentation</h3><a id="user-content-building-the-documentation" class="anchor" aria-label="Permalink: Building the Documentation" href="#building-the-documentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To build documentation in various formats, you will need <a href="http://www.sphinx-doc.org" rel="nofollow">Sphinx</a> and the readthedocs theme.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd docs/ pip install -r requirements.txt make html make serve"><pre><span class="pl-c1">cd</span> docs/ pip install -r requirements.txt make html make serve</pre></div> <p dir="auto">Run <code>make</code> to get a list of all available output formats.</p> <p dir="auto">If you get a katex error run <code>npm install katex</code>. If it persists, try <code>npm install -g katex</code></p> <blockquote> <p dir="auto">Note: if you installed <code>nodejs</code> with a different package manager (e.g., <code>conda</code>) then <code>npm</code> will probably install a version of <code>katex</code> that is not compatible with your version of <code>nodejs</code> and doc builds will fail. A combination of versions that is known to work is <code>node@6.13.1</code> and <code>katex@0.13.18</code>. To install the latter with <code>npm</code> you can run <code>npm install -g katex@0.13.18</code></p> </blockquote> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Previous Versions</h3><a id="user-content-previous-versions" class="anchor" aria-label="Permalink: Previous Versions" href="#previous-versions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Installation instructions and binaries for previous PyTorch versions may be found on <a href="https://pytorch.org/previous-versions" rel="nofollow">our website</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Three-pointers to get you started:</p> <ul dir="auto"> <li><a href="https://pytorch.org/tutorials/" rel="nofollow">Tutorials: get you started with understanding and using PyTorch</a></li> <li><a href="https://github.com/pytorch/examples">Examples: easy to understand PyTorch code across all domains</a></li> <li><a href="https://pytorch.org/docs/" rel="nofollow">The API Reference</a></li> <li><a href="https://github.com/pytorch/pytorch/blob/main/GLOSSARY.md">Glossary</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Resources</h2><a id="user-content-resources" class="anchor" aria-label="Permalink: Resources" href="#resources"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://pytorch.org/" rel="nofollow">PyTorch.org</a></li> <li><a href="https://pytorch.org/tutorials/" rel="nofollow">PyTorch Tutorials</a></li> <li><a href="https://github.com/pytorch/examples">PyTorch Examples</a></li> <li><a href="https://pytorch.org/hub/" rel="nofollow">PyTorch Models</a></li> <li><a href="https://www.udacity.com/course/deep-learning-pytorch--ud188" rel="nofollow">Intro to Deep Learning with PyTorch from Udacity</a></li> <li><a href="https://www.udacity.com/course/intro-to-machine-learning-nanodegree--nd229" rel="nofollow">Intro to Machine Learning with PyTorch from Udacity</a></li> <li><a href="https://www.coursera.org/learn/deep-neural-networks-with-pytorch" rel="nofollow">Deep Neural Networks with PyTorch from Coursera</a></li> <li><a href="https://twitter.com/PyTorch" rel="nofollow">PyTorch Twitter</a></li> <li><a href="https://pytorch.org/blog/" rel="nofollow">PyTorch Blog</a></li> <li><a href="https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw" rel="nofollow">PyTorch YouTube</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Communication</h2><a id="user-content-communication" class="anchor" aria-label="Permalink: Communication" href="#communication"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Forums: Discuss implementations, research, etc. <a href="https://discuss.pytorch.org" rel="nofollow">https://discuss.pytorch.org</a></li> <li>GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.</li> <li>Slack: The <a href="https://pytorch.slack.com/" rel="nofollow">PyTorch Slack</a> hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is <a href="https://discuss.pytorch.org" rel="nofollow">PyTorch Forums</a>. If you need a slack invite, please fill this form: <a href="https://goo.gl/forms/PP1AGvNHpSaJP8to1" rel="nofollow">https://goo.gl/forms/PP1AGvNHpSaJP8to1</a></li> <li>Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: <a href="https://eepurl.com/cbG0rv" rel="nofollow">https://eepurl.com/cbG0rv</a></li> <li>Facebook Page: Important announcements about PyTorch. <a href="https://www.facebook.com/pytorch" rel="nofollow">https://www.facebook.com/pytorch</a></li> <li>For brand guidelines, please visit our website at <a href="https://pytorch.org/" rel="nofollow">pytorch.org</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Releases and Contributing</h2><a id="user-content-releases-and-contributing" class="anchor" aria-label="Permalink: Releases and Contributing" href="#releases-and-contributing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Typically, PyTorch has three minor releases a year. Please let us know if you encounter a bug by <a href="https://github.com/pytorch/pytorch/issues">filing an issue</a>.</p> <p dir="auto">We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.</p> <p dir="auto">If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.</p> <p dir="auto">To learn more about making a contribution to Pytorch, please see our <a href="/pytorch/pytorch/blob/main/CONTRIBUTING.md">Contribution page</a>. For more information about PyTorch releases, see <a href="/pytorch/pytorch/blob/main/RELEASE.md">Release page</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">The Team</h2><a id="user-content-the-team" class="anchor" aria-label="Permalink: The Team" href="#the-team"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch is a community-driven project with several skillful engineers and researchers contributing to it.</p> <p dir="auto">PyTorch is currently maintained by <a href="http://soumith.ch" rel="nofollow">Soumith Chintala</a>, <a href="https://github.com/gchanan">Gregory Chanan</a>, <a href="https://github.com/dzhulgakov">Dmytro Dzhulgakov</a>, <a href="https://github.com/ezyang">Edward Yang</a>, and <a href="https://github.com/malfet">Nikita Shulga</a> with major contributions coming from hundreds of talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: <a href="https://github.com/killeent">Trevor Killeen</a>, <a href="https://github.com/chsasank">Sasank Chilamkurthy</a>, <a href="https://github.com/szagoruyko">Sergey Zagoruyko</a>, <a href="https://github.com/adamlerer">Adam Lerer</a>, <a href="https://github.com/fmassa">Francisco Massa</a>, <a href="https://github.com/alykhantejani">Alykhan Tejani</a>, <a href="https://github.com/lantiga">Luca Antiga</a>, <a href="https://github.com/albanD">Alban Desmaison</a>, <a href="https://github.com/andreaskoepf">Andreas Koepf</a>, <a href="https://github.com/jamesb93">James Bradbury</a>, <a href="https://github.com/ebetica">Zeming Lin</a>, <a href="https://github.com/yuandong-tian">Yuandong Tian</a>, <a href="https://github.com/glample">Guillaume Lample</a>, <a href="https://github.com/Maratyszcza">Marat Dukhan</a>, <a href="https://github.com/ngimel">Natalia Gimelshein</a>, <a href="https://github.com/csarofeen">Christian Sarofeen</a>, <a href="https://github.com/martinraison">Martin Raison</a>, <a href="https://github.com/ezyang">Edward Yang</a>, <a href="https://github.com/zdevito">Zachary Devito</a>.</p> <p dir="auto">Note: This project is unrelated to <a href="https://github.com/hughperkins/pytorch">hughperkins/pytorch</a> with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">License</h2><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">PyTorch has a 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