PyTorch

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style="display:none;visibility:hidden"></iframe></noscript>  <div class="hello-bar"> <div class="container"> Join us in Silicon Valley September 18-19 at the 2024 PyTorch Conference. <a target="_blank" href="https://events.linuxfoundation.org/pytorch-conference/?utm_source=www&utm_medium=homepage&utm_campaign=Pytorch-Conference-2024&utm_content=hello">Learn more</a>. </div> </div> <div class="container-fluid header-holder homepage-header"> <div class="container"> <div class="header-container"> <a class="header-logo" href="https://pytorch.org" aria-label="PyTorch"></a> <div class="main-menu"> <ul> <li class="main-menu-item"> <div id="dropdownMenuButton" data-toggle="resources-dropdown" class="resources-dropdown"> <a class="with-down-arrow"> Learn </a> <div class="resources-dropdown-menu"> <a class="nav-dropdown-item" href="/get-started"> <span class=dropdown-title>Get Started</span> <p>Run PyTorch locally or get started quickly with one of the supported cloud platforms</p> </a> <a 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class="nav-dropdown-item" href="/pytorch-domains"> <span class="dropdown-title">PyTorch Domains</span> <p> Read the PyTorch Domains documentation to learn more about domain-specific libraries.</p> </a> </div> </div> </li> <li class="main-menu-item"> <div id="dropdownMenuButton" data-toggle="resources-dropdown" class="resources-dropdown"> <a class="with-down-arrow"> Blog & News </a> <div class="resources-dropdown-menu"> <a class="nav-dropdown-item" href="/blog"> <span class="dropdown-title">PyTorch Blog</span> <p>Catch up on the latest technical news and happenings</p> </a> <a class="nav-dropdown-item" href="/community-blog"> <span class="dropdown-title">Community Blog</span> <p>Stories from the PyTorch ecosystem</p> </a> <a class="nav-dropdown-item" href="/videos"> <span class="dropdown-title">Videos</span> <p>Learn about the latest PyTorch tutorials, new, and more </p> </a> <a class="nav-dropdown-item" href="/community-stories"> <span class="dropdown-title">Community Stories</span> 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width="275px" alt="PyTorch logo"/><br />Get Started</h1> <p class="lead">Choose Your Path: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms</p> <a href="/get-started" class="btn btn-lg with-right-arrow" data-cta="join"> Get started </a> </div> </div> <div class="main-content-wrapper"> <div class="container homepage-box-module"> <div class="row"> <div class="col-md"> <h3>Blog</h3> <p> Stay up-to-date on the latest news and technical topics from the PyTorch Foundation. </p> <p> <a href="/blog/">Read more</a> </p> </div> <div class="col-md"> <h3>PyTorch 2.5</h3> <p> Featuring a new CuDNN backend for SDPA, improvements to TorchDynamo, regional compilation of torch.compile, and more. <p> <a href="/blog/pytorch2-5/">Learn more</a> </p> </div> <div class="col-md"> <h3>Membership Available</h3> <p> Become an integral part of the PyTorch Foundation, to build and shape the future of AI. </p> <p> <a href="/join">Join</a> </p> </div> </div> </div> <div class="container homepage-feature-module key-features-module"> <div class="row"> <div class="col-md-12"> <div class="row"> <div class="col-md-12"> <h2>Key Features &<br />Capabilities</h2> <a href="/features" class="btn btn-lg with-right-arrow module-button"> See all Features </a> </div> </div> <div class="row key-features-boxes"> <div class="col-md-3 key-feature-box"> <h5>Production Ready</h5> <p>Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.</p> </div> <div class="col-md-3 key-feature-box"> <h5>Distributed Training</h5> <p>Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.</p> </div> <div class="col-md-3 key-feature-box"> <h5>Robust Ecosystem</h5> <p>A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.</p> </div> <div class="col-md-3 key-feature-box"> <h5>Cloud Support</h5> <p>PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.</p> </div> </div> </div> </div> </div> <div class="container-fluid quick-start-module quick-starts"> <div class="container"> <div class="row"> <div class="col-md-8 start-locally-col"> <h3>Install PyTorch</h3> <p>Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have <b>met the prerequisites below (e.g., numpy)</b>, depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. You can also <a href="/get-started/previous-versions">install previous versions of PyTorch</a>. Note that LibTorch is only available for C++. </p> <p><b>NOTE:</b> Latest PyTorch requires Python 3.9 or later.</p> <div class="row"> <div class="col-md-3 headings"> <div class="col-md-12 title-block"> <div class="option-text">PyTorch Build</div> </div> <div class="col-md-12 title-block"> <div class="option-text os-text">Your OS</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Package</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Language</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Compute Platform</div> </div> <div class="col-md-12 title-block command-block"> <div class="option-text command-text">Run this Command:</div> </div> </div> <div class="col-md-9"> <div class="row ptbuild"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">PyTorch Build</div> </div> <div class="col-md-6 option block version selected" id="stable"> <div class="option-text">Stable (1.13.0)</div> </div> <div class="col-md-6 option block version" id="preview"> <div class="option-text">Preview (Nightly)</div> </div> </div> <div class="row os"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Your OS</div> </div> <div class="col-md-4 option block" id="linux"> <div class="option-text">Linux</div> </div> <div class="col-md-4 option block" id="macos"> <div class="option-text">Mac</div> </div> <div class="col-md-4 option block" id="windows"> <div class="option-text">Windows</div> </div> </div> <div class="row package"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Package</div> </div> <div class="col-md-3 option block" id="conda"> <div class="option-text">Conda</div> </div> <div class="col-md-3 option block selected" id="pip"> <div class="option-text">Pip</div> </div> <div class="col-md-3 option block" id="libtorch"> <div class="option-text">LibTorch</div> </div> <div class="col-md-3 option block" id="source"> <div class="option-text">Source</div> </div> </div> <div class="row language"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Language</div> </div> <div class="col-md-6 option block version selected" id="python"> <div class="option-text">Python</div> </div> <div class="col-md-6 option block" id="cplusplus"> <div class="option-text">C++ / Java</div> </div> </div> <div class="row cuda"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Compute Platform</div> </div> <div class="col-md-2 option block version" id="cuda.x"> <div class="option-text">CUDA 11.8</div> </div> <div class="col-md-2 option block version" id="cuda.y"> <div class="option-text">CUDA 12.1</div> </div> <div class="col-md-2 option block version" id="cuda.z"> <div class="option-text">CUDA 12.4</div> </div> <div class="col-md-3 option block version" id="rocm5.x"> <div class="option-text">ROCm 5.2</div> </div> <div class="col-md-3 option block version" id="accnone"> <div class="option-text">CPU</div> </div> </div> <div class="row"> <div class="col-md-12 title-block command-mobile-heading"> <div class="option-text">Run this Command:</div> </div> <div class="command-container"> <div class="col-md-12" id="command">conda install pytorch torchvision -c pytorch</div> </div> </div> </div> </div> <br> <a href="/get-started/previous-versions" class="btn btn-lg with-right-arrow btn-white prev-versions-btn"> Previous versions of PyTorch </a> </div> <div class="col-md-3 offset-md-1 cloud-options-col"> <h3>Quick Start With<br />Cloud Partners</h3> <p>Get up and running with PyTorch quickly through popular cloud platforms and machine learning services.</p> <div class="quick-start-guides"> <div class="cloud-option-row"> <div class="cloud-option" data-toggle="cloud-dropdown"> <div class="cloud-option-body aws" id="aws"> Amazon Web Services </div> <ul> <li><a href="https://aws.amazon.com/pytorch/">PyTorch on AWS</a></li> <li><a href="https://aws.amazon.com/sagemaker">Amazon SageMaker</a></li> <li><a href="https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-ec2-tutorials-training.html#deep-learning-containers-ec2-tutorials-training-pytorch">AWS Deep Learning Containers</a></li> <li><a href="https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-pytorch.html">AWS Deep Learning AMIs</a></li> </ul> </div> </div> <div class="cloud-option-row"> <div class="cloud-option" data-toggle="cloud-dropdown"> <div class="cloud-option-body google-cloud" id="google-cloud"> Google Cloud Platform </div> <ul> <li><a href="https://cloud.google.com/deep-learning-vm/docs/pytorch_start_instance">Cloud Deep Learning VM Image</a></li> </ul> <ul> <li><a href="https://cloud.google.com/ai-platform/deep-learning-containers/">Deep Learning Containers</a></li> </ul> </div> </div> <div class="cloud-option-row"> <div class="cloud-option" data-toggle="cloud-dropdown"> <div class="cloud-option-body microsoft-azure" id="microsoft-azure"> <p>Microsoft Azure</p> </div> <ul> <li><a href="https://azure.microsoft.com/en-us/develop/pytorch/">PyTorch on Azure</a></li> <li><a href="https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch">Azure Machine Learning</a></li> <li><a href="https://docs.microsoft.com/en-us/azure/azure-functions/machine-learning-pytorch?tabs=bash">Azure Functions</a></li> </ul> </div> </div> </div> </div> </div> </div> </div> <script src="/assets/quick-start-module.js"></script> <div class="homepage-feature-module community-module container-fluid"> <div class="container"> <div class="row"> <div class="col-md-12"> <h2>Ecosystem </h2> <h5>Feature Projects</h5> <a href="/ecosystem" class="btn btn-lg with-right-arrow module-button"> See all Projects </a> </div> <div class="col-md-8"> <p class="h2-subheadline">Explore a rich ecosystem of libraries, tools, and more to support development.</p> </div> </div> <div class="row ecosystem-row"> <div class="col-md-4"> <div class="card ecosystem-card"> <a href="/ecosystem/Captum/"> <div class="card-body "> <h4>Captum</h4> <p class="card-summary">Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.</p> </div> </a> </div> </div> <div class="col-md-4"> <div class="card ecosystem-card"> <a href="/ecosystem/pytorch-geometric/"> <div class="card-body "> <h4>PyTorch Geometric</h4> <p class="card-summary">PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.</p> </div> </a> </div> </div> <div class="col-md-4"> <div class="card ecosystem-card"> <a href="/ecosystem/skorch/"> <div class="card-body "> <h4>skorch</h4> <p class="card-summary">skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.</p> </div> </a> </div> </div> </div> <div id="community-module" class="row"> <div class="col-md-12 community-heading"> <h2>Community</h2> 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