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'block' : 'none'; } // Determine the default search type let defaultSearchType = 'google'; if (window.location.href.startsWith('https://docs-preview.pytorch.org/')) { defaultSearchType = 'sphinx'; } else { defaultSearchType = localStorage.getItem('searchType') || 'google'; } // Set the default search type document.querySelector(`input[name="searchType"][value="${defaultSearchType}"]`).checked = true; toggleSearchBox(defaultSearchType); // Event listener for changes in search type searchForm.addEventListener('change', function(event) { const selectedSearchType = event.target.value; localStorage.setItem('searchType', selectedSearchType); toggleSearchBox(selectedSearchType); }); // Set placeholder text for Google search box window.onload = function() { var placeholderText = "Search Docs"; var googleSearchboxText = document.querySelector("#gsc-i-id1"); if (googleSearchboxText) { googleSearchboxText.placeholder = placeholderText; googleSearchboxText.style.fontFamily = 'FreightSans'; googleSearchboxText.style.fontSize = "1.2rem"; googleSearchboxText.style.color = '#262626'; } }; }); </script> </div> <p class="caption" role="heading"><span class="caption-text">PyTorch Recipes</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="recipes/recipes_index.html">See All Recipes</a></li> <li class="toctree-l1"><a class="reference internal" href="prototype/prototype_index.html">See All Prototype Recipes</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Introduction to PyTorch</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/basics/intro.html">Learn the Basics</a><ul> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/quickstart_tutorial.html">Quickstart</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/tensorqs_tutorial.html">Tensors</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/data_tutorial.html">Datasets & DataLoaders</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/transforms_tutorial.html">Transforms</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/buildmodel_tutorial.html">Build the Neural Network</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/autogradqs_tutorial.html">Automatic Differentiation with <code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code></a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/optimization_tutorial.html">Optimizing Model Parameters</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/basics/saveloadrun_tutorial.html">Save and Load the Model</a></li> </ul> </li> <li class="toctree-l1"><a class="reference internal" href="beginner/introyt/introyt_index.html">Introduction to PyTorch - YouTube Series</a><ul> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/introyt1_tutorial.html">Introduction to PyTorch</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/tensors_deeper_tutorial.html">Introduction to PyTorch Tensors</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/trainingyt.html">Training with PyTorch</a></li> <li class="toctree-l2"><a class="reference internal" href="beginner/introyt/captumyt.html">Model Understanding with Captum</a></li> </ul> </li> </ul> <p class="caption" role="heading"><span class="caption-text">Learning PyTorch</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/deep_learning_60min_blitz.html">Deep Learning with PyTorch: A 60 Minute Blitz</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/pytorch_with_examples.html">Learning PyTorch with Examples</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/nn_tutorial.html">What is <cite>torch.nn</cite> <em>really</em>?</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/nlp_from_scratch_index.html">NLP from Scratch</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/tensorboard_tutorial.html">Visualizing Models, Data, and Training with TensorBoard</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/pinmem_nonblock.html">A guide on good usage of <code class="docutils literal notranslate"><span class="pre">non_blocking</span></code> and <code class="docutils literal notranslate"><span class="pre">pin_memory()</span></code> in PyTorch</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Image and Video</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="intermediate/torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/transfer_learning_tutorial.html">Transfer Learning for Computer Vision Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/fgsm_tutorial.html">Adversarial Example Generation</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/dcgan_faces_tutorial.html">DCGAN Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/spatial_transformer_tutorial.html">Spatial Transformer Networks Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/vt_tutorial.html">Optimizing Vision Transformer Model for Deployment</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Audio</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_io_tutorial.html">Audio I/O</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_resampling_tutorial.html">Audio Resampling</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/audio_datasets_tutorial.html">Audio Datasets</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/forced_alignment_with_torchaudio_tutorial.html">Forced Alignment with Wav2Vec2</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Backends</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Reinforcement Learning</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="intermediate/reinforcement_q_learning.html">Reinforcement Learning (DQN) Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/mario_rl_tutorial.html">Train a Mario-playing RL Agent</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Deploying PyTorch Models in Production</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/flask_rest_api_tutorial.html">Deploying PyTorch in Python via a REST API with Flask</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/Intro_to_TorchScript_tutorial.html">Introduction to TorchScript</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_export.html">Loading a TorchScript Model in C++</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/super_resolution_with_onnxruntime.html">(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/realtime_rpi.html">Real Time Inference on Raspberry Pi 4 (30 fps!)</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Profiling PyTorch</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/profiler.html">Profiling your PyTorch Module</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/hta_intro_tutorial.html">Introduction to Holistic Trace Analysis</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/hta_trace_diff_tutorial.html">Trace Diff using Holistic Trace Analysis</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="intermediate/fx_conv_bn_fuser.html">(beta) Building a Convolution/Batch Norm fuser in FX</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Frontend APIs</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/ensembling.html">Model ensembling</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/per_sample_grads.html">Per-sample-gradients</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/torch-script-parallelism.html">Dynamic Parallelism in TorchScript</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_autograd.html">Autograd in C++ Frontend</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Extending PyTorch</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="advanced/custom_ops_landing_page.html">PyTorch Custom Operators</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/python_custom_ops.html">Custom Python Operators</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_custom_ops.html">Custom C++ and CUDA Operators</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/torch_script_custom_classes.html">Extending TorchScript with Custom C++ Classes</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/dispatcher.html">Registering a Dispatched Operator in C++</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Model Optimization</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/profiler.html">Profiling your PyTorch Module</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/tensorboard_profiler_tutorial.html">PyTorch Profiler With TensorBoard</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/hyperparameter_tuning_tutorial.html">Hyperparameter tuning with Ray Tune</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/vt_tutorial.html">Optimizing Vision Transformer Model for Deployment</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/parametrizations.html">Parametrizations Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/pruning_tutorial.html">Pruning Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/dynamic_quantization_bert_tutorial.html">(beta) Dynamic Quantization on BERT</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/quantized_transfer_learning_tutorial.html">(beta) Quantized Transfer Learning for Computer Vision Tutorial</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/compiled_autograd_tutorial.html">Compiled Autograd: Capturing a larger backward graph for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/scaled_dot_product_attention_tutorial.html">(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Parallel and Distributed Training</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="distributed/home.html">Distributed and Parallel Training Tutorials</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/dist_overview.html">PyTorch Distributed Overview</a></li> <li class="toctree-l1"><a class="reference internal" href="beginner/ddp_series_intro.html">Distributed Data Parallel in PyTorch - Video Tutorials</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/dist_tuto.html">Writing Distributed Applications with PyTorch</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/FSDP_advanced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/TCPStore_libuv_backend.html">Introduction to Libuv TCPStore Backend</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/pipelining_tutorial.html">Introduction to Distributed Pipeline Parallelism</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/process_group_cpp_extension_tutorial.html">Customize Process Group Backends Using Cpp Extensions</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li> <li class="toctree-l1"><a class="reference internal" href="intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/rpc_ddp_tutorial.html">Combining Distributed DataParallel with Distributed RPC Framework</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Edge with ExecuTorch</span></p> <ul> <li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html">Exporting to ExecuTorch Tutorial</a></li> <li class="toctree-l1"><a class="reference external" href=" https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html">Running an ExecuTorch Model in C++ Tutorial</a></li> <li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li> <li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li> <li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-android.html">Building an ExecuTorch Android Demo App</a></li> <li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Recommendation Systems</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="intermediate/torchrec_intro_tutorial.html">Introduction to TorchRec</a></li> <li class="toctree-l1"><a class="reference internal" href="advanced/sharding.html">Exploring TorchRec sharding</a></li> </ul> <p class="caption" role="heading"><span class="caption-text">Multimodality</span></p> <ul> <li class="toctree-l1"><a class="reference internal" href="beginner/flava_finetuning_tutorial.html">TorchMultimodal Tutorial: Finetuning FLAVA</a></li> </ul> </div> </div> </nav> <div class="pytorch-container"> <div class="pytorch-page-level-bar" id="pytorch-page-level-bar"> <div class="pytorch-breadcrumbs-wrapper"> <div role="navigation" aria-label="breadcrumbs navigation"> <ul class="pytorch-breadcrumbs"> <li> <a href="#"> Tutorials </a> > </li> <li>Welcome to PyTorch Tutorials</li> <li class="pytorch-breadcrumbs-aside"> <a href="_sources/index.rst.txt" rel="nofollow"><img src="_static/images/view-page-source-icon.svg"></a> </li> </ul> </div> </div> <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper"> Shortcuts </div> </div> <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap"> <div class="pytorch-content-left"> <div class="pytorch-call-to-action-links"> <div id="tutorial-type">index</div> <div id="google-colab-link"> <img class="call-to-action-img" src="_static/images/pytorch-colab.svg"/> <div class="call-to-action-desktop-view">Run in Google Colab</div> <div class="call-to-action-mobile-view">Colab</div> </div> <div id="download-notebook-link"> <img class="call-to-action-notebook-img" src="_static/images/pytorch-download.svg"/> <div class="call-to-action-desktop-view">Download Notebook</div> <div class="call-to-action-mobile-view">Notebook</div> </div> <div id="github-view-link"> <img class="call-to-action-img" src="_static/images/pytorch-github.svg"/> <div class="call-to-action-desktop-view">View on GitHub</div> <div class="call-to-action-mobile-view">GitHub</div> </div> </div>  <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T8XT4PS" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>  <div class="rst-content"> <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article"> <article itemprop="articleBody" id="pytorch-article" class="pytorch-article"> <div class="section" id="welcome-to-pytorch-tutorials"> <h1>Welcome to PyTorch Tutorials<a class="headerlink" href="#welcome-to-pytorch-tutorials" title="Permalink to this heading">¶</a></h1> <p><strong>What’s new in PyTorch tutorials?</strong></p> <ul class="simple"> <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/torch_compiler_set_stance_tutorial.html">Dynamic Compilation Control with torch.compiler.set_stance</a></p></li> <li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/transformer_building_blocks.html">Accelerating PyTorch Transformers by replacing nn.Transformer with Nested Tensors and torch.compile()</a></p></li> <li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/torch_export_challenges_solutions.html">Understanding the torch.export Flow and Solutions to Common Challenges</a></p></li> <li><p>Updated <a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_export_tutorial.html#constraints-dynamic-shapes">torch.export Tutorial</a> with automatic dynamic shapes <code class="docutils literal notranslate"><span class="pre">Dim.AUTO</span></code></p></li> <li><p>Updated <a class="reference external" href="https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html">torch.export AOTInductor Tutorial for Python runtime</a></p></li> <li><p>Updated <a class="reference external" href="https://pytorch.org/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html#composability">Using User-Defined Triton Kernels with torch.compile</a> with new <code class="docutils literal notranslate"><span class="pre">torch.library.triton_op</span></code></p></li> </ul> <div class="tutorials-callout-container"> <div class="row"><p><div class="col-md-6"> <div class="text-container"> <h3>Learn the Basics</h3> <p class="body-paragraph">Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide.</p> <a class="btn with-right-arrow callout-button" href="beginner/basics/intro.html">Get started with PyTorch</a> </div> </div></p> <p><div class="col-md-6"> <div class="text-container"> <h3>PyTorch Recipes</h3> <p class="body-paragraph">Bite-size, ready-to-deploy PyTorch code examples.</p> <a class="btn with-right-arrow callout-button" href="recipes/recipes_index.html">Explore Recipes</a> </div> </div></p> </div> </div> <div id="tutorial-cards-container"> <nav class="navbar navbar-expand-lg navbar-light tutorials-nav col-12"> <div class="tutorial-tags-container"> <div id="dropdown-filter-tags"> <div class="tutorial-filter-menu"> <div class="tutorial-filter filter-btn all-tag-selected" data-tag="all">All</div> </div> </div> </div> </nav> <hr class="tutorials-hr"> <div class="row"> <div id="tutorial-cards"> <div class="list"><p><div class="col-md-12 tutorials-card-container" data-tags=Getting-Started> <div class="card tutorials-card"> <a href="beginner/basics/intro.html"> <div class="card-body"> <div class="card-title-container"> <h4>Learn the Basics</h4> </div> <p class="card-summary">A step-by-step guide to building a complete ML workflow with PyTorch.</p> <p class="tags">Getting-Started</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/60-min-blitz.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Getting-Started> <div class="card tutorials-card"> <a href="beginner/introyt/introyt_index.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to PyTorch on YouTube</h4> </div> <p class="card-summary">An introduction to building a complete ML workflow with PyTorch. Follows the PyTorch Beginner Series on YouTube.</p> <p class="tags">Getting-Started</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Getting-Started> <div class="card tutorials-card"> <a href="beginner/pytorch_with_examples.html"> <div class="card-body"> <div class="card-title-container"> <h4>Learning PyTorch with Examples</h4> </div> <p class="card-summary">This tutorial introduces the fundamental concepts of PyTorch through self-contained examples.</p> <p class="tags">Getting-Started</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/learning-pytorch-with-examples.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Getting-Started> <div class="card tutorials-card"> <a href="beginner/nn_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>What is torch.nn really?</h4> </div> <p class="card-summary">Use torch.nn to create and train a neural network.</p> <p class="tags">Getting-Started</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torch-nn.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Interpretability,Getting-Started,TensorBoard> <div class="card tutorials-card"> <a href="intermediate/tensorboard_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Visualizing Models, Data, and Training with TensorBoard</h4> </div> <p class="card-summary">Learn to use TensorBoard to visualize data and model training.</p> <p class="tags">Interpretability,Getting-Started,TensorBoard</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/visualizing-with-tensorboard.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Getting-Started> <div class="card tutorials-card"> <a href="intermediate/pinmem_nonblock.html"> <div class="card-body"> <div class="card-title-container"> <h4>Good usage of `non_blocking` and `pin_memory()` in PyTorch</h4> </div> <p class="card-summary">A guide on best practices to copy data from CPU to GPU.</p> <p class="tags">Getting-Started</p> <div class="tutorials-image"><img src='_static/img/pinmem.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="intermediate/torchvision_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>TorchVision Object Detection Finetuning Tutorial</h4> </div> <p class="card-summary">Finetune a pre-trained Mask R-CNN model.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/TorchVision-Object-Detection-Finetuning-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="beginner/transfer_learning_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Transfer Learning for Computer Vision Tutorial</h4> </div> <p class="card-summary">Train a convolutional neural network for image classification using transfer learning.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Transfer-Learning-for-Computer-Vision-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="beginner/vt_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Optimizing Vision Transformer Model</h4> </div> <p class="card-summary">Apply cutting-edge, attention-based transformer models to computer vision tasks.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/60-min-blitz.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="beginner/fgsm_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Adversarial Example Generation</h4> </div> <p class="card-summary">Train a convolutional neural network for image classification using transfer learning.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Adversarial-Example-Generation.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="beginner/dcgan_faces_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>DCGAN Tutorial</h4> </div> <p class="card-summary">Train a generative adversarial network (GAN) to generate new celebrities.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/DCGAN-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="intermediate/spatial_transformer_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Spatial Transformer Networks Tutorial</h4> </div> <p class="card-summary">Learn how to augment your network using a visual attention mechanism.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/stn/Five.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="intermediate/tiatoolbox_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Inference on Whole Slide Images with TIAToolbox</h4> </div> <p class="card-summary">Learn how to use the TIAToolbox to perform inference on whole slide images.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/TIAToolbox-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video> <div class="card tutorials-card"> <a href="advanced/usb_semisup_learn.html"> <div class="card-body"> <div class="card-title-container"> <h4>Semi-Supervised Learning Tutorial Based on USB</h4> </div> <p class="card-summary">Learn how to train semi-supervised learning algorithms (on custom data) using USB and PyTorch.</p> <p class="tags">Image/Video</p> <div class="tutorials-image"><img src='_static/img/usb_semisup_learn/code.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_io_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio IO</h4> </div> <p class="card-summary">Learn to load data with torchaudio.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_resampling_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio Resampling</h4> </div> <p class="card-summary">Learn to resample audio waveforms using torchaudio.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_data_augmentation_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio Data Augmentation</h4> </div> <p class="card-summary">Learn to apply data augmentations using torchaudio.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_feature_extractions_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio Feature Extractions</h4> </div> <p class="card-summary">Learn to extract features using torchaudio.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_feature_augmentation_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio Feature Augmentation</h4> </div> <p class="card-summary">Learn to augment features using torchaudio.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="beginner/audio_datasets_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Audio Datasets</h4> </div> <p class="card-summary">Learn to use torchaudio datasets.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="intermediate/speech_recognition_pipeline_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Automatic Speech Recognition with Wav2Vec2 in torchaudio</h4> </div> <p class="card-summary">Learn how to use torchaudio's pretrained models for building a speech recognition application.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-asr.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="intermediate/speech_command_classification_with_torchaudio_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Speech Command Classification</h4> </div> <p class="card-summary">Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-speech.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="intermediate/text_to_speech_with_torchaudio.html"> <div class="card-body"> <div class="card-title-container"> <h4>Text-to-Speech with torchaudio</h4> </div> <p class="card-summary">Learn how to use torchaudio's pretrained models for building a text-to-speech application.</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-speech.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Audio> <div class="card tutorials-card"> <a href="intermediate/forced_alignment_with_torchaudio_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Forced Alignment with Wav2Vec2 in torchaudio</h4> </div> <p class="card-summary">Learn how to use torchaudio's Wav2Vec2 pretrained models for aligning text to speech</p> <p class="tags">Audio</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/torchaudio-alignment.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=NLP> <div class="card tutorials-card"> <a href="intermediate/char_rnn_classification_tutorial"> <div class="card-body"> <div class="card-title-container"> <h4>NLP from Scratch: Classifying Names with a Character-level RNN</h4> </div> <p class="card-summary">Build and train a basic character-level RNN to classify word from scratch without the use of torchtext. First in a series of three tutorials.</p> <p class="tags">NLP</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/NLP-From-Scratch-Classifying-Names-with-a-Character-Level-RNN.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=NLP> <div class="card tutorials-card"> <a href="intermediate/char_rnn_generation_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>NLP from Scratch: Generating Names with a Character-level RNN</h4> </div> <p class="card-summary">After using character-level RNN to classify names, learn how to generate names from languages. Second in a series of three tutorials.</p> <p class="tags">NLP</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/NLP-From-Scratch-Generating-Names-with-a-Character-Level-RNN.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=NLP> <div class="card tutorials-card"> <a href="intermediate/seq2seq_translation_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>NLP from Scratch: Translation with a Sequence-to-sequence Network and Attention</h4> </div> <p class="card-summary">This is the third and final tutorial on doing “NLP From Scratch”, where we write our own classes and functions to preprocess the data to do our NLP modeling tasks.</p> <p class="tags">NLP</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/NLP-From-Scratch-Translation-with-a-Sequence-to-Sequence-Network-and-Attention.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production,ONNX,Backends> <div class="card tutorials-card"> <a href="beginner/onnx/export_simple_model_to_onnx_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(optional) Exporting a PyTorch model to ONNX using TorchDynamo backend and Running it using ONNX Runtime</h4> </div> <p class="card-summary">Build a image classifier model in PyTorch and convert it to ONNX before deploying it with ONNX Runtime.</p> <p class="tags">Production,ONNX,Backends</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Exporting-PyTorch-Models-to-ONNX-Graphs.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production,ONNX,Backends> <div class="card tutorials-card"> <a href="advanced/onnx_registry_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to ONNX Registry</h4> </div> <p class="card-summary">Demonstrate end-to-end how to address unsupported operators by using ONNX Registry.</p> <p class="tags">Production,ONNX,Backends</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Exporting-PyTorch-Models-to-ONNX-Graphs.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="intermediate/reinforcement_q_learning.html"> <div class="card-body"> <div class="card-title-container"> <h4>Reinforcement Learning (DQN)</h4> </div> <p class="card-summary">Learn how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym.</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/cartpole.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="intermediate/reinforcement_ppo.html"> <div class="card-body"> <div class="card-title-container"> <h4>Reinforcement Learning (PPO) with TorchRL</h4> </div> <p class="card-summary">Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the Inverted Pendulum task from Gym.</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/invpendulum.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="intermediate/mario_rl_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Train a Mario-playing RL Agent</h4> </div> <p class="card-summary">Use PyTorch to train a Double Q-learning agent to play Mario.</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/mario.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="intermediate/dqn_with_rnn_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Recurrent DQN</h4> </div> <p class="card-summary">Use TorchRL to train recurrent policies</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/rollout_recurrent.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="advanced/coding_ddpg.html"> <div class="card-body"> <div class="card-title-container"> <h4>Code a DDPG Loss</h4> </div> <p class="card-summary">Use TorchRL to code a DDPG Loss</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/half_cheetah.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Reinforcement-Learning> <div class="card tutorials-card"> <a href="advanced/pendulum.html"> <div class="card-body"> <div class="card-title-container"> <h4>Writing your environment and transforms</h4> </div> <p class="card-summary">Use TorchRL to code a Pendulum</p> <p class="tags">Reinforcement-Learning</p> <div class="tutorials-image"><img src='_static/img/pendulum.gif'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production> <div class="card tutorials-card"> <a href="intermediate/flask_rest_api_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Deploying PyTorch in Python via a REST API with Flask</h4> </div> <p class="card-summary">Deploy a PyTorch model using Flask and expose a REST API for model inference using the example of a pretrained DenseNet 121 model which detects the image.</p> <p class="tags">Production</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production,TorchScript> <div class="card tutorials-card"> <a href="beginner/Intro_to_TorchScript_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to TorchScript</h4> </div> <p class="card-summary">Introduction to TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.</p> <p class="tags">Production,TorchScript</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Introduction-to-TorchScript.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production,TorchScript> <div class="card tutorials-card"> <a href="advanced/cpp_export.html"> <div class="card-body"> <div class="card-title-container"> <h4>Loading a TorchScript Model in C++</h4> </div> <p class="card-summary">Learn how PyTorch provides to go from an existing Python model to a serialized representation that can be loaded and executed purely from C++, with no dependency on Python.</p> <p class="tags">Production,TorchScript</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Loading-a-TorchScript-Model-in-Cpp.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Production,ONNX> <div class="card tutorials-card"> <a href="advanced/super_resolution_with_onnxruntime.html"> <div class="card-body"> <div class="card-title-container"> <h4>(optional) Exporting a PyTorch Model to ONNX using TorchScript backend and Running it using ONNX Runtime</h4> </div> <p class="card-summary">Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime.</p> <p class="tags">Production,ONNX</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/optional-Exporting-a-Model-from-PyTorch-to-ONNX-and-Running-it-using-ONNX-Runtime.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Profiling> <div class="card tutorials-card"> <a href="beginner/profiler.html"> <div class="card-body"> <div class="card-title-container"> <h4>Profiling PyTorch</h4> </div> <p class="card-summary">Learn how to profile a PyTorch application</p> <p class="tags">Profiling</p> <div class="tutorials-image">_static/img/thumbnails/default.png</div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Profiling> <div class="card tutorials-card"> <a href="beginner/hta_intro_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Profiling PyTorch</h4> </div> <p class="card-summary">Introduction to Holistic Trace Analysis</p> <p class="tags">Profiling</p> <div class="tutorials-image">_static/img/thumbnails/default.png</div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Profiling> <div class="card tutorials-card"> <a href="beginner/hta_trace_diff_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Profiling PyTorch</h4> </div> <p class="card-summary">Trace Diff using Holistic Trace Analysis</p> <p class="tags">Profiling</p> <div class="tutorials-image">_static/img/thumbnails/default.png</div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=FX> <div class="card tutorials-card"> <a href="intermediate/fx_conv_bn_fuser.html"> <div class="card-body"> <div class="card-title-container"> <h4>Building a Convolution/Batch Norm fuser in FX</h4> </div> <p class="card-summary">Build a simple FX pass that fuses batch norm into convolution to improve performance during inference.</p> <p class="tags">FX</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=FX> <div class="card tutorials-card"> <a href="intermediate/fx_profiling_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Building a Simple Performance Profiler with FX</h4> </div> <p class="card-summary">Build a simple FX interpreter to record the runtime of op, module, and function calls and report statistics</p> <p class="tags">FX</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Memory-Format,Best-Practice,Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/memory_format_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Channels Last Memory Format in PyTorch</h4> </div> <p class="card-summary">Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions.</p> <p class="tags">Memory-Format,Best-Practice,Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/experimental-Channels-Last-Memory-Format-in-PyTorch.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs,C++> <div class="card tutorials-card"> <a href="advanced/cpp_frontend.html"> <div class="card-body"> <div class="card-title-container"> <h4>Using the PyTorch C++ Frontend</h4> </div> <p class="card-summary">Walk through an end-to-end example of training a model with the C++ frontend by training a DCGAN – a kind of generative model – to generate images of MNIST digits.</p> <p class="tags">Frontend-APIs,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Using-the-PyTorch-Cpp-Frontend.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++,CUDA> <div class="card tutorials-card"> <a href="advanced/custom_ops_landing_page.html"> <div class="card-body"> <div class="card-title-container"> <h4>PyTorch Custom Operators Landing Page</h4> </div> <p class="card-summary">This is the landing page for all things related to custom operators in PyTorch.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++,CUDA</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++,CUDA> <div class="card tutorials-card"> <a href="advanced/python_custom_ops.html"> <div class="card-body"> <div class="card-title-container"> <h4>Custom Python Operators</h4> </div> <p class="card-summary">Create Custom Operators in Python. Useful for black-boxing a Python function for use with torch.compile.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++,CUDA</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,CUDA> <div class="card tutorials-card"> <a href="intermediate/compiled_autograd_tutorial"> <div class="card-body"> <div class="card-title-container"> <h4>Compiled Autograd: Capturing a larger backward graph for ``torch.compile``</h4> </div> <p class="card-summary">Learn how to use compiled autograd to capture a larger backward graph.</p> <p class="tags">Model-Optimization,CUDA</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++,CUDA> <div class="card tutorials-card"> <a href="advanced/cpp_custom_ops.html"> <div class="card-body"> <div class="card-title-container"> <h4>Custom C++ and CUDA Operators</h4> </div> <p class="card-summary">How to extend PyTorch with custom C++ and CUDA operators.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++,CUDA</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++,CUDA> <div class="card tutorials-card"> <a href="advanced/cpp_extension.html"> <div class="card-body"> <div class="card-title-container"> <h4>Custom C++ and CUDA Extensions</h4> </div> <p class="card-summary">Create a neural network layer with no parameters using numpy. Then use scipy to create a neural network layer that has learnable weights.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++,CUDA</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,TorchScript,C++> <div class="card tutorials-card"> <a href="advanced/torch_script_custom_ops.html"> <div class="card-body"> <div class="card-title-container"> <h4>Extending TorchScript with Custom C++ Operators</h4> </div> <p class="card-summary">Implement a custom TorchScript operator in C++, how to build it into a shared library, how to use it in Python to define TorchScript models and lastly how to load it into a C++ application for inference workloads.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,TorchScript,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Operators.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,TorchScript,C++> <div class="card tutorials-card"> <a href="advanced/torch_script_custom_classes.html"> <div class="card-body"> <div class="card-title-container"> <h4>Extending TorchScript with Custom C++ Classes</h4> </div> <p class="card-summary">This is a continuation of the custom operator tutorial, and introduces the API we’ve built for binding C++ classes into TorchScript and Python simultaneously.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,TorchScript,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Classes.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs,TorchScript,C++> <div class="card tutorials-card"> <a href="advanced/torch-script-parallelism.html"> <div class="card-body"> <div class="card-title-container"> <h4>Dynamic Parallelism in TorchScript</h4> </div> <p class="card-summary">This tutorial introduces the syntax for doing *dynamic inter-op parallelism* in TorchScript.</p> <p class="tags">Frontend-APIs,TorchScript,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/TorchScript-Parallelism.jpg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=TorchScript,Model-Optimization,Image/Video,Quantization> <div class="card tutorials-card"> <a href="intermediate/realtime_rpi.html"> <div class="card-body"> <div class="card-title-container"> <h4>Real Time Inference on Raspberry Pi 4</h4> </div> <p class="card-summary">This tutorial covers how to run quantized and fused models on a Raspberry Pi 4 at 30 fps.</p> <p class="tags">TorchScript,Model-Optimization,Image/Video,Quantization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/realtime_rpi.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs,C++> <div class="card tutorials-card"> <a href="advanced/cpp_autograd.html"> <div class="card-body"> <div class="card-title-container"> <h4>Autograd in C++ Frontend</h4> </div> <p class="card-summary">The autograd package helps build flexible and dynamic nerural netorks. In this tutorial, exploreseveral examples of doing autograd in PyTorch C++ frontend</p> <p class="tags">Frontend-APIs,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Autograd-in-Cpp-Frontend.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++> <div class="card tutorials-card"> <a href="advanced/dispatcher.html"> <div class="card-body"> <div class="card-title-container"> <h4>Registering a Dispatched Operator in C++</h4> </div> <p class="card-summary">The dispatcher is an internal component of PyTorch which is responsible for figuring out what code should actually get run when you call a function like torch::add.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++> <div class="card tutorials-card"> <a href="advanced/extend_dispatcher.html"> <div class="card-body"> <div class="card-title-container"> <h4>Extending Dispatcher For a New Backend in C++</h4> </div> <p class="card-summary">Learn how to extend the dispatcher to add a new device living outside of the pytorch/pytorch repo and maintain it to keep in sync with native PyTorch devices.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs,C++> <div class="card tutorials-card"> <a href="advanced/privateuseone.html"> <div class="card-body"> <div class="card-title-container"> <h4>Facilitating New Backend Integration by PrivateUse1</h4> </div> <p class="card-summary">Learn how to integrate a new backend living outside of the pytorch/pytorch repo and maintain it to keep in sync with the native PyTorch backend.</p> <p class="tags">Extending-PyTorch,Frontend-APIs,C++</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/custom_function_double_backward_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Custom Function Tutorial: Double Backward</h4> </div> <p class="card-summary">Learn how to write a custom autograd Function that supports double backward.</p> <p class="tags">Extending-PyTorch,Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Extending-PyTorch,Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/custom_function_conv_bn_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Custom Function Tutorial: Fusing Convolution and Batch Norm</h4> </div> <p class="card-summary">Learn how to create a custom autograd Function that fuses batch norm into a convolution to improve memory usage.</p> <p class="tags">Extending-PyTorch,Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/forward_ad_usage.html"> <div class="card-body"> <div class="card-title-container"> <h4>Forward-mode Automatic Differentiation</h4> </div> <p class="card-summary">Learn how to use forward-mode automatic differentiation.</p> <p class="tags">Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/jacobians_hessians.html"> <div class="card-body"> <div class="card-title-container"> <h4>Jacobians, Hessians, hvp, vhp, and more</h4> </div> <p class="card-summary">Learn how to compute advanced autodiff quantities using torch.func</p> <p class="tags">Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/ensembling.html"> <div class="card-body"> <div class="card-title-container"> <h4>Model Ensembling</h4> </div> <p class="card-summary">Learn how to ensemble models using torch.vmap</p> <p class="tags">Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/per_sample_grads.html"> <div class="card-body"> <div class="card-title-container"> <h4>Per-Sample-Gradients</h4> </div> <p class="card-summary">Learn how to compute per-sample-gradients using torch.func</p> <p class="tags">Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/neural_tangent_kernels.html"> <div class="card-body"> <div class="card-title-container"> <h4>Neural Tangent Kernels</h4> </div> <p class="card-summary">Learn how to compute neural tangent kernels using torch.func</p> <p class="tags">Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice,Profiling> <div class="card tutorials-card"> <a href="beginner/profiler.html"> <div class="card-body"> <div class="card-title-container"> <h4>Performance Profiling in PyTorch</h4> </div> <p class="card-summary">Learn how to use the PyTorch Profiler to benchmark your module's performance.</p> <p class="tags">Model-Optimization,Best-Practice,Profiling</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/profiler.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice,Profiling,TensorBoard> <div class="card tutorials-card"> <a href="intermediate/tensorboard_profiler_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Performance Profiling in TensorBoard</h4> </div> <p class="card-summary">Learn how to use the TensorBoard plugin to profile and analyze your model's performance.</p> <p class="tags">Model-Optimization,Best-Practice,Profiling,TensorBoard</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/profiler.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice> <div class="card tutorials-card"> <a href="beginner/hyperparameter_tuning_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Hyperparameter Tuning Tutorial</h4> </div> <p class="card-summary">Learn how to use Ray Tune to find the best performing set of hyperparameters for your model.</p> <p class="tags">Model-Optimization,Best-Practice</p> <div class="tutorials-image"><img src='_static/img/ray-tune.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice> <div class="card tutorials-card"> <a href="intermediate/parametrizations.html"> <div class="card-body"> <div class="card-title-container"> <h4>Parametrizations Tutorial</h4> </div> <p class="card-summary">Learn how to use torch.nn.utils.parametrize to put constraints on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...)</p> <p class="tags">Model-Optimization,Best-Practice</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/parametrizations.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice> <div class="card tutorials-card"> <a href="intermediate/pruning_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Pruning Tutorial</h4> </div> <p class="card-summary">Learn how to use torch.nn.utils.prune to sparsify your neural networks, and how to extend it to implement your own custom pruning technique.</p> <p class="tags">Model-Optimization,Best-Practice</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Pruning-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice,CUDA,Frontend-APIs> <div class="card tutorials-card"> <a href="intermediate/optimizer_step_in_backward_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>How to save memory by fusing the optimizer step into the backward pass</h4> </div> <p class="card-summary">Learn a memory-saving technique through fusing the optimizer step into the backward pass using memory snapshots.</p> <p class="tags">Model-Optimization,Best-Practice,CUDA,Frontend-APIs</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Text,Model-Optimization> <div class="card tutorials-card"> <a href="advanced/semi_structured_sparse.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Accelerating BERT with semi-structured sparsity</h4> </div> <p class="card-summary">Train BERT, prune it to be 2:4 sparse, and then accelerate it to achieve 2x inference speedups with semi-structured sparsity and torch.compile.</p> <p class="tags">Text,Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Pruning-Tutorial.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Text,Quantization,Model-Optimization> <div class="card tutorials-card"> <a href="advanced/dynamic_quantization_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Dynamic Quantization on an LSTM Word Language Model</h4> </div> <p class="card-summary">Apply dynamic quantization, the easiest form of quantization, to a LSTM-based next word prediction model.</p> <p class="tags">Text,Quantization,Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-an-LSTM-Word-Language-Model.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Text,Quantization,Model-Optimization> <div class="card tutorials-card"> <a href="intermediate/dynamic_quantization_bert_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Dynamic Quantization on BERT</h4> </div> <p class="card-summary">Apply the dynamic quantization on a BERT (Bidirectional Embedding Representations from Transformers) model.</p> <p class="tags">Text,Quantization,Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-BERT.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Image/Video,Quantization,Model-Optimization> <div class="card tutorials-card"> <a href="intermediate/quantized_transfer_learning_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Quantized Transfer Learning for Computer Vision Tutorial</h4> </div> <p class="card-summary">Extends the Transfer Learning for Computer Vision Tutorial using a quantized model.</p> <p class="tags">Image/Video,Quantization,Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/60-min-blitz.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Quantization> <div class="card tutorials-card"> <a href="advanced/static_quantization_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Static Quantization with Eager Mode in PyTorch</h4> </div> <p class="card-summary">This tutorial shows how to do post-training static quantization.</p> <p class="tags">Quantization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/60-min-blitz.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Production> <div class="card tutorials-card"> <a href="intermediate/torchserve_with_ipex"> <div class="card-body"> <div class="card-title-container"> <h4>Grokking PyTorch Intel CPU Performance from First Principles</h4> </div> <p class="card-summary">A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch.</p> <p class="tags">Model-Optimization,Production</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Production> <div class="card tutorials-card"> <a href="intermediate/torchserve_with_ipex_2"> <div class="card-body"> <div class="card-title-container"> <h4>Grokking PyTorch Intel CPU Performance from First Principles (Part 2)</h4> </div> <p class="card-summary">A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).</p> <p class="tags">Model-Optimization,Production</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Best-Practice,Ax,TorchX> <div class="card tutorials-card"> <a href="intermediate/ax_multiobjective_nas_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Multi-Objective Neural Architecture Search with Ax</h4> </div> <p class="card-summary">Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.</p> <p class="tags">Model-Optimization,Best-Practice,Ax,TorchX</p> <div class="tutorials-image"><img src='_static/img/ax_logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization> <div class="card tutorials-card"> <a href="intermediate/torch_compile_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>torch.compile Tutorial</h4> </div> <p class="card-summary">Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.</p> <p class="tags">Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization> <div class="card tutorials-card"> <a href="intermediate/inductor_debug_cpu.html"> <div class="card-body"> <div class="card-title-container"> <h4>Inductor CPU Backend Debugging and Profiling</h4> </div> <p class="card-summary">Learn the usage, debugging and performance profiling for ``torch.compile`` with Inductor CPU backend.</p> <p class="tags">Model-Optimization</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/generic-pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Attention,Transformer> <div class="card tutorials-card"> <a href="intermediate/scaled_dot_product_attention_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>(beta) Implementing High-Performance Transformers with SCALED DOT PRODUCT ATTENTION</h4> </div> <p class="card-summary">This tutorial explores the new torch.nn.functional.scaled_dot_product_attention and how it can be used to construct Transformer components.</p> <p class="tags">Model-Optimization,Attention,Transformer</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Model-Optimization,Image/Video> <div class="card tutorials-card"> <a href="beginner/knowledge_distillation_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Knowledge Distillation in Convolutional Neural Networks</h4> </div> <p class="card-summary">Learn how to improve the accuracy of lightweight models using more powerful models as teachers.</p> <p class="tags">Model-Optimization,Image/Video</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/knowledge_distillation_pytorch_logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Transformer> <div class="card tutorials-card"> <a href="intermediate/transformer_building_blocks.html"> <div class="card-body"> <div class="card-title-container"> <h4>Accelerating PyTorch Transformers by replacing nn.Transformer with Nested Tensors and torch.compile()</h4> </div> <p class="card-summary">This tutorial goes over recommended best practices for implementing Transformers with native PyTorch.</p> <p class="tags">Transformer</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/pytorch-logo.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="beginner/dist_overview.html"> <div class="card-body"> <div class="card-title-container"> <h4>PyTorch Distributed Overview</h4> </div> <p class="card-summary">Briefly go over all concepts and features in the distributed package. Use this document to find the distributed training technology that can best serve your application.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="beginner/ddp_series_intro.html"> <div class="card-body"> <div class="card-title-container"> <h4>Distributed Data Parallel in PyTorch - Video Tutorials</h4> </div> <p class="card-summary">This series of video tutorials walks you through distributed training in PyTorch via DDP.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/model_parallel_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Single-Machine Model Parallel Best Practices</h4> </div> <p class="card-summary">Learn how to implement model parallel, a distributed training technique which splits a single model onto different GPUs, rather than replicating the entire model on each GPU</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Model-Parallel-Best-Practices.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/ddp_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Getting Started with Distributed Data Parallel</h4> </div> <p class="card-summary">Learn the basics of when to use distributed data paralle versus data parallel and work through an example to set it up.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Getting-Started-with-Distributed-Data-Parallel.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/dist_tuto.html"> <div class="card-body"> <div class="card-title-container"> <h4>Writing Distributed Applications with PyTorch</h4> </div> <p class="card-summary">Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Writing-Distributed-Applications-with-PyTorch.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/TP_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Large Scale Transformer model training with Tensor Parallel</h4> </div> <p class="card-summary">Learn how to train large models with Tensor Parallel package.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Large-Scale-Transformer-model-training-with-Tensor-Parallel.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/process_group_cpp_extension_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Customize Process Group Backends Using Cpp Extensions</h4> </div> <p class="card-summary">Extend ProcessGroup with custom collective communication implementations.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Customize-Process-Group-Backends-Using-Cpp-Extensions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/rpc_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Getting Started with Distributed RPC Framework</h4> </div> <p class="card-summary">Learn how to build distributed training using the torch.distributed.rpc package.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Getting Started with Distributed-RPC-Framework.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/rpc_param_server_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Implementing a Parameter Server Using Distributed RPC Framework</h4> </div> <p class="card-summary">Walk through a through a simple example of implementing a parameter server using PyTorch’s Distributed RPC framework.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Implementing-a-Parameter-Server-Using-Distributed-RPC-Framework.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/pipelining_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to Distributed Pipeline Parallelism</h4> </div> <p class="card-summary">Demonstrate how to implement pipeline parallelism using torch.distributed.pipelining</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Introduction-to-Distributed-Pipeline-Parallelism.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/rpc_async_execution.html"> <div class="card-body"> <div class="card-title-container"> <h4>Implementing Batch RPC Processing Using Asynchronous Executions</h4> </div> <p class="card-summary">Learn how to use rpc.functions.async_execution to implement batch RPC</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Implementing-Batch-RPC-Processing-Using-Asynchronous-Executions.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="advanced/rpc_ddp_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Combining Distributed DataParallel with Distributed RPC Framework</h4> </div> <p class="card-summary">Walk through a through a simple example of how to combine distributed data parallelism with distributed model parallelism.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Combining-Distributed-DataParallel-with-Distributed-RPC-Framework.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/FSDP_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Getting Started with Fully Sharded Data Parallel(FSDP)</h4> </div> <p class="card-summary">Learn how to train models with Fully Sharded Data Parallel package.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Getting-Started-with-FSDP.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/FSDP_advanced_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Advanced Model Training with Fully Sharded Data Parallel (FSDP)</h4> </div> <p class="card-summary">Explore advanced model training with Fully Sharded Data Parallel package.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Getting-Started-with-FSDP.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Parallel-and-Distributed-Training> <div class="card tutorials-card"> <a href="intermediate/TCPStore_libuv_backend.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to Libuv TCPStore Backend</h4> </div> <p class="card-summary">TCPStore now uses a new server backend for faster connection and better scalability.</p> <p class="tags">Parallel-and-Distributed-Training</p> <div class="tutorials-image"><img src='_static/img/thumbnails/cropped/Introduction-to-Libuv-Backend-TCPStore.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Exporting to ExecuTorch Tutorial</h4> </div> <p class="card-summary">Learn about how to use ExecuTorch, a unified ML stack for lowering PyTorch models to edge devices.</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Running an ExecuTorch Model in C++ Tutorial</h4> </div> <p class="card-summary">Learn how to load and execute an ExecuTorch model in C++</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Using the ExecuTorch SDK to Profile a Model</h4> </div> <p class="card-summary">Explore how to use the ExecuTorch SDK to profile, debug, and visualize ExecuTorch models</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/demo-apps-ios.html"> <div class="card-body"> <div class="card-title-container"> <h4>Building an ExecuTorch iOS Demo App</h4> </div> <p class="card-summary">Explore how to set up the ExecuTorch iOS Demo App, which uses the MobileNet v3 model to process live camera images leveraging three different backends: XNNPACK, Core ML, and Metal Performance Shaders (MPS).</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/demo-apps-android.html"> <div class="card-body"> <div class="card-title-container"> <h4>Building an ExecuTorch Android Demo App</h4> </div> <p class="card-summary">Learn how to set up the ExecuTorch Android Demo App for image segmentation tasks using the DeepLab v3 model and XNNPACK FP32 backend.</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=Edge> <div class="card tutorials-card"> <a href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html"> <div class="card-body"> <div class="card-title-container"> <h4>Lowering a Model as a Delegate</h4> </div> <p class="card-summary">Learn to accelerate your program using ExecuTorch by applying delegates through three methods: lowering the whole module, composing it with another module, and partitioning parts of a module.</p> <p class="tags">Edge</p> <div class="tutorials-image"><img src='_static/img/ExecuTorch-Logo-cropped.svg'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=TorchRec,Recommender> <div class="card tutorials-card"> <a href="intermediate/torchrec_intro_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to TorchRec</h4> </div> <p class="card-summary">TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems.</p> <p class="tags">TorchRec,Recommender</p> <div class="tutorials-image"><img src='_static/img/thumbnails/torchrec.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=TorchRec,Recommender> <div class="card tutorials-card"> <a href="advanced/sharding.html"> <div class="card-body"> <div class="card-title-container"> <h4>Exploring TorchRec sharding</h4> </div> <p class="card-summary">This tutorial covers the sharding schemes of embedding tables by using <code>EmbeddingPlanner</code> and <code>DistributedModelParallel</code> API.</p> <p class="tags">TorchRec,Recommender</p> <div class="tutorials-image"><img src='_static/img/thumbnails/torchrec.png'></div> </div> </a> </div> </div></p> <p><div class="col-md-12 tutorials-card-container" data-tags=TorchMultimodal> <div class="card tutorials-card"> <a href="beginner/flava_finetuning_tutorial.html"> <div class="card-body"> <div class="card-title-container"> <h4>Introduction to TorchMultimodal</h4> </div> <p class="card-summary">TorchMultimodal is a library that provides models, primitives and examples for training multimodal tasks</p> <p class="tags">TorchMultimodal</p> <div class="tutorials-image"><img src='_static/img/thumbnails/torchrec.png'></div> </div> </a> </div> </div></p> </div> <div class="pagination d-flex justify-content-center"></div> </div> </div> <br> <br></div> <div class="section" id="additional-resources"> <h1>Additional Resources<a class="headerlink" href="#additional-resources" title="Permalink to 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