What's new in PyTorch tutorials?
- Introduction to TorchRec
- Getting Started with Fully Sharded Data Parallel (FSDP)
- Grokking PyTorch Intel CPU Performance from First Principles
- Customize Process Group Backends Using Cpp Extensions
- Forward-mode Automatic Differentiation (added functorch API capabilities)
- Real Time Inference on Raspberry Pi 4 (30 fps!)
.. customcalloutitem:: :description: 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. :header: Learn the Basics :button_link: beginner/basics/intro.html :button_text: Get started with PyTorch
.. customcalloutitem:: :description: Bite-size, ready-to-deploy PyTorch code examples. :header: PyTorch Recipes :button_link: recipes/recipes_index.html :button_text: Explore Recipes
.. customcarditem:: :header: Learn the Basics :card_description: A step-by-step guide to building a complete ML workflow with PyTorch. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/basics/intro.html :tags: Getting-Started
.. customcarditem:: :header: Introduction to PyTorch on YouTube :card_description: An introduction to building a complete ML workflow with PyTorch. Follows the PyTorch Beginner Series on YouTube. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: beginner/introyt.html :tags: Getting-Started
.. customcarditem:: :header: Learning PyTorch with Examples :card_description: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. :image: _static/img/thumbnails/cropped/learning-pytorch-with-examples.png :link: beginner/pytorch_with_examples.html :tags: Getting-Started
.. customcarditem:: :header: What is torch.nn really? :card_description: Use torch.nn to create and train a neural network. :image: _static/img/thumbnails/cropped/torch-nn.png :link: beginner/nn_tutorial.html :tags: Getting-Started
.. customcarditem:: :header: Visualizing Models, Data, and Training with TensorBoard :card_description: Learn to use TensorBoard to visualize data and model training. :image: _static/img/thumbnails/cropped/visualizing-with-tensorboard.png :link: intermediate/tensorboard_tutorial.html :tags: Interpretability,Getting-Started,TensorBoard
.. customcarditem:: :header: TorchVision Object Detection Finetuning Tutorial :card_description: Finetune a pre-trained Mask R-CNN model. :image: _static/img/thumbnails/cropped/TorchVision-Object-Detection-Finetuning-Tutorial.png :link: intermediate/torchvision_tutorial.html :tags: Image/Video
.. customcarditem:: :header: Transfer Learning for Computer Vision Tutorial :card_description: Train a convolutional neural network for image classification using transfer learning. :image: _static/img/thumbnails/cropped/Transfer-Learning-for-Computer-Vision-Tutorial.png :link: beginner/transfer_learning_tutorial.html :tags: Image/Video
.. customcarditem:: :header: Optimizing Vision Transformer Model :card_description: Apply cutting-edge, attention-based transformer models to computer vision tasks. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/vt_tutorial.html :tags: Image/Video
.. customcarditem:: :header: Adversarial Example Generation :card_description: Train a convolutional neural network for image classification using transfer learning. :image: _static/img/thumbnails/cropped/Adversarial-Example-Generation.png :link: beginner/fgsm_tutorial.html :tags: Image/Video
.. customcarditem:: :header: DCGAN Tutorial :card_description: Train a generative adversarial network (GAN) to generate new celebrities. :image: _static/img/thumbnails/cropped/DCGAN-Tutorial.png :link: beginner/dcgan_faces_tutorial.html :tags: Image/Video
.. customcarditem:: :header: Spatial Transformer Networks Tutorial :card_description: Learn how to augment your network using a visual attention mechanism. :image: _static/img/stn/Five.gif :link: intermediate/spatial_transformer_tutorial.html :tags: Image/Video
.. customcarditem:: :header: Audio IO :card_description: Learn to load data with torchaudio. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_io_tutorial.html :tags: Audio
.. customcarditem:: :header: Audio Resampling :card_description: Learn to resample audio waveforms using torchaudio. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_resampling_tutorial.html :tags: Audio
.. customcarditem:: :header: Audio Data Augmentation :card_description: Learn to apply data augmentations using torchaudio. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_data_augmentation_tutorial.html :tags: Audio
.. customcarditem:: :header: Audio Feature Extractions :card_description: Learn to extract features using torchaudio. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_feature_extractions_tutorial.html :tags: Audio
.. customcarditem:: :header: Audio Feature Augmentation :card_description: Learn to augment features using torchaudio. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_feature_augmentation_tutorial.html :tags: Audio
.. customcarditem:: :header: Audio Datasets :card_description: Learn to use torchaudio datasets. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_datasets_tutorial.html :tags: Audio
.. customcarditem:: :header: Automatic Speech Recognition with Wav2Vec2 in torchaudio :card_description: Learn how to use torchaudio's pretrained models for building a speech recognition application. :image: _static/img/thumbnails/cropped/torchaudio-asr.png :link: intermediate/speech_recognition_pipeline_tutorial.html :tags: Audio
.. customcarditem:: :header: Speech Command Classification :card_description: Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset. :image: _static/img/thumbnails/cropped/torchaudio-speech.png :link: intermediate/speech_command_classification_with_torchaudio_tutorial.html :tags: Audio
.. customcarditem:: :header: Text-to-Speech with torchaudio :card_description: Learn how to use torchaudio's pretrained models for building a text-to-speech application. :image: _static/img/thumbnails/cropped/torchaudio-speech.png :link: intermediate/text_to_speech_with_torchaudio.html :tags: Audio
.. customcarditem:: :header: Forced Alignment with Wav2Vec2 in torchaudio :card_description: Learn how to use torchaudio's Wav2Vec2 pretrained models for aligning text to speech :image: _static/img/thumbnails/cropped/torchaudio-alignment.png :link: intermediate/forced_alignment_with_torchaudio_tutorial.html :tags: Audio
.. customcarditem:: :header: Sequence-to-Sequence Modeling with nn.Transformer and torchtext :card_description: Learn how to train a sequence-to-sequence model that uses the nn.Transformer module. :image: _static/img/thumbnails/cropped/Sequence-to-Sequence-Modeling-with-nnTransformer-andTorchText.png :link: beginner/transformer_tutorial.html :tags: Text
.. customcarditem:: :header: NLP from Scratch: Classifying Names with a Character-level RNN :card_description: 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. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Classifying-Names-with-a-Character-Level-RNN.png :link: intermediate/char_rnn_classification_tutorial :tags: Text
.. customcarditem:: :header: NLP from Scratch: Generating Names with a Character-level RNN :card_description: After using character-level RNN to classify names, leanr how to generate names from languages. Second in a series of three tutorials. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Generating-Names-with-a-Character-Level-RNN.png :link: intermediate/char_rnn_generation_tutorial.html :tags: Text
.. customcarditem:: :header: NLP from Scratch: Translation with a Sequence-to-sequence Network and Attention :card_description: 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. :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Translation-with-a-Sequence-to-Sequence-Network-and-Attention.png :link: intermediate/seq2seq_translation_tutorial.html :tags: Text
.. customcarditem:: :header: Text Classification with Torchtext :card_description: Learn how to build the dataset and classify text using torchtext library. :image: _static/img/thumbnails/cropped/Text-Classification-with-TorchText.png :link: beginner/text_sentiment_ngrams_tutorial.html :tags: Text
.. customcarditem:: :header: Language Translation with Transformer :card_description: Train a language translation model from scratch using Transformer. :image: _static/img/thumbnails/cropped/Language-Translation-with-TorchText.png :link: beginner/translation_transformer.html :tags: Text
.. customcarditem:: :header: Reinforcement Learning (DQN) :card_description: Learn how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym. :image: _static/img/cartpole.gif :link: intermediate/reinforcement_q_learning.html :tags: Reinforcement-Learning
.. customcarditem:: :header: Train a Mario-playing RL Agent :card_description: Use PyTorch to train a Double Q-learning agent to play Mario. :image: _static/img/mario.gif :link: intermediate/mario_rl_tutorial.html :tags: Reinforcement-Learning
.. customcarditem:: :header: Deploying PyTorch in Python via a REST API with Flask :card_description: 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. :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/flask_rest_api_tutorial.html :tags: Production
.. customcarditem:: :header: Introduction to TorchScript :card_description: 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++. :image: _static/img/thumbnails/cropped/Introduction-to-TorchScript.png :link: beginner/Intro_to_TorchScript_tutorial.html :tags: Production,TorchScript
.. customcarditem:: :header: Loading a TorchScript Model in C++ :card_description: 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. :image: _static/img/thumbnails/cropped/Loading-a-TorchScript-Model-in-Cpp.png :link: advanced/cpp_export.html :tags: Production,TorchScript
.. customcarditem:: :header: (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime :card_description: Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime. :image: _static/img/thumbnails/cropped/optional-Exporting-a-Model-from-PyTorch-to-ONNX-and-Running-it-using-ONNX-Runtime.png :link: advanced/super_resolution_with_onnxruntime.html :tags: Production
.. customcarditem:: :header: Building a Convolution/Batch Norm fuser in FX :card_description: Build a simple FX pass that fuses batch norm into convolution to improve performance during inference. :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/fx_conv_bn_fuser.html :tags: FX
.. customcarditem:: :header: Building a Simple Performance Profiler with FX :card_description: Build a simple FX interpreter to record the runtime of op, module, and function calls and report statistics :image: _static/img/thumbnails/cropped/Deploying-PyTorch-in-Python-via-a-REST-API-with-Flask.png :link: intermediate/fx_profiling_tutorial.html :tags: FX
.. customcarditem:: :header: (beta) Channels Last Memory Format in PyTorch :card_description: Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions. :image: _static/img/thumbnails/cropped/experimental-Channels-Last-Memory-Format-in-PyTorch.png :link: intermediate/memory_format_tutorial.html :tags: Memory-Format,Best-Practice,Frontend-APIs
.. customcarditem:: :header: Using the PyTorch C++ Frontend :card_description: 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. :image: _static/img/thumbnails/cropped/Using-the-PyTorch-Cpp-Frontend.png :link: advanced/cpp_frontend.html :tags: Frontend-APIs,C++
.. customcarditem:: :header: Custom C++ and CUDA Extensions :card_description: Create a neural network layer with no parameters using numpy. Then use scipy to create a neural network layer that has learnable weights. :image: _static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png :link: advanced/cpp_extension.html :tags: Extending-PyTorch,Frontend-APIs,C++,CUDA
.. customcarditem:: :header: Extending TorchScript with Custom C++ Operators :card_description: 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. :image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Operators.png :link: advanced/torch_script_custom_ops.html :tags: Extending-PyTorch,Frontend-APIs,TorchScript,C++
.. customcarditem:: :header: Extending TorchScript with Custom C++ Classes :card_description: 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. :image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Classes.png :link: advanced/torch_script_custom_classes.html :tags: Extending-PyTorch,Frontend-APIs,TorchScript,C++
.. customcarditem:: :header: Dynamic Parallelism in TorchScript :card_description: This tutorial introduces the syntax for doing *dynamic inter-op parallelism* in TorchScript. :image: _static/img/thumbnails/cropped/TorchScript-Parallelism.jpg :link: advanced/torch-script-parallelism.html :tags: Frontend-APIs,TorchScript,C++
.. customcarditem:: :header: Real Time Inference on Raspberry Pi 4 :card_description: This tutorial covers how to run quantized and fused models on a Raspberry Pi 4 at 30 fps. :image: _static/img/thumbnails/cropped/realtime_rpi.png :link: intermediate/realtime_rpi.html :tags: TorchScript,Model-Optimization,Image/Video,Quantization
.. customcarditem:: :header: Autograd in C++ Frontend :card_description: The autograd package helps build flexible and dynamic nerural netorks. In this tutorial, exploreseveral examples of doing autograd in PyTorch C++ frontend :image: _static/img/thumbnails/cropped/Autograd-in-Cpp-Frontend.png :link: advanced/cpp_autograd.html :tags: Frontend-APIs,C++
.. customcarditem:: :header: Registering a Dispatched Operator in C++ :card_description: 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. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: advanced/dispatcher.html :tags: Extending-PyTorch,Frontend-APIs,C++
.. customcarditem:: :header: Extending Dispatcher For a New Backend in C++ :card_description: 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. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: advanced/extend_dispatcher.html :tags: Extending-PyTorch,Frontend-APIs,C++
.. customcarditem:: :header: Custom Function Tutorial: Double Backward :card_description: Learn how to write a custom autograd Function that supports double backward. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: intermediate/custom_function_double_backward_tutorial.html :tags: Extending-PyTorch,Frontend-APIs
.. customcarditem:: :header: Custom Function Tutorial: Fusing Convolution and Batch Norm :card_description: Learn how to create a custom autograd Function that fuses batch norm into a convolution to improve memory usage. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: intermediate/custom_function_conv_bn_tutorial.html :tags: Extending-PyTorch,Frontend-APIs
.. customcarditem:: :header: Forward-mode Automatic Differentiation :card_description: Learn how to use forward-mode automatic differentiation. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: intermediate/forward_ad_usage.html :tags: Frontend-APIs
.. customcarditem:: :header: Performance Profiling in PyTorch :card_description: Learn how to use the PyTorch Profiler to benchmark your module's performance. :image: _static/img/thumbnails/cropped/profiler.png :link: beginner/profiler.html :tags: Model-Optimization,Best-Practice,Profiling
.. customcarditem:: :header: Performance Profiling in TensorBoard :card_description: Learn how to use the TensorBoard plugin to profile and analyze your model's performance. :image: _static/img/thumbnails/cropped/profiler.png :link: intermediate/tensorboard_profiler_tutorial.html :tags: Model-Optimization,Best-Practice,Profiling,TensorBoard
.. customcarditem::
:header: Hyperparameter Tuning Tutorial
:card_description: Learn how to use Ray Tune to find the best performing set of hyperparameters for your model.
:image: _static/img/ray-tune.png
:link: beginner/hyperparameter_tuning_tutorial.html
:tags: Model-Optimization,Best-Practice
.. customcarditem::
:header: Optimizing Vision Transformer Model
:card_description: Learn how to use Facebook Data-efficient Image Transformers DeiT and script and optimize it for mobile.
:image: _static/img/thumbnails/cropped/mobile.png
:link: beginner/vt_tutorial.html
:tags: Model-Optimization,Best-Practice,Mobile
.. customcarditem:: :header: Parametrizations Tutorial :card_description: Learn how to use torch.nn.utils.parametrize to put constriants on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...) :image: _static/img/thumbnails/cropped/parametrizations.png :link: intermediate/parametrizations.html :tags: Model-Optimization,Best-Practice
.. customcarditem:: :header: Pruning Tutorial :card_description: 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. :image: _static/img/thumbnails/cropped/Pruning-Tutorial.png :link: intermediate/pruning_tutorial.html :tags: Model-Optimization,Best-Practice
.. customcarditem:: :header: (beta) Dynamic Quantization on an LSTM Word Language Model :card_description: Apply dynamic quantization, the easiest form of quantization, to a LSTM-based next word prediction model. :image: _static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-an-LSTM-Word-Language-Model.png :link: advanced/dynamic_quantization_tutorial.html :tags: Text,Quantization,Model-Optimization
.. customcarditem:: :header: (beta) Dynamic Quantization on BERT :card_description: Apply the dynamic quantization on a BERT (Bidirectional Embedding Representations from Transformers) model. :image: _static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-BERT.png :link: intermediate/dynamic_quantization_bert_tutorial.html :tags: Text,Quantization,Model-Optimization
.. customcarditem:: :header: (beta) Quantized Transfer Learning for Computer Vision Tutorial :card_description: Extends the Transfer Learning for Computer Vision Tutorial using a quantized model. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: intermediate/quantized_transfer_learning_tutorial.html :tags: Image/Video,Quantization,Model-Optimization
.. customcarditem:: :header: (beta) Static Quantization with Eager Mode in PyTorch :card_description: This tutorial shows how to do post-training static quantization. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: advanced/static_quantization_tutorial.html :tags: Quantization
.. customcarditem:: :header: Grokking PyTorch Intel CPU Performance from First Principles :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch. :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png :link: intermediate/torchserve_with_ipex :tags: Model-Optimization,Production
.. customcarditem:: :header: PyTorch Distributed Overview :card_description: 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. :image: _static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png :link: beginner/dist_overview.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Single-Machine Model Parallel Best Practices :card_description: 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 :image: _static/img/thumbnails/cropped/Model-Parallel-Best-Practices.png :link: intermediate/model_parallel_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Getting Started with Distributed Data Parallel :card_description: Learn the basics of when to use distributed data paralle versus data parallel and work through an example to set it up. :image: _static/img/thumbnails/cropped/Getting-Started-with-Distributed-Data-Parallel.png :link: intermediate/ddp_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Writing Distributed Applications with PyTorch :card_description: Set up the distributed package of PyTorch, use the different communication strategies, and go over some the internals of the package. :image: _static/img/thumbnails/cropped/Writing-Distributed-Applications-with-PyTorch.png :link: intermediate/dist_tuto.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Customize Process Group Backends Using Cpp Extensions :card_description: Extend ProcessGroup with custom collective communication implementations. :image: _static/img/thumbnails/cropped/Customize-Process-Group-Backends-Using-Cpp-Extensions.png :link: intermediate/process_group_cpp_extension_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Getting Started with Distributed RPC Framework :card_description: Learn how to build distributed training using the torch.distributed.rpc package. :image: _static/img/thumbnails/cropped/Getting Started with Distributed-RPC-Framework.png :link: intermediate/rpc_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Implementing a Parameter Server Using Distributed RPC Framework :card_description: Walk through a through a simple example of implementing a parameter server using PyTorch’s Distributed RPC framework. :image: _static/img/thumbnails/cropped/Implementing-a-Parameter-Server-Using-Distributed-RPC-Framework.png :link: intermediate/rpc_param_server_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Distributed Pipeline Parallelism Using RPC :card_description: Demonstrate how to implement distributed pipeline parallelism using RPC :image: _static/img/thumbnails/cropped/Distributed-Pipeline-Parallelism-Using-RPC.png :link: intermediate/dist_pipeline_parallel_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Implementing Batch RPC Processing Using Asynchronous Executions :card_description: Learn how to use rpc.functions.async_execution to implement batch RPC :image: _static/img/thumbnails/cropped/Implementing-Batch-RPC-Processing-Using-Asynchronous-Executions.png :link: intermediate/rpc_async_execution.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Combining Distributed DataParallel with Distributed RPC Framework :card_description: Walk through a through a simple example of how to combine distributed data parallelism with distributed model parallelism. :image: _static/img/thumbnails/cropped/Combining-Distributed-DataParallel-with-Distributed-RPC-Framework.png :link: advanced/rpc_ddp_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Training Transformer models using Pipeline Parallelism :card_description: Walk through a through a simple example of how to train a transformer model using pipeline parallelism. :image: _static/img/thumbnails/cropped/Training-Transformer-models-using-Pipeline-Parallelism.png :link: intermediate/pipeline_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Training Transformer models using Distributed Data Parallel and Pipeline Parallelism :card_description: Walk through a through a simple example of how to train a transformer model using Distributed Data Parallel and Pipeline Parallelism :image: _static/img/thumbnails/cropped/Training-Transformer-Models-using-Distributed-Data-Parallel-and-Pipeline-Parallelism.png :link: advanced/ddp_pipeline.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Getting Started with Fully Sharded Data Parallel(FSDP) :card_description: Learn how to train models with Fully Sharded Data Parallel package. :image: _static/img/thumbnails/cropped/Getting Started with FSDP.png :link: intermediate/FSDP_tutorial.html :tags: Parallel-and-Distributed-Training
.. customcarditem:: :header: Image Segmentation DeepLabV3 on iOS :card_description: A comprehensive step-by-step tutorial on how to prepare and run the PyTorch DeepLabV3 image segmentation model on iOS. :image: _static/img/thumbnails/cropped/ios.png :link: beginner/deeplabv3_on_ios.html :tags: Mobile
.. customcarditem:: :header: Image Segmentation DeepLabV3 on Android :card_description: A comprehensive step-by-step tutorial on how to prepare and run the PyTorch DeepLabV3 image segmentation model on Android. :image: _static/img/thumbnails/cropped/android.png :link: beginner/deeplabv3_on_android.html :tags: Mobile
.. customcarditem:: :header: Introduction to TorchRec :card_description: TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems. :image: _static/img/thumbnails/torchrec.png :link: intermediate/torchrec_tutorial.html :tags: TorchRec,Recommender
.. customcarditem:: :header: Exploring TorchRec sharding :card_description: This tutorial covers the sharding schemes of embedding tables by using <code>EmbeddingPlanner</code> and <code>DistributedModelParallel</code> API. :image: _static/img/thumbnails/torchrec.png :link: advanced/sharding.html :tags: TorchRec,Recommender
.. customcalloutitem:: :header: Examples of PyTorch :description: A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc. :button_link: https://github.com/pytorch/examples :button_text: Checkout Examples
.. customcalloutitem:: :header: PyTorch Cheat Sheet :description: Quick overview to essential PyTorch elements. :button_link: beginner/ptcheat.html :button_text: Open
.. customcalloutitem:: :header: Tutorials on GitHub :description: Access PyTorch Tutorials from GitHub. :button_link: https://github.com/pytorch/tutorials :button_text: Go To GitHub
.. customcalloutitem:: :header: Run Tutorials on Google Colab :description: Learn how to copy tutorial data into Google Drive so that you can run tutorials on Google Colab. :button_link: beginner/colab.html :button_text: Open
.. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: PyTorch Recipes See All Recipes <recipes/recipes_index> See All Prototype Recipes <prototype/prototype_index>
.. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: Introduction to PyTorch beginner/basics/intro beginner/basics/quickstart_tutorial beginner/basics/tensorqs_tutorial beginner/basics/data_tutorial beginner/basics/transforms_tutorial beginner/basics/buildmodel_tutorial beginner/basics/autogradqs_tutorial beginner/basics/optimization_tutorial beginner/basics/saveloadrun_tutorial
.. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: Introduction to PyTorch on YouTube beginner/introyt beginner/introyt/introyt1_tutorial beginner/introyt/tensors_deeper_tutorial beginner/introyt/autogradyt_tutorial beginner/introyt/modelsyt_tutorial beginner/introyt/tensorboardyt_tutorial beginner/introyt/trainingyt beginner/introyt/captumyt
.. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: Learning PyTorch beginner/deep_learning_60min_blitz beginner/pytorch_with_examples beginner/nn_tutorial intermediate/tensorboard_tutorial
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Image and Video intermediate/torchvision_tutorial beginner/transfer_learning_tutorial beginner/fgsm_tutorial beginner/dcgan_faces_tutorial intermediate/spatial_transformer_tutorial beginner/vt_tutorial
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Audio beginner/audio_io_tutorial beginner/audio_resampling_tutorial beginner/audio_data_augmentation_tutorial beginner/audio_feature_extractions_tutorial beginner/audio_feature_augmentation_tutorial beginner/audio_datasets_tutorial intermediate/speech_recognition_pipeline_tutorial intermediate/speech_command_classification_with_torchaudio_tutorial intermediate/text_to_speech_with_torchaudio intermediate/forced_alignment_with_torchaudio_tutorial
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Text beginner/transformer_tutorial intermediate/char_rnn_classification_tutorial intermediate/char_rnn_generation_tutorial intermediate/seq2seq_translation_tutorial beginner/text_sentiment_ngrams_tutorial beginner/translation_transformer
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Reinforcement Learning intermediate/reinforcement_q_learning intermediate/mario_rl_tutorial
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Deploying PyTorch Models in Production intermediate/flask_rest_api_tutorial beginner/Intro_to_TorchScript_tutorial advanced/cpp_export advanced/super_resolution_with_onnxruntime intermediate/realtime_rpi
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Code Transforms with FX intermediate/fx_conv_bn_fuser intermediate/fx_profiling_tutorial
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Frontend APIs intermediate/memory_format_tutorial intermediate/forward_ad_usage advanced/cpp_frontend advanced/torch-script-parallelism advanced/cpp_autograd
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Extending PyTorch intermediate/custom_function_double_backward_tutorial intermediate/custom_function_conv_bn_tutorial advanced/cpp_extension advanced/torch_script_custom_ops advanced/torch_script_custom_classes advanced/dispatcher advanced/extend_dispatcher
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Model Optimization beginner/profiler intermediate/tensorboard_profiler_tutorial beginner/hyperparameter_tuning_tutorial beginner/vt_tutorial intermediate/parametrizations intermediate/pruning_tutorial advanced/dynamic_quantization_tutorial intermediate/dynamic_quantization_bert_tutorial intermediate/quantized_transfer_learning_tutorial advanced/static_quantization_tutorial intermediate/torchserve_with_ipex
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Parallel and Distributed Training beginner/dist_overview intermediate/model_parallel_tutorial intermediate/ddp_tutorial intermediate/dist_tuto intermediate/FSDP_tutorial intermediate/process_group_cpp_extension_tutorial intermediate/rpc_tutorial intermediate/rpc_param_server_tutorial intermediate/dist_pipeline_parallel_tutorial intermediate/rpc_async_execution advanced/rpc_ddp_tutorial intermediate/pipeline_tutorial advanced/ddp_pipeline advanced/generic_join
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Mobile beginner/deeplabv3_on_ios beginner/deeplabv3_on_android
.. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Recommendation Systems intermediate/torchrec_tutorial advanced/sharding