Welcome to PyTorch Tutorials ============================ .. raw:: html
.. Add callout items below this line .. customcalloutitem:: :description: The 60 min blitz is the most common starting point and provides a broad view on how to use PyTorch. It covers the basics all the way to constructing deep neural networks. :header: New to PyTorch? :button_link: beginner/deep_learning_60min_blitz.html :button_text: Start 60-min blitz .. customcalloutitem:: :description: Bite-size, ready-to-deploy PyTorch code examples. :header: PyTorch Recipes :button_link: recipes/recipes_index.html :button_text: Explore Recipes .. End of callout item section .. raw:: html
.. Add tutorial cards below this line .. Learning PyTorch .. customcarditem:: :header: Deep Learning with PyTorch: A 60 Minute Blitz :card_description: Understand PyTorch’s Tensor library and neural networks at a high level. :image: _static/img/thumbnails/cropped/60-min-blitz.png :link: beginner/deep_learning_60min_blitz.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 .. Image/Video .. 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: 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 .. Audio .. customcarditem:: :header: torchaudio Tutorial :card_description: Learn to load and preprocess data from a simple dataset with PyTorch's torchaudio library. :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png :link: beginner/audio_preprocessing_tutorial.html :tags: Audio .. Text .. 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: 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/Text-Classification-with-TorchText.png :link: beginner/text_sentiment_ngrams_tutorial.html :tags: Text .. customcarditem:: :header: Language Translation with Torchtext :card_description: Use torchtext to reprocess data from a well-known datasets containing both English and German. Then use it to train a sequence-to-sequence model. :image: _static/img/thumbnails/cropped/Language-Translation-with-TorchText.png :link: beginner/torchtext_translation_tutorial.html :tags: Text .. Reinforcement Learning .. 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 .. Deploying PyTorch Models in Production .. 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 .. 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 .. 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 .. Frontend APIs .. customcarditem:: :header: (prototype) Introduction to Named Tensors in PyTorch :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/thumbnails/cropped/experimental-Introduction-to-Named-Tensors-in-PyTorch.png :link: intermediate/memory_format_tutorial.html :tags: Frontend-APIs,Named-Tensor,Best-Practice .. 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 .. 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: 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: 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: Frontend-APIs,TorchScript,C++ .. 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++ .. Model Optimization .. 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) Static Quantization with Eager Mode in PyTorch :card_description: Learn techniques to impove a model's accuracy = post-training static quantization, per-channel quantization, and quantization-aware training. :image: _static/img/thumbnails/cropped/experimental-Static-Quantization-with-Eager-Mode-in-PyTorch.png :link: advanced/static_quantization_tutorial.html :tags: Image/Video,Quantization,Model-Optimization .. customcarditem:: :header: (beta) Quantized Transfer Learning for Computer Vision Tutorial :card_description: Learn techniques to impove a model's accuracy - post-training static quantization, per-channel quantization, and quantization-aware training. :image: _static/img/thumbnails/cropped/experimental-Quantized-Transfer-Learning-for-Computer-Vision-Tutorial.png :link: advanced/static_quantization_tutorial.html :tags: Image/Video,Quantization,Model-Optimization .. 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: 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: (advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS :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/advanced-PyTorch-1point0-Distributed-Trainer-with-Amazon-AWS.png :link: beginner/aws_distributed_training_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 .. 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Additional Resources ============================ .. raw:: html
.. Add callout items below this line .. 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: Download .. customcalloutitem:: :header: Tutorials on GitHub :description: Access PyTorch Tutorials from GitHub. :button_link: https://github.com/pytorch/tutorials :button_text: Go To GitHub .. End of callout section .. raw:: html
.. ----------------------------------------- .. Page TOC .. ----------------------------------------- .. toctree:: :maxdepth: 2 :hidden: :includehidden: :caption: PyTorch Recipes See All Recipes .. 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/Video intermediate/torchvision_tutorial beginner/transfer_learning_tutorial beginner/fgsm_tutorial beginner/dcgan_faces_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Audio beginner/audio_preprocessing_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/torchtext_translation_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Reinforcement Learning intermediate/reinforcement_q_learning .. 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 .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Frontend APIs intermediate/named_tensor_tutorial intermediate/memory_format_tutorial advanced/cpp_frontend advanced/cpp_extension advanced/torch_script_custom_ops advanced/torch_script_custom_classes advanced/cpp_autograd .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Model Optimization intermediate/pruning_tutorial advanced/dynamic_quantization_tutorial intermediate/dynamic_quantization_bert_tutorial advanced/static_quantization_tutorial intermediate/quantized_transfer_learning_tutorial .. toctree:: :maxdepth: 2 :includehidden: :hidden: :caption: Parallel and Distributed Training intermediate/model_parallel_tutorial intermediate/ddp_tutorial intermediate/dist_tuto intermediate/rpc_tutorial beginner/aws_distributed_training_tutorial intermediate/rpc_param_server_tutorial