| Welcome to PyTorch Tutorials |
| ============================ |
|
|
| .. raw:: html |
|
|
| <div class="tutorials-callout-container"> |
| <div class="row"> |
|
|
| .. 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 |
|
|
| </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"> |
|
|
| .. 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 |
| |
| .. customcarditem:: |
| :header: Speech Command Recognition |
| :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_recognition_with_torchaudio.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/named_tensor_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: 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: 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: 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: 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: 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: 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 |
| |
| .. End of tutorial card section |
| |
| .. raw:: html |
| |
| </div> |
| |
| <div class="pagination d-flex justify-content-center"></div> |
| |
| </div> |
| |
| </div> |
| <br> |
| <br> |
| |
| |
| Additional Resources |
| ============================ |
| |
| .. raw:: html |
| |
| <div class="tutorials-callout-container"> |
| <div class="row"> |
| |
| .. 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 |
| |
| </div> |
| </div> |
| |
| <div style='clear:both'></div> |
| |
| .. ----------------------------------------- |
| .. Page TOC |
| .. ----------------------------------------- |
| .. toctree:: |
| :maxdepth: 2 |
| :hidden: |
| :includehidden: |
| :caption: PyTorch Recipes |
| |
| See All Recipes <recipes/recipes_index> |
| |
| .. 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 |
| intermediate/speech_command_recognition_with_torchaudio |
| |
| |
| .. 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/torch-script-parallelism |
| advanced/cpp_autograd |
| advanced/dispatcher |
| |
| .. toctree:: |
| :maxdepth: 2 |
| :includehidden: |
| :hidden: |
| :caption: Model Optimization |
| |
| beginner/hyperparameter_tuning_tutorial |
| 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 |
| |
| beginner/dist_overview |
| intermediate/model_parallel_tutorial |
| intermediate/ddp_tutorial |
| intermediate/dist_tuto |
| intermediate/rpc_tutorial |
| intermediate/rpc_param_server_tutorial |
| intermediate/dist_pipeline_parallel_tutorial |
| intermediate/rpc_async_execution |
| advanced/rpc_ddp_tutorial |
| |
