.. 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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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_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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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
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: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++
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: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++
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: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++
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: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++
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: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++
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: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
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: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
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: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
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: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: 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
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: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
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: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
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: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
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: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
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: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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: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
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: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
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: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
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: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
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: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
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: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
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: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