Welcome to PyTorch Tutorials

To learn how to use PyTorch, begin with our Getting Started Tutorials. The 60-minute blitz is the most common starting point, and provides a broad view into how to use PyTorch from the basics all the way into constructing deep neural networks.

Some considerations:

• We’ve added a new feature to tutorials that allows users to open the notebook associated with a tutorial in Google Colab. Visit this page for more information.

• If you would like to do the tutorials interactively via IPython / Jupyter, each tutorial has a download link for a Jupyter Notebook and Python source code.

• Additional high-quality examples are available, including image classification, unsupervised learning, reinforcement learning, machine translation, and many other applications, in PyTorch Examples.

• You can find reference documentation for the PyTorch API and layers in PyTorch Docs or via inline help.

• If you would like the tutorials section improved, please open a github issue here with your feedback.

• Check out our PyTorch Cheat Sheet for additional useful information.

• Finally, here’s a link to the PyTorch Release Notes

Getting Started

Deep Learning with PyTorch: A 60 Minute Blitz

Writing Custom Datasets, DataLoaders and Transforms

Visualizing Models, Data, and Training with TensorBoard

Image

TorchVision Object Detection Finetuning Tutorial

Transfer Learning for Computer Vision Tutorial

Spatial Transformer Networks Tutorial

Neural Transfer Using PyTorch

Adversarial Example Generation

DCGAN Tutorial

Named Tensor (experimental)

(experimental) Introduction to Named Tensors in PyTorch

Audio

torchaudio Tutorial

Text

NLP From Scratch: Classifying Names with a Character-Level RNN

NLP From Scratch: Generating Names with a Character-Level RNN

NLP From Scratch: Translation with a Sequence to Sequence Network and Attention

Text Classification with TorchText

Language Translation with TorchText

Sequence-to-Sequence Modeling with nn.Transformer and TorchText

Reinforcement Learning

Reinforcement Learning (DQN) Tutorial

Deploying PyTorch Models in Production

Deploying PyTorch in Python via a REST API with Flask

Introduction to TorchScript

Loading a TorchScript Model in C++

(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime

Parallel and Distributed Training

Model Parallel Best Practices

Getting Started with Distributed Data Parallel

Writing Distributed Applications with PyTorch

(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS

Extending PyTorch

Extending TorchScript with Custom C++ Operators

Creating Extensions Using numpy and scipy

Custom C++ and CUDA Extensions

Quantization (experimental)

(experimental) Dynamic Quantization on an LSTM Word Language Model

(experimental) Static Quantization with Eager Mode in PyTorch

(experimental) Quantized Transfer Learning for Computer Vision Tutorial

(experimental) Dynamic Quantization on BERT

PyTorch in Other Languages

Using the PyTorch C++ Frontend

PyTorch Fundamentals In-Depth

Learning PyTorch with Examples

What is torch.nn really?