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PyTorch 新增教程

基础知识

熟悉 PyTorch 的概念和模块。通过本快速入门指南，学习如何加载数据、构建深度神经网络、训练和保存模型。

开启 PyTorch 旅程

PyTorch 示例

小巧易用、即时部署的 PyTorch 代码示例。

查看示例

基础知识

逐步教你如何用PyTorch构建完整的机器学习流程。

Getting-Started

YouTube PyTorch 介绍视频

用PyTorch构建完整的机器学习工作流程，PyTorch初学者系列。

Getting-Started

通过示例学习 PyTorch

本教程通过独立的示例介绍了 PyTorch 的基本概念。

Getting-Started

什么是 torch.nn ?

使用 torch.nn 来创建和训练神经网络。

Getting-Started

使用 TensorBoard 展现模型、数据和训练过程

学习使用 TensorBoard 可视化数据集和模型训练过程。

Interpretability,Getting-Started,TensorBoard

TorchVision 目标检测微调教程

微调预训练的 Mask R-CNN 模型。

Image/Video

计算机视觉迁移学习教程

使用迁移学习训练卷积神经网络进行图像分类。

Image/Video

优化视觉Transformer模型

应用最前沿的、基于 attention-based transformer 模型到计算机视觉任务中。

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对抗性样本生成

使用迁移学习训练卷积神经网络进行图像分类。

Image/Video

DCGAN 教程

Train a generative adversarial network (GAN) to generate new celebrities.

Image/Video

Spatial Transformer Networks 教程

学习如何通过视觉注意机制增强你的网络。

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使用 TIAToolbox 对图像进行推理

学习如何使用TIAToolbox对图像进行推理。

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基于 USB 的半监督学习教程

学习如何使用 USB 和 PyTorch 对自定义数据进行半监督学习算法的训练。

Image/Video

Audio IO

使用 torchaudio 加载数据。

Audio

Audio 重采样

学习使用 torchaudio 对音频波形进行重新采样。

Audio

Audio 数据增强

学习使用 torchaudio 应用数据增强。

Audio

Audio 特征提取

学习使用 torchaudio 提取特征。

Audio

Audio 特征增强

学习使用 torchaudio 对特征进行增强。

Audio

Audio 数据集

学习如何使用 torchaudio 数据集。

Audio

在 torchaudio 中使用 Wav2Vec2 进行自动语音识别

学习如何使用 torchaudio 的预训练模型来构建语音识别应用程序。

Audio

语音命令分类

学习如何正确格式化音频数据集，然后在该数据集上训练/测试音频分类器网络。

Audio

使用 torchaudio 进行文本转语音

学习如何使用 torchaudio 的预训练模型构建文本转语音应用程序。

Audio

在 torchaudio 中使用 Wav2Vec2 进行对齐

学习如何使用 torchaudio 的 Wav2Vec2 预训练模型对文本进行与语音对齐。

Audio

使用 Better Transformer 提升推理效率

使用 Better Transformer 实现的 PyTorch Transformer 模型，以实现高性能的推断。

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从零开始的自然语言处理：使用字符级 RNN 对姓名进行分类

1.构建并训练一个基本的字符级循环神经网络，从零开始分类单词，而不使用 torchtext。

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从零开始的自然语言处理：使用字符级 RNN 生成姓名

2.在使用字符级循环神经网络对姓名进行分类之后，学习如何从语言中生成姓名。

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从零开始的自然语言处理：使用序列到序列网络和注意力进行翻译

3.在这里我们编写自己的类和函数来预处理数据以执行我们的自然语言处理建模任务。

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使用 Torchtext 进行文本分类

学习如何使用 torchtext 库构建数据集并对文本进行分类。

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使用 Transformer 进行语言翻译

从零开始训练一个使用 Transformer 的语言翻译模型。

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使用Torchtext预处理自定义文本数据集

学习如何使用 torchtext 准备自定义数据集

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（可选）使用 TorchDynamo 将 PyTorch 模型导出为 ONNX，并使用 ONNX Runtime 运行它

构建一个 PyTorch 图像分类器模型，然后将其转换为 ONNX 格式，最后使用 ONNX Runtime 部署它。

Production,ONNX,Backends

ONNX Registry 介绍

演示如何通过使用 ONNX Registry 来解决不支持的操作符，从而实现端到端的流程。

Production,ONNX,Backends

强化学习 (DQN)

学习如何使用 PyTorch 在 OpenAI Gym 的 CartPole-v0 任务上训练一个 Deep Q Learning（DQN）代理。

Reinforcement-Learning

使用TorchRL进行强化学习（PPO）

Learn how to use PyTorch and TorchRL to train a Proximal Policy Optimization agent on the Inverted Pendulum task from Gym.

Reinforcement-Learning

训练一个马里奥游戏的 RL Agent

Use PyTorch to train a Double Q-learning agent to play Mario.

Reinforcement-Learning

Recurrent DQN

Use TorchRL to train recurrent policies

Reinforcement-Learning

Code a DDPG Loss

Use TorchRL to code a DDPG Loss

Reinforcement-Learning

Writing your environment and transforms

Use TorchRL to code a Pendulum

Reinforcement-Learning

使用 Flask 在 Python 中部署 PyTorch REST API

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.

Production

Introduction to TorchScript

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++.

Production,TorchScript

在 C++ 中加载 TorchScript 模型

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.

Production,TorchScript

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

Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime.

Production,ONNX

Profiling PyTorch

Learn how to profile a PyTorch application

Profiling

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Profiling PyTorch

Introduction to Holistic Trace Analysis

Profiling

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Profiling PyTorch

Trace Diff using Holistic Trace Analysis

Profiling

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Building a Convolution/Batch Norm fuser in FX

Build a simple FX pass that fuses batch norm into convolution to improve performance during inference.

FX

Building a Simple Performance Profiler with FX

Build a simple FX interpreter to record the runtime of op, module, and function calls and report statistics

FX

(beta) Channels Last Memory Format in PyTorch

Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions.

Memory-Format,Best-Practice,Frontend-APIs

Using the PyTorch C++ Frontend

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.

Frontend-APIs,C++

Custom C++ and CUDA Extensions

Create a neural network layer with no parameters using numpy. Then use scipy to create a neural network layer that has learnable weights.

Extending-PyTorch,Frontend-APIs,C++,CUDA

Extending TorchScript with Custom C++ Operators

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.

Extending-PyTorch,Frontend-APIs,TorchScript,C++

Extending TorchScript with Custom C++ Classes

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.

Extending-PyTorch,Frontend-APIs,TorchScript,C++

Dynamic Parallelism in TorchScript

This tutorial introduces the syntax for doing *dynamic inter-op parallelism* in TorchScript.

Frontend-APIs,TorchScript,C++

Real Time Inference on Raspberry Pi 4

This tutorial covers how to run quantized and fused models on a Raspberry Pi 4 at 30 fps.

TorchScript,Model-Optimization,Image/Video,Quantization

Autograd in C++ Frontend

The autograd package helps build flexible and dynamic nerural netorks. In this tutorial, exploreseveral examples of doing autograd in PyTorch C++ frontend

Frontend-APIs,C++

Registering a Dispatched Operator in C++

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.

Extending-PyTorch,Frontend-APIs,C++

Extending Dispatcher For a New Backend in C++

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.

Extending-PyTorch,Frontend-APIs,C++

Facilitating New Backend Integration by PrivateUse1

Learn how to integrate a new backend living outside of the pytorch/pytorch repo and maintain it to keep in sync with the native PyTorch backend.

Extending-PyTorch,Frontend-APIs,C++

Custom Function Tutorial: Double Backward

Learn how to write a custom autograd Function that supports double backward.

Extending-PyTorch,Frontend-APIs

Custom Function Tutorial: Fusing Convolution and Batch Norm

Learn how to create a custom autograd Function that fuses batch norm into a convolution to improve memory usage.

Extending-PyTorch,Frontend-APIs

Forward-mode Automatic Differentiation

Learn how to use forward-mode automatic differentiation.

Frontend-APIs

Jacobians, Hessians, hvp, vhp, and more

Learn how to compute advanced autodiff quantities using torch.func

Frontend-APIs

Model Ensembling

Learn how to ensemble models using torch.vmap

Frontend-APIs

Per-Sample-Gradients

Learn how to compute per-sample-gradients using torch.func

Frontend-APIs

Neural Tangent Kernels

Learn how to compute neural tangent kernels using torch.func

Frontend-APIs

Performance Profiling in PyTorch

Learn how to use the PyTorch Profiler to benchmark your module's performance.

Model-Optimization,Best-Practice,Profiling

Performance Profiling in TensorBoard

Learn how to use the TensorBoard plugin to profile and analyze your model's performance.

Model-Optimization,Best-Practice,Profiling,TensorBoard

Hyperparameter Tuning Tutorial

Learn how to use Ray Tune to find the best performing set of hyperparameters for your model.

Model-Optimization,Best-Practice

Parametrizations Tutorial

Learn how to use torch.nn.utils.parametrize to put constraints on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...)

Model-Optimization,Best-Practice

Pruning Tutorial

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.

Model-Optimization,Best-Practice

How to save memory by fusing the optimizer step into the backward pass

Learn a memory-saving technique through fusing the optimizer step into the backward pass using memory snapshots.

Model-Optimization,Best-Practice,CUDA,Frontend-APIs

(beta) Accelerating BERT with semi-structured sparsity

Train BERT, prune it to be 2:4 sparse, and then accelerate it to achieve 2x inference speedups with semi-structured sparsity and torch.compile.

Text,Model-Optimization

(beta) Dynamic Quantization on an LSTM Word Language Model

Apply dynamic quantization, the easiest form of quantization, to a LSTM-based next word prediction model.

Text,Quantization,Model-Optimization

(beta) Dynamic Quantization on BERT

Apply the dynamic quantization on a BERT (Bidirectional Embedding Representations from Transformers) model.

Text,Quantization,Model-Optimization

(beta) Quantized Transfer Learning for Computer Vision Tutorial

Extends the Transfer Learning for Computer Vision Tutorial using a quantized model.

Image/Video,Quantization,Model-Optimization

(beta) Static Quantization with Eager Mode in PyTorch

This tutorial shows how to do post-training static quantization.

Quantization

Grokking PyTorch Intel CPU Performance from First Principles

A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch.

Model-Optimization,Production

Grokking PyTorch Intel CPU Performance from First Principles (Part 2)

A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).

Model-Optimization,Production

Multi-Objective Neural Architecture Search with Ax

Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.

Model-Optimization,Best-Practice,Ax,TorchX

torch.compile Tutorial

Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.

Model-Optimization

Inductor CPU Backend Debugging and Profiling

Learn the usage, debugging and performance profiling for ``torch.compile`` with Inductor CPU backend.

Model-Optimization

(beta) Implementing High-Performance Transformers with SCALED DOT PRODUCT ATTENTION

This tutorial explores the new torch.nn.functional.scaled_dot_product_attention and how it can be used to construct Transformer components.

Model-Optimization,Attention,Transformer

Knowledge Distillation in Convolutional Neural Networks

Learn how to improve the accuracy of lightweight models using more powerful models as teachers.

Model-Optimization,Image/Video

PyTorch Distributed Overview

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.