파이토치(PyTorch) 한국어 튜토리얼에 오신 것을 환영합니다!¶

아래 튜토리얼들이 새로 추가되었습니다:

PyTorch 기본 익히기

PyTorch 개념과 모듈을 익힙니다. 데이터를 불러오고, 심층 신경망을 구성하고, 모델을 학습하고 저장하는 방법을 배웁니다.

PyTorch 시작하기

파이토치(PyTorch) 레시피

한 입 크기의, 바로 사용할 수 있는 PyTorch 코드 예제들을 확인해보세요.

레시피 찾아보기

PyTorch 기본 익히기

PyTorch로 전체 ML워크플로우를 구축하기 위한 단계별 학습 가이드입니다.

Getting-Started

Introduction to PyTorch on YouTube

An introduction to building a complete ML workflow with PyTorch. Follows the PyTorch Beginner Series on YouTube.

Getting-Started

예제로 배우는 파이토치(PyTorch)

튜토리얼에 포함된 예제들로 PyTorch의 기본 개념을 이해합니다.

Getting-Started

torch.nn이 실제로 무엇인가요?

torch.nn을 사용하여 신경망을 생성하고 학습합니다.

Getting-Started

TensorBoard로 모델, 데이터, 학습 시각화하기

TensorBoard로 데이터 및 모델 교육을 시각화하는 방법을 배웁니다.

Interpretability,Getting-Started,Tensorboard

TorchVision 객체 검출 미세조정(Finetuning) 튜토리얼

이미 훈련된 Mask R-CNN 모델을 미세조정합니다.

Image/Video

컴퓨터 비전을 위한 전이학습(Transfer Learning) 튜토리얼

전이학습으로 이미지 분류를 위한 합성곱 신경망을 학습합니다.

Image/Video

Optimizing Vision Transformer Model

Apply cutting-edge, attention-based transformer models to computer vision tasks.

Image/Video

적대적 예제 생성(Adversarial Example Generation)

가장 많이 사용되는 공격 방법 중 하나인 FGSM (Fast Gradient Sign Attack)을 이용해 MNIST 분류기를 속이는 방법을 배웁니다.

Image/Video

DCGAN Tutorial

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

Image/Video

Spatial Transformer Networks Tutorial

Learn how to augment your network using a visual attention mechanism.

Image/Video

Inference on Whole Slide Images with TIAToolbox

Learn how to use the TIAToolbox to perform inference on whole slide images.

Image/Video

Semi-Supervised Learning Tutorial Based on USB

Learn how to train semi-supervised learning algorithms (on custom data) using USB and PyTorch.

Image/Video

Audio IO

Learn to load data with torchaudio.

Audio

Audio Resampling

Learn to resample audio waveforms using torchaudio.

Audio

Audio Data Augmentation

Learn to apply data augmentations using torchaudio.

Audio

Audio Feature Extractions

Learn to extract features using torchaudio.

Audio

Audio Feature Augmentation

Learn to augment features using torchaudio.

Audio

Audio Datasets

Learn to use torchaudio datasets.

Audio

Automatic Speech Recognition with Wav2Vec2 in torchaudio

Learn how to use torchaudio's pretrained models for building a speech recognition application.

Audio

Speech Command Classification

Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset.

Audio

Text-to-Speech with torchaudio

Learn how to use torchaudio's pretrained models for building a text-to-speech application.

Audio

Forced Alignment with Wav2Vec2 in torchaudio

Learn how to use torchaudio's Wav2Vec2 pretrained models for aligning text to speech

Audio

Fast Transformer Inference with Better Transformer

Deploy a PyTorch Transformer model using Better Transformer with high performance for inference

Production,Text

기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기

torchtext를 사용하지 않고 기본적인 문자-단위 RNN을 사용하여 단어를 분류하는 모델을 기초부터 만들고 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈의 첫번째 편입니다.

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기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 생성하기

문자-단위 RNN을 사용하여 이름을 분류해봤으니, 이름을 생성하는 방법을 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈 중 두번째 편입니다.

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기초부터 시작하는 NLP: 시퀀스-투-시퀀스 네트워크와 어텐션을 이용한 번역

“기초부터 시작하는 NLP”의 세번째이자 마지막 편으로, NLP 모델링 작업을 위한 데이터 전처리에 사용할 자체 클래스와 함수들을 작성해보겠습니다.

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torchtext로 텍스트 분류하기

torchtext 라이브러리를 사용하여 어떻게 텍스트 분류 분석을 위한 데이터셋을 만드는지를 살펴봅니다.

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Language Translation with Transformer

Train a language translation model from scratch using Transformer.

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Pre-process custom text dataset using Torchtext

Learn how to use torchtext to prepare a custom dataset

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(optional) Exporting a PyTorch model to ONNX using TorchDynamo backend and Running it using ONNX Runtime

Build a image classifier model in PyTorch and convert it to ONNX before deploying it with ONNX Runtime.

Production,ONNX,Backends

Introduction to ONNX Registry

Demonstrate end-to-end how to address unsupported operators by using ONNX Registry.

Production,ONNX,Backends

강화 학습(DQN) 튜토리얼

PyTorch를 사용하여 OpenAI Gym의 CartPole-v0 태스크에서 DQN(Deep Q Learning) 에이전트를 학습하는 방법을 살펴봅니다.

Reinforcement-Learning

Reinforcement Learning (PPO) with TorchRL

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

Reinforcement-Learning

Train a Mario-playing 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

Flask를 사용하여 Python에서 PyTorch를 REST API로 배포하기

Flask를 사용하여 PyTorch 모델을 배포하고, 미리 학습된 DenseNet 121 모델을 예제로 활용하여 모델 추론(inference)을 위한 REST API를 만들어보겠습니다.

Production

TorchScript 소개

C++과 같은 고성능 환경에서 실행할 수 있도록 (nn.Module의 하위 클래스인) PyTorch 모델의 중간 표현(intermediate representation)을 제공하는 TorchScript를 소개합니다.

Production,TorchScript

C++에서 TorchScript 모델 로딩하기

PyTorch가 어떻게 기존의 Python 모델을 직렬화된 표현으로 변환하여 Python 의존성 없이 순수하게 C++에서 불러올 수 있는지 배웁니다.

Production,TorchScript

(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기

PyTorch로 정의한 모델을 ONNX 형식으로 변환하고 ONNX 런타임에서 실행합니다.

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

(베타) PyTorch의 Channels Last 메모리 형식

Channels Last 메모리 형식에 대한 개요를 확인하고 차원 순서를 유지하며 메모리 상의 NCHW 텐서를 정렬하는 방법을 이해합니다.

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

가지치기 기법(pruning) 튜토리얼

torch.nn.utils.prune을 사용하여 신경망을 희소화(sparsify)하는 방법과, 이를 확장하여 사용자 정의 가지치기 기법을 구현하는 방법을 알아봅니다.

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

(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화

가장 간단한 양자화 기법인 동적 양자화(dynamic quantization)를 LSTM 기반의 단어 예측 모델에 적용합니다.

Text,Quantization,Model-Optimization

(베타) BERT 모델 동적 양자화하기

BERT(Bidirectional Embedding Representations from Transformers) 모델에 동적 양자화(dynamic quantization)를 적용합니다.

Text,Quantization,Model-Optimization

(베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)

양자화된 모델을 사용하여 전이학습을 컴퓨터 비전 튜토리얼에 확장합니다.

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.