index.rst

og:description:	파이토치(PyTorch) 한국어 튜토리얼에 오신 것을 환영합니다. 파이토치 한국 사용자 모임은 한국어를 사용하시는 많은 분들께 PyTorch를 소개하고 함께 배우며 성장하는 것을 목표로 하고 있습니다.

파이토치(PyTorch) 한국어 튜토리얼에 오신 것을 환영합니다!

아래 튜토리얼들이 새로 추가되었습니다.

• Introduction to TorchRec
• Getting Started with Fully Sharded Data Parallel (FSDP)
• Grokking PyTorch Intel CPU Performance from First Principles
• Customize Process Group Backends Using Cpp Extensions
• Forward-mode Automatic Differentiation (added functorch API capabilities)
• Real Time Inference on Raspberry Pi 4 (30 fps!)

.. customcalloutitem::
   :description: PyTorch 개념과 모듈을 익힙니다. 데이터를 불러오고, 심층 신경망을 구성하고, 모델을 학습하고 저장하는 방법을 배웁니다.
   :header: PyTorch 기본 익히기
   :button_link: beginner/basics/intro.html
   :button_text: PyTorch 시작하기

.. customcalloutitem::
   :description: 한 입 크기의, 바로 사용할 수 있는 PyTorch 코드 예제들을 확인해보세요.
   :header: 파이토치(PyTorch) 레시피
   :button_link: recipes/recipes_index.html
   :button_text: 레시피 찾아보기

All

---

.. customcarditem::
   :header: PyTorch 기본 익히기
   :card_description: PyTorch로 전체 ML워크플로우를 구축하기 위한 단계별 학습 가이드입니다.
   :image: _static/img/thumbnails/cropped/60-min-blitz.png
   :link: beginner/basics/intro.html
   :tags: Getting-Started

.. customcarditem::
   :header: Introduction to PyTorch on YouTube
   :card_description: An introduction to building a complete ML workflow with PyTorch. Follows the PyTorch Beginner Series on YouTube.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: beginner/introyt.html
   :tags: Getting-Started

.. customcarditem::
   :header: 예제로 배우는 파이토치(PyTorch)
   :card_description: 튜토리얼에 포함된 예제들로 PyTorch의 기본 개념을 이해합니다.
   :image: _static/img/thumbnails/cropped/learning-pytorch-with-examples.png
   :link: beginner/pytorch_with_examples.html
   :tags: Getting-Started

.. customcarditem::
   :header: torch.nn이 실제로 무엇인가요?
   :card_description: torch.nn을 사용하여 신경망을 생성하고 학습합니다.
   :image: _static/img/thumbnails/cropped/torch-nn.png
   :link: beginner/nn_tutorial.html
   :tags: Getting-Started

.. customcarditem::
   :header: TensorBoard로 모델, 데이터, 학습 시각화하기
   :card_description: TensorBoard로 데이터 및 모델 교육을 시각화하는 방법을 배웁니다.
   :image: _static/img/thumbnails/cropped/visualizing-with-tensorboard.png
   :link: intermediate/tensorboard_tutorial.html
   :tags: Interpretability,Getting-Started,Tensorboard

.. customcarditem::
   :header: TorchVision 객체 검출 미세조정(Finetuning) 튜토리얼
   :card_description: 이미 훈련된 Mask R-CNN 모델을 미세조정합니다.
   :image: _static/img/thumbnails/cropped/TorchVision-Object-Detection-Finetuning-Tutorial.png
   :link: intermediate/torchvision_tutorial.html
   :tags: Image/Video

.. customcarditem::
   :header: 컴퓨터 비전을 위한 전이학습(Transfer Learning) 튜토리얼
   :card_description: 전이학습으로 이미지 분류를 위한 합성곱 신경망을 학습합니다.
   :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

.. customcarditem::
   :header: 적대적 예제 생성(Adversarial Example Generation)
   :card_description: 가장 많이 사용되는 공격 방법 중 하나인 FGSM (Fast Gradient Sign Attack)을 이용해 MNIST 분류기를 속이는 방법을 배웁니다.
   :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

.. customcarditem::
   :header: Spatial Transformer Networks Tutorial
   :card_description: Learn how to augment your network using a visual attention mechanism.
   :image: _static/img/stn/Five.gif
   :link: intermediate/spatial_transformer_tutorial.html
   :tags: Image/Video

.. customcarditem::
   :header: Audio IO
   :card_description: Learn to load data with torchaudio.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_io_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Audio Resampling
   :card_description: Learn to resample audio waveforms using torchaudio.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_resampling_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Audio Data Augmentation
   :card_description: Learn to apply data augmentations using torchaudio.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_data_augmentation_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Audio Feature Extractions
   :card_description: Learn to extract features using torchaudio.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_feature_extractions_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Audio Feature Augmentation
   :card_description: Learn to augment features using torchaudio.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_feature_augmentation_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Audio Datasets
   :card_description: Learn to use torchaudio datasets.
   :image: _static/img/thumbnails/cropped/torchaudio-Tutorial.png
   :link: beginner/audio_datasets_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Automatic Speech Recognition with Wav2Vec2 in torchaudio
   :card_description: Learn how to use torchaudio's pretrained models for building a speech recognition application.
   :image: _static/img/thumbnails/cropped/torchaudio-asr.png
   :link: intermediate/speech_recognition_pipeline_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Speech Command Classification
   :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_classification_with_torchaudio_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: Text-to-Speech with torchaudio
   :card_description: Learn how to use torchaudio's pretrained models for building a text-to-speech application.
   :image: _static/img/thumbnails/cropped/torchaudio-speech.png
   :link: intermediate/text_to_speech_with_torchaudio.html
   :tags: Audio

.. customcarditem::
   :header: Forced Alignment with Wav2Vec2 in torchaudio
   :card_description: Learn how to use torchaudio's Wav2Vec2 pretrained models for aligning text to speech
   :image: _static/img/thumbnails/cropped/torchaudio-alignment.png
   :link: intermediate/forced_alignment_with_torchaudio_tutorial.html
   :tags: Audio

.. customcarditem::
   :header: nn.Transformer와 TorchText로 시퀀스-투-시퀀스 모델링하기
   :card_description: nn.Transformer 모듈을 사용하여 어떻게 시퀀스-투-시퀀스(Seq-to-Seq) 모델을 학습하는지 배웁니다.
   :image: _static/img/thumbnails/cropped/Sequence-to-Sequence-Modeling-with-nnTransformer-andTorchText.png
   :link: beginner/transformer_tutorial.html
   :tags: Text

.. customcarditem::
   :header: 기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기
   :card_description: torchtext를 사용하지 않고 기본적인 문자-단위 RNN을 사용하여 단어를 분류하는 모델을 기초부터 만들고 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈의 첫번째 편입니다.
   :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: 기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 생성하기
   :card_description: 문자-단위 RNN을 사용하여 이름을 분류해봤으니, 이름을 생성하는 방법을 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈 중 두번째 편입니다.
   :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: 기초부터 시작하는 NLP: 시퀀스-투-시퀀스 네트워크와 어텐션을 이용한 번역
   :card_description: “기초부터 시작하는 NLP”의 세번째이자 마지막 편으로, NLP 모델링 작업을 위한 데이터 전처리에 사용할 자체 클래스와 함수들을 작성해보겠습니다.
   :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: torchtext로 텍스트 분류하기
   :card_description: torchtext 라이브러리를 사용하여 어떻게 텍스트 분류 분석을 위한 데이터셋을 만드는지를 살펴봅니다.
   :image: _static/img/thumbnails/cropped/Text-Classification-with-TorchText.png
   :link: beginner/text_sentiment_ngrams_tutorial.html
   :tags: Text

.. customcarditem::
   :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

.. customcarditem::
   :header: 강화 학습(DQN) 튜토리얼
   :card_description: PyTorch를 사용하여 OpenAI Gym의 CartPole-v0 태스크에서 DQN(Deep Q Learning) 에이전트를 학습하는 방법을 살펴봅니다.
   :image: _static/img/cartpole.gif
   :link: intermediate/reinforcement_q_learning.html
   :tags: Reinforcement-Learning

.. customcarditem::
   :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

.. customcarditem::
   :header: Flask를 사용하여 Python에서 PyTorch를 REST API로 배포하기
   :card_description: Flask를 사용하여 PyTorch 모델을 배포하고, 미리 학습된 DenseNet 121 모델을 예제로 활용하여 모델 추론(inference)을 위한 REST API를 만들어보겠습니다.
   :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: TorchScript 소개
   :card_description: C++과 같은 고성능 환경에서 실행할 수 있도록 (nn.Module의 하위 클래스인) PyTorch 모델의 중간 표현(intermediate representation)을 제공하는 TorchScript를 소개합니다.
   :image: _static/img/thumbnails/cropped/Introduction-to-TorchScript.png
   :link: beginner/Intro_to_TorchScript_tutorial.html
   :tags: Production,TorchScript

.. customcarditem::
   :header: C++에서 TorchScript 모델 로딩하기
   :card_description: PyTorch가 어떻게 기존의 Python 모델을 직렬화된 표현으로 변환하여 Python 의존성 없이 순수하게 C++에서 불러올 수 있는지 배웁니다.
   :image: _static/img/thumbnails/cropped/Loading-a-TorchScript-Model-in-Cpp.png
   :link: advanced/cpp_export.html
   :tags: Production,TorchScript

.. customcarditem::
   :header: (선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기
   :card_description: PyTorch로 정의한 모델을 ONNX 형식으로 변환하고 ONNX 런타임에서 실행합니다.
   :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

.. customcarditem::
   :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: (베타) PyTorch의 Channels Last 메모리 형식
   :card_description: Channels Last 메모리 형식에 대한 개요를 확인하고 차원 순서를 유지하며 메모리 상의 NCHW 텐서를 정렬하는 방법을 이해합니다.
   :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

.. 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: 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++

.. 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: Real Time Inference on Raspberry Pi 4
   :card_description: This tutorial covers how to run quantized and fused models on a Raspberry Pi 4 at 30 fps.
   :image: _static/img/thumbnails/cropped/realtime_rpi.png
   :link: intermediate/realtime_rpi.html
   :tags: TorchScript,Model-Optimization,Image/Video,Quantization

.. 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++

.. customcarditem::
   :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++

.. customcarditem::
   :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++

.. customcarditem::
   :header: Custom Function Tutorial: Double Backward
   :card_description: Learn how to write a custom autograd Function that supports double backward.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/custom_function_double_backward_tutorial.html
   :tags: Extending-PyTorch,Frontend-APIs

.. customcarditem::
   :header: Custom Function Tutorial: Fusing Convolution and Batch Norm
   :card_description: Learn how to create a custom autograd Function that fuses batch norm into a convolution to improve memory usage.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/custom_function_conv_bn_tutorial.html
   :tags: Extending-PyTorch,Frontend-APIs

.. customcarditem::
   :header: Forward-mode Automatic Differentiation
   :card_description: Learn how to use forward-mode automatic differentiation.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/forward_ad_usage.html
   :tags: Frontend-APIs

.. customcarditem::
   :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

.. customcarditem::
    :header: Optimizing Vision Transformer Model
    :card_description: Learn how to use Facebook Data-efficient Image Transformers DeiT and script and optimize it for mobile.
    :image: _static/img/thumbnails/cropped/mobile.png
    :link: beginner/vt_tutorial.html
    :tags: Model-Optimization,Best-Practice,Mobile

.. customcarditem::
   :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

.. customcarditem::
   :header: 가지치기 기법(pruning) 튜토리얼
   :card_description: torch.nn.utils.prune을 사용하여 신경망을 희소화(sparsify)하는 방법과, 이를 확장하여 사용자 정의 가지치기 기법을 구현하는 방법을 알아봅니다.
   :image: _static/img/thumbnails/cropped/Pruning-Tutorial.png
   :link: intermediate/pruning_tutorial.html
   :tags: Model-Optimization,Best-Practice

.. customcarditem::
   :header: (베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화
   :card_description: 가장 간단한 양자화 기법인 동적 양자화(dynamic quantization)를 LSTM 기반의 단어 예측 모델에 적용합니다.
   :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: (베타) BERT 모델 동적 양자화하기
   :card_description: BERT(Bidirectional Embedding Representations from Transformers) 모델에 동적 양자화(dynamic quantization)를 적용합니다.
   :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: (베타) 컴퓨터 비전 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)
   :card_description: 양자화된 모델을 사용하여 전이학습을 컴퓨터 비전 튜토리얼에 확장합니다.
   :image: _static/img/thumbnails/cropped/60-min-blitz.png
   :link: intermediate/quantized_transfer_learning_tutorial.html
   :tags: Image/Video,Quantization,Model-Optimization

.. customcarditem::
   :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: Grokking PyTorch Intel CPU Performance from First Principles
   :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch.
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/torchserve_with_ipex
   :tags: Model-Optimization,Production

.. 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: 단일 머신을 사용한 모델 병렬화 모범 사례
   :card_description: 개별 GPU들에 전체 모델을 복제하는 대신, 하나의 모델을 여러 GPU에 분할하여 분산학습을 하는 모델 병렬 처리를 구현하는 방법을 배웁니다.
   :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: PyTorch로 분산 어플리케이션 개발하기
   :card_description: PyTorch의 분산 패키지를 설정하고, 서로 다른 통신 전략을 사용하고, 내부를 살펴봅니다.
   :image: _static/img/thumbnails/cropped/Writing-Distributed-Applications-with-PyTorch.png
   :link: intermediate/dist_tuto.html
   :tags: Parallel-and-Distributed-Training

.. customcarditem::
   :header: Customize Process Group Backends Using Cpp Extensions
   :card_description: Extend ProcessGroup with custom collective communication implementations.
   :image: _static/img/thumbnails/cropped/Customize-Process-Group-Backends-Using-Cpp-Extensions.png
   :link: intermediate/process_group_cpp_extension_tutorial.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

.. customcarditem::
   :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

.. customcarditem::
   :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

.. customcarditem::
   :header: Getting Started with Fully Sharded Data Parallel(FSDP)
   :card_description: Learn how to train models with Fully Sharded Data Parallel package.
   :image: _static/img/thumbnails/cropped/Getting Started with FSDP.png
   :link: intermediate/FSDP_tutorial.html
   :tags: Parallel-and-Distributed-Training

.. customcarditem::
   :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

.. customcarditem::
   :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

.. customcarditem::
   :header: Introduction to TorchRec
   :card_description: TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems.
   :image: _static/img/thumbnails/torchrec.png
   :link: intermediate/torchrec_tutorial.html
   :tags: TorchRec,Recommender

.. customcarditem::
   :header: Exploring TorchRec sharding
   :card_description: This tutorial covers the sharding schemes of embedding tables by using <code>EmbeddingPlanner</code> and <code>DistributedModelParallel</code> API.
   :image: _static/img/thumbnails/torchrec.png
   :link: advanced/sharding.html
   :tags: TorchRec,Recommender

추가 자료

.. customcalloutitem::
   :header: 파이토치(PyTorch) 예제
   :description: 비전, 텍스트, Reinforcement-Learning 등의 분야에서의 PyTorch 예제 모음
   :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: Open

.. customcalloutitem::
   :header: 공식 튜토리얼 저장소(GitHub)
   :description: GitHub에서 공식 튜토리얼을 만나보세요.
   :button_link: https://github.com/pytorch/tutorials
   :button_text: Go To GitHub

.. customcalloutitem::
   :header: 튜토리얼을 Google Colab에서 실행하기
   :description: Google Colab에서 튜토리얼을 실행하기 위해 튜토리얼 데이터를 Google Drive로 복사하는 방법을 배웁니다.
   :button_link: beginner/colab.html
   :button_text: Open

.. customcalloutitem::
   :header: (비공식) 한국어 튜토리얼 저장소(GitHub)
   :description: GitHub에서 (비공식) 한국어 튜토리얼을 만나보세요.
   :button_link: https://github.com/PyTorchKorea/tutorials-kr
   :button_text: Go To GitHub

.. customcalloutitem::
   :header: 파이토치 한국어 사용자 모임
   :description: 파이토치를 사용하는 다른 사용자들과 의견을 나눠보세요.
   :button_link: https://discuss.pytorch.kr
   :button_text: Open

.. toctree::
   :maxdepth: 2
   :hidden:
   :includehidden:
   :caption: 파이토치(PyTorch) 레시피

   모든 레시피 보기 <recipes/recipes_index>
   모든 프로토타입 레시피 보기 <prototype/prototype_index>

.. toctree::
   :maxdepth: 2
   :hidden:
   :includehidden:
   :caption: 파이토치(PyTorch) 시작하기

   beginner/basics/intro
   beginner/basics/quickstart_tutorial
   beginner/basics/tensorqs_tutorial
   beginner/basics/data_tutorial
   beginner/basics/transforms_tutorial
   beginner/basics/buildmodel_tutorial
   beginner/basics/autogradqs_tutorial
   beginner/basics/optimization_tutorial
   beginner/basics/saveloadrun_tutorial

.. toctree::
   :maxdepth: 2
   :hidden:
   :includehidden:
   :caption: Introduction to PyTorch on YouTube

   beginner/introyt
   beginner/introyt/introyt1_tutorial
   beginner/introyt/tensors_deeper_tutorial
   beginner/introyt/autogradyt_tutorial
   beginner/introyt/modelsyt_tutorial
   beginner/introyt/tensorboardyt_tutorial
   beginner/introyt/trainingyt
   beginner/introyt/captumyt

.. toctree::
   :maxdepth: 2
   :hidden:
   :includehidden:
   :caption: 파이토치(PyTorch) 배우기

   beginner/deep_learning_60min_blitz
   beginner/pytorch_with_examples
   beginner/nn_tutorial
   intermediate/tensorboard_tutorial

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 이미지/비디오

   intermediate/torchvision_tutorial
   beginner/transfer_learning_tutorial
   beginner/fgsm_tutorial
   beginner/dcgan_faces_tutorial
   beginner/vt_tutorial

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 오디오

   beginner/audio_io_tutorial
   beginner/audio_resampling_tutorial
   beginner/audio_data_augmentation_tutorial
   beginner/audio_feature_extractions_tutorial
   beginner/audio_feature_augmentation_tutorial
   beginner/audio_datasets_tutorial
   intermediate/speech_recognition_pipeline_tutorial
   intermediate/speech_command_classification_with_torchaudio_tutorial
   intermediate/text_to_speech_with_torchaudio
   intermediate/forced_alignment_with_torchaudio_tutorial

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 텍스트

   beginner/transformer_tutorial
   intermediate/char_rnn_classification_tutorial
   intermediate/char_rnn_generation_tutorial
   intermediate/seq2seq_translation_tutorial
   beginner/text_sentiment_ngrams_tutorial
   beginner/translation_transformer

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 강화학습

   intermediate/reinforcement_q_learning
   intermediate/mario_rl_tutorial

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: PyTorch 모델을 프로덕션 환경에 배포하기

   intermediate/flask_rest_api_tutorial
   beginner/Intro_to_TorchScript_tutorial
   advanced/cpp_export
   advanced/super_resolution_with_onnxruntime
   intermediate/realtime_rpi

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: Code Transforms with FX

   intermediate/fx_conv_bn_fuser
   intermediate/fx_profiling_tutorial

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 프론트엔드 API

   intermediate/memory_format_tutorial
   intermediate/forward_ad_usage
   advanced/cpp_frontend
   advanced/torch-script-parallelism
   advanced/cpp_autograd

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: PyTorch 확장하기

   intermediate/custom_function_double_backward_tutorial
   intermediate/custom_function_conv_bn_tutorial
   advanced/cpp_extension
   advanced/torch_script_custom_ops
   advanced/torch_script_custom_classes
   advanced/dispatcher
   advanced/extend_dispatcher

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 모델 최적화

   beginner/profiler
   intermediate/tensorboard_profiler_tutorial
   beginner/hyperparameter_tuning_tutorial
   beginner/vt_tutorial
   intermediate/parametrizations
   intermediate/pruning_tutorial
   advanced/dynamic_quantization_tutorial
   intermediate/dynamic_quantization_bert_tutorial
   intermediate/quantized_transfer_learning_tutorial
   advanced/static_quantization_tutorial
   intermediate/torchserve_with_ipex

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: 병렬 및 분산 학습

   beginner/dist_overview
   intermediate/model_parallel_tutorial
   intermediate/ddp_tutorial
   intermediate/dist_tuto
   intermediate/FSDP_tutorial
   intermediate/process_group_cpp_extension_tutorial
   intermediate/rpc_tutorial
   intermediate/rpc_param_server_tutorial
   intermediate/dist_pipeline_parallel_tutorial
   intermediate/rpc_async_execution
   advanced/rpc_ddp_tutorial
   intermediate/pipeline_tutorial
   advanced/ddp_pipeline
   advanced/generic_join

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: Mobile

   beginner/deeplabv3_on_ios
   beginner/deeplabv3_on_android

.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: Recommendation Systems

   intermediate/torchrec_tutorial
   advanced/sharding