.. 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: Fast Transformer Inference with Better Transformer
   :card_description: Deploy a PyTorch Transformer model using Better Transformer with high performance for inference
   :image: _static/img/thumbnails/cropped/pytorch-logo.png
   :link: beginner/bettertransformer_tutorial.html
   :tags: Production,Text

.. 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: Grokking PyTorch Intel CPU Performance from First Principles (Part 2)
   :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/torchserve_with_ipex_2
   :tags: Model-Optimization,Production

.. customcarditem::
   :header: Introduction to nvFuser
   :card_description: An introduction to nvFuser
   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
   :link: intermediate/nvfuser_intro_tutorial.html
   :tags: Model-Optimization

.. customcarditem::
   :header: Multi-Objective Neural Architecture Search with Ax
   :card_description: Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.
   :image: _static/img/ax_logo.png
   :link: intermediate/ax_multiobjective_nas_tutorial.html
   :tags: Model-Optimization,Best-Practice,Ax,TorchX

.. 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: Distributed Data Parallel in PyTorch - Video Tutorials
   :card_description: This series of video tutorials walks you through distributed training in PyTorch via DDP.
   :image: _static/img/thumbnails/cropped/PyTorch-Distributed-Overview.png
   :link: beginner/ddp_series_intro.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: Advanced Model Training with Fully Sharded Data Parallel (FSDP)
   :card_description: Explore advanced model training with Fully Sharded Data Parallel package.
   :image: _static/img/thumbnails/cropped/Getting-Started-with-FSDP.png
   :link: intermediate/FSDP_adavnced_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

.. customcarditem::
   :header: Introduction to TorchMultimodal
   :card_description: TorchMultimodal is a library that provides models, primitives and examples for training multimodal tasks
   :image: _static/img/thumbnails/torchrec.png
   :link: beginner/flava_finetuning_tutorial.html
   :tags: TorchMultimodal