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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ DataLoader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
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<p class="caption" role="heading"><span class="caption-text">Introduction to PyTorch on YouTube</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt.html">PyTorch ์๊ฐ - YouTube ์๋ฆฌ์ฆ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/introyt1_tutorial.html">PyTorch ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensors_deeper_tutorial.html">Pytorch Tensor ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/autogradyt_tutorial.html">The Fundamentals of Autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/modelsyt_tutorial.html">Building Models with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/tensorboardyt_tutorial.html">PyTorch TensorBoard Support</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/trainingyt.html">Training with PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/introyt/captumyt.html">Model Understanding with Captum</a></li>
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<p class="caption" role="heading"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
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<p class="caption" role="heading"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision Object Detection Finetuning Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tiatoolbox_tutorial.html">Whole Slide Image Classification Using PyTorch and TIAToolbox</a></li>
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<p class="caption" role="heading"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_io_tutorial.html">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_resampling_tutorial.html">Audio Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_data_augmentation_tutorial.html">Audio Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_extractions_tutorial.html">Audio Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_feature_augmentation_tutorial.html">Audio Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_datasets_tutorial.html">Audio Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_recognition_pipeline_tutorial.html">Speech Recognition with Wav2Vec2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/text_to_speech_with_torchaudio.html">Text-to-speech with Tacotron2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forced_alignment_with_torchaudio_tutorial.html">wav2vec2์ ์ด์ฉํ ๊ฐ์ ์ ๋ ฌ</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/bettertransformer_tutorial.html">Fast Transformer Inference with Better Transformer</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html"><code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> ์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/torchtext_custom_dataset_tutorial.html">Preprocess custom text dataset using Torchtext</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ฐฑ์๋</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_q_learning.html">๊ฐํ ํ์ต (DQN) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/reinforcement_ppo.html">Reinforcement Learning (PPO) with TorchRL Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/mario_rl_tutorial.html">๋ง๋ฆฌ์ค ๊ฒ์ RL ์์ด์ ํธ๋ก ํ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="pendulum.html">Pendulum: Writing your environment and transforms with TorchRL</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ๋ชจ๋ธ์ ํ๋ก๋์
ํ๊ฒฝ์ ๋ฐฐํฌํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/onnx/intro_onnx.html">Introduction to ONNX</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/flask_rest_api_tutorial.html">Flask๋ฅผ ์ฌ์ฉํ์ฌ Python์์ PyTorch๋ฅผ REST API๋ก ๋ฐฐํฌํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/Intro_to_TorchScript_tutorial.html">TorchScript ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_export.html">C++์์ TorchScript ๋ชจ๋ธ ๋ก๋ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="super_resolution_with_onnxruntime.html">(์ ํ) PyTorch ๋ชจ๋ธ์ ONNX์ผ๋ก ๋ณํํ๊ณ ONNX ๋ฐํ์์์ ์คํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/realtime_rpi.html">Raspberry Pi 4 ์์ ์ค์๊ฐ ์ถ๋ก (Inference) (30fps!)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ๋กํ์ผ๋ง</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_intro_tutorial.html">Introduction to Holistic Trace Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hta_trace_diff_tutorial.html">Trace Diff using Holistic Trace Analysis</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Code Transforms with FX</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_conv_bn_fuser.html">(๋ฒ ํ) FX์์ ํฉ์ฑ๊ณฑ/๋ฐฐ์น ์ ๊ทํ(Convolution/Batch Norm) ๊ฒฐํฉ๊ธฐ(Fuser) ๋ง๋ค๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/fx_profiling_tutorial.html">(beta) Building a Simple CPU Performance Profiler with FX</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">ํ๋ก ํธ์๋ API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(๋ฒ ํ) PyTorch๋ฅผ ์ฌ์ฉํ Channels Last ๋ฉ๋ชจ๋ฆฌ ํ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/forward_ad_usage.html">Forward-mode Automatic Differentiation (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/jacobians_hessians.html">Jacobians, Hessians, hvp, vhp, and more: composing function transforms</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ensembling.html">๋ชจ๋ธ ์์๋ธ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/per_sample_grads.html">Per-sample-gradients</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">PyTorch C++ ํ๋ก ํธ์๋ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch-script-parallelism.html">TorchScript์ ๋์ ๋ณ๋ ฌ ์ฒ๋ฆฌ(Dynamic Parallelism)</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_autograd.html">C++ ํ๋ก ํธ์๋์ ์๋ ๋ฏธ๋ถ (autograd)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">PyTorch ํ์ฅํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_double_backward_tutorial.html">Double Backward with Custom Functions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/custom_function_conv_bn_tutorial.html">Fusing Convolution and Batch Norm using Custom Function</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_script_custom_classes.html">์ปค์คํ
C++ ํด๋์ค๋ก TorchScript ํ์ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dispatcher.html">Registering a Dispatched Operator in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="extend_dispatcher.html">Extending dispatcher for a new backend in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="privateuseone.html">Facilitating New Backend Integration by PrivateUse1</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ชจ๋ธ ์ต์ ํ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/profiler.html">PyTorch ๋ชจ๋ ํ๋กํ์ผ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_profiler_tutorial.html">ํ
์๋ณด๋๋ฅผ ์ด์ฉํ ํ์ดํ ์น ํ๋กํ์ผ๋ฌ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/hyperparameter_tuning_tutorial.html">Ray Tune์ ์ฌ์ฉํ ํ์ดํผํ๋ผ๋ฏธํฐ ํ๋</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">๋ฐฐํฌ๋ฅผ ์ํด ๋น์ ํธ๋์คํฌ๋จธ(Vision Transformer) ๋ชจ๋ธ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/parametrizations.html">Parametrizations Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">๊ฐ์ง์น๊ธฐ ๊ธฐ๋ฒ(Pruning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(๋ฒ ํ) LSTM ๊ธฐ๋ฐ ๋จ์ด ๋จ์ ์ธ์ด ๋ชจ๋ธ์ ๋์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(๋ฒ ํ) BERT ๋ชจ๋ธ ๋์ ์์ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(๋ฒ ํ) ์ปดํจํฐ ๋น์ ํํ ๋ฆฌ์ผ์ ์ํ ์์ํ๋ ์ ์ดํ์ต(Quantized Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(๋ฒ ํ) PyTorch์์ Eager Mode๋ฅผ ์ด์ฉํ ์ ์ ์์ํ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchserve_with_ipex.html">Grokking PyTorch Intel CPU performance from first principles</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchserve_with_ipex_2.html">Grokking PyTorch Intel CPU performance from first principles (Part 2)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/nvfuser_intro_tutorial.html">Getting Started - Accelerate Your Scripts with nvFuser</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ax_multiobjective_nas_tutorial.html">Multi-Objective NAS with Ax</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torch_compile_tutorial.html">Introduction to <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/inductor_debug_cpu.html">Inductor CPU backend debugging and profiling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html">(Beta) Scaled Dot Product Attention (SDPA)๋ก ๊ณ ์ฑ๋ฅ ํธ๋์คํฌ๋จธ(Transformers) ๊ตฌํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#torch-compile-sdpa"><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ๊ณผ ํจ๊ป SDPA ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#sdpa-atteition-bias">SDPA๋ฅผ <code class="docutils literal notranslate"><span class="pre">atteition.bias</span></code> ํ์ ํด๋์ค์ ์ฌ์ฉํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/scaled_dot_product_attention_tutorial.html#id8">๊ฒฐ๋ก </a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/knowledge_distillation_tutorial.html">Knowledge Distillation Tutorial</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">๋ณ๋ ฌ ๋ฐ ๋ถ์ฐ ํ์ต</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../distributed/home.html">Distributed and Parallel Training Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dist_overview.html">PyTorch Distributed Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/ddp_series_intro.html">Distributed Data Parallel in PyTorch - Video Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">๋จ์ผ ๋จธ์ ์ ์ฌ์ฉํ ๋ชจ๋ธ ๋ณ๋ ฌํ ๋ชจ๋ฒ ์ฌ๋ก</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ ์์ํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch๋ก ๋ถ์ฐ ์ดํ๋ฆฌ์ผ์ด์
๊ฐ๋ฐํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/FSDP_tutorial.html">Getting Started with Fully Sharded Data Parallel(FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/FSDP_adavnced_tutorial.html">Advanced Model Training with Fully Sharded Data Parallel (FSDP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/TP_tutorial.html">Large Scale Transformer model training with Tensor Parallel (TP)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/process_group_cpp_extension_tutorial.html">Cpp ํ์ฅ์ ์ฌ์ฉํ ํ๋ก์ธ์ค ๊ทธ๋ฃน ๋ฐฑ์๋ ์ฌ์ฉ์ ์ ์</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_param_server_tutorial.html">Implementing a Parameter Server Using Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_pipeline_parallel_tutorial.html">Distributed Pipeline Parallelism Using RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_async_execution.html">Implementing Batch RPC Processing Using Asynchronous Executions</a></li>
<li class="toctree-l1"><a class="reference internal" href="rpc_ddp_tutorial.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ(DDP)๊ณผ ๋ถ์ฐ RPC ํ๋ ์์ํฌ ๊ฒฐํฉ</a></li>
<li class="toctree-l1"><a class="reference internal" href="ddp_pipeline.html">๋ถ์ฐ ๋ฐ์ดํฐ ๋ณ๋ ฌ ์ฒ๋ฆฌ์ ๋ณ๋ ฌ ์ฒ๋ฆฌ ํ์ดํ๋ผ์ธ์ ์ฌ์ฉํ ํธ๋์คํฌ๋จธ ๋ชจ๋ธ ํ์ต</a></li>
<li class="toctree-l1"><a class="reference internal" href="generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Edge with ExecuTorch</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html">Exporting to ExecuTorch Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href=" https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html">Running an ExecuTorch Model in C++ Tutorial</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html">Using the ExecuTorch SDK to Profile a Model</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-ios.html">Building an ExecuTorch iOS Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/demo-apps-android.html">Building an ExecuTorch Android Demo App</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html">Lowering a Model as a Delegate</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">์ถ์ฒ ์์คํ
</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchrec_tutorial.html">TorchRec ์๊ฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="sharding.html">Exploring TorchRec sharding</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Multimodality</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/flava_finetuning_tutorial.html">TorchMultimodal ํํ ๋ฆฌ์ผ: FLAVA ๋ฏธ์ธ์กฐ์ </a></li>
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<div class="section" id="using-cuda-graphs-in-pytorch-c-api">
<h1>Using CUDA Graphs in PyTorch C++ API<a class="headerlink" href="#using-cuda-graphs-in-pytorch-c-api" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h1>
<div class="admonition note">
<p class="admonition-title">์ฐธ๊ณ </p>
<p><a class="reference internal" href="../_images/pencil-16.png"><img alt="edit" src="../_images/pencil-16.png" style="width: 16px; height: 16px;" /></a> View and edit this tutorial in <a class="reference external" href="https://github.com/pytorch/tutorials/blob/main/advanced_source/cpp_cuda_graphs.rst">GitHub</a>. The full source code is available on <a class="reference external" href="https://github.com/pytorch/tutorials/blob/main/advanced_source/cpp_cuda_graphs">GitHub</a>.</p>
</div>
<p>Prerequisites:</p>
<ul class="simple">
<li><p><a class="reference external" href="../advanced_source/cpp_frontend.html">Using the PyTorch C++ Frontend</a></p></li>
<li><p><a class="reference external" href="https://pytorch.org/docs/master/notes/cuda.html">CUDA semantics</a></p></li>
<li><p>Pytorch 2.0 or later</p></li>
<li><p>CUDA 11 or later</p></li>
</ul>
<p>NVIDIAโs CUDA Graphs have been a part of CUDA Toolkit library since the
release of <a class="reference external" href="https://developer.nvidia.com/blog/cuda-graphs/">version 10</a>.
They are capable of greatly reducing the CPU overhead increasing the
performance of applications.</p>
<p>In this tutorial, we will be focusing on using CUDA Graphs for <a class="reference external" href="https://tutorials.pytorch.kr/advanced/cpp_frontend.html">C++
frontend of PyTorch</a>.
The C++ frontend is mostly utilized in production and deployment applications which
are important parts of PyTorch use cases. Since <a class="reference external" href="https://pytorch.org/blog/accelerating-pytorch-with-cuda-graphs/">the first appearance</a>
the CUDA Graphs won usersโ and developerโs hearts for being a very performant
and at the same time simple-to-use tool. In fact, CUDA Graphs are used by default
in <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> of PyTorch 2.0 to boost the productivity of training and inference.</p>
<p>We would like to demonstrate CUDA Graphs usage on PyTorchโs <a class="reference external" href="https://github.com/pytorch/examples/tree/main/cpp/mnist">MNIST
example</a>.
The usage of CUDA Graphs in LibTorch (C++ Frontend) is very similar to its
<a class="reference external" href="https://pytorch.org/docs/main/notes/cuda.html#cuda-graphs">Python counterpart</a>
but with some differences in syntax and functionality.</p>
<div class="section" id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>The main training loop consists of the several steps and depicted in the
following code chunk:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="o">&</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">data_loader</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">targets</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batch</span><span class="p">.</span><span class="n">target</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">);</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">zero_grad</span><span class="p">();</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data</span><span class="p">);</span>
<span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">loss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">);</span>
<span class="w"> </span><span class="n">loss</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">step</span><span class="p">();</span>
<span class="p">}</span>
</pre></div>
</div>
<p>The example above includes a forward pass, a backward pass, and weight updates.</p>
<p>In this tutorial, we will be applying CUDA Graph on all the compute steps through the whole-network
graph capture. But before doing so, we need to slightly modify the source code. What we need
to do is preallocate tensors for reusing them in the main training loop. Here is an example
implementation:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">TensorOptions</span><span class="w"> </span><span class="n">FloatCUDA</span><span class="w"> </span><span class="o">=</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">TensorOptions</span><span class="p">(</span><span class="n">device</span><span class="p">).</span><span class="n">dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kFloat</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">TensorOptions</span><span class="w"> </span><span class="n">LongCUDA</span><span class="w"> </span><span class="o">=</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">TensorOptions</span><span class="p">(</span><span class="n">device</span><span class="p">).</span><span class="n">dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kLong</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">zeros</span><span class="p">({</span><span class="n">kTrainBatchSize</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">28</span><span class="p">,</span><span class="w"> </span><span class="mi">28</span><span class="p">},</span><span class="w"> </span><span class="n">FloatCUDA</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">targets</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">zeros</span><span class="p">({</span><span class="n">kTrainBatchSize</span><span class="p">},</span><span class="w"> </span><span class="n">LongCUDA</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">zeros</span><span class="p">({</span><span class="mi">1</span><span class="p">},</span><span class="w"> </span><span class="n">FloatCUDA</span><span class="p">);</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">loss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">zeros</span><span class="p">({</span><span class="mi">1</span><span class="p">},</span><span class="w"> </span><span class="n">FloatCUDA</span><span class="p">);</span>
<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="o">&</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">data_loader</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">data</span><span class="p">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">);</span>
<span class="w"> </span><span class="n">targets</span><span class="p">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">target</span><span class="p">);</span>
<span class="w"> </span><span class="n">training_step</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">optimizer</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">,</span><span class="w"> </span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">loss</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Where <code class="docutils literal notranslate"><span class="pre">training_step</span></code> simply consists of forward and backward passes with corresponding optimizer calls:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="kt">void</span><span class="w"> </span><span class="nf">training_step</span><span class="p">(</span>
<span class="w"> </span><span class="n">Net</span><span class="o">&</span><span class="w"> </span><span class="n">model</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">optim</span><span class="o">::</span><span class="n">Optimizer</span><span class="o">&</span><span class="w"> </span><span class="n">optimizer</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&</span><span class="w"> </span><span class="n">data</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&</span><span class="w"> </span><span class="n">targets</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&</span><span class="w"> </span><span class="n">output</span><span class="p">,</span>
<span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&</span><span class="w"> </span><span class="n">loss</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">zero_grad</span><span class="p">();</span>
<span class="w"> </span><span class="n">output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data</span><span class="p">);</span>
<span class="w"> </span><span class="n">loss</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">);</span>
<span class="w"> </span><span class="n">loss</span><span class="p">.</span><span class="n">backward</span><span class="p">();</span>
<span class="w"> </span><span class="n">optimizer</span><span class="p">.</span><span class="n">step</span><span class="p">();</span>
<span class="p">}</span>
</pre></div>
</div>
<p>PyTorchโs CUDA Graphs API is relying on Stream Capture which in our case would be used like this:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">CUDAGraph</span><span class="w"> </span><span class="n">graph</span><span class="p">;</span>
<span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">CUDAStream</span><span class="w"> </span><span class="n">captureStream</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">getStreamFromPool</span><span class="p">();</span>
<span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">setCurrentCUDAStream</span><span class="p">(</span><span class="n">captureStream</span><span class="p">);</span>
<span class="n">graph</span><span class="p">.</span><span class="n">capture_begin</span><span class="p">();</span>
<span class="n">training_step</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">optimizer</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">,</span><span class="w"> </span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">loss</span><span class="p">);</span>
<span class="n">graph</span><span class="p">.</span><span class="n">capture_end</span><span class="p">();</span>
</pre></div>
</div>
<p>Before the actual graph capture, it is important to run several warm-up iterations on side stream to
prepare CUDA cache as well as CUDA libraries (like CUBLAS and CUDNN) that will be used during
the training:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">CUDAStream</span><span class="w"> </span><span class="n">warmupStream</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">getStreamFromPool</span><span class="p">();</span>
<span class="n">at</span><span class="o">::</span><span class="n">cuda</span><span class="o">::</span><span class="n">setCurrentCUDAStream</span><span class="p">(</span><span class="n">warmupStream</span><span class="p">);</span>
<span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">iter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">iter</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">num_warmup_iters</span><span class="p">;</span><span class="w"> </span><span class="n">iter</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">training_step</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="w"> </span><span class="n">optimizer</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">targets</span><span class="p">,</span><span class="w"> </span><span class="n">output</span><span class="p">,</span><span class="w"> </span><span class="n">loss</span><span class="p">);</span>
<span class="p">}</span>
</pre></div>
</div>
<p>After the successful graph capture, we can replace <code class="docutils literal notranslate"><span class="pre">training_step(model,</span> <span class="pre">optimizer,</span> <span class="pre">data,</span> <span class="pre">targets,</span> <span class="pre">output,</span> <span class="pre">loss);</span></code>
call via <code class="docutils literal notranslate"><span class="pre">graph.replay();</span></code> to do the training step.</p>
</div>
<div class="section" id="training-results">
<h2>Training Results<a class="headerlink" href="#training-results" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>Taking the code for a spin we can see the following output from ordinary non-graphed training:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">time</span><span class="w"> </span>./mnist
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.3921
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.2051<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.938
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.1826
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.1273<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.960
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.1796
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.1012<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.968
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.1603
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0869<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.973
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">5</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.2315
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0736<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.978
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">6</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0511
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0704<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.977
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">7</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0802
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0654<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.979
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">8</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0774
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0604<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.980
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">9</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0669
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0544<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.984
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0219
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0517<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.983
real<span class="w"> </span>0m44.287s
user<span class="w"> </span>0m44.018s
sys<span class="w"> </span>0m1.116s
</pre></div>
</div>
<p>While the training with the CUDA Graph produces the following output:</p>
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">time</span><span class="w"> </span>./mnist<span class="w"> </span>--use-train-graph
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.4092
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.2037<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.938
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.2039
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.1274<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.961
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.1779
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.1017<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.968
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">4</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.1559
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0871<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.972
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">5</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.2240
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0735<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.977
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">6</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0520
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0710<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.978
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">7</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0935
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0666<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.979
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">8</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0744
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0603<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.981
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">9</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0762
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0547<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.983
Train<span class="w"> </span>Epoch:<span class="w"> </span><span class="m">10</span><span class="w"> </span><span class="o">[</span><span class="m">59584</span>/60000<span class="o">]</span><span class="w"> </span>Loss:<span class="w"> </span><span class="m">0</span>.0207
Test<span class="w"> </span>set:<span class="w"> </span>Average<span class="w"> </span>loss:<span class="w"> </span><span class="m">0</span>.0525<span class="w"> </span><span class="p">|</span><span class="w"> </span>Accuracy:<span class="w"> </span><span class="m">0</span>.983
real<span class="w"> </span>0m6.952s
user<span class="w"> </span>0m7.048s
sys<span class="w"> </span>0m0.619s
</pre></div>
</div>
</div>
<div class="section" id="conclusion">
<h2>Conclusion<a class="headerlink" href="#conclusion" title="์ด ์ ๋ชฉ์ ๋ํ ํผ๋จธ๋งํฌ">ยถ</a></h2>
<p>As we can see, just by applying a CUDA Graph on the <a class="reference external" href="https://github.com/pytorch/examples/tree/main/cpp/mnist">MNIST example</a> we were able to gain the performance
by more than six times for training. This kind of large performance improvement was achievable due to
the small model size. In case of larger models with heavy GPU usage, the CPU overhead is less impactful
so the improvement will be smaller. Nevertheless, it is always advantageous to use CUDA Graphs to
gain the performance of GPUs.</p>
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