diff --git a/docs/_downloads/7d5771891cc8e2f733352e4fc8fc63b6/saving_loading_models.py b/docs/_downloads/7d5771891cc8e2f733352e4fc8fc63b6/saving_loading_models.py index 847e50b25..f44c5d611 100644 --- a/docs/_downloads/7d5771891cc8e2f733352e4fc8fc63b6/saving_loading_models.py +++ b/docs/_downloads/7d5771891cc8e2f733352e4fc8fc63b6/saving_loading_models.py @@ -13,14 +13,14 @@ 1) `torch.save `__: 직렬화된 객체를 디스크에 저장합니다. 이 함수는 Python의 - `pickle `__ 을 사용하여 직렬화합니다 + `pickle `__ 을 사용하여 직렬화합니다. 이 함수를 사용하여 모든 종류의 객체의 모델, Tensor 및 사전을 저장할 수 있습니다. 2) `torch.load `__: `pickle `__\ 을 사용하여 - 저장된 객체 파일들을 역직렬화하여 메모리에 올립니다. 이 함수는 데이터를 장치에 - 불러올 때도 사용합니다. - (`장치간 모델 저장하기 & 불러오기 <#device>`__ 참고) + 저장된 객체 파일들을 역직렬화하여 메모리에 올립니다. 이 함수는 데이터를 장치에 불러올 + 때에도 사용됩니다. + (`장치 간 모델 저장하기 & 불러오기 <#device>`__ 참고) 3) `torch.nn.Module.load_state_dict `__: 역직렬화된 *state_dict* 를 사용하여 모델의 매개변수들을 불러옵니다. @@ -145,29 +145,29 @@ # model.eval() # # .. note:: -# PyTorch 버전 1.6에서는 새로운 Zip파일-기반의 파일 포맷을 사용하도록 -# ``torch.save`` 가 변경되었습니다. ``torch.load`` 는 예전 방식의 파일들을 +# PyTorch 버전 1.6에서는 ``torch.save`` 가 새로운 Zip파일-기반의 파일 +# 포맷을 사용하도록 변경되었습니다. ``torch.load`` 는 예전 방식의 파일들을 # 읽어올 수 있도록 하고 있습니다. 어떤 이유에서든 ``torch.save`` 가 예전 # 방식을 사용하도록 하고 싶다면, ``_use_new_zipfile_serialization=False`` 을 # kwarg로 전달하세요. # -# 추론을 위해 모델을 저장할 때는 학습된 모델의 학습된 매개변수만 저장하면 됩니다. +# 추론을 위해 모델을 저장할 때는 그 모델의 학습된 매개변수만 저장하면 됩니다. # ``torch.save()`` 를 사용하여 모델의 *state_dict* 를 저장하는 것이 나중에 모델을 # 사용할 때 가장 유연하게 사용할 수 있는, 모델 저장 시 권장하는 방법입니다. # # PyTorch에서는 모델을 저장할 때 ``.pt`` 또는 ``.pth`` 확장자를 사용하는 것이 # 일반적인 규칙입니다. # -# 추론을 실행하기 전에는 반드시 ``model.eval()`` 을 호출하여 드롭아웃 및 배치 -# 정규화를 평가 모드로 설정하여야 합니다. 이것을 하지 않으면 추론 결과가 일관성 -# 없게 출력됩니다. +# 추론을 실행하기 전에 반드시 ``model.eval()`` 을 호출하여 드롭아웃 및 배치 +# 정규화를 평가 모드로 설정하여야 합니다. 이 과정을 거치지 않으면 일관성 없는 +# 추론 결과가 출력됩니다. # # .. Note :: # # ``load_state_dict()`` 함수에는 저장된 객체의 경로가 아닌, 사전 객체를 # 전달해야 하는 것에 유의하세요. 따라서 저장된 *state_dict* 를 ``load_state_dict()`` # 함수에 전달하기 전에 반드시 역직렬화를 해야 합니다. 예를 들어, -# ``model.load_state_dict(PATH)`` 과 같은 식으로는 사용하면 안됩니다. +# ``model.load_state_dict(PATH)`` 과 같은 식으로 사용하면 안됩니다. # # .. Note :: # @@ -192,7 +192,7 @@ # # .. code:: python # -# # 모델 클래스는 어딘가에 반드시 선언되어 있어야 합니다 +# # 모델 클래스는 어딘가에 반드시 선언되어 있어야 합니다. # model = torch.load(PATH) # model.eval() # @@ -213,25 +213,23 @@ # 정규화를 평가 모드로 설정하여야 합니다. 이것을 하지 않으면 추론 결과가 일관성 # 없게 출력됩니다. # -# Export/Load Model in TorchScript Format +# TorchScript 포맷으로 모델 내보내기/가져오기 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # -# One common way to do inference with a trained model is to use -# `TorchScript `__, an intermediate -# representation of a PyTorch model that can be run in Python as well as in a -# high performance environment like C++. TorchScript is actually the recommended model format -# for scaled inference and deployment. +# 훈련된 모델로 추론을 수행하는 일반적인 방법 중 하나는 `TorchScript `__ 를 사용하는 것입니다. +# TorchScript는 파이썬 환경이나 C++와 같은 고성능 환경에서 실행할 수 있는 +# 파이토치 모델의 중간 표현(IR; Intermediate Representation)입니다. +# TorchScript는 확장된 추론 및 배포에 권장되는 모델 형식이기도 합니다. # # .. note:: -# Using the TorchScript format, you will be able to load the exported model and -# run inference without defining the model class. +# TorchScript 형식을 사용하면 모델 클래스를 정의하지 않고도 내보낸 모델을 읽어 오거나 추론을 실행할 수 있습니다. # # **Export:** # # .. code:: python # -# model_scripted = torch.jit.script(model) # Export to TorchScript -# model_scripted.save('model_scripted.pt') # Save +# model_scripted = torch.jit.script(model) # TorchScript 형식으로 내보내기 +# model_scripted.save('model_scripted.pt') # 저장하기 # # **Load:** # @@ -240,14 +238,14 @@ # model = torch.jit.load('model_scripted.pt') # model.eval() # -# Remember that you must call ``model.eval()`` to set dropout and batch -# normalization layers to evaluation mode before running inference. -# Failing to do this will yield inconsistent inference results. +# 추론 실행 전, 드롭아웃 및 배치(batch) 정규화 레이어를 평가 모드로 설정하기 위해 ``model.eval()`` 을 호출해야 +# 합니다. 이 호출 과정이 없으면 일관성 없는 추론 결과가 나타납니다. # -# For more information on TorchScript, feel free to visit the dedicated -# `tutorials `__. -# You will get familiar with the tracing conversion and learn how to -# run a TorchScript module in a `C++ environment `__. +# TorchScript에 대한 추가 정보는 전용 +# `자습서 `__ 에서 찾을 수 있습니다. +# `C++ 환경 `__ 문서를 참고하여 트레이싱(Tracing) 변환을 +# 수행하는 방법과 C++ 환경에서 TorchScript 모듈을 실행하는 방법을 익힐 수 있습니다. + ###################################################################### diff --git a/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb b/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb index 7356b237f..7a5419cfc 100644 --- a/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb +++ b/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n=========================\n**Author:** `Matthew Inkawhich `_\n **\ubc88\uc5ed**: `\ubc15\uc815\ud658 `_\n\n\uc774 \ubb38\uc11c\uc5d0\uc11c\ub294 PyTorch \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ubb38\uc11c \uc804\uccb4\ub97c \ub2e4 \uc77d\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud544\uc694\ud55c \uc0ac\uc6a9 \uc608\uc758 \ucf54\ub4dc\ub9cc \ucc38\uace0\ud558\ub294\n\uac83\ub3c4 \uace0\ub824\ud574\ubcf4\uc138\uc694.\n\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc62c \ub54c\ub294 3\uac00\uc9c0\uc758 \ud575\uc2ec \ud568\uc218\uc640 \uc775\uc219\ud574\uc9c8 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4:\n\n1) `torch.save `__:\n \uc9c1\ub82c\ud654\ub41c \uac1d\uccb4\ub97c \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 Python\uc758\n `pickle `__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc9c1\ub82c\ud654\ud569\ub2c8\ub2e4\n \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4e0 \uc885\ub958\uc758 \uac1d\uccb4\uc758 \ubaa8\ub378, Tensor \ubc0f \uc0ac\uc804\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n2) `torch.load `__:\n `pickle `__\\ \uc744 \uc0ac\uc6a9\ud558\uc5ec\n \uc800\uc7a5\ub41c \uac1d\uccb4 \ud30c\uc77c\ub4e4\uc744 \uc5ed\uc9c1\ub82c\ud654\ud558\uc5ec \uba54\ubaa8\ub9ac\uc5d0 \uc62c\ub9bd\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub370\uc774\ud130\ub97c \uc7a5\uce58\uc5d0\n \ubd88\ub7ec\uc62c \ub54c\ub3c4 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n (`\uc7a5\uce58\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__ \ucc38\uace0)\n\n3) `torch.nn.Module.load_state_dict `__:\n \uc5ed\uc9c1\ub82c\ud654\ub41c *state_dict* \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n *state_dict* \uc5d0 \ub300\ud55c \ub354 \uc790\uc138\ud55c \uc815\ubcf4\ub294 `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n <#state-dict>`__ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n\n\n**\ubaa9\ucc28:**\n\n- `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694? <#state-dict>`__\n- `\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#inference>`__\n- `\uc77c\ubc18 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#checkpoint>`__\n- `\uc5ec\ub7ec\uac1c(multiple)\uc758 \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 <#multiple>`__\n- `\ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart) <#warmstart>`__\n- `\uc7a5\uce58(device)\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__\n" + "\n\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n=========================\n**Author:** `Matthew Inkawhich `_\n **\ubc88\uc5ed**: `\ubc15\uc815\ud658 `_\n\n\uc774 \ubb38\uc11c\uc5d0\uc11c\ub294 PyTorch \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ubb38\uc11c \uc804\uccb4\ub97c \ub2e4 \uc77d\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud544\uc694\ud55c \uc0ac\uc6a9 \uc608\uc758 \ucf54\ub4dc\ub9cc \ucc38\uace0\ud558\ub294\n\uac83\ub3c4 \uace0\ub824\ud574\ubcf4\uc138\uc694.\n\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc62c \ub54c\ub294 3\uac00\uc9c0\uc758 \ud575\uc2ec \ud568\uc218\uc640 \uc775\uc219\ud574\uc9c8 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4:\n\n1) `torch.save `__:\n \uc9c1\ub82c\ud654\ub41c \uac1d\uccb4\ub97c \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 Python\uc758\n `pickle `__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc9c1\ub82c\ud654\ud569\ub2c8\ub2e4.\n \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4e0 \uc885\ub958\uc758 \uac1d\uccb4\uc758 \ubaa8\ub378, Tensor \ubc0f \uc0ac\uc804\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n2) `torch.load `__:\n `pickle `__\\ \uc744 \uc0ac\uc6a9\ud558\uc5ec\n \uc800\uc7a5\ub41c \uac1d\uccb4 \ud30c\uc77c\ub4e4\uc744 \uc5ed\uc9c1\ub82c\ud654\ud558\uc5ec \uba54\ubaa8\ub9ac\uc5d0 \uc62c\ub9bd\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub370\uc774\ud130\ub97c \uc7a5\uce58\uc5d0 \ubd88\ub7ec\uc62c\n \ub54c\uc5d0\ub3c4 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n (`\uc7a5\uce58 \uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__ \ucc38\uace0)\n\n3) `torch.nn.Module.load_state_dict `__:\n \uc5ed\uc9c1\ub82c\ud654\ub41c *state_dict* \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n *state_dict* \uc5d0 \ub300\ud55c \ub354 \uc790\uc138\ud55c \uc815\ubcf4\ub294 `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n <#state-dict>`__ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n\n\n**\ubaa9\ucc28:**\n\n- `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694? <#state-dict>`__\n- `\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#inference>`__\n- `\uc77c\ubc18 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#checkpoint>`__\n- `\uc5ec\ub7ec\uac1c(multiple)\uc758 \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 <#multiple>`__\n- `\ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart) <#warmstart>`__\n- `\uc7a5\uce58(device)\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__\n" ] }, { @@ -29,7 +29,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n------------------------------------------------\n\n``state_dict`` \uc800\uc7a5\ud558\uae30 / \ubd88\ub7ec\uc624\uae30 (\uad8c\uc7a5)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**\uc800\uc7a5\ud558\uae30:**\n\n.. code:: python\n\n torch.save(model.state_dict(), PATH)\n\n**\ubd88\ub7ec\uc624\uae30:**\n\n.. code:: python\n\n model = TheModelClass(*args, **kwargs)\n model.load_state_dict(torch.load(PATH))\n model.eval()\n\n

Note

PyTorch \ubc84\uc804 1.6\uc5d0\uc11c\ub294 \uc0c8\ub85c\uc6b4 Zip\ud30c\uc77c-\uae30\ubc18\uc758 \ud30c\uc77c \ud3ec\ub9f7\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d\n ``torch.save`` \uac00 \ubcc0\uacbd\ub418\uc5c8\uc2b5\ub2c8\ub2e4. ``torch.load`` \ub294 \uc608\uc804 \ubc29\uc2dd\uc758 \ud30c\uc77c\ub4e4\uc744\n \uc77d\uc5b4\uc62c \uc218 \uc788\ub3c4\ub85d \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5b4\ub5a4 \uc774\uc720\uc5d0\uc11c\ub4e0 ``torch.save`` \uac00 \uc608\uc804\n \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud558\uace0 \uc2f6\ub2e4\uba74, ``_use_new_zipfile_serialization=False`` \uc744\n kwarg\ub85c \uc804\ub2ec\ud558\uc138\uc694.

\n\n\ucd94\ub860\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c\ub294 \ud559\uc2b5\ub41c \ubaa8\ub378\uc758 \ud559\uc2b5\ub41c \ub9e4\uac1c\ubcc0\uc218\ub9cc \uc800\uc7a5\ud558\uba74 \ub429\ub2c8\ub2e4.\n``torch.save()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 *state_dict* \ub97c \uc800\uc7a5\ud558\ub294 \uac83\uc774 \ub098\uc911\uc5d0 \ubaa8\ub378\uc744\n\uc0ac\uc6a9\ud560 \ub54c \uac00\uc7a5 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294, \ubaa8\ub378 \uc800\uc7a5 \uc2dc \uad8c\uc7a5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n\nPyTorch\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c ``.pt`` \ub610\ub294 ``.pth`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4.\n\n\ucd94\ub860\uc744 \uc2e4\ud589\ud558\uae30 \uc804\uc5d0\ub294 \ubc18\ub4dc\uc2dc ``model.eval()`` \uc744 \ud638\ucd9c\ud558\uc5ec \ub4dc\ub86d\uc544\uc6c3 \ubc0f \ubc30\uce58\n\uc815\uaddc\ud654\ub97c \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uc5ec\uc57c \ud569\ub2c8\ub2e4. \uc774\uac83\uc744 \ud558\uc9c0 \uc54a\uc73c\uba74 \ucd94\ub860 \uacb0\uacfc\uac00 \uc77c\uad00\uc131\n\uc5c6\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\n.. Note ::\n\n ``load_state_dict()`` \ud568\uc218\uc5d0\ub294 \uc800\uc7a5\ub41c \uac1d\uccb4\uc758 \uacbd\ub85c\uac00 \uc544\ub2cc, \uc0ac\uc804 \uac1d\uccb4\ub97c\n \uc804\ub2ec\ud574\uc57c \ud558\ub294 \uac83\uc5d0 \uc720\uc758\ud558\uc138\uc694. \ub530\ub77c\uc11c \uc800\uc7a5\ub41c *state_dict* \ub97c ``load_state_dict()``\n \ud568\uc218\uc5d0 \uc804\ub2ec\ud558\uae30 \uc804\uc5d0 \ubc18\ub4dc\uc2dc \uc5ed\uc9c1\ub82c\ud654\ub97c \ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4,\n ``model.load_state_dict(PATH)`` \uacfc \uac19\uc740 \uc2dd\uc73c\ub85c\ub294 \uc0ac\uc6a9\ud558\uba74 \uc548\ub429\ub2c8\ub2e4.\n\n.. Note ::\n\n \ub9cc\uc57d (\uac80\uc99d \uc190\uc2e4(validation loss) \uacb0\uacfc\uc5d0 \ub530\ub77c) \uac00\uc7a5 \uc131\ub2a5\uc774 \uc88b\uc740 \ubaa8\ub378\ub9cc \uc720\uc9c0\ud560\n \uacc4\ud68d\uc774\ub77c\uba74, ``best_model_state = model.state_dict()`` \uc740 \ubaa8\ub378\uc758 \ubcf5\uc0ac\ubcf8\uc774 \uc544\ub2cc\n \ubaa8\ub378\uc758 \ud604\uc7ac \uc0c1\ud0dc\uc5d0 \ub300\ud55c \ucc38\uc870(reference)\ub9cc \ubc18\ud658\ud55c\ub2e4\ub294 \uc0ac\uc2e4\uc744 \uc78a\uc73c\uc2dc\uba74 \uc548\ub429\ub2c8\ub2e4!\n \ub530\ub77c\uc11c ``best_model_state`` \uc744 \uc9c1\ub82c\ud654(serialize)\ud558\uac70\ub098,\n ``best_model_state = deepcopy(model.state_dict())`` \uc744 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.\n \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74, \uc81c\uc77c \uc88b\uc740 \uc131\ub2a5\uc744 \ub0b4\ub294 ``best_model_state`` \uc740 \uacc4\uc18d\ub418\ub294 \ud559\uc2b5 \ub2e8\uacc4\uc5d0\uc11c\n \uac31\uc2e0\ub420 \uac83\uc785\ub2c8\ub2e4. \uacb0\uacfc\uc801\uc73c\ub85c, \ucd5c\uc885 \ubaa8\ub378\uc758 \uc0c1\ud0dc\ub294 \uacfc\uc801\ud569(overfit)\ub41c \uc0c1\ud0dc\uac00 \ub429\ub2c8\ub2e4.\n\n\uc804\uccb4 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30/\ubd88\ub7ec\uc624\uae30\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**\uc800\uc7a5\ud558\uae30:**\n\n.. code:: python\n\n torch.save(model, PATH)\n\n**\ubd88\ub7ec\uc624\uae30:**\n\n.. code:: python\n\n # \ubaa8\ub378 \ud074\ub798\uc2a4\ub294 \uc5b4\ub518\uac00\uc5d0 \ubc18\ub4dc\uc2dc \uc120\uc5b8\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4\n model = torch.load(PATH)\n model.eval()\n\n\uc774 \uc800\uc7a5\ud558\uae30/\ubd88\ub7ec\uc624\uae30 \uacfc\uc815\uc740 \uac00\uc7a5 \uc9c1\uad00\uc801\uc778 \ubb38\ubc95\uc744 \uc0ac\uc6a9\ud558\uba70 \uc801\uc740 \uc591\uc758\n\ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc73c\ub85c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294 \uac83\uc740 Python\uc758\n`pickle `__ \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud558\uc5ec\n\uc804\uccb4 \ubaa8\ub4c8\uc744 \uc800\uc7a5\ud558\uac8c \ub429\ub2c8\ub2e4. \ud558\uc9c0\ub9cc pickle\uc740 \ubaa8\ub378 \uadf8 \uc790\uccb4\ub97c \uc800\uc7a5\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0\n\uc9c1\ub82c\ud654\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c \uc0ac\uc6a9\ud55c \ud2b9\uc815 \ud074\ub798\uc2a4 \ubc0f \ub514\ub809\ud1a0\ub9ac \uacbd\ub85c(\uad6c\uc870)\uc5d0\n\uc5bd\ub9e4\uc778\ub2e4\ub294 \uac83\uc774 \uc774 \ubc29\uc2dd\uc758 \ub2e8\uc810\uc785\ub2c8\ub2e4. \ub300\uc2e0\uc5d0 \ud074\ub798\uc2a4\uac00 \uc704\uce58\ud55c \ud30c\uc77c\uc758 \uacbd\ub85c\ub97c\n\uc800\uc7a5\ud574\ub450\uace0, \ubd88\ub7ec\uc624\ub294 \uc2dc\uc810\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc774\uc720 \ub54c\ubb38\uc5d0, \ub9cc\ub4e4\uc5b4\ub454 \ucf54\ub4dc\ub97c\n\ub2e4\ub978 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c \uc0ac\uc6a9\ud558\uac70\ub098 \ub9ac\ud329\ud1a0\ub9c1 \ud6c4\uc5d0 \ub2e4\uc591\ud55c \uc774\uc720\ub85c \ub3d9\uc791\ud558\uc9c0 \uc54a\uc744 \uc218\n\uc788\uc2b5\ub2c8\ub2e4.\n\nPyTorch\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c ``.pt`` \ub610\ub294 ``.pth`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4.\n\n\ucd94\ub860\uc744 \uc2e4\ud589\ud558\uae30 \uc804\uc5d0\ub294 \ubc18\ub4dc\uc2dc ``model.eval()`` \uc744 \ud638\ucd9c\ud558\uc5ec \ub4dc\ub86d\uc544\uc6c3 \ubc0f \ubc30\uce58\n\uc815\uaddc\ud654\ub97c \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uc5ec\uc57c \ud569\ub2c8\ub2e4. \uc774\uac83\uc744 \ud558\uc9c0 \uc54a\uc73c\uba74 \ucd94\ub860 \uacb0\uacfc\uac00 \uc77c\uad00\uc131\n\uc5c6\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\nExport/Load Model in TorchScript Format\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nOne common way to do inference with a trained model is to use\n`TorchScript `__, an intermediate\nrepresentation of a PyTorch model that can be run in Python as well as in a\nhigh performance environment like C++. TorchScript is actually the recommended model format\nfor scaled inference and deployment.\n\n

Note

Using the TorchScript format, you will be able to load the exported model and\n run inference without defining the model class.

\n\n**Export:**\n\n.. code:: python\n\n model_scripted = torch.jit.script(model) # Export to TorchScript\n model_scripted.save('model_scripted.pt') # Save\n\n**Load:**\n\n.. code:: python\n\n model = torch.jit.load('model_scripted.pt')\n model.eval()\n\nRemember that you must call ``model.eval()`` to set dropout and batch\nnormalization layers to evaluation mode before running inference.\nFailing to do this will yield inconsistent inference results.\n\nFor more information on TorchScript, feel free to visit the dedicated\n`tutorials `__.\nYou will get familiar with the tracing conversion and learn how to\nrun a TorchScript module in a `C++ environment `__.\n\n" + "\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n------------------------------------------------\n\n``state_dict`` \uc800\uc7a5\ud558\uae30 / \ubd88\ub7ec\uc624\uae30 (\uad8c\uc7a5)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**\uc800\uc7a5\ud558\uae30:**\n\n.. code:: python\n\n torch.save(model.state_dict(), PATH)\n\n**\ubd88\ub7ec\uc624\uae30:**\n\n.. code:: python\n\n model = TheModelClass(*args, **kwargs)\n model.load_state_dict(torch.load(PATH))\n model.eval()\n\n

Note

PyTorch \ubc84\uc804 1.6\uc5d0\uc11c\ub294 ``torch.save`` \uac00 \uc0c8\ub85c\uc6b4 Zip\ud30c\uc77c-\uae30\ubc18\uc758 \ud30c\uc77c\n \ud3ec\ub9f7\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ubcc0\uacbd\ub418\uc5c8\uc2b5\ub2c8\ub2e4. ``torch.load`` \ub294 \uc608\uc804 \ubc29\uc2dd\uc758 \ud30c\uc77c\ub4e4\uc744\n \uc77d\uc5b4\uc62c \uc218 \uc788\ub3c4\ub85d \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc5b4\ub5a4 \uc774\uc720\uc5d0\uc11c\ub4e0 ``torch.save`` \uac00 \uc608\uc804\n \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud558\uace0 \uc2f6\ub2e4\uba74, ``_use_new_zipfile_serialization=False`` \uc744\n kwarg\ub85c \uc804\ub2ec\ud558\uc138\uc694.

\n\n\ucd94\ub860\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c\ub294 \uadf8 \ubaa8\ub378\uc758 \ud559\uc2b5\ub41c \ub9e4\uac1c\ubcc0\uc218\ub9cc \uc800\uc7a5\ud558\uba74 \ub429\ub2c8\ub2e4.\n``torch.save()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 *state_dict* \ub97c \uc800\uc7a5\ud558\ub294 \uac83\uc774 \ub098\uc911\uc5d0 \ubaa8\ub378\uc744\n\uc0ac\uc6a9\ud560 \ub54c \uac00\uc7a5 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294, \ubaa8\ub378 \uc800\uc7a5 \uc2dc \uad8c\uc7a5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n\nPyTorch\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c ``.pt`` \ub610\ub294 ``.pth`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4.\n\n\ucd94\ub860\uc744 \uc2e4\ud589\ud558\uae30 \uc804\uc5d0 \ubc18\ub4dc\uc2dc ``model.eval()`` \uc744 \ud638\ucd9c\ud558\uc5ec \ub4dc\ub86d\uc544\uc6c3 \ubc0f \ubc30\uce58\n\uc815\uaddc\ud654\ub97c \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uc5ec\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \uac70\uce58\uc9c0 \uc54a\uc73c\uba74 \uc77c\uad00\uc131 \uc5c6\ub294\n\ucd94\ub860 \uacb0\uacfc\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\n.. Note ::\n\n ``load_state_dict()`` \ud568\uc218\uc5d0\ub294 \uc800\uc7a5\ub41c \uac1d\uccb4\uc758 \uacbd\ub85c\uac00 \uc544\ub2cc, \uc0ac\uc804 \uac1d\uccb4\ub97c\n \uc804\ub2ec\ud574\uc57c \ud558\ub294 \uac83\uc5d0 \uc720\uc758\ud558\uc138\uc694. \ub530\ub77c\uc11c \uc800\uc7a5\ub41c *state_dict* \ub97c ``load_state_dict()``\n \ud568\uc218\uc5d0 \uc804\ub2ec\ud558\uae30 \uc804\uc5d0 \ubc18\ub4dc\uc2dc \uc5ed\uc9c1\ub82c\ud654\ub97c \ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4,\n ``model.load_state_dict(PATH)`` \uacfc \uac19\uc740 \uc2dd\uc73c\ub85c \uc0ac\uc6a9\ud558\uba74 \uc548\ub429\ub2c8\ub2e4.\n\n.. Note ::\n\n \ub9cc\uc57d (\uac80\uc99d \uc190\uc2e4(validation loss) \uacb0\uacfc\uc5d0 \ub530\ub77c) \uac00\uc7a5 \uc131\ub2a5\uc774 \uc88b\uc740 \ubaa8\ub378\ub9cc \uc720\uc9c0\ud560\n \uacc4\ud68d\uc774\ub77c\uba74, ``best_model_state = model.state_dict()`` \uc740 \ubaa8\ub378\uc758 \ubcf5\uc0ac\ubcf8\uc774 \uc544\ub2cc\n \ubaa8\ub378\uc758 \ud604\uc7ac \uc0c1\ud0dc\uc5d0 \ub300\ud55c \ucc38\uc870(reference)\ub9cc \ubc18\ud658\ud55c\ub2e4\ub294 \uc0ac\uc2e4\uc744 \uc78a\uc73c\uc2dc\uba74 \uc548\ub429\ub2c8\ub2e4!\n \ub530\ub77c\uc11c ``best_model_state`` \uc744 \uc9c1\ub82c\ud654(serialize)\ud558\uac70\ub098,\n ``best_model_state = deepcopy(model.state_dict())`` \uc744 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.\n \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74, \uc81c\uc77c \uc88b\uc740 \uc131\ub2a5\uc744 \ub0b4\ub294 ``best_model_state`` \uc740 \uacc4\uc18d\ub418\ub294 \ud559\uc2b5 \ub2e8\uacc4\uc5d0\uc11c\n \uac31\uc2e0\ub420 \uac83\uc785\ub2c8\ub2e4. \uacb0\uacfc\uc801\uc73c\ub85c, \ucd5c\uc885 \ubaa8\ub378\uc758 \uc0c1\ud0dc\ub294 \uacfc\uc801\ud569(overfit)\ub41c \uc0c1\ud0dc\uac00 \ub429\ub2c8\ub2e4.\n\n\uc804\uccb4 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30/\ubd88\ub7ec\uc624\uae30\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**\uc800\uc7a5\ud558\uae30:**\n\n.. code:: python\n\n torch.save(model, PATH)\n\n**\ubd88\ub7ec\uc624\uae30:**\n\n.. code:: python\n\n # \ubaa8\ub378 \ud074\ub798\uc2a4\ub294 \uc5b4\ub518\uac00\uc5d0 \ubc18\ub4dc\uc2dc \uc120\uc5b8\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n model = torch.load(PATH)\n model.eval()\n\n\uc774 \uc800\uc7a5\ud558\uae30/\ubd88\ub7ec\uc624\uae30 \uacfc\uc815\uc740 \uac00\uc7a5 \uc9c1\uad00\uc801\uc778 \ubb38\ubc95\uc744 \uc0ac\uc6a9\ud558\uba70 \uc801\uc740 \uc591\uc758\n\ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc73c\ub85c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294 \uac83\uc740 Python\uc758\n`pickle `__ \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud558\uc5ec\n\uc804\uccb4 \ubaa8\ub4c8\uc744 \uc800\uc7a5\ud558\uac8c \ub429\ub2c8\ub2e4. \ud558\uc9c0\ub9cc pickle\uc740 \ubaa8\ub378 \uadf8 \uc790\uccb4\ub97c \uc800\uc7a5\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0\n\uc9c1\ub82c\ud654\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c \uc0ac\uc6a9\ud55c \ud2b9\uc815 \ud074\ub798\uc2a4 \ubc0f \ub514\ub809\ud1a0\ub9ac \uacbd\ub85c(\uad6c\uc870)\uc5d0\n\uc5bd\ub9e4\uc778\ub2e4\ub294 \uac83\uc774 \uc774 \ubc29\uc2dd\uc758 \ub2e8\uc810\uc785\ub2c8\ub2e4. \ub300\uc2e0\uc5d0 \ud074\ub798\uc2a4\uac00 \uc704\uce58\ud55c \ud30c\uc77c\uc758 \uacbd\ub85c\ub97c\n\uc800\uc7a5\ud574\ub450\uace0, \ubd88\ub7ec\uc624\ub294 \uc2dc\uc810\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc774\uc720 \ub54c\ubb38\uc5d0, \ub9cc\ub4e4\uc5b4\ub454 \ucf54\ub4dc\ub97c\n\ub2e4\ub978 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c \uc0ac\uc6a9\ud558\uac70\ub098 \ub9ac\ud329\ud1a0\ub9c1 \ud6c4\uc5d0 \ub2e4\uc591\ud55c \uc774\uc720\ub85c \ub3d9\uc791\ud558\uc9c0 \uc54a\uc744 \uc218\n\uc788\uc2b5\ub2c8\ub2e4.\n\nPyTorch\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c ``.pt`` \ub610\ub294 ``.pth`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4.\n\n\ucd94\ub860\uc744 \uc2e4\ud589\ud558\uae30 \uc804\uc5d0\ub294 \ubc18\ub4dc\uc2dc ``model.eval()`` \uc744 \ud638\ucd9c\ud558\uc5ec \ub4dc\ub86d\uc544\uc6c3 \ubc0f \ubc30\uce58\n\uc815\uaddc\ud654\ub97c \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uc5ec\uc57c \ud569\ub2c8\ub2e4. \uc774\uac83\uc744 \ud558\uc9c0 \uc54a\uc73c\uba74 \ucd94\ub860 \uacb0\uacfc\uac00 \uc77c\uad00\uc131\n\uc5c6\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\nTorchScript \ud3ec\ub9f7\uc73c\ub85c \ubaa8\ub378 \ub0b4\ubcf4\ub0b4\uae30/\uac00\uc838\uc624\uae30\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\ud6c8\ub828\ub41c \ubaa8\ub378\ub85c \ucd94\ub860\uc744 \uc218\ud589\ud558\ub294 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95 \uc911 \ud558\ub098\ub294 `TorchScript `__ \ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\nTorchScript\ub294 \ud30c\uc774\uc36c \ud658\uacbd\uc774\ub098 C++\uc640 \uac19\uc740 \uace0\uc131\ub2a5 \ud658\uacbd\uc5d0\uc11c \uc2e4\ud589\ud560 \uc218 \uc788\ub294\n\ud30c\uc774\ud1a0\uce58 \ubaa8\ub378\uc758 \uc911\uac04 \ud45c\ud604(IR; Intermediate Representation)\uc785\ub2c8\ub2e4.\nTorchScript\ub294 \ud655\uc7a5\ub41c \ucd94\ub860 \ubc0f \ubc30\ud3ec\uc5d0 \uad8c\uc7a5\ub418\ub294 \ubaa8\ub378 \ud615\uc2dd\uc774\uae30\ub3c4 \ud569\ub2c8\ub2e4.\n\n

Note

TorchScript \ud615\uc2dd\uc744 \uc0ac\uc6a9\ud558\uba74 \ubaa8\ub378 \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud558\uc9c0 \uc54a\uace0\ub3c4 \ub0b4\ubcf4\ub0b8 \ubaa8\ub378\uc744 \uc77d\uc5b4 \uc624\uac70\ub098 \ucd94\ub860\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.

\n\n**Export:**\n\n.. code:: python\n\n model_scripted = torch.jit.script(model) # TorchScript \ud615\uc2dd\uc73c\ub85c \ub0b4\ubcf4\ub0b4\uae30\n model_scripted.save('model_scripted.pt') # \uc800\uc7a5\ud558\uae30\n\n**Load:**\n\n.. code:: python\n\n model = torch.jit.load('model_scripted.pt')\n model.eval()\n\n\ucd94\ub860 \uc2e4\ud589 \uc804, \ub4dc\ub86d\uc544\uc6c3 \ubc0f \ubc30\uce58(batch) \uc815\uaddc\ud654 \ub808\uc774\uc5b4\ub97c \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uae30 \uc704\ud574 ``model.eval()`` \uc744 \ud638\ucd9c\ud574\uc57c\n\ud569\ub2c8\ub2e4. \uc774 \ud638\ucd9c \uacfc\uc815\uc774 \uc5c6\uc73c\uba74 \uc77c\uad00\uc131 \uc5c6\ub294 \ucd94\ub860 \uacb0\uacfc\uac00 \ub098\ud0c0\ub0a9\ub2c8\ub2e4.\n\nTorchScript\uc5d0 \ub300\ud55c \ucd94\uac00 \uc815\ubcf4\ub294 \uc804\uc6a9\n`\uc790\uc2b5\uc11c `__ \uc5d0\uc11c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n`C++ \ud658\uacbd `__ \ubb38\uc11c\ub97c \ucc38\uace0\ud558\uc5ec \ud2b8\ub808\uc774\uc2f1(Tracing) \ubcc0\ud658\uc744\n\uc218\ud589\ud558\ub294 \ubc29\ubc95\uacfc C++ \ud658\uacbd\uc5d0\uc11c TorchScript \ubaa8\ub4c8\uc744 \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc775\ud790 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n" ] }, { diff --git a/docs/beginner/saving_loading_models.html b/docs/beginner/saving_loading_models.html index a8202425c..e18fc8603 100644 --- a/docs/beginner/saving_loading_models.html +++ b/docs/beginner/saving_loading_models.html @@ -13,7 +13,7 @@ - + @@ -409,13 +409,13 @@

  1. torch.save: 직렬화된 객체를 디스크에 저장합니다. 이 함수는 Python의 -pickle 을 사용하여 직렬화합니다 +pickle 을 사용하여 직렬화합니다. 이 함수를 사용하여 모든 종류의 객체의 모델, Tensor 및 사전을 저장할 수 있습니다.

  2. torch.load: pickle을 사용하여 -저장된 객체 파일들을 역직렬화하여 메모리에 올립니다. 이 함수는 데이터를 장치에 -불러올 때도 사용합니다. -(장치간 모델 저장하기 & 불러오기 참고)

    3. +저장된 객체 파일들을 역직렬화하여 메모리에 올립니다. 이 함수는 데이터를 장치에 불러올 +때에도 사용됩니다. +(장치 간 모델 저장하기 & 불러오기 참고)

  3. torch.nn.Module.load_state_dict: 역직렬화된 state_dict 를 사용하여 모델의 매개변수들을 불러옵니다. state_dict 에 대한 더 자세한 정보는 state_dict가 무엇인가요? 를 참고하세요.

    4. @@ -518,26 +518,26 @@

    state_dict

    Note

    -

    PyTorch 버전 1.6에서는 새로운 Zip파일-기반의 파일 포맷을 사용하도록 -torch.save 가 변경되었습니다. torch.load 는 예전 방식의 파일들을 +

    PyTorch 버전 1.6에서는 torch.save 가 새로운 Zip파일-기반의 파일 +포맷을 사용하도록 변경되었습니다. torch.load 는 예전 방식의 파일들을 읽어올 수 있도록 하고 있습니다. 어떤 이유에서든 torch.save 가 예전 방식을 사용하도록 하고 싶다면, _use_new_zipfile_serialization=False 을 kwarg로 전달하세요.

    -

    추론을 위해 모델을 저장할 때는 학습된 모델의 학습된 매개변수만 저장하면 됩니다. +

    추론을 위해 모델을 저장할 때는 그 모델의 학습된 매개변수만 저장하면 됩니다. torch.save() 를 사용하여 모델의 state_dict 를 저장하는 것이 나중에 모델을 사용할 때 가장 유연하게 사용할 수 있는, 모델 저장 시 권장하는 방법입니다.

    PyTorch에서는 모델을 저장할 때 .pt 또는 .pth 확장자를 사용하는 것이 일반적인 규칙입니다.

    -

    추론을 실행하기 전에는 반드시 model.eval() 을 호출하여 드롭아웃 및 배치 -정규화를 평가 모드로 설정하여야 합니다. 이것을 하지 않으면 추론 결과가 일관성 -없게 출력됩니다.

    +

    추론을 실행하기 전에 반드시 model.eval() 을 호출하여 드롭아웃 및 배치 +정규화를 평가 모드로 설정하여야 합니다. 이 과정을 거치지 않으면 일관성 없는 +추론 결과가 출력됩니다.

    Note

    load_state_dict() 함수에는 저장된 객체의 경로가 아닌, 사전 객체를 전달해야 하는 것에 유의하세요. 따라서 저장된 state_dict 를 load_state_dict() 함수에 전달하기 전에 반드시 역직렬화를 해야 합니다. 예를 들어, -model.load_state_dict(PATH) 과 같은 식으로는 사용하면 안됩니다.

    +model.load_state_dict(PATH) 과 같은 식으로 사용하면 안됩니다.

    Note

    @@ -557,7 +557,7 @@

    전체 모델 저장하기/불러오기

    불러오기:

    -
    # 모델 클래스는 어딘가에 반드시 선언되어 있어야 합니다
+
    # 모델 클래스는 어딘가에 반드시 선언되어 있어야 합니다.
 model = torch.load(PATH)
 model.eval()
 
    
@@ -577,21 +577,19 @@ 
    전체 모델 저장하기/불러오기
 
    
-
    
-
    Export/Load Model in TorchScript Format
    
-
    One common way to do inference with a trained model is to use
-TorchScript, an intermediate
-representation of a PyTorch model that can be run in Python as well as in a
-high performance environment like C++. TorchScript is actually the recommended model format
-for scaled inference and deployment.
    
+
    
+
    TorchScript 포맷으로 모델 내보내기/가져오기
    
+
    훈련된 모델로 추론을 수행하는 일반적인 방법 중 하나는 TorchScript 를 사용하는 것입니다.
+TorchScript는 파이썬 환경이나 C++와 같은 고성능 환경에서 실행할 수 있는
+파이토치 모델의 중간 표현(IR; Intermediate Representation)입니다.
+TorchScript는 확장된 추론 및 배포에 권장되는 모델 형식이기도 합니다.
    
 
    
 
    Note
    
-
    Using the TorchScript format, you will be able to load the exported model and
-run inference without defining the model class.
    
+
    TorchScript 형식을 사용하면 모델 클래스를 정의하지 않고도 내보낸 모델을 읽어 오거나 추론을 실행할 수 있습니다.
    
 
    
 
    Export:
    
-
    model_scripted = torch.jit.script(model) # Export to TorchScript
-model_scripted.save('model_scripted.pt') # Save
+
    model_scripted = torch.jit.script(model) # TorchScript 형식으로 내보내기
+model_scripted.save('model_scripted.pt') # 저장하기
 
    
 
    
 
    Load:
    
@@ -599,13 +597,12 @@ 
    Export/Load Model in TorchScript Formatmodel.eval()
 
    
 
    
-
    Remember that you must call model.eval() to set dropout and batch
-normalization layers to evaluation mode before running inference.
-Failing to do this will yield inconsistent inference results.
    
-
    For more information on TorchScript, feel free to visit the dedicated
-tutorials.
-You will get familiar with the tracing conversion and learn how to
-run a TorchScript module in a C++ environment.
    
+
    추론 실행 전, 드롭아웃 및 배치(batch) 정규화 레이어를 평가 모드로 설정하기 위해 model.eval() 을 호출해야
+합니다. 이 호출 과정이 없으면 일관성 없는 추론 결과가 나타납니다.
    
+
    TorchScript에 대한 추가 정보는 전용
+자습서 에서 찾을 수 있습니다.
+C++ 환경 문서를 참고하여 트레이싱(Tracing) 변환을
+수행하는 방법과 C++ 환경에서 TorchScript 모듈을 실행하는 방법을 익힐 수 있습니다.
    
 
    
 
    
 
    
@@ -884,7 +881,7 @@ 
    torch.nn.DataPa
 
  4. 추론(inference)를 위해 모델 저장하기 & 불러오기
 
    5. 
 
  5. 추론 / 학습 재개를 위해 일반 체크포인트(checkpoint) 저장하기 & 불러오기
      
diff --git a/docs/searchindex.js b/docs/searchindex.js
index e5c53fa72..81957a73e 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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\ubcd1\ub82c(DDP)\uacfc \ubd84\uc0b0 RPC \ud504\ub808\uc784\uc6cc\ud06c \uacb0\ud569","Computation times","Exploring TorchRec sharding","(\ubca0\ud0c0) PyTorch\uc5d0\uc11c Eager Mode\ub97c \uc774\uc6a9\ud55c \uc815\uc801 \uc591\uc790\ud654","(\uc120\ud0dd) PyTorch \ubaa8\ub378\uc744 ONNX\uc73c\ub85c \ubcc0\ud658\ud558\uace0 ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud558\uae30","TorchScript\uc758 \ub3d9\uc801 \ubcd1\ub82c \ucc98\ub9ac(Dynamic Parallelism)","\ucee4\uc2a4\ud140 C++ \ud074\ub798\uc2a4\ub85c TorchScript \ud655\uc7a5\ud558\uae30","Extending TorchScript with Custom C++ Operators","TorchScript \uc18c\uac1c","\uc624\ub514\uc624 \ub370\uc774\ud130 \uc99d\uac15","Audio Datasets","Audio Feature Augmentation","Audio Feature Extractions","Audio I/O","Audio Resampling","torch.autograd\ub97c \uc0ac\uc6a9\ud55c \uc790\ub3d9 \ubbf8\ubd84","\uc2e0\uacbd\ub9dd \ubaa8\ub378 \uad6c\uc131\ud558\uae30","Dataset\uacfc DataLoader","\ud30c\uc774\ud1a0\uce58(PyTorch) \uae30\ubcf8 \uc775\ud788\uae30","\ubaa8\ub378 \ub9e4\uac1c\ubcc0\uc218 \ucd5c\uc801\ud654\ud558\uae30","\ube60\ub978 \uc2dc\uc791(Quickstart)","\ubaa8\ub378 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30","Computation times","\ud150\uc11c(Tensor)","\ubcc0\ud615(Transform)","torch.autograd \uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc18c\uac1c","\ubd84\ub958\uae30(Classifier) \ud559\uc2b5\ud558\uae30","\uc120\ud0dd \uc0ac\ud56d: \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac (Data Parallelism)","\uc2e0\uacbd\ub9dd(Neural Networks)","Computation times","\ud150\uc11c(Tensor)","\ucc57\ubd07 \ud29c\ud1a0\ub9ac\uc5bc","Colab\uc5d0\uc11c Google Drive\uc758 \ud29c\ud1a0\ub9ac\uc5bc \ub370\uc774\ud130 \uc0ac\uc6a9\ud558\uae30","\uc0ac\uc6a9\uc790 \uc815\uc758 Dataset, Dataloader, Transforms \uc791\uc131\ud558\uae30","DCGAN \ud29c\ud1a0\ub9ac\uc5bc","PyTorch\ub85c \ub525\ub7ec\ub2dd\ud558\uae30: 60\ubd84\ub9cc\uc5d0 \ub05d\uc7a5\ub0b4\uae30","Deep Learning for NLP with Pytorch","\uc548\ub4dc\ub85c\uc774\ub4dc\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubd84\ud560 DeepLapV3","iOS\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubd84\ud560 DeepLapV3","Deploying a Seq2Seq Model with TorchScript","PyTorch Distributed Overview","PyTorch: \ud150\uc11c(Tensor)\uc640 autograd","PyTorch: \uc0c8 autograd Function \uc815\uc758\ud558\uae30","PyTorch: \uc81c\uc5b4 \ud750\ub984(Control Flow) + \uac00\uc911\uce58 \uacf5\uc720(Weight Sharing)","PyTorch: \uc0ac\uc6a9\uc790 \uc815\uc758 nn.Module","PyTorch: nn","PyTorch: optim","\uc900\ube44 \uc6b4\ub3d9: NumPy","\ud30c\uc774\ud1a0\uce58(PyTorch): \ud150\uc11c(Tensor)","\uc801\ub300\uc801 \uc608\uc81c \uc0dd\uc131(Adversarial Example Generation)","Finetuning Torchvision Models","Autograd","nn \ud328\ud0a4\uc9c0","\uba40\ud2f0-GPU \uc608\uc81c","Computation times","Tensor","Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c PyTorch","Learning Hybrid Frontend Syntax Through Example","Computation times","Hybrid Frontend Tutorials","Ray Tune\uc744 \uc774\uc6a9\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd","PyTorch \uc18c\uac1c - YouTube \uc2dc\ub9ac\uc988","The Fundamentals of Autograd","Model Understanding with Captum","PyTorch \uc18c\uac1c","Building Models with PyTorch","Computation times","PyTorch TensorBoard Support","Introduction to PyTorch Tensors","Training with PyTorch","\uc2ec\ud654 \uacfc\uc815 : Bi-LSTM CRF\uc640 \ub3d9\uc801 \uacb0\uc815","PyTorch\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd","PyTorch \uc18c\uac1c","Sequence Models and Long Short-Term Memory Networks","Computation times","\ub2e8\uc5b4 \uc784\ubca0\ub529: \uc5b4\ud718\uc758 \uc758\ubbf8(Lexical semantics)\ub97c \uc778\ucf54\ub529\ud558\uae30","torch.nn \uc774 \uc2e4\uc81c\ub85c \ubb34\uc5c7\uc778\uac00\uc694?","PyTorch \ubaa8\ub4c8 \ud504\ub85c\ud30c\uc77c\ub9c1 \ud558\uae30","PyTorch Cheat Sheet","\uc608\uc81c\ub85c \ubc30\uc6b0\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)","\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","Computation times","torchtext \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ud558\uae30","TorchText\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30","\ucef4\ud4e8\ud130 \ube44\uc804(Vision)\uc744 \uc704\ud55c \uc804\uc774\ud559\uc2b5(Transfer Learning)","nn.Transformer \uc640 TorchText \ub85c \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\ub9c1\ud558\uae30","nn.Transformer\uc640 torchtext\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30","\ubc30\ud3ec\ub97c \uc704\ud55c \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Vision Transformer) \ubaa8\ub378 \ucd5c\uc801\ud654\ud558\uae30","\ud30c\uc774\ud1a0\uce58(PyTorch) \ud55c\uad6d\uc5b4 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0 \uc624\uc2e0 \uac83\uc744 \ud658\uc601\ud569\ub2c8\ub2e4!","Advanced Fully Sharded Data Parallel(FSDP) Tutorial","Getting Started with Fully Sharded Data Parallel(FSDP)","\ubbf8\ubd84\uc790\ub3d9\ud654(autograd) \uc800\uc7a5\ub41c \ud150\uc11c\ub97c \uc704\ud55c Hooks","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \ubd84\ub958\ud558\uae30","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP:  \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \uc0dd\uc131\ud558\uae30","Fusing Convolution and Batch Norm using Custom Function","Double Backward with Custom Functions","\ubd84\uc0b0 \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac \uc2dc\uc791\ud558\uae30","Distributed Pipeline Parallelism Using RPC","PyTorch\ub85c \ubd84\uc0b0 \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158 \uac1c\ubc1c\ud558\uae30","(\ubca0\ud0c0) BERT \ubaa8\ub378 \ub3d9\uc801 \uc591\uc790\ud654\ud558\uae30","Flask\ub97c \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c PyTorch\ub97c REST API\ub85c \ubc30\ud3ec\ud558\uae30","Forced Alignment with Wav2Vec2","Forward-mode Automatic Differentiation (Beta)","(\ubca0\ud0c0) FX\uc5d0\uc11c \ud569\uc131\uacf1/\ubc30\uce58 \uc815\uaddc\ud654(Convolution/Batch Norm) \uacb0\ud569\uae30(Fuser) \ub9cc\ub4e4\uae30","(beta) Building a Simple CPU Performance Profiler with FX","\ub9c8\ub9ac\uc624 \uac8c\uc784 RL \uc5d0\uc774\uc804\ud2b8\ub85c \ud559\uc2b5\ud558\uae30","(\ubca0\ud0c0) PyTorch\ub97c \uc0ac\uc6a9\ud55c Channels Last \uba54\ubaa8\ub9ac \ud615\uc2dd","\ub2e8\uc77c \uba38\uc2e0\uc744 \uc0ac\uc6a9\ud55c \ubaa8\ub378 \ubcd1\ub82c\ud654 \ubaa8\ubc94 \uc0ac\ub840","Parametrizations Tutorial","\ud30c\uc774\ud504\ub77c\uc778 \ubcd1\ub82c\ud654\ub85c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378 \ud559\uc2b5\uc2dc\ud0a4\uae30","Customize Process Group Backends Using Cpp Extensions","\uac00\uc9c0\uce58\uae30 \uae30\ubc95(Pruning) \ud29c\ud1a0\ub9ac\uc5bc","(\ubca0\ud0c0) \ucef4\ud4e8\ud130 \ube44\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc704\ud55c \uc591\uc790\ud654\ub41c \uc804\uc774\ud559\uc2b5(Quantized Transfer Learning)","Raspberry Pi 4 \uc5d0\uc11c \uc2e4\uc2dc\uac04 \ucd94\ub860(Inference) (30fps!)","\uac15\ud654 \ud559\uc2b5 (DQN) \ud29c\ud1a0\ub9ac\uc5bc","Implementing Batch RPC Processing Using Asynchronous Executions","Implementing a Parameter Server Using Distributed RPC Framework","Getting Started with Distributed RPC Framework","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: Sequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 Attention\uc744 \uc774\uc6a9\ud55c \ubc88\uc5ed","Computation times","\uacf5\uac04 \ubcc0\ud615 \ub124\ud2b8\uc6cc\ud06c(Spatial Transformer Networks) \ud29c\ud1a0\ub9ac\uc5bc","Speech Command Classification with torchaudio","Wav2Vec2\ub97c \uc774\uc6a9\ud574\uc11c \uc74c\uc131 \uc778\uc2dd\ud558\uae30","PyTorch Profiler With TensorBoard","TensorBoard\ub85c \ubaa8\ub378, \ub370\uc774\ud130, \ud559\uc2b5 \uc2dc\uac01\ud654\ud558\uae30","torchaudio\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\uc5d0\uc11c \uc74c\uc131\uc73c\ub85c \ubcc0\ud658(text-to-speech)","TorchRec \uc18c\uac1c","Grokking PyTorch Intel CPU performance from first principles","TorchVision \uac1d\uccb4 \uac80\ucd9c \ubbf8\uc138\uc870\uc815(Finetuning) \ud29c\ud1a0\ub9ac\uc5bc","Profiling PyTorch RPC-Based Workloads","(prototype) FX Graph Mode Post Training Dynamic Quantization","(prototype) FX Graph Mode Post Training Static Quantization","(prototype) FX Graph Mode Quantization User Guide","PyTorch FX Numeric Suite Core APIs Tutorial","(prototype) Graph Mode Dynamic Quantization on BERT","(Prototype) Convert Mobilenetv2 to Core ML","(Prototype) Use iOS GPU in PyTorch","(Beta) Convert MobileNetV2 to NNAPI","PyTorch Numeric Suite Tutorial","PyTorch Prototype Recipes","Computation times","\ubaa8\ub4c8 \ub9e4\uac1c\ubcc0\uc218 \ucd08\uae30\ud654 \uac74\ub108\ub6f0\uae30","Model Freezing in TorchScript","(prototype) \ucd94\uc801-\uae30\ubc18 \uc120\ud0dd\uc801 \ube4c\ub4dc \ubaa8\ubc14\uc77c Android, iOS \uc778\ud130\ud504\ub9ac\ud130","torch.vmap","PyTorch Vulkan Backend User Workflow","PyTorch \uc0ac\uc804 \ube4c\ub4dc\ub41c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \ub124\uc774\ud2f0\ube0c Android \uc560\ud50c\ub9ac\ucf00\uc774\uc158 \ub9cc\ub4e4\uae30","(beta) Bundling inputs to PyTorch Models","Direct Device-to-Device Communication with TensorPipe CUDA RPC","Flask\ub85c \ubc30\ud3ec\ud558\uae30","Distributed Optimizer with TorchScript support","Profiling PyTorch RPC-Based Workloads","Fuse Modules Recipe","Intel\u00ae Extension for PyTorch*","Ease-of-use quantization for PyTorch with Intel\u00ae Neural Compressor","(beta) Efficient mobile interpreter in Android and iOS","PyTorch \ubaa8\ubc14\uc77c \uc131\ub2a5 \ub808\uc2dc\ud53c","Model Preparation for Android Recipe","Model Preparation for iOS Recipe","Summary of PyTorch Mobile Recipes","\uc591\uc790\ud654 \ub808\uc2dc\ud53c","Captum\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ud574\uc11d\ud558\uae30","Automatic Mixed Precision","PyTorch Benchmark","Pytorch\ub97c \uc0ac\uc6a9\ud574 \uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30","\ub3d9\uc801 \uc591\uc790\ud654","PyTorch\uc5d0\uc11c \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30","PyTorch \ud504\ub85c\ud30c\uc77c\ub7ec(Profiler)","PyTorch\uc5d0\uc11c \ub2e4\uc591\ud55c \uc7a5\uce58 \uac04 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \uc77c\ubc18\uc801\uc778 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \uc5ec\ub7ec \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\ub85c TensorBoard \uc0ac\uc6a9\ud558\uae30","Timer \ube60\ub974\uac8c \uc2dc\uc791\ud558\uae30","Performance Tuning Guide","PyTorch\uc5d0\uc11c \ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart)","PyTorch\uc5d0\uc11c state_dict\ub780 \ubb34\uc5c7\uc778\uac00\uc694?","PyTorch\uc5d0\uc11c \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uae30","PyTorch Recipes","Script and Optimize for Mobile Recipe","TorchScript\ub85c \ubc30\ud3ec\ud558\uae30","Shard Optimizer States with 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\ubcd1\ub82c(DDP)\uacfc \ubd84\uc0b0 RPC \ud504\ub808\uc784\uc6cc\ud06c \uacb0\ud569","Computation times","Exploring TorchRec sharding","(\ubca0\ud0c0) PyTorch\uc5d0\uc11c Eager Mode\ub97c \uc774\uc6a9\ud55c \uc815\uc801 \uc591\uc790\ud654","(\uc120\ud0dd) PyTorch \ubaa8\ub378\uc744 ONNX\uc73c\ub85c \ubcc0\ud658\ud558\uace0 ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud558\uae30","TorchScript\uc758 \ub3d9\uc801 \ubcd1\ub82c \ucc98\ub9ac(Dynamic Parallelism)","\ucee4\uc2a4\ud140 C++ \ud074\ub798\uc2a4\ub85c TorchScript \ud655\uc7a5\ud558\uae30","Extending TorchScript with Custom C++ Operators","TorchScript \uc18c\uac1c","\uc624\ub514\uc624 \ub370\uc774\ud130 \uc99d\uac15","Audio Datasets","Audio Feature Augmentation","Audio Feature Extractions","Audio I/O","Audio Resampling","torch.autograd\ub97c \uc0ac\uc6a9\ud55c \uc790\ub3d9 \ubbf8\ubd84","\uc2e0\uacbd\ub9dd \ubaa8\ub378 \uad6c\uc131\ud558\uae30","Dataset\uacfc DataLoader","\ud30c\uc774\ud1a0\uce58(PyTorch) \uae30\ubcf8 \uc775\ud788\uae30","\ubaa8\ub378 \ub9e4\uac1c\ubcc0\uc218 \ucd5c\uc801\ud654\ud558\uae30","\ube60\ub978 \uc2dc\uc791(Quickstart)","\ubaa8\ub378 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30","Computation times","\ud150\uc11c(Tensor)","\ubcc0\ud615(Transform)","torch.autograd \uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc18c\uac1c","\ubd84\ub958\uae30(Classifier) \ud559\uc2b5\ud558\uae30","\uc120\ud0dd \uc0ac\ud56d: \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac (Data Parallelism)","\uc2e0\uacbd\ub9dd(Neural Networks)","Computation times","\ud150\uc11c(Tensor)","\ucc57\ubd07 \ud29c\ud1a0\ub9ac\uc5bc","Colab\uc5d0\uc11c Google Drive\uc758 \ud29c\ud1a0\ub9ac\uc5bc \ub370\uc774\ud130 \uc0ac\uc6a9\ud558\uae30","\uc0ac\uc6a9\uc790 \uc815\uc758 Dataset, Dataloader, Transforms \uc791\uc131\ud558\uae30","DCGAN \ud29c\ud1a0\ub9ac\uc5bc","PyTorch\ub85c \ub525\ub7ec\ub2dd\ud558\uae30: 60\ubd84\ub9cc\uc5d0 \ub05d\uc7a5\ub0b4\uae30","Deep Learning for NLP with Pytorch","\uc548\ub4dc\ub85c\uc774\ub4dc\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubd84\ud560 DeepLapV3","iOS\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubd84\ud560 DeepLapV3","Deploying a Seq2Seq Model with TorchScript","PyTorch Distributed Overview","PyTorch: \ud150\uc11c(Tensor)\uc640 autograd","PyTorch: \uc0c8 autograd Function \uc815\uc758\ud558\uae30","PyTorch: \uc81c\uc5b4 \ud750\ub984(Control Flow) + \uac00\uc911\uce58 \uacf5\uc720(Weight Sharing)","PyTorch: \uc0ac\uc6a9\uc790 \uc815\uc758 nn.Module","PyTorch: nn","PyTorch: optim","\uc900\ube44 \uc6b4\ub3d9: NumPy","\ud30c\uc774\ud1a0\uce58(PyTorch): \ud150\uc11c(Tensor)","\uc801\ub300\uc801 \uc608\uc81c \uc0dd\uc131(Adversarial Example Generation)","Finetuning Torchvision Models","Autograd","nn \ud328\ud0a4\uc9c0","\uba40\ud2f0-GPU \uc608\uc81c","Computation times","Tensor","Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c PyTorch","Learning Hybrid Frontend Syntax Through Example","Computation times","Hybrid Frontend Tutorials","Ray Tune\uc744 \uc774\uc6a9\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd","PyTorch \uc18c\uac1c - YouTube \uc2dc\ub9ac\uc988","The Fundamentals of Autograd","Model Understanding with Captum","PyTorch \uc18c\uac1c","Building Models with PyTorch","Computation times","PyTorch TensorBoard Support","Introduction to PyTorch Tensors","Training with PyTorch","\uc2ec\ud654 \uacfc\uc815 : Bi-LSTM CRF\uc640 \ub3d9\uc801 \uacb0\uc815","PyTorch\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd","PyTorch \uc18c\uac1c","Sequence Models and Long Short-Term Memory Networks","Computation times","\ub2e8\uc5b4 \uc784\ubca0\ub529: \uc5b4\ud718\uc758 \uc758\ubbf8(Lexical semantics)\ub97c \uc778\ucf54\ub529\ud558\uae30","torch.nn \uc774 \uc2e4\uc81c\ub85c \ubb34\uc5c7\uc778\uac00\uc694?","PyTorch \ubaa8\ub4c8 \ud504\ub85c\ud30c\uc77c\ub9c1 \ud558\uae30","PyTorch Cheat Sheet","\uc608\uc81c\ub85c \ubc30\uc6b0\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)","\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","Computation times","torchtext \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ud558\uae30","TorchText\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30","\ucef4\ud4e8\ud130 \ube44\uc804(Vision)\uc744 \uc704\ud55c \uc804\uc774\ud559\uc2b5(Transfer Learning)","nn.Transformer \uc640 TorchText \ub85c \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\ub9c1\ud558\uae30","nn.Transformer\uc640 torchtext\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30","\ubc30\ud3ec\ub97c \uc704\ud55c \ube44\uc804 \ud2b8\ub79c\uc2a4\ud3ec\uba38(Vision Transformer) \ubaa8\ub378 \ucd5c\uc801\ud654\ud558\uae30","\ud30c\uc774\ud1a0\uce58(PyTorch) \ud55c\uad6d\uc5b4 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0 \uc624\uc2e0 \uac83\uc744 \ud658\uc601\ud569\ub2c8\ub2e4!","Advanced Fully Sharded Data Parallel(FSDP) Tutorial","Getting Started with Fully Sharded Data Parallel(FSDP)","\ubbf8\ubd84\uc790\ub3d9\ud654(autograd) \uc800\uc7a5\ub41c \ud150\uc11c\ub97c \uc704\ud55c Hooks","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \ubd84\ub958\ud558\uae30","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP:  \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \uc0dd\uc131\ud558\uae30","Fusing Convolution and Batch Norm using Custom Function","Double Backward with Custom Functions","\ubd84\uc0b0 \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac \uc2dc\uc791\ud558\uae30","Distributed Pipeline Parallelism Using RPC","PyTorch\ub85c \ubd84\uc0b0 \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158 \uac1c\ubc1c\ud558\uae30","(\ubca0\ud0c0) BERT \ubaa8\ub378 \ub3d9\uc801 \uc591\uc790\ud654\ud558\uae30","Flask\ub97c \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c PyTorch\ub97c REST API\ub85c \ubc30\ud3ec\ud558\uae30","Forced Alignment with Wav2Vec2","Forward-mode Automatic Differentiation (Beta)","(\ubca0\ud0c0) FX\uc5d0\uc11c \ud569\uc131\uacf1/\ubc30\uce58 \uc815\uaddc\ud654(Convolution/Batch Norm) \uacb0\ud569\uae30(Fuser) \ub9cc\ub4e4\uae30","(beta) Building a Simple CPU Performance Profiler with FX","\ub9c8\ub9ac\uc624 \uac8c\uc784 RL \uc5d0\uc774\uc804\ud2b8\ub85c \ud559\uc2b5\ud558\uae30","(\ubca0\ud0c0) PyTorch\ub97c \uc0ac\uc6a9\ud55c Channels Last \uba54\ubaa8\ub9ac \ud615\uc2dd","\ub2e8\uc77c \uba38\uc2e0\uc744 \uc0ac\uc6a9\ud55c \ubaa8\ub378 \ubcd1\ub82c\ud654 \ubaa8\ubc94 \uc0ac\ub840","Parametrizations Tutorial","\ud30c\uc774\ud504\ub77c\uc778 \ubcd1\ub82c\ud654\ub85c \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378 \ud559\uc2b5\uc2dc\ud0a4\uae30","Customize Process Group Backends Using Cpp Extensions","\uac00\uc9c0\uce58\uae30 \uae30\ubc95(Pruning) \ud29c\ud1a0\ub9ac\uc5bc","(\ubca0\ud0c0) \ucef4\ud4e8\ud130 \ube44\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc704\ud55c \uc591\uc790\ud654\ub41c \uc804\uc774\ud559\uc2b5(Quantized Transfer Learning)","Raspberry Pi 4 \uc5d0\uc11c \uc2e4\uc2dc\uac04 \ucd94\ub860(Inference) (30fps!)","\uac15\ud654 \ud559\uc2b5 (DQN) \ud29c\ud1a0\ub9ac\uc5bc","Implementing Batch RPC Processing Using Asynchronous Executions","Implementing a Parameter Server Using Distributed RPC Framework","Getting Started with Distributed RPC Framework","\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: Sequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 Attention\uc744 \uc774\uc6a9\ud55c \ubc88\uc5ed","Computation times","\uacf5\uac04 \ubcc0\ud615 \ub124\ud2b8\uc6cc\ud06c(Spatial Transformer Networks) \ud29c\ud1a0\ub9ac\uc5bc","Speech Command Classification with torchaudio","Wav2Vec2\ub97c \uc774\uc6a9\ud574\uc11c \uc74c\uc131 \uc778\uc2dd\ud558\uae30","PyTorch Profiler With TensorBoard","TensorBoard\ub85c \ubaa8\ub378, \ub370\uc774\ud130, \ud559\uc2b5 \uc2dc\uac01\ud654\ud558\uae30","torchaudio\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\uc5d0\uc11c \uc74c\uc131\uc73c\ub85c \ubcc0\ud658(text-to-speech)","TorchRec \uc18c\uac1c","Grokking PyTorch Intel CPU performance from first principles","TorchVision \uac1d\uccb4 \uac80\ucd9c \ubbf8\uc138\uc870\uc815(Finetuning) \ud29c\ud1a0\ub9ac\uc5bc","Profiling PyTorch RPC-Based Workloads","(prototype) FX Graph Mode Post Training Dynamic Quantization","(prototype) FX Graph Mode Post Training Static Quantization","(prototype) FX Graph Mode Quantization User Guide","PyTorch FX Numeric Suite Core APIs Tutorial","(prototype) Graph Mode Dynamic Quantization on BERT","(Prototype) Convert Mobilenetv2 to Core ML","(Prototype) Use iOS GPU in PyTorch","(Beta) Convert MobileNetV2 to NNAPI","PyTorch Numeric Suite Tutorial","PyTorch Prototype Recipes","Computation times","\ubaa8\ub4c8 \ub9e4\uac1c\ubcc0\uc218 \ucd08\uae30\ud654 \uac74\ub108\ub6f0\uae30","Model Freezing in TorchScript","(prototype) \ucd94\uc801-\uae30\ubc18 \uc120\ud0dd\uc801 \ube4c\ub4dc \ubaa8\ubc14\uc77c Android, iOS \uc778\ud130\ud504\ub9ac\ud130","torch.vmap","PyTorch Vulkan Backend User Workflow","PyTorch \uc0ac\uc804 \ube4c\ub4dc\ub41c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \ub124\uc774\ud2f0\ube0c Android \uc560\ud50c\ub9ac\ucf00\uc774\uc158 \ub9cc\ub4e4\uae30","(beta) Bundling inputs to PyTorch Models","Direct Device-to-Device Communication with TensorPipe CUDA RPC","Flask\ub85c \ubc30\ud3ec\ud558\uae30","Distributed Optimizer with TorchScript support","Profiling PyTorch RPC-Based Workloads","Fuse Modules Recipe","Intel\u00ae Extension for PyTorch*","Ease-of-use quantization for PyTorch with Intel\u00ae Neural Compressor","(beta) Efficient mobile interpreter in Android and iOS","PyTorch \ubaa8\ubc14\uc77c \uc131\ub2a5 \ub808\uc2dc\ud53c","Model Preparation for Android Recipe","Model Preparation for iOS Recipe","Summary of PyTorch Mobile Recipes","\uc591\uc790\ud654 \ub808\uc2dc\ud53c","Captum\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ud574\uc11d\ud558\uae30","Automatic Mixed Precision","PyTorch Benchmark","Pytorch\ub97c \uc0ac\uc6a9\ud574 \uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30","\ub3d9\uc801 \uc591\uc790\ud654","PyTorch\uc5d0\uc11c \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30","PyTorch \ud504\ub85c\ud30c\uc77c\ub7ec(Profiler)","PyTorch\uc5d0\uc11c \ub2e4\uc591\ud55c \uc7a5\uce58 \uac04 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \uc77c\ubc18\uc801\uc778 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\uc5d0\uc11c \uc5ec\ub7ec \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30","PyTorch\ub85c TensorBoard \uc0ac\uc6a9\ud558\uae30","Timer \ube60\ub974\uac8c \uc2dc\uc791\ud558\uae30","Performance Tuning Guide","PyTorch\uc5d0\uc11c \ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart)","PyTorch\uc5d0\uc11c state_dict\ub780 \ubb34\uc5c7\uc778\uac00\uc694?","PyTorch\uc5d0\uc11c \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uae30","PyTorch Recipes","Script and Optimize for Mobile Recipe","TorchScript\ub85c \ubc30\ud3ec\ud558\uae30","Shard Optimizer States with 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