{"id":29606,"date":"2024-10-28T01:55:42","date_gmt":"2024-10-28T01:55:42","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29606"},"modified":"2024-11-26T06:52:06","modified_gmt":"2024-11-26T06:52:06","slug":"%ed%97%88%ea%b9%85%ed%8e%98%ec%9d%b4%ec%8a%a4-%ed%8a%b8%eb%a0%8c%ec%8a%a4%ed%8f%ac%eb%a8%b8-%ed%99%9c%ec%9a%a9%ea%b0%95%ec%a2%8c-gpu%eb%a1%9c-%ec%a0%84%ec%86%a1","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29606\/","title":{"rendered":"\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, GPU\ub85c \uc804\uc1a1"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\ub294 \uc190\uc26c\uc6b4 Transformer \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\uc5ec \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc774 \uc190\uc27d\uac8c NLP \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uae30\ubcf8\uc801\uc778 Transformer \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uacfc GPU \uac00\uc18d\uc744 \ud1b5\ud55c \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780?<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc744 \uc704\ud574 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 \uc5b8\uc5b4 \uc774\ud574, \ud14d\uc2a4\ud2b8 \uc0dd\uc131, \ubc88\uc5ed, \uc9c8\ubb38 \uc751\ub2f5 \ub4f1 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub294 \uc0ac\uc6a9\ud558\uae30 \uc26c\uc6b4 API\ub97c \uc81c\uacf5\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac04\ud3b8\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ud658\uacbd \uc124\uc815<\/h2>\n

\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 Python\uacfc pip\ub97c \uc124\uce58\ud558\uace0, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud574\ubd05\uc2dc\ub2e4.<\/p>\n

pip install transformers torch<\/code><\/pre>\n
\uc704 \uba85\ub839\uc5b4\ub294 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 PyTorch\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. \uc774\ud6c4 GPU\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574 \ub2e4\uc74c\uacfc \uac19\uc740 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch\nprint(\"CUDA \uac00\uc6a9\uc131:\", torch.cuda.is_available())\nprint(\"\uc0ac\uc6a9 \uc911\uc778 CUDA \uc7a5\uce58:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"\uc5c6\uc74c\")\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 CUDA\uac00 \uc0ac\uc6a9 \uac00\ub2a5\ud55c\uc9c0 \uc5ec\ubd80\uc640 \uc0ac\uc6a9 \uc911\uc778 GPU\uc758 \uc774\ub984\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h2>\n
\uc774\uc81c \ubcf8\uaca9\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \ubd88\ub7ec\uc624\uace0 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud5c8\uae45\ud398\uc774\uc2a4\uc758 transformers<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \ubbf8\ub9ac \ud559\uc2b5\ub41c \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 BERT \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc608\uc2dc\ub85c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom transformers import BertTokenizer, BertForSequenceClassification\nfrom torch.nn import functional as F\n\n# BERT \ud1a0\ud06c\ub098\uc774\uc800 \ubc0f \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n\n# GPU\ub85c \uc804\uc1a1\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\n<\/code><\/pre>\n
\uc704\uc758 \ucf54\ub4dc\ub294 BERT \ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \ubd88\ub7ec\uc624\uace0, \uc0ac\uc6a9 \uac00\ub2a5\ud55c \uacbd\uc6b0 GPU\ub85c \ubaa8\ub378\uc744 \uc804\uc1a1\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
4. \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ubb38\uc7a5\uc744 \uc785\ub825\ud558\uc5ec \ud1a0\ud070\ud654\ud55c \ud6c4, \uc785\ub825 \ud150\uc11c\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
\n# \uc785\ub825 \ubb38\uc7a5\ntext = \"\ud5c8\uae45\ud398\uc774\uc2a4\uc758 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \uac15\ub825\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\"\n# \ud1a0\ud070\ud654 \ubc0f \uc778\ub371\uc2a4\ub85c \ubcc0\ud658\ninputs = tokenizer(text, return_tensors=\"pt\").to(device)\n<\/code><\/pre>\n
\uc5ec\uae30\uc11c return_tensors=\"pt\"<\/code>\ub294 PyTorch \ud150\uc11c\ub97c \ubc18\ud658\ud558\uaca0\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uc774\uc81c \uc6b0\ub9ac\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \uc804\ub2ec\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \ubaa8\ub378 \uc608\uce21<\/h2>\n
\ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc5d0 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc804\ub2ec\ud558\uace0, \ub85c\uc9d3\uc744 \uc0ac\uc6a9\ud574 \uacb0\uacfc\ub97c \ud574\uc11d\ud569\ub2c8\ub2e4.<\/p>\n
\n# \ubaa8\ub378 \uc608\uce21\nwith torch.no_grad():\n    outputs = model(**inputs)\n\n# \ub85c\uc9d3 \ucd9c\ub825\nlogits = outputs.logits\npredicted_class = logits.argmax(dim=1).item()\nprint(\"\uc608\uc0c1 \ud074\ub798\uc2a4:\", predicted_class)\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 \uc785\ub825 \ubb38\uc7a5\uc5d0 \ub300\ud55c \ubaa8\ub378\uc758 \uc608\uce21 \ud074\ub798\uc2a4\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.<\/p>\n
6. \ub370\uc774\ud130 \ubc30\uce58 \ucc98\ub9ac<\/h2>\n
\uc2e4\uc81c \uc751\uc6a9\uc5d0\uc11c\ub294 \uc5ec\ub7ec \ubb38\uc7a5\uc744 \ub3d9\uc2dc\uc5d0 \ucc98\ub9ac\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc5ec\ub7ec \ubb38\uc7a5\uc744 \ubc30\uce58\ub85c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
\ntexts = [\n    \"\uccab \ubc88\uc9f8 \ubb38\uc7a5\uc785\ub2c8\ub2e4.\",\n    \"\ub450 \ubc88\uc9f8 \ubb38\uc7a5\uc785\ub2c8\ub2e4.\",\n    \"\uc138 \ubc88\uc9f8 \ubb38\uc7a5\uc785\ub2c8\ub2e4.\"\n]\n\n# \ud1a0\ud070\ud654 \ubc0f \uc778\ub371\uc2a4 \ubcc0\ud658\ninputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\").to(device)\n\n# \ubaa8\ub378 \uc608\uce21\nwith torch.no_grad():\n    outputs = model(**inputs)\n\n# \ub85c\uc9d3 \ucd9c\ub825\nlogits = outputs.logits\npredicted_classes = logits.argmax(dim=1).tolist()\nprint(\"\uc608\uc0c1 \ud074\ub798\uc2a4\ub4e4:\", predicted_classes)\n<\/code><\/pre>\n
\uc704\uc640 \uac19\uc774 \uc5ec\ub7ec \ubb38\uc7a5\uc744 \ud55c \ubc88\uc5d0 \ucc98\ub9ac\ud558\uba74, \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ub354\uc6b1 \ud6a8\uc728\uc801\uc73c\ub85c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \ucd5c\uc801\ud654 \ubc0f GPU \ud65c\uc6a9<\/h2>\n
\ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \ub54c\ub294 GPU\ub97c \ud65c\uc6a9\ud558\uc5ec \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub192\uc774\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ub2e8\uc21c\ud55c \uc608\uc81c\ub97c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc774 \uc0d8\ud50c \uc608\uc81c\uc5d0\uc11c\ub294 Adadelta \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud558\uc600\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom torch.optim import AdamW\n\n# \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\noptimizer = AdamW(model.parameters(), lr=5e-5)\n\n# \uac00\uc0c1\uc758 \ub370\uc774\ud130\uc640 \ub808\uc774\ube14\ntrain_texts = [\"\uae0d\uc815\uc801\uc778 \ubb38\uc7a5\uc785\ub2c8\ub2e4.\", \"\ubd80\uc815\uc801\uc778 \ubb38\uc7a5\uc785\ub2c8\ub2e4.\"]\ntrain_labels = [1, 0]\n\n# \ubc30\uce58 \ucc98\ub9ac\ntrain_inputs = tokenizer(train_texts, padding=True, truncation=True, return_tensors=\"pt\").to(device)\ntrain_labels = torch.tensor(train_labels).to(device)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(3): # \uc5d0\ud3ed \uc218\n    optimizer.zero_grad()\n    outputs = model(**train_inputs, labels=train_labels)\n    loss = outputs.loss\n    loss.backward()\n    optimizer.step()\n    print(f\"\uc5d0\ud3ed {epoch + 1}, \uc190\uc2e4: {loss.item()}\")\n<\/code><\/pre>\n
\uc704 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c \ub450 \uac1c\uc758 \ubb38\uc7a5\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \uc5d0\ud3ed\ub9c8\ub2e4 \uc190\uc2e4\uc744 \ucd9c\ub825\ud558\uc5ec \ud559\uc2b5 \uc9c4\ud589 \uc0c1\ud669\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
8. \ubaa8\ub378 \uc800\uc7a5 \ubc0f \ub85c\ub4dc<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc740 \uc800\uc7a5\ud558\uc5ec \ub098\uc911\uc5d0 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ub85c\ub4dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
\n# \ubaa8\ub378 \uc800\uc7a5\nmodel.save_pretrained(\".\/model_directory\")\ntokenizer.save_pretrained(\".\/model_directory\")\n\n# \ubaa8\ub378 \ub85c\ub4dc\nmodel = BertForSequenceClassification.from_pretrained(\".\/model_directory\")\ntokenizer = BertTokenizer.from_pretrained(\".\/model_directory\")\nmodel.to(device)\n<\/code><\/pre>\n
\ubaa8\ub378\uacfc \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc800\uc7a5\ud558\uace0, \uc774\ud6c4 \ud544\uc694\ud560 \ub54c \ub85c\ub4dc\ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
9. \ub9c8\ubb34\ub9ac<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec BERT \ubaa8\ub378\uc744 \uc774\uc6a9\ud55c NLP \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uacfc GPU\ub97c \ud65c\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc774 \uc810\ucc28 \uc911\uc694\ud574\uc838\uac00\ub294 \ub9cc\ud07c, \ub2e4\uc591\ud55c \ub3c4\uad6c\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc775\ud600 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \ud0a4\uc6b0\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 AI\uc640 NLP \ubd84\uc57c\uc5d0\uc11c \ub354 \ub9ce\uc740 \ubc1c\uc804\uc774 \uc77c\uc5b4\ub098\uae38 \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud5c8\uae45\ud398\uc774\uc2a4(Hugging Face)\ub294 \uc190\uc26c\uc6b4 Transformer \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\uc5ec \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc774 \uc190\uc27d\uac8c NLP \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud5c8\uae45\ud398\uc774\uc2a4 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uae30\ubcf8\uc801\uc778 Transformer \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uacfc GPU \uac00\uc18d\uc744 \ud1b5\ud55c \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub780? \ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub79c\uc2a4\ud3ec\uba38\ub294 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc791\uc5c5\uc744 … \ub354 \ubcf4\uae30 “\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, GPU\ub85c \uc804\uc1a1”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[30],"tags":[],"class_list":["post-29606","post","type-post","status-publish","format-standard","hentry","category-30"],"yoast_head":"\n\ud5c8\uae45\ud398\uc774\uc2a4 \ud2b8\ub80c\uc2a4\ud3ec\uba38 \ud65c\uc6a9\uac15\uc88c, GPU\ub85c \uc804\uc1a1 - 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