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CPU与CUDA(GPU)的计算能力对比之二: Keras Resnet 运算效率比较

發布時間:2024/3/13 编程问答 39 豆豆
生活随笔 收集整理的這篇文章主要介紹了 CPU与CUDA(GPU)的计算能力对比之二: Keras Resnet 运算效率比较 小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

CPU與CUDA(GPU)的計算能力對比之二: Keras Resnet 運算效率比較

結論: CUDA(GPU : NVIDIA RTX2070 MQ 筆記本版本) 啟動后,效率將近是 CPU 單獨運行的 17倍左右 : 每個 EPOCH 運行時間平均分別為 370秒, 22秒。

運算環境:
GPU: NVIDIA RTX2070 MQ
CPU : Intel 9750 i7 六核 2.3Ghz
CUDA : 10.1 版本
cuDNN : 7.6.5
Keras : 2.4.3
OS : Windows 10
Python :3.8.5

代碼鏈接:
https://github.com/keras-team/keras-docs-zh/blob/master/sources/examples/cifar10_resnet.md

使用 CPU (未啟動CUDA功能)時,每個 EPOCH 需要將近 370 秒。

1563/1563 [==============================] - 372s 238ms/step - loss: 1.5579 - accuracy: 0.4921 - val_loss: 1.7732 - val_accuracy: 0.4694 Learning rate: 0.001 Epoch 2/200 1563/1563 [==============================] - ETA: 0s - loss: 1.1675 - accuracy: 0.6428 WARNING:tensorflow:Can save best model only with val_acc available, skipping. 1563/1563 [==============================] - 369s 236ms/step - loss: 1.1675 - accuracy: 0.6428 - val_loss: 1.3538 - val_accuracy: 0.6097 Learning rate: 0.001 Epoch 3/200 1563/1563 [==============================] - ETA: 0s - loss: 1.0121 - accuracy: 0.7031 WARNING:tensorflow:Can save best model only with val_acc available, skipping. 1563/1563 [==============================] - 379s 243ms/step - loss: 1.0121 - accuracy: 0.7031 - val_loss: 0.9784 - val_accuracy: 0.7139 Learning rate: 0.001 Epoch 4/200 1563/1563 [==============================] - ETA: 0s - loss: 0.9183 - accuracy: 0.7376 WARNING:tensorflow:Can save best model only with val_acc available, skipping. 1563/1563 [==============================] - 371s 237ms/step - loss: 0.9183 - accuracy: 0.7376 - val_loss: 0.9903 - val_accuracy: 0.7200

啟動 CUDA 功能后的運算效率:每個 EPOCH 需要 22 秒左右。

1563/1563 [==============================] - ETA: 0s - loss: 0.2890 - accuracy: 0.9597 Epoch 00094: val_accuracy did not improve from 0.91200 1563/1563 [==============================] - 22s 14ms/step - loss: 0.2890 - accuracy: 0.9597 - val_loss: 0.4482 - val_accuracy: 0.9089 Learning rate: 0.0001 Epoch 95/200 1561/1563 [============================>.] - ETA: 0s - loss: 0.2835 - accuracy: 0.9601 Epoch 00095: val_accuracy improved from 0.91200 to 0.91240, saving model to C:\Users\AERO15\saved_models\cifar10_ResNet20v1_model.095.h5 1563/1563 [==============================] - 22s 14ms/step - loss: 0.2836 - accuracy: 0.9601 - val_loss: 0.4446 - val_accuracy: 0.9124 Learning rate: 0.0001 Epoch 96/200 1561/1563 [============================>.] - ETA: 0s - loss: 0.2792 - accuracy: 0.9612 Epoch 00096: val_accuracy improved from 0.91240 to 0.91290, saving model to C:\Users\AERO15\saved_models\cifar10_ResNet20v1_model.096.h5 1563/1563 [==============================] - 22s 14ms/step - loss: 0.2791 - accuracy: 0.9612 - val_loss: 0.4397 - val_accuracy: 0.9129```

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