![]() That is results in 85% less training time. Each Epoch took ~75 seconds or about 0.5s per step. In contrast, after enabling the GPU version, it was immediately obvious that the training is considerably faster. The whole model is built using Keras, which offers considerably improved integration in TensorFLow 2. ![]() The neural network has ~58 million parameters and I will benchmark the performance by running it for 10 epochs on a dataset with ~10k 256x256 images loaded via generator with image augmentation. The test will compare the speed of a fairly standard task of training a Convolutional Neural Network using tensorflow=2.0.0-rc1 and tensorflow-gpu=2.0.0-rc1. 8 core Ryzen 2700x CPU (16 threads, 20MB cache, 4.3GHz max boost).In this test, I am using a local machine with: Since using GPU for deep learning task has became particularly popular topic after the release of NVIDIA’s Turing architecture, I was interested to get a closer look at how the CPU training speed compares to GPU while using the latest TF2 package. ![]() TensorFlow 2 has finally became available this fall and as expected, it offers support for both standard CPU as well as GPU based deep learning.
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