They require special software to unlock their potential (which was used when compiling TF from sources).New, optimized processors compute quickly (even the energy-efficient versions for laptops), but.~ 415 examples/sec, now we’re talking! Seems like energy-efficient i7–7500U with only 2 cores performs on par with monstrous pair of 12-core processors (AMD Opteron 6168) and god knows what results the energy consumption comparison will yield! So, important takeaways are: So, I decided not to compile TF from sources and moved on to running tests: I suppose that this family of CPUs doesn’t support the matrix multiplication optimization tricks available for exploitation in TensorFlow. The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.īut, fortunately, AMD Opteron 6168 seems too old for this shit (there were no warnings at runtime). The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. Will install tensorflow in CPU mode not optimally, and during runtime one would be able to see warnings such as: The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. I remembered from my past experience, that simply doing pip install tensorflow Simply go to directory tutorials/image/cifar10Īnd run the following code from terminal python cifar10_train.py Test metric:Ī single comparison metric is number of examples processed per second (the more the better). To reproduce the test, you’ll require internet connection and a python environment with installed tensorflow on top. In order to test every piece of equipment fairly, I decided to focus on a common and reproducible deep learning task, such as training CNN on Cifar-10 dataset using tensorflow/models, which you can download on your PC using git clone 2 x AMD Opteron 6168 1.9 GHz Processor (2x12 cores total) taken from PowerEdge R715 server (yes, I have one installed at home.GPU NVidia GeForce 1070, 8GB (ASUS DUAL-GTX1070-O8G) from my desktop.GPU NVidia GeForce 940MX, 2GB (also from my Ultrabook Samsung NP-900X5N).CPU 7th gen i7–7500U, 2.7 GHz (from my Ultrabook Samsung NP-900X5N).So, I decided to setup a fair test using some of the equipment I had at hand to answer that question.
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