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I have a NetTrain call with the following parameters:

tNet = NetTrain[inVecs -> outVecs, TargetDevice -> "GPU"];

But for some reason, the training is still occurring on the CPU rather than the GPU. I also tried TargetDevice -> {"GPU", 1}, matching the GPU number of the NVIDIA card on my computer. But that did not help either.

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    $\begingroup$ Can you use CUDA? What is the result of << CUDALink`; CUDAQ[] For me, training on GPU works and I see a clear difference in CPU usage. $\endgroup$ – halirutan Oct 22 '18 at 8:05
  • $\begingroup$ @halirutan sorry for the long delay. The output of CUDAQ[] is True. And the output of CUDADriverVersion[] is 416.3, which is the latest driver for the NVIDIA Quadro M1000M chip. $\endgroup$ – Shredderroy Oct 22 '18 at 17:08
  • $\begingroup$ The windows resource monitor can be deceptive. Click the GPU you want to monitor, and change the graph to compute 0 or 1. You'll then see the proper utilization. $\endgroup$ – Yss Oct 23 '18 at 2:23
  • $\begingroup$ Do you have the same issue when you run the example n = 100; trainingData = RandomReal[1, {n, 4}] -> RandomReal[1, {n, 4}]; net = NetChain[{8, Tanh, 2048, Tanh, 2048, Tanh, 4}]; trained = NetTrain[net, trainingData, TargetDevice -> "GPU"] I have a Nvidia M1000M with driver 391.03 on windows 10 (1607) and Intel graphics 530 in a HP ZBook g3. $\endgroup$ – Lou Oct 23 '18 at 6:16
  • $\begingroup$ Unfortunately, yes. I still see a spike in CPU usage, but not in GPU usage. $\endgroup$ – Shredderroy Oct 23 '18 at 6:42

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