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I'v trained a deep network (e.g. LeNet) using NetTrain with TargetDevice -> "GPU". Everything works well and it is really fast. But now I have a trained network and tens of thousands of samples to classify. The GPU based training was evaluating ~ 1200 samples/second but seems like I can only use CPU based ParallelMap to evaluate trained network with samples (i.e. slow).

Is there a way to apply the trained network using the GPU as target device?

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  • $\begingroup$ Not yet, but I hear gpu inference is coming in 11.1 $\endgroup$
    – M.R.
    Oct 7, 2016 at 16:14
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    $\begingroup$ Yes, we're trying to get it into 11.1, and will be available by writing net[input, TargetDevice -> "GPU"] instead of net[input]. $\endgroup$
    – Taliesin Beynon
    Oct 9, 2016 at 10:33
  • $\begingroup$ I cannot test the GPU at the moment, as I'm having issues with TargetDevice->"GPU" on my Quadro K2100M (compute capability 3.0, latest drivers) for some reason, but would love to know if this was added, as I was hoping to use it. It shouldn't be that difficult in theory, as this is done every single time a loss is computed for training data as well as for validation data. $\endgroup$
    – Gregory Klopper
    Apr 3, 2017 at 23:35
  • $\begingroup$ Nevermind, just confirmed! It works in 11.1. trained[data,TargetDevice->"GPU"] $\endgroup$
    – Gregory Klopper
    Apr 5, 2017 at 5:54

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Works in 11.1.

trained[data,TargetDevice->"GPU"]
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