predict fucntion in deep learning toolbox does not use gpu

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yongbo chen
yongbo chen on 27 Jul 2021
Answered: Joss Knight on 14 Aug 2021
I use a pre-trained network from tensorflow 2.0 to predict a depth image from an RGB image. The code is:
dlX = dlarray(double(I)./255,'SSCB');
dlY = predict(dlnet,dlX);
The code works fine, but it is very slow. I find that it seems that the code only use the cpu core instead of gpu.
From the online help document, I find the following explanation:
It seems that the default way to run predict is to use a gpu. I find my gpu seems to be avaliable in MATLAB by running the gpu test function like:
gpuDevice;
A = gpuArray([1 0 1; -1 -2 0; 0 1 -1]);
e = eig(A);
It works fine with my gpu:
Name: 'GeForce RTX 2060'
Index: 1
ComputeCapability: '7.5'
SupportsDouble: 1
DriverVersion: 11.2000
ToolkitVersion: 11
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 6.4425e+09
AvailableMemory: 4.9872e+09
MultiprocessorCount: 30
ClockRateKHz: 1200000
ComputeMode: 'Default'
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceAvailable: 1
DeviceSelected: 1
Any way to deal with this problem? Thank you very much.
  2 Comments
yongbo chen
yongbo chen on 27 Jul 2021
Thank you for your answer. Do you know how to take the weights to gpu in MATLAB. Any function?

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Answers (1)

Joss Knight
Joss Knight on 14 Aug 2021
That is the documentation for DAGNetwork, not dlnetwork. dlnetwork does not have an ExecutionEnvironment, it chooses its environment in the same way that other GPU operations do, by reacting to the incoming data. As KSSV points out, converting to a gpuArray is the correct solution in this case.

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