Comments (5)
Thank. I solved this issue.
function SpatialConvolution(nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, groups)
local conv = cudnn.SpatialConvolution(nInputPlane, nOutputPlane,
kW, kH, dW, dH, padW, padH, groups)
-- set deterministic algorithm
conv:setMode('CUDNN_CONVOLUTION_FWD_ALGO_GEMM',
'CUDNN_CONVOLUTION_BWD_DATA_ALGO_1',
'CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1')
return conv
end
The performance is close to default algorithm in my setting.
EDIT: remove :fastest()
from cudnn.torch.
this is expected. cudnn convolutions and pooling are not deterministic implementations.
from cudnn.torch.
Thank you for your explaining.
This means we cannot reproduce the submitted solution in the data prediction competition like a kaggle. 😭
from cudnn.torch.
It should be also possible to choose a deterministic algorithm using SpatialConvolution:setMode()
with proper arguments (see cudnn manual), but the last time I tried it it was slower than the nn
equivalent for my tensor sizes. But it's worth a try and maybe with cudnn v4 it's even better now. SpatialMaxPoolings from nn and cudnn should be more or less interchangeable in performance, I'd say.
from cudnn.torch.
After setting mode, i don't think you should do :fastest()
from cudnn.torch.
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from cudnn.torch.