[GitHub] [incubator-mxnet] Neutron3529 edited a comment on issue #19649: Results are significant different between RTX 2080Ti and RTX 3090

[GitBox <gi...@apache.org>]

Neutron3529 edited a comment on issue #19649:
URL: https://github.com/apache/incubator-mxnet/issues/19649#issuecomment-758323589


   > After more tests, I found that the result also varies on RTX2080Ti on both MXNet 1.9.0 and MXNet 2.0.0.
   > ~The result have 0.005 difference in the shallow layer. I think it will have more difference as the layer grows.~
   > 
   > ```python
   > import os
   > # os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
   > import mxnet as mx
   > import numpy as np
   > from mxnet.gluon.model_zoo.vision.resnet import resnet18_v1
   > 
   > def testrestnet():
   >     ctx = mx.gpu(0)
   >     mx_model = resnet18_v1(pretrained=True,ctx=ctx)
   >     mx_model.hybridize()
   > 
   >     x_mx = mx.nd.ones(shape=(1,3,224,224), ctx=ctx)
   > 
   >     y_mx = mx_model.features[0:6](x_mx)
   > 
   >     # the res is always 13064.977 on CPU
   >     # the res varies on RTX2080Ti/RTX3090 on both MXNet 1.9.0 and 2.0.0 without 
   >     # MXNET_CUDNN_AUTOTUNE_DEFAULT=0: 13064.971, 13064.976
   >     res = y_mx.asnumpy().sum()
   > 
   >     print(res)
   > 
   > if __name__ == '__main__':
   >     testrestnet()
   > ```
   
   have you ever tried `NVIDIA_TF32_OVERRIDE=0 python`?
   3090 using tf32 to accelerate training&testing by default, and using `NVIDIA_TF32_OVERRIDE=0` will disable it.


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