I have some issues with v100 GPU. A tiny-yolov2 classification example takes around 25 seconds for one mini-batch(batch size 50). It consumes around 2.5 GB of ram and GPU utilization hovering around 0.
I’m using windows server 12 with the v100 and all of the installation is done via anaconda environment.
Python = 3.7
pytorch = 1.0
Same code runs in 2-2.2 second on one p2000 GPU and on a 1080 1.8 seconds. Both p2000 and 1080 are on a ubuntu 16.04 machine with anaconda, pytorch 1.0, python 3.7.
one weird issue is that in windows machine nvidia-smi does not show the Python Processes even if the GPU ram is fully utilized by increasing the batch size.
this is somewhat similar with the

  
    
    
    Slow pytorch on V100 
  
  
    I have installed pytorch on a Tesla V100. The problem is that it is way too slow; after 16 hours, not even one epoch of imagenet was completed! I believe this should be much faster in normal circumstances. 
I have cuda9 installed and the driver version is NVIDIA-SMI 384.98, which I think it is good enough. I have also the most NCLL2 and the most recent version of cuDNN. 
What else should I check to make sure the V100 is configured properly? And how to fix the problem? 
Thank you!
  


want to open a new post to get some attention.
thanks for your help