I’m currently working my way through this post:

  
    
    
    How to prefetch data when processing with GPU? 
  
  
    I’ve spent some time working on this problem over a variety of projects. I’ve cut and pasted some past thoughts, bullets, etc from previous discussions. My background involves architecting systems that move large volumes of data from network cards to storage and then back again on request, with processing between the steps. A very similar set of concerns. 
The two main constraints that usually dominate your PyTorch training performance and ability to saturate the shiny GPUs are your total CPU IP…
  


Let me know if any of you have thoughts specifically related to eval(), but for now, it looks like I have plenty of best practices to consider before tackling my eval() issue… 
–SEH