I am new to Pytorch and when I ran the neural language model from the tutorial page I noticed that the program was using one out of four of my machine cores (Mid 2014 MacBook Pro).
After reading some documentation I found that I could change this behaviour through
torch.set_num_threads(n), althogh after setting the amount of threads to
4, for instance,
torch.get_num_threads() still returns
Following, I reinstalled Pytorch through conda and then I noticed that the CPU usage moved to about 270% (ie: about three cores are being used), which is a little better, but still
1. Next I tried compiling Pytorch from source following the instructions on Github, but I observed the same behaviour as before regarding the amount of threads returned. My question is: is there a way to make
torch.get_num_threads() reflect the actual amount of threads that are going to be used by the model?
I have seen some issue posts on Github discussing about setting MKL or OMP options, but I’m not sure how to use these or even how to install MKL properly (to my understanding this is already done when installing Pytorch through conda).
Some additional details:
- pytorch version: 0.4
- python version: 3.6