You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I was running an optimization using default Bayesian optimization advisor, it takes around 400 s to get a suggestion at Iter 400. Is this normal and how to make it run faster?
The text was updated successfully, but these errors were encountered:
Hi @Dalton2333, there are many reasons that may cause optimization to slow down:
The typical reason is using GP as surrogate model. When there are hundreds of observations, GP is significantly slower than the PRF model. It is normal and you can switch to PRF instead in this case. However, please also check the following reason that may cause GP to be extremely slow.
If there are too many CPU cores on the machine (e.g. 100 CPU cores), GP might be extremely slow because: (1) GP attempts to occupy all CPU cores to compute; (2) GP is running in a docker environment and the CPU usage is limited, or multiple experiments are running simultaneously. Then for each GP, it occupies all CPU cores on the machine but the occupancy rate on each core is extremely low. To fix this, add the following code in the beginning of your startup script (at least before import numpy):
I was running an optimization using default Bayesian optimization advisor, it takes around 400 s to get a suggestion at Iter 400. Is this normal and how to make it run faster?
The text was updated successfully, but these errors were encountered: