Comments (6)
I verified the suspicion above by running your code snippet with the two different acquisition functions:
- With
qNoisyExpectedImprovement
(the default before #1792) the average sample time is 0.65s. - With
qLogNoisyExpectedImprovement
(the default after #1792) the average sample time is 2.06s.
from ax.
My guess is that this slowdown is coming from #1792, which improved the default acquisition functions in Ax. This is not a bug, but rather a significant improvement.
Before this change, the acquisition functions were much harder to optimize (because of the gradients being numerically zero) so .gen()
would sometimes terminate very quickly. After #1792, we are doing a much better job optimizing the acquisition function. While this will result in better optimization performance as Ax generates better candidates, it will also take longer since we will take many more gradient steps when optimizing the acquisition function.
from ax.
Thanks for reporting this. A slowdown definitely wasn't expected behavior. We'll look into this.
from ax.
Hi, also taking a look through the code changes between 0.3.4 and 0.3.5 here 0.3.4...0.3.5
from ax.
Haven't identified the source of the regression, but the long running method in the .get_next_trial() call is GenerationStrategy::_gen_multiple, which contains the long running call GenerationNode::gen
I ran the code example on my own, and noticed that as "ax_client.get_next_trial()" was called repeatedly, the run time for _gen_multiple would increase over time. Over 50 calls, its run time increased from 0.03 to 15 seconds.
So far, I haven't been able to find the change in GenerationNode that accounts for the increase between versions 0.3.4 and 0.3.5.
from ax.
Ah, I see! That does seem like a worthwhile trade-off then.
Many thanks everyone for the quick response and diagnosis. I'll close this issue now.
from ax.
Related Issues (20)
- attach trials HOT 8
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- Question : Generation 12 trials HOT 8
- Question: Multi-Task Multi Objective HOT 1
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- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed
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- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
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