-
Notifications
You must be signed in to change notification settings - Fork 72
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
seg fault when predicting local uncertainty with trained sgp calculator #379
Comments
Hi @aaronchen0316 , did you use this line to load the sgp model calculator, kernels = sgp_calculator.from_file('path-to-flare.json') Basically, make sure you have the "kernels" returned (even if not used) in the current work space. If you just want to get the local uncertainties on a set of test structures, yes you can use FakeMD with a very large threshold and a warm start (by loading the SGP from file) |
Hi @YuuuXie thank you for your suggestion as always! Yes I used the correct line to load the sgp model: Yes, I was able to get around with seg fault by using OTF() with FakeMD and either large Thank you for confirming that, but the seg fault persists. Is there a problem with my workflow or difficult to load a sgp calculator and predict on a few isolated atomic structures? Thank you! |
Just realized I forgot to reply to this thread. The seg fault can happen in different places, we encountered in the kernel, the structure etc. which might need to debug in specific case |
Describe the bug
I tried to predict the forces on a structure for testing purpose using an already trained sgp calculator/model. However, it threw a segmentation fault. I identified the seg fault happened at predict_local_uncertainties function in the sparse_gp.cpp.
My workflow is:
I tried to make a
flare_pp.Structure
object andpredict_on_structure_descriptor()
directly but it also threw a seg fault. Is the issue with "local" uncertainty estimate or there is a more efficient way to run a test set evaluation on existing sgp calculator/model? For example, should I supply a series of dft trajectories toOTF(trajectory)
and useFakeMD
to only predict propertiesThank you so much for your help!
NOTE: updated with better syntax
The text was updated successfully, but these errors were encountered: