An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Describe the issue: I just started using nni and followed the /hpo_quickstart_pytorch/ example to inplement.
But, instead of accuracy, I need to thave the main monitor and optimize for the loss, and the lower loss, the better, obviously.
I used nni.report_intermediate_result(valLossSingle) to report the loss. I think I need to let the tuner know that this is actually loss, rather than accuracy. How to do that?
Environment:
NNI version: 2.7
Training service (local|remote|pai|aml|etc): local
Describe the issue: I just started using nni and followed the /hpo_quickstart_pytorch/ example to inplement. But, instead of accuracy, I need to thave the main monitor and optimize for the loss, and the lower loss, the better, obviously.
I used nni.report_intermediate_result(valLossSingle) to report the loss. I think I need to let the tuner know that this is actually loss, rather than accuracy. How to do that?
Environment:
Configuration:
Log message:
How to reproduce it?: