cmu-catalyst / GradSign

Code for paper GradSign: Model Performance Inference with Theoretical Insights

GradSign

Code for paper GradSign: Model Performance Inference with Theoretical Insights

Spearman's rho experiments:

Clone and follow the installation steps in zero-cost-nas. Then type the following command in the cmd:

cp zero-cost-nas-code/*.py zero-cost-nas/
cp -r zero-cost-nas-code/foresight/pruners zero-cost-nas/foresight
cd zero-cost-nas/
python build.py
python gs.py

Modify args to test for different datasets.

Kendall's Tau experiments:

Clone and follow the installation steps in NASWOT. Then type the following command in the cmd:

cp naswot-code/*.py naswot/
cd naswot
python gradsign.py

Modify args to test for different datasets.

Architecture selection experiments:

After finishing above steps from NASWOT. Run:

python grad_search.py

Modify args to test for different datasets.

GradSign assisted NAS:

Clone and follow the installation steps in AutoDL. Then type the following command in the cmd:

# Replace exps/NAS-Bench-201-algos/*.py with ours
cp autodl/*.py AutoDL-Projects/exps/NAS-Bench-201-algos/

Then simply run the command listed in here. The GsApi is built in the zero-cost-nas step and could be directly linked by changing gs_root. In order to run BOHB, you need to modify the source file of package hpbandster by replacing their optimizers directory with ours.