This official code for Stable Nonconvex-Nonconcave Training via Linear Interpolation accepted at NeurIPS 2023.

conda create -n rapp python=3.8
conda activate rapp

# On GPU
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

pip install -r requirements.txt
python setup.py develop
source .env
python rapp/runner.py -h

For wandb:

• Delete wandb entry from /home/<user>/.netrc if present to prevent auto-login to a different account
• Storage your key with vim .env:

  export WANDB_API_KEY=<mykey>

• Before running a script run source .env

1. This code contains 13 optimizers on CIFAR-10 listed in Table 2&3 in the paper. Use --opt RAPP to modify it.

   --opt	GDA	EG	EGplus	LA-GDA	LA-EG	LA-EGplus	RAPP
   optimizer	GDA	EG	EG+	LA-GDA	LA-EG	LA-EG+	RAPP

   --opt	Adam	EA	EAplus	LA-Adam	LA-EA	LA-EAplus
   optimizer	Adam	ExtraAdam	ExtraAdam+	LA-Adam	LA-ExtraAdam	LA-ExtraAdam+

2. Example scripts:

   python rapp/runner.py --model resnet --dataset cifar10 --loss hinge --spectral-norm --batch-size 128 --epochs 3200 --lrG 0.02 --lrD 0.1 --nz 128 --ngf 128 --ndf 64 --opt RAPP --num-D-step 1 --num-metrics-samples 50000 --wandb-name "cifar10/RAPP/hinge/resnet/bs128/net64/lr(0.02_0.1)/eval50000/anchor_change(multi3_lam0.9)" --gpus 1 --workers 200 --inner-steps 3 --lam 0.9

@inproceedings{pethick2023stable,
  title={Stable Nonconvex-Nonconcave Training via Linear Interpolation},
  author={Pethick, Thomas and Xie, Wanyun and Cevher, Volkan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023}
}