To quickly reproduce results for DP fine-tuning on CIFAR10, just install the following libraries via pip;

• pytorch
• numpy
• torchvision
• timm
• fastdp from https://github.com/awslabs/fast-differential-privacy (preferred to opacus for a number of reasons)
• opacus (not preferred)
• tqdm
• prv_accountant

And then just cd into dp_finetuning and run python finetune_classifier_dp.py. To run the accounting (which can be quite slow) pass sigma=-1. There are a number of command-line arguments as described below.

To run hyperparameter tuning with the linear scaling rule, run python linear_scaling.py.

For more general experiment reproduction, run the following command:

python {script}.py\
    --dataset_path ${0}\
    --dataset ${1}\
    --lr ${2}\
    --epsilon ${3}\
    --epochs ${4}\
    --arch ${5}\

For conventional CV experiments (ImageNet, CIFAR10, CIFAR100, FashionMNIST, EMNIST, MNIST, STL10, SVHN) {script} = finetune_classifier_dp. If there are any errors during extracting features please raise a comment to the authors -this

For OOD experiments in Wilds (waterbirds, fmow, domainnet, camelyon) {script} = wilds_finetune_classifier_dp after following directions in the cited papers to download and split the datasets accordingly.

For OOD experiments transferring from CIFAR10/CIFAR100 first add the "--save_weights" flag when running a conventional CV experiment, then use {script} = test_transfer and specify the transfer dataset with --transfer_dataset ${6}. Make sure to download CIFAR10C/CIFAR100C from the cited paper.

Dependencies (non-exhaustive):

• pytorch
• numpy
• torchvision
• opacus
• timm
• tqdm
• fastdp