DataAuditing/script2.sh at master · ayush-alag/DataAuditing · GitHub

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#!/bin/bash
#SBATCH --job-name=memRunK      # create a short name for your job
#SBATCH --nodes=1                # node count
#SBATCH --ntasks=1               # total number of tasks across all nodes
#SBATCH --cpus-per-task=8        # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem-per-cpu=2G         # memory per cpu-core (4G is default)
#SBATCH --time=0-04:00:00        # total run time limit (days-HH:MM:SS)
#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --gres=gpu:1             # number of gpus per node
#SBATCH --mail-user=aalag@princeton.edu
#SBATCH --output=slurm-%j-%x.out

module purge
module load anaconda3/2022.5
conda activate audit
wandb offline

# epoch=50
epoch=200
# I=$1
I=0.0
# P=$1
P=-1
k=$1

## Train the base + calibration model and run audit
experiment="MemGuard"
# experiment="re-plateau$P"
dataset="Location"
# dataset="MNIST"

# # Train the base model
# python train_model.py --mode base --dataset $dataset --batch_size 64 --epoch $epoch --train_size 10000 --dropout $I --expt $experiment --plateau $P
# # For MemGuard: train defense model
# python train_model.py --mode defense --dataset $dataset --batch_size 64 --epoch 400 --train_size 10000 --expt $experiment

python train_model.py --mode cal --dataset $dataset --batch_size 64 --epoch $epoch --train_size 10000 --k $k --cal_data $dataset --dropout $I --expt $experiment --plateau $P
# python run_audit.py --k $k --fold 0 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I
python run_audit.py --k $k --fold 1 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True
python run_audit.py --k $k --fold 2 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True
python run_audit.py --k $k --fold 3 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True
python run_audit.py --k $k --fold 4 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True
python run_audit.py --k $k --fold 5 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True
python run_audit.py --k $k --fold 6 --audit EMA --epoch $epoch --cal_data $dataset --dataset $dataset --cal_size 10000 --expt $experiment --dropout $I --memguard True