runAdvTraining.pbs

#!/bin/bash
#PBS -P RDS-FEI-tcar-RW
#PBS -l select=1:ngpus=1:ncpus=1:mem=64G
#PBS -l walltime=48:00:00
#PBS -j oe


cd /project/RDS-FEI-tcar-RW/vaecnnAdvTraining
# module load python/3.7.7
# module load cmake
# module load openmpi-gcc/3.1.5
# module load clang/7.0.0
# module load gcc/7.4.0
module load llvm/14.0.0 python/3.7.7 magma/2.5.3 openmpi-gcc/3.1.5 cmake cuda/10.2.89
module load binutils
module load bazel
virtualenv --system-site-packages tf
module load gcc
source tf/bin/activate
pip install /usr/local/tensorflow/v100/cuda10.2/tensorflow-2.5.0-cp37-cp37m-linux_x86_64.whl
# pip install /usr/local/pytorch/torch-1.1.0a0+c1643ec-cp35-cp35m-linux_x86_64.whl
pip install /usr/local/pytroch/torch-1.1.0a0+4f45bc7-cp36-cp36m-linux_x86_64.whl
pip install tensorflow_probability
export CFLAGS="$CFLAGS -D__STDC_FORMAT_MACROS=1"
export CPPFLAGS="$CPPFLAGS -D__STDC_FORMAT_MACROS=1"
export CXXFLAGS="$CXXFLAGS -D__STDC_FORMAT_MACROS=1"
export CC=gcc
# pip install open_spiel
pip install numpy
pip install absl-py
# pip install tensorly
pip install matplotlib
# pip install networkx
pip install scipy
pip install pandas
# pip install pyspark

# python3 setup.py install
# python3 adversarial_training_main.py --data_root . --batch_size=64 --learning_rate=0.05
python3 adversarial_training_main.py --data_root . --batch_size=128 --learning_rate=0.05 --model_name=VAEResNet18 --model_root=checkpoint-adv-training-VAEResNet18 -e 0.0157 -p 'linf' --adv_train --affix 'linf'