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train_NIHAEs_vector.sh
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train_NIHAEs_vector.sh
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#!/usr/bin/env bash
#SBATCH --gres=gpu:1
#SBATCH --mem=32G
#SBATCH -p gpu
#SBATCH --cpus-per-task=4
#SBATCH -a 0-4
PARRAY1=(NIHTrainAE.py NIHTrainAEBCE.py NIHTrainVAE.py NIHTrainVAEBCE.py NIHTrainALILikeAE.py NIHTrainALILikeAEBCE.py PADTrainALILikeVAE.py PADTrainALILikeVAEBCE.py)
for i in {0..8}
do
let task_id=$i
# printf $task_id"\n" # comment in for testing
if [ "$task_id" -eq "$SLURM_ARRAY_TASK_ID" ]
then
p1=${PARRAY1[$i]}
source /pkgs/anaconda3/bin/activate pytorch
#module load python/3.6
#mkdir -p $SLURM_TMPDIR/env/temp
#mkdir -p $SLURM_TMPDIR/data
#cp -r ~/Venv/temp/* $SLURM_TMPDIR/env/temp
#cp -r ~/projects/rpp-bengioy/caotians/data/NIHCC $SLURM_TMPDIR/data
#mkdir -p $SLURM_TMPDIR/data/NIHCC/images_224
#tar -xzf $SLURM_TMPDIR/data/NIHCC/images_224.tar.gz -C $SLURM_TMPDIR/data/NIHCC/images_224 --strip-components=1
#source $SLURM_TMPDIR/env/temp/bin/activate
#ln -sf $SLURM_TMPDIR/data workspace/datasets-$SLURM_JOBID
export DISABLE_TQDM="True"
python setup/NIH/$p1 --root_path=workspace/datasets --exp=model_ref_nih --batch-size=64 --no-visualize --save --workers=8
fi
done