run.sh

#!/bin/bash --login
########## SBATCH Lines for Resource Request ##########

#SBATCH --time=4:00:00             # limit of wall clock time - how long the job will run (same as -t)
#SBATCH --nodes=1                 # number of different nodes - could be an exact number or a range of nodes (same as -N)
#SBATCH --ntasks=1                  # number of tasks - how many tasks (nodes) that you require (same as -n)
#SBATCH --cpus-per-task=4         # number of CPUs (or cores) per task (same as -c)
#SBATCH --mem-per-cpu=48G            # memory required per allocated CPU (or core) - amount of memory (in bytes)
#SBATCH --job-name hgnn4epi      # you can give your job a name for easier identification (same as -J)
#SBATCH --gres=gpu:v100:1

########## Command Lines for Job Running ##########
ssh dev-amd20-v100 
conda activate torch18

cd /mnt/home/jinwei2/simon/HGNN4EPIDEMIC  ### change to the directory where your code is located.

models=("DTGNN" "DTHGNN" "MSTGCN" "ASTGCN")

pred_interval=(5 10 20)
for interval in ${pred_interval[@]}; do
    python ftrain.py --timestep_hidden 20 --device "cuda" --model "ASTGCN" --partial --pred $interval
done

#python dtrain.py --timestep_hidden 20 --device "cuda" --model "ASTGCN" --partial --agg

scontrol show job $SLURM_JOB_ID     ### write job information to SLURM output file.