#!/bin/bash
#SBATCH -J diffusion # Job name
#SBATCH -p rtx #-dev
#SBATCH -N8            # Number of nodes and cores per node required
#SBATCH --ntasks-per-node=1
#SBATCH -t 47:59:59                                    # Duration of the job (Ex: 15 mins)
#SBATCH -oReport-%j                         # Combined output and error messages file
#SBATCH --mail-type=BEGIN,END,FAIL              # Mail preferences
#SBATCH --mail-user=xiabin@gatech.edu

pwd
date
#module load anaconda3/2022.05 # Load module dependencies
#module load pytorch
#conda activate diffusers 
conda env list
module list
python -c "import torch; print(torch.cuda.is_available(), torch.__version__, torch.__path__, torch.version.cuda)"
cat $0

MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
MASTER_PORT=$((10000 + RANDOM % 10000)) #12355

export MASTER_ADDR=$MASTER_ADDR
export MASTER_PORT=$MASTER_PORT

srun python diffusion.py \
    --num_new_img_per_gpu 50 \
    --max_num_img_per_gpu 2 \
    --gradient_accumulation_steps 5 \
    --train "$SCRATCH/LEN128-DIM64-CUB16-Tvir[4, 6]-zeta[10, 250]-0809-123640.h5" \
    #--resume outputs/model-N3000-device_count4-node2-epoch49-23040531 \
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