#!/bin/bash

#SBATCH --job-name=nomaisi
#SBATCH --mail-type=END,FAIL    
#SBATCH --mail-user=ft42@duke.edu
#SBATCH -p vram48
#SBATCH --ntasks=1  # 
#SBATCH --gpus=1    # 2 GPU per task, chose more if model is capable of multi gpu training
#SBATCH --cpus-per-task=16 # More if it is CPU intensive job too NNUNET demands lot of CPU

## Make sure logs directory is present on current directory (same as this script)
#SBATCH --output=logs/NoMAISI-infr-log-%j.out
#SBATCH --error=logs/NoMAISI-infr-log-%j.out



echo "Job starting"
echo "GPUs Given:   $CUDA_VISIBLE_DEVICES"
module load miniconda/py39_4.12.0
source activate monai-auto3dseg


# Add the correct path to PYTHONPATH
export MONAI_DATA_DIRECTORY=/home/ft42/NoMAISI/

python -m scripts.infer_testV2_controlnet -c ./configs/config_maisi3d-rflow.json -e ./configs/infr_env_NoMAISI_DLCSD24_demo.json -t ./configs/infr_config_NoMAISI_controlnet.json