| #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 | |