slurm_smol_launch.sh

#!/bin/bash
# ======= SLURM OPTIONS ======= (user input required)
### See inline comments for what each option means
#SBATCH --partition=GPU
### Set the job name
#SBATCH --job-name=saliency_mistral
### Specify the # of cpus for your job.
#SBATCH --nodes=1
# set cpus per node
#SBATCH --cpus-per-task=8
#SBATCH --mem=64gb
#SBATCH --gres=gpu:4
#SBATCH --time=30:01:01
### pass the full environment
#SBATCH --export=ALL
#SBATCH --output=%j.o
#SBATCH --error=%j.e
# ===== END SLURM OPTIONS =====
### IMPORTANT: load Python 3 environment with Pytorch and cuda enabled
#module load pytorch/1.5.1-anaconda3-cuda10.2
source activate citation
#module load pytorch/1.6.0-cuda10.2 
echo "loaded module"
### Go to the directory of the sample.sh file
cd $SLURM_SUBMIT_DIR
### Make a folder for job_logs if one doesn't exist
mkdir -p job_logs
### Run the python file
echo "running code"
# Run the script
# prompt_based, gradient_based, sliding_window
python run_pipeline.py && python run_pipeline.py --attribution_system prompt_based && python run_pipeline.py --attribution_system gradient_based && python run_pipeline.py --yaml_config best.yaml --output_path attribution_results_best && python run_pipeline.py --yaml_config smoothing.yaml --output_path attribution_results_smoothing && python run_pipeline.py --yaml_config thresholding.yaml --output_path attribution_results_thresholding
echo "finished running"
### move the log files inside the folder
mv $SLURM_JOB_ID.o job_logs/$SLURM_JOB_ID.o
mv $SLURM_JOB_ID.e job_logs/$SLURM_JOB_ID.e