slurm_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=salsa_70b
### 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:A40:4
#SBATCH --time=36:01:01
### pass the full environment
#SBATCH --export=ALL
#SBATCH --output=%j.o
#SBATCH --error=%j.e
# ===== END SLURM OPTIONS =====

### IMPORTANT: load Python environment
source activate citation
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
# print the available GPUs
echo "Available GPUs:"
nvidia-smi
### Run the experiments
echo "running code"
bash launch_experiment.sh 3  # Run Llama-3.1-70B experiments
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


# #!/bin/bash
# # ======= SLURM OPTIONS ======= (user input required)
# ### See inline comments for what each option means
# #SBATCH --partition=Bunescu
# ### Set the job name
# #SBATCH --job-name=saliency_big
# ### 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=36:01:01
# ### 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 --yaml_config llama31_big.yaml && python run_pipeline.py --attribution_system prompt_based --yaml_config llama31_big.yaml && python run_pipeline.py --attribution_system gradient_based --yaml_config llama31_big.yaml && python run_pipeline.py --yaml_config llama31_big_best.yaml --output_path attribution_results_best && python run_pipeline.py --yaml_config llama31_big_smoothing.yaml --output_path attribution_results_smoothing && python run_pipeline.py --yaml_config llama31_big_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