run_script_with_env_rew.batch

#!/bin/bash

####
#a) Define slurm job parameters
####

#SBATCH --job-name=test

#resources:

#SBATCH --cpus-per-task=4
# the job can use and see 12 CPUs (from max 24).

#SBATCH --partition=day
# the slurm partition the job is queued to.

#SBATCH --mem-per-cpu=3G
# the job will need 12GB of memory equally distributed on 4 cpus.  (251GB are available in total on one node)

#SBATCH --gres=gpu:1
#the job can use and see 1 GPUs (4 GPUs are available in total on one node)

#SBATCH --time=20:00:00
# the maximum time the scripts needs to run
# "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds"



#SBATCH --error=tcml_out/job.ellm%J.err
# write the error output to job.ellm*jobID*.err

#SBATCH --output=tcml_out/job.ellm%J.out
# write the standard output to job.ellm*jobID*.out

#SBATCH --mail-type=ALL
#write a mail if a job begins, ends, fails, gets requeued or stages out

#SBATCH --mail-user=clara.grotehans@student.uni-tuebingen.de
# your mail address

#SBATCH 

####
#b) copy all needed data to the jobs scratch folder
####

####
#c) Execute your tensorflow code in a specific singularity container
#d) Write your checkpoints to your home directory, so that you still have them if your job fails
####

singularity exec --nv /common/singularityImages/TCML-CUDA12_0_TF2_12_PT1_13.simg \
/home/grotehans/miniconda3/envs/text-crafter-latest/bin/python -u train.py --config configs/EnvReward/run_seed-1_env_reward.yaml

echo DONE!