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GPU Example

#SBATCH --job-name=test_job 
#SBATCH --account=gpu_gres               # to access gpu resources
#SBATCH --partition=gpu                                           
#SBATCH --nodes=1                        # request to run job on single node                                       
##SBATCH --ntasks=10                     # request 10 CPU's (t3gpu01/02: balance between CPU and GPU : 5CPU/1GPU)      
#SBATCH --gres=gpu:2                     # request  for two GPU's on machine, this is total  amount of GPUs for job        
##SBATCH --mem=4000M                     # memory (per job)
#SBATCH --time=0-00:30                   # time  in format DD-HH:MM

# each node has local /scratch space to be used during job run
mkdir -p /scratch/$USER/${SLURM_JOB_ID}
export TMPDIR=/scratch/$USER/${SLURM_JOB_ID}

# Slurm reserves two GPU's (according to requirement above), those ones that are recorded in shell variable CUDA_VISIBLE_DEVICES
# python program script.py should use CUDA_VISIBLE_DEVICES variable (*NOT* hardcoded GPU's numbers)
python script.py 

# cleaning of temporal working dir when job was completed:
rmdir  -rf /scratch/$USER/${SLURM_JOB_ID}


-- NinaLoktionova - 2019-09-24

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Topic revision: r4 - 2020-04-27 - NinaLoktionova
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