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#!
/bin/bash
#
SBATCH -t 01:00:00
#
SBATCH -N 4
#
SBATCH --ntasks-per-node=4
#
SBATCH --ntasks-per-socket=2
#
SBATCH --gres=gpu:4
#
SBATCH -c 4
#
SBATCH --mem-per-cpu=0
#
Example Slurm configuration for TigerGPU nodes (4 nodes, 16 GPUs total)
#
Each node = 2.4 GHz Xeon Broadwell E5-2680 v4 + 4x 1328 MHz P100 GPU
module load anaconda3
conda activate my_env
module load cudatoolkit
module load cudnn
module load openmpi/cuda-8.0/intel-17.0/3.0.0/64
module load intel/19.0/64/19.0.3.199
module load hdf5/intel-17.0/intel-mpi/1.10.0
#
remove checkpoints for a benchmark run
rm /tigress/
$USER
/model_checkpoints/
*
rm /tigress/
$USER
/results/
*
rm /tigress/
$USER
/csv_logs/
*
rm /tigress/
$USER
/Graph/
*
rm /tigress/
$USER
/normalization/
*
export
OMPI_MCA_btl=
"
tcp,self,vader
"
srun python mpi_learn.py
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