Add summit scripts.

#!/bin/bash

#BSUB -P MED107

#BSUB -W 2:00

#BSUB -nnodes 6

#BSUB -J boston

#BSUB -o output/boston.o

#BSUB -e output/boston.e

#BSUB -q batch

#BSUB -rn

# this must match the -nnodes argument above

NNODES=6

set -ex

PROJDIR=$PROJWORK/med107

USERDIR=$PROJDIR/$USER

REPODIR=$USERDIR/bottleneck_nngp

JOBSCRIPTDIR=$REPODIR/torch_experiments

# set up env

. /sw/summit/init/profile

module load ibm-wml-ce

conda activate $USERDIR/condaenvs/torch

jsrun -n$((NNODES*6)) -a1 -g1 -c7 -r6 \

	-E LD_LIBRARY_PATH \

	--bind=proportional-packed:7 --launch_distribution=packed \

	$JOBSCRIPTDIR/mapper.sh \

		$(which python) $JOBSCRIPTDIR/main.py \

		--dataset=boston \

		--depths=1,%depth% \

		--widths=%width% \

		--vb=0.1 \

		--vw=1.0 \

		--vn=0.1 \

		--train_samples=100 \

		--test_samples=1000 \

		--lr=1e-1 \

		--iters=100 \

		--gpu=0 \

		--manual_grad \

#!/bin/bash

set -e

# post-bottleneck depths and bottleneck widths

depths=(10 20 30 40 50 60 70 80 90)

widths=(2 8 32 128)

# get argument

CMD=$*

# list env variables

#env

# get MPI env variables

LOCALRANK=$OMPI_COMM_WORLD_LOCAL_RANK

RANK=$OMPI_COMM_WORLD_RANK

JOB=$LSB_JOBID

# calculate architecture given rank

DEPTH=${depths[$((RANK/4))]}

WIDTH=${widths[$((RANK%4))]}

# substitute into command

CMD=${CMD//%depth%/$DEPTH}

CMD=${CMD//%width%/$WIDTH}

CMD=${CMD//%rank%/$RANK}

CMD=${CMD//%localrank%/$LOCALRANK}

CMD=${CMD//%jobid%/$JOB}

# run command

echo Running command: \"$CMD\"

$CMD