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6,765 changes: 6,765 additions & 0 deletions ai-testbed/cerebras/getting-started/index.html

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84 changes: 84 additions & 0 deletions ai-testbed/files/dictionary.txt
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aitestbed
ALCFUserID
analyser
ANL
arnoldw
AUTOTUNE
Cerebras
conv
cosmictagger
cpus
cuda
cudart
DATADIR
dlerror
dlopen
elif
finetune
flos
gbps
GEMM
Graphcore
graphcore_login
gres
inet
inplace
jsons
kaggle
keras
keygen
keyscan
layernorm
lenet
libcudart
libnvinfer
LOGDIR
logreg
mgmt
mnist
MNIST
modelzoo
nodelist
ntasks
OUTDIR
passcode
petaFLOPS
POPART
POPLIBS
poptorch
popvision
pretrain
pretraining
PYTHONPATH
relu
RELU
resnet
run_unet_256_256_single_4
SambaFlow
sambanova
sbatch
scancel
Slurm
snconfig
snpath
snthreads
sntilestat
snvenv
softmax
squeue
SRAM
srun
tensorrt
tf2tensorrt
TFLOPs
unet
UNet
unet
unet_compile_run_all
Venkat
venv
venvs
vipu
virtualenv
wilsonb
XRDU
64 changes: 64 additions & 0 deletions ai-testbed/files/example-multi-node-programs.sh
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#! /bin/bash -x
set -e
#
# Usage: ./unet_all.sh 256 256
#
SECONDS=0

# IMage size.
IM=${1}
# Batch Size
BS=${2}
NUM_WORKERS=1
export OMP_NUM_THREADS=16

source /opt/sambaflow/venv/bin/activate
UNET=$(pwd)/unet

echo "Model: UNET"
echo "Date: " $(date +%m/%d/%y)
echo "Time: " $(date +%H:%M)

echo "COMPILE"

# Compile for parallel RDUs
if [ ! -e out/unet_train_${BS}_${IM}_NN/unet_train_${BS}_${IM}_NN.pef ] ; then
python ${UNET}/unet.py compile -b ${BS} --in-channels=3 --in-width=${IM} --in-height=${IM} --enable-conv-tiling --mac-v2 --compiler-configs-file ${UNET}/jsons/compiler_configs/unet_compiler_configs_no_inst.json --pef-name="unet_train_${BS}_${IM}_NN" --data-parallel -ws 2 > compile_${BS}_${IM}_NN.log 2>&1
fi

# Run Multi-Node, Data Parallel
NN=2
echo "RUN"
echo "NN=${NN}"
sbatch --gres=rdu:1 --tasks-per-node 8 --nodes 2 --nodelist sm-02,sm-01 --cpus-per-task=16 ./unet_batch.sh ${NN} ${NUM_WORKERS}
echo "Duration: " $SECONDS

#! /bin/bash -x
set -e
#
# Usage: ./unet_batch.sh 2 1
#
SECONDS=0

# Batch Size
BS=256

# IMage size
IM=256
NN=${1}
NUM_WORKERS=${2}
export OMP_NUM_THREADS=16
DATADIR=/software/sambanova/dataset/kaggle_3m
UNET=$(pwd)/unet
export SAMBA_CCL_USE_PCIE_TRANSPORT=0

# TODO: Update this.
source /opt/sambaflow/venv/bin/activate

echo "Model: UNET_TRAIN"
echo "Date: " $(date +%m/%d/%y)
echo "Time: " $(date +%H:%M)

srun --mpi=pmi2 python ${UNET}/unet_hook.py run --do-train --in-channels=3 --in-width=${IM} --in-height=${IM} --init-features 32 --batch-size=${BS} --epochs 2 --data-dir ${DATADIR} --log-dir log_dir_unet_${NN}_train_kaggle --pef=$(pwd)/out/unet_train_${BS}_${IM}_NN/unet_train_${BS}_${IM}_NN.pef --data-parallel --reduce-on-rdu --num-workers=${NUM_WORKERS}

echo "Duration: " $SECONDS
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