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nerfstudio/scripts/benchmarking/launch_train_blender.sh

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#!/bin/bash
helpFunction_launch_train()
{
echo "Usage: $0 -c config_name -g gpu_list"
echo -e "\t-c name of config to benchmark (e.g. graph_mipnerf, graph_instant_ngp)"
echo -e "\t-g list of space-separated gpu numbers to launch train on (e.g. 0 2 4 5)"
exit 1 # Exit program after printing help
}
while getopts "c:g" opt; do
case "$opt" in
c ) config_name="$OPTARG" ;;
g ) gpu_list="$OPTARG" ;;
? ) helpFunction ;;
esac
done
if [ -z "$config_name" ]; then
echo "Missing method name"
helpFunction_launch_train
fi
shift $((OPTIND-1))
# Deal with gpu's. If passed in, use those.
GPU_IDX=("$@")
if [ -z "$GPU_IDX" ]; then
echo "no gpus set... finding available gpus"
# Find available devices
num_device=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
START=0
END=${num_device}-1
GPU_IDX=()
for (( id=$START; id<$END; id++ )); do
free_mem=$(nvidia-smi --query-gpu=memory.free --format=csv -i $id | grep -Eo [0-9]+)
if [[ $free_mem -gt 10000 ]]; then
GPU_IDX+=( $id )
fi
done
fi
echo available gpus... ${GPU_IDX[@]}
DATASETS=("mic" "ficus" "chair" "hotdog" "materials" "drums" "ship" "lego")
date
now=$(date)
tag=$(date +'%m-%d-%Y')
idx=0
len=${#GPU_IDX[@]}
((len=len-1))
for dataset in ${DATASETS[@]}; do
export CUDA_VISIBLE_DEVICES=${GPU_IDX[$idx]}
python scripts/run_train.py \
--config-name ${config_name} \
'~logging.writer.LocalWriter' \
pipeline.dataloader.train_dataset.data_directory=data/blender/${dataset} \
experiment_name=blender_${dataset}_${tag} \
trainer.model_dir=nerfactory_models/ \
trainer.steps_per_save=25000 \
trainer.max_num_iterations=2000000 \
viewer.enable=False \
logging.enable_profiler=False &
echo "Launched ${config_name} ${dataset} on gpu ${GPU_IDX[$idx]}, ${tag}"
# update gpu
if [ $idx == $len ]; then
idx=0
else
((idx=idx+1))
fi
done