6effb6f127
we use an abstract class NASBenchMetaAPI to define the spec of an API; it can be inherited to support different kinds of NAS API, while keep the query interface the same.
50 lines
2.1 KiB
Bash
50 lines
2.1 KiB
Bash
#!/bin/bash
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
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#####################################################
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# [mars6] CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/X-X/train-shapes.sh 00000-05000 12 777
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# [mars6] bash ./scripts-search/X-X/train-shapes.sh 05001-10000 12 777
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# [mars20] bash ./scripts-search/X-X/train-shapes.sh 10001-14500 12 777
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# [mars20] bash ./scripts-search/X-X/train-shapes.sh 14501-18000 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 18001-19500 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 19501-23500 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 23501-27500 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 27501-30000 12 777
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# [x] bash ./scripts-search/X-X/train-shapes.sh 30001-32767 12 777
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#
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# CUDA_VISIBLE_DEVICES=2 bash ./scripts-search/X-X/train-shapes.sh 01000-03999,04050-05000,06000-09000,11000-14500,15000-18500,20000-23500,25000-27500,29000-30000 12 777
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# SLURM_PROCID=1 SLURM_NTASKS=5 bash ./scripts-search/X-X/train-shapes.sh 01000-03999,04050-05000,06000-09000,11000-14500,15000-18500,20000-23500,25000-27500,29000-30000 90 777
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# [GCP] bash ./scripts-search/X-X/train-shapes.sh 00000-09999 90 777
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# [UTS] bash ./scripts-search/X-X/train-shapes.sh 30000-32767 90 777
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 3 ] ;then
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echo "Input illegal number of parameters " $#
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echo "Need 3 parameters for start-and-end, hyper-parameters-opt-file, and seeds"
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exit 1
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fi
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if [ "$TORCH_HOME" = "" ]; then
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echo "Must set TORCH_HOME envoriment variable for data dir saving"
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exit 1
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else
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echo "TORCH_HOME : $TORCH_HOME"
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fi
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srange=$1
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opt=$2
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all_seeds=$3
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cpus=4
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save_dir=./output/NAS-BENCH-202/
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OMP_NUM_THREADS=${cpus} python exps/NAS-Bench-201/xshapes.py \
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--mode new --srange ${srange} --hyper ${opt} --save_dir ${save_dir} \
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--datasets cifar10 cifar10 cifar100 ImageNet16-120 \
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--splits 1 0 0 0 \
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--xpaths $TORCH_HOME/cifar.python \
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$TORCH_HOME/cifar.python \
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$TORCH_HOME/cifar.python \
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$TORCH_HOME/cifar.python/ImageNet16 \
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--workers ${cpus} \
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--seeds ${all_seeds}
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