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65 lines
1.3 KiB
65 lines
1.3 KiB
#!/bin/bash
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profiler_options=
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# seed may break model convergence
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seed=0
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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if [ ${seed} != 0 ]; then
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export FLAGS_cudnn_deterministic=True
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echo "using seed $seed & FLAGS_cudnn_deterministic=True ..."
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fi
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if [ $# -lt 2 ] && [ $# -gt 3 ];then
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echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)"
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exit -1
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fi
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config_path=$1
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ckpt_name=$2
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ips=$3
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if [ ! $ips ];then
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ips_config=
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else
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ips_config="--ips="${ips}
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fi
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mkdir -p exp
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# default memeory allocator strategy may case gpu training hang
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# for no OOM raised when memory exhaused
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export FLAGS_allocator_strategy=naive_best_fit
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if [ ${ngpu} == 0 ]; then
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python3 -u ${BIN_DIR}/train.py \
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--ngpu ${ngpu} \
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--config ${config_path} \
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--output exp/${ckpt_name} \
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--profiler-options "${profiler_options}" \
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--seed ${seed}
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else
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python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
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--ngpu ${ngpu} \
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--config ${config_path} \
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--output exp/${ckpt_name} \
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--profiler-options "${profiler_options}" \
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--seed ${seed}
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fi
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if [ ${seed} != 0 ]; then
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unset FLAGS_cudnn_deterministic
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fi
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if [ $? -ne 0 ]; then
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echo "Failed in training!"
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exit 1
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fi
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exit 0
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