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PaddleSpeech/examples/tiny/asr0/local/train.sh

65 lines
1.3 KiB

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