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81 lines
2.6 KiB
81 lines
2.6 KiB
#!/bin/bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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echo "=============================================================================================================="
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echo "Please run the scipt as: "
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echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET MINDSPORE_HCCL_CONFIG_PATH PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
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echo "for example: sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json /opt/ssd-300.ckpt(optional) 200(optional)"
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echo "It is better to use absolute path."
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echo "================================================================================================================="
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if [ $# != 5 ] && [ $# != 7 ]
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then
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echo "Usage: sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
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[MINDSPORE_HCCL_CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
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exit 1
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fi
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# Before start distribute train, first create mindrecord files.
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python train.py --only_create_dataset=1
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echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
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export RANK_SIZE=$1
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EPOCH_SIZE=$2
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LR=$3
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DATASET=$4
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PRE_TRAINED=$6
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PRE_TRAINED_EPOCH_SIZE=$7
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export MINDSPORE_HCCL_CONFIG_PATH=$5
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for((i=0;i<RANK_SIZE;i++))
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do
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export DEVICE_ID=$i
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rm -rf LOG$i
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mkdir ./LOG$i
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cp ../*.py ./LOG$i
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cp -r ../src ./LOG$i
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cd ./LOG$i || exit
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export RANK_ID=$i
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echo "start training for rank $i, device $DEVICE_ID"
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env > env.log
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if [ $# == 5 ]
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then
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python train.py \
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--distribute=1 \
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--lr=$LR \
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--dataset=$DATASET \
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--device_num=$RANK_SIZE \
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--device_id=$DEVICE_ID \
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--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
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fi
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if [ $# == 7 ]
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then
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python train.py \
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--distribute=1 \
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--lr=$LR \
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--dataset=$DATASET \
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--device_num=$RANK_SIZE \
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--device_id=$DEVICE_ID \
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--pre_trained=$PRE_TRAINED \
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--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
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--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
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fi
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cd ../
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done
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