[add]上传训练benchmark by z00560161
This commit is contained in:
liang_chaoming@huawei.com
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#!/usr/bin/env bash
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yamlPath=$1
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toolsPath=$2
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currentDir=$(cd "$(dirname "$0")/.."; pwd)
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export REMARK_LOG_FILE=hw_mobilenet.log
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benchmark_log_path=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils
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export PYTHONPATH=$PYTHONPATH:${benchmark_log_path}
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eval $(${toolsPath}/get_params_for_yaml.sh ${yamlPath} "common_config")
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cd ${ckpt_path%results*}/results
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rm -rf ./hw_mobilenet.log
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rm -rf ./eval.out
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python3.7 ${currentDir}/code/eval_image_classifier_mobilenet.py --dataset_dir=${data_url} \
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--checkpoint_path=${ckpt_path} > ./eval.out 2>&1
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#!/bin/bash
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rank_size=$1
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yamlPath=$2
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toolsPath=$3
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currentDir=$(cd "$(dirname "$0")/.."; pwd)
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# 从 yaml 获取配置
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eval $(${toolsPath}/get_params_for_yaml.sh ${yamlPath} "tensorflow_config")
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if [ -f /.dockerenv ];then
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CLUSTER=$4
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MPIRUN_ALL_IP="$5"
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export CLUSTER=${CLUSTER}
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fi
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if [ x"$mode" != x"evaluate" ];then
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currtime=`date +%Y%m%d%H%M%S`
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mkdir -p ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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train_job_dir=${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] ${train_job_dir} &"
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fi
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# device 列表, 若无指定 device 根据 rank_size 顺序选择
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eval device_group=\$device_group_${rank_size}p
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if [ x"${device_group}" == x"" ] || [ ${rank_size} -ge 8 ];then
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device_group="$(seq 0 "$(expr $rank_size - 1)")"
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fi
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# get last device id in device_group, hw log in performance from the dir named last_device_id
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device_group_str=`echo ${device_group} | sed 's/ //g'`
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first_device_id=`echo ${device_group_str: 0:1}`
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if [ x"${CLUSTER}" == x"True" ];then
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# ln hw log
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ln -snf ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/0/hw_mobilenet.log ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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this_ip=$(hostname -I |awk '{print $1}')
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for ip in $MPIRUN_ALL_IP;do
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if [ x"$ip" != x"$this_ip" ];then
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scp $yamlPath root@$ip:$yamlPath
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fi
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done
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export PATH=$PATH:/usr/local/mpirun4.0/bin
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mpirun -H ${mpirun_ip} \
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--bind-to none -map-by slot\
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--allow-run-as-root \
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--mca btl_tcp_if_exclude lo,docker0,endvnic,virbr0,vethf40501b,docker_gwbridge,br-f42ac38052b4\
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--prefix /usr/local/mpirun4.0/ \
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${currentDir}/scripts/train.sh 0 $rank_size $yamlPath $currtime ${toolsPath} ${CLUSTER}
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elif [ x"$mode" == x"train" ];then
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# ln hw log
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ln -snf ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/${first_device_id}/hw_mobilenet.log ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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rank_id=0
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for device_id in $device_group;do
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${currentDir}/scripts/train.sh $device_id $rank_size $yamlPath $currtime ${toolsPath} $rank_id &
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let rank_id++
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done
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else
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echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] ${ckpt_path%results*}/results &"
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ln -snf ${ckpt_path%results*}/results/hw_mobilenet.log ${ckpt_path%results*}/..
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bash ${currentDir}/scripts/eval.sh ${yamlPath} ${toolsPath}
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fi
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wait
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+161
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#!/usr/bin/env bash
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device_id=$1
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rank_size=$2
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yamlPath=$3
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currentDir=$(cd "$(dirname "$0")/.."; pwd)
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currtime=$4
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toolsPath=$5
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export YAML_PATH=$3
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mkdir -p ${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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export train_job_dir=${currentDir%train*}/train/result/tf_mobilenet/training_job_${currtime}/
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# 从 yaml 获取配置
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eval $(${toolsPath}/get_params_for_yaml.sh ${yamlPath} "tensorflow_config")
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export REMARK_LOG_FILE=hw_mobilenet.log
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benchmark_log_path=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils
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export PYTHONPATH=$PYTHONPATH:${benchmark_log_path}
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source ${currentDir}/config/npu_set_env.sh
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# user env
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export HCCL_CONNECT_TIMEOUT=600
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export JOB_ID=9999001
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export RANK_TABLE_FILE=${currentDir}/config/${rank_size}p.json
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export RANK_SIZE=${rank_size}
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export SLOG_PRINT_TO_STDOUT=0
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export DEVICE_ID=${device_id}
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DEVICE_INDEX=$(( DEVICE_ID + RANK_INDEX * 8 ))
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export DEVICE_INDEX=${DEVICE_INDEX}
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if [ ${profiling_mode} == True ];
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then
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export PROFILING_MODE=true
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else
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export PROFILING_MODE=false
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fi
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if [ ${aicpu_profiling_mode} == True ];
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then
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export AICPU_PROFILING_MODE=true
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else
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export AICPU_PROFILING_MODE=false
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fi
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export PROFILING_OPTIONS=${profiling_options}
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export FP_POINT=${fp_point}
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export BP_POINT=${bp_point}
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cd ${train_job_dir}
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curd_dir=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils/atlasboost
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export PYTHONPATH=$PYTHONPATH:${curd_dir}
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if [ x"$6" != x"True" ];then
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rank_id=$6
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export RANK_ID=$6
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else
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device_id_mo=$(python3.7 -c "import src.tensorflow.mpi_ops as atlasboost;atlasboost.init(); \
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device_id = atlasboost.local_rank();cluster_device_id = str(device_id); \
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atlasboost.set_device_id(device_id);print(atlasboost.rank())")
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device_id_mo=`echo $device_id_mo`
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rank_id=${device_id_mo##* }
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export RANK_ID=${rank_id}
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device=${device_id_mo##*deviceid = }
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device_id=${device%% phyid=*}
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export DEVICE_ID=${device_id}
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hccljson=${train_job_dir}/*.json
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cp ${hccljson} ${currentDir}/config/${rank_size}p.json
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fi
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#mkdir exec path
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mkdir -p ${train_job_dir}/${device_id}
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cd ${train_job_dir}/${device_id}
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startTime=`date +%Y%m%d-%H:%M:%S`
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startTime_s=`date +%s`
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if [ x"${mode}" == x"evaluate" ];then
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# 评测
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python3.7 ${currentDir}/code/eval_image_classifier_mobilenet.py \
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--checkpoint_path="${ckpt_path}" \
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--dataset_dir=${data_url} > ./train.log 2>&1
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else
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# 根据单卡/多卡区分调用参数
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if [ x"$6" == x"True" ];then
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export CLUSTER=True
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python3.7 ${currentDir}/code/train.py \
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--dataset_dir=${data_url} \
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--max_epoch=${epoches} \
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--model_name="mobilenet_v2" \
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--moving_average_decay=0.9999 \
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--label_smoothing=0.1 \
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--preprocessing_name="inception_v2" \
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--weight_decay='0.00004' \
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--batch_size=${batch_size} \
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--learning_rate_decay_type='cosine_annealing' \
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--learning_rate=0.8 \
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--optimizer='momentum' \
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--momentum='0.9' \
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--warmup_epochs=5 > ${train_job_dir}/train_${device_id}.log 2>&1
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elif [ x"${rank_size}" == x"1" ];then
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# 单卡
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python3.7 ${currentDir}/code/train.py \
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--dataset_dir=${data_url} \
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--max_train_steps=${max_steps} \
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--iterations_per_loop=50 \
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--model_name="mobilenet_v2" \
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--moving_average_decay=0.9999 \
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--label_smoothing=0.1 \
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--preprocessing_name="inception_v2" \
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--weight_decay='0.00004' \
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--batch_size=${batch_size} \
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--learning_rate_decay_type='cosine_annealing' \
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--learning_rate=0.4 \
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--optimizer='momentum' \
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--momentum='0.9' \
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--warmup_epochs=5 > ${train_job_dir}/train_${device_id}.log 2>&1
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elif [ ${rank_size} -le 8 ];then
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# 多卡单机
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python3.7 ${currentDir}/code/train.py \
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--dataset_dir=${data_url} \
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--max_epoch=${epoches} \
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--model_name="mobilenet_v2" \
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--moving_average_decay=0.9999 \
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--label_smoothing=0.1 \
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--preprocessing_name="inception_v2" \
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--weight_decay='0.00004' \
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--batch_size=${batch_size} \
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--learning_rate_decay_type='cosine_annealing' \
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--learning_rate=0.8 \
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--optimizer='momentum' \
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--momentum='0.9' \
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--warmup_epochs=5 > ${train_job_dir}/train_${device_id}.log 2>&1
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fi
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fi
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if [ $? -eq 0 ];then
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echo ":::ABK 1.0.0 hw_mobilenet train success"
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echo ":::ABK 1.0.0 hw_mobilenet train success" >> ${train_job_dir}/train_${device_id}.log 2
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echo ":::ABK 1.0.0 hw_mobilenet train success" >> ./hw_mobilenet.log
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else
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echo ":::ABK 1.0.0 hw_mobilenet train failed"
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echo ":::ABK 1.0.0 hw_mobilenet train failed" >> ${train_job_dir}/train_${device_id}.log 2
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echo ":::ABK 1.0.0 hw_mobilenet train failed" >> ./hw_mobilenet.log
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fi
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endTime=`date +%Y%m%d-%H:%M:%S`
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endTime_s=`date +%s`
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sumTime=$[ $endTime_s - $startTime_s ]
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hour=$(( $sumTime/3600 ))
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min=$(( ($sumTime-${hour}*3600)/60 ))
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sec=$(( $sumTime-${hour}*3600-${min}*60 ))
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echo ":::ABK 1.0.0 mobilenet train total time：${hour}:${min}:${sec}"
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echo ":::ABK 1.0.0 mobilenet train total time： ${hour}:${min}:${sec}" >> ./hw_mobilenet.log
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