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train_small_retrain.sh
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executable file
·73 lines (72 loc) · 2.74 KB
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#!/bin/bash
date
dataset_list=("beauty")
echo ${dataset_list}
line_num_list=(7828 21189 30819)
cuda_num_list=(2)
echo ${line_num_list}
seed=2022
length=${#dataset_list[@]}
for ((i=0; i<${length}; i++));
do
{
dataset=${dataset_list[i]}
cuda_num=${cuda_num_list[i]}
for model in t5-small
do
{
for split in train
do
{
for recommendation_model in sasrec
do
{
for framework in small_recommendation_model
do
{
for type_small in base
do
{
framework=${framework}_${type_small}
set |grep RANDOM
# 0~65536
port=$[${RANDOM}%10000+20000]
echo $port
if [ ${dataset} = "yelp" ];then
learning_rate=1e-3
warmup_ratio=0.02
else
learning_rate=1e-3
warmup_ratio=0.05
fi
# for type in traditional
for type in sequential
do
{
# file="pretrain_${type}_${split}"
file="recommendation_train_retrain"
bash scripts/${file}.sh ${dataset} ${model} ${cuda_num} ${port} ${warmup_ratio} ${learning_rate} ${seed} ${recommendation_model} ${framework} ${type_small} ${type}
} &
done
} &
done
} &
done
} &
done
# } &
# done
} &
done
} &
done
} &
done
wait # 等待所有任务结束
date
# bash scripts/pretrain.sh beauty ddd 1 11111 0 0.001 2022 sasrec small_recommendation_model
# bash scripts/pretrain_all_train.sh beauty t5-small 1 11113 0.05 1e-3 2022
# bash scripts/pretrain_sequential_train.sh beauty t5-small 1 11111 0.05 1e-3 2022
# bash scripts/recommendation_train.sh beauty ddd 7 11115 0 0.001 2022 sasrec small_recommendation_model
# bash scripts/recommendation_train.sh beauty ddd 7 11116 0 0.001 2022 din small_recommendation_model
# bash scripts/recommendation_train.sh beauty ddd 7 11117 0 0.001 2022 gru4rec small_recommendation_model