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run.sh
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## M
online_learning='full'
i=1
ns=(1 )
bszs=(1 )
lens=(1 24 48)
methods=('onenet_fsnet')
for n in ${ns[*]}; do
for bsz in ${bszs[*]}; do
for len in ${lens[*]}; do
for m in ${methods[*]}; do
CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --method $m --root_path ./data/ --n_inner $n --test_bsz $bsz --data ETTh2 --features M --seq_len 60 --label_len 0 --pred_len $len --des 'Exp' --itr $i --train_epochs 15 --learning_rate 1e-3 --online_learning $online_learning --use_adbfgs > ETTh2$len$online_learning.out 2>&1 &
CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --method $m --root_path ./data/ --n_inner $n --test_bsz $bsz --data ETTm1 --features M --seq_len 60 --label_len 0 --pred_len $len --des 'Exp' --itr $i --train_epochs 15 --learning_rate 1e-3 --online_learning $online_learning > ETTm1$len$online_learning.out 2>&1 &
CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --method $m --root_path ./data/ --n_inner $n --test_bsz $bsz --data WTH --features M --seq_len 60 --label_len 0 --pred_len $len --des 'Exp' --itr $i --train_epochs 15 --learning_rate 1e-3 --online_learning $online_learning --use_adbfgs > WTH$len$online_learning.out 2>&1 &
CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --method $m --root_path ./data/ --n_inner $n --test_bsz $bsz --data ECL --features M --seq_len 60 --label_len 0 --pred_len $len --des 'Exp' --itr $i --train_epochs 15 --learning_rate 3e-3 --online_learning $online_learning --use_adbfgs > ECL$len$online_learning.out 2>&1 &
done
done
done
done