-
Notifications
You must be signed in to change notification settings - Fork 2
/
run_all_for_overhead.sh
executable file
·54 lines (43 loc) · 3.26 KB
/
run_all_for_overhead.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
#!/bin/bash
cd /home/yhao/d/benchmark
output=/home/yhao/d/testing/runlog_100ms_train_all.txt
func(){
echo "" > $output
# for model in fambench_dlrm fambench_xlmr detectron2_maskrcnn vision_maskrcnn timm_efficientnet timm_vision_transformer hf_Bert hf_GPT2 hf_T5
for model in fambench_dlrm fambench_xlmr detectron2_maskrcnn vision_maskrcnn timm_efficientnet timm_vision_transformer hf_Bert hf_GPT2 hf_T5 resnet50 timm_resnest resnext50_32x4d hf_BigBird hf_Bart soft_actor_critic alexnet timm_vovnet mobilenet_v3_large vgg16 shufflenet_v2_x1_0 pytorch_unet dlrm mnasnet1_0 resnet50_quantized_qat tts_angular hf_Reformer nvidia_deeprecommender mobilenet_v2_quantized_qat mobilenet_v2 LearningToPaint hf_Longformer opacus_cifar10 resnet18 timm_regnet dcgan maml BERT_pytorch Super_SloMo pytorch_struct pplbench_beanmachine drq pyhpc_isoneutral_mixing hf_Albert attention_is_all_you_need_pytorch moco Background_Matting pyhpc_turbulent_kinetic_energy maml_omniglot pyhpc_equation_of_state timm_nfnet demucs densenet121 pytorch_CycleGAN_and_pix2pix tacotron2 squeezenet1_1 fastNLP_Bert pytorch_stargan hf_DistilBert speech_transformer yolov3 timm_efficientdet
do
# this part is for total overhead
# echo "@Yueming Hao: start model tests" >> $output
# echo "@Yueming Hao: Run $model" >> $output
# { time python run.py -d cuda -t train $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train $model >> $output 2>&1 ; } 2>> $output
# echo "@Yueming Hao: Run $model with dcgm flops" >> $output 2>&1
# { time python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1 ; } 2>> $output
# { time python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1 ; } 2>> $output
# echo "@Yueming Hao: end model tests" >> $output
# this part is for computation overhead
echo "@Yueming Hao: start model tests" >> $output
echo "@Yueming Hao: Run $model" >> $output
python run.py -d cuda -t train $model >> $output 2>&1
python run.py -d cuda -t train $model >> $output 2>&1
python run.py -d cuda -t train $model >> $output 2>&1
python run.py -d cuda -t train $model >> $output 2>&1
python run.py -d cuda -t train $model >> $output 2>&1
echo "@Yueming Hao: Run $model with dcgm flops" >> $output 2>&1
python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1
python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1
python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1
python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1
python run.py -d cuda -t train --flops dcgm $model >> $output 2>&1
echo "@Yueming Hao: end model tests" >> $output
done
}
func
# output=runlog_100ms_all.txt
# func