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Yixin Liu
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update readme
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Procfile
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web: sh setup.sh && streamlit run main.py
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main.py
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# import imp
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import streamlit as st
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import pandas as pd
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import numpy as np
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import time
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# import matplotlib.pyplot as plt
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# import seaborn as sns
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# import plotly.figure_factory as ff
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# import altair as alt
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# from PIL import Image
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# import base64
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# import tarfile
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# import os
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# import requests
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# title
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st.title("Exp Command Generator")
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## 检查框
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debug = st.checkbox("Debug:选择则会串行地执行命令", value=True)
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# st.write(f"checkbox的值是{res}")
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setup = st.text_area("Some setup of env at beginning.", """cd $(dirname $(dirname $0))
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source activate xai
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export PYTHONPATH=${PYTHONPATH}:/Users/apple/Desktop/workspace/research_project/attention:/mnt/yixin/:/home/yila22/prj""")
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exp_hyper = st.text_area("Hyperparameters", """exp_name="debug-adv-training-emotion"
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dataset=emotion
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n_epoch=3
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K=3
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encoder=bert
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lambda_1=1
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lambda_2=1
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x_pgd_radius=0.01
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pgd_radius=0.001
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seed=2
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bsize=8
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lr=5e-5""")
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## gpu 相关参数
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gpu_list = st.multiselect("multi select", range(10), [1, 2, 3, 4, 5, 6, 7, 8, 9])
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print(gpu_list)
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allow_gpu_memory_threshold = st.number_input("最小单卡剩余容量", value=5000, min_value=0, max_value=30000, step=1000)
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gpu_threshold = st.number_input("最大单卡利用率", value=70, min_value=0, max_value=100, step=10)
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sleep_time_after_loading_task= st.number_input("加载任务后等待秒数", value=20, min_value=0,step=5)
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all_full_sleep_time = st.number_input("全满之后等待秒数", value=20, min_value=0,step=5)
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gpu_list_str = ' '.join([str(i) for i in gpu_list])
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gpu_hyper = f"gpu=({gpu_list_str})\n"
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gpu_hyper+=f"allow_gpu_memory_threshold={allow_gpu_memory_threshold}\n"
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gpu_hyper+=f"gpu_threshold={gpu_threshold}\n"
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gpu_hyper+=f"sleep_time_after_loading_task={sleep_time_after_loading_task}s\n"
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gpu_hyper+=f"all_full_sleep_time={all_full_sleep_time}s\n"
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gpu_hyper+=f"gpunum={len(gpu_list)}\n"
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main_loop = st.text_area("Main loop", """for lambda_1 in 1 3;do
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for lambda_2 in 1 10;do
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for n_epoch in 3;do
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for x_pgd_radius in 0.005 0.01;do
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for pgd_radius in 0.0005 0.001 0.002;do
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python train.py --dataset $dataset --data_dir . --output_dir ./outputs/ --attention tanh \
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--encoder $encoder \
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--exp_name $exp_name --lambda_1 $lambda_1 --lambda_2 $lambda_2 --pgd_radius $pgd_radius --x_pgd_radius $x_pgd_radius \
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--K $K --seed $seed --train_mode adv_train --bsize $bsize --n_epoch $n_epoch --lr $lr \
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--eval_baseline
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done;done;done;done;done;""")
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hyper_loop = main_loop.split("python")[0]
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print(hyper_loop)
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python_cmd = main_loop.split(";do\n")[-1].split('done;')[0]
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print(python_cmd)
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end_loop = "done;"*hyper_loop.count("\n")
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print(end_loop)
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g = st.button("Generate")
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if g:
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s = ""
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s += setup + "\n\n"
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s += exp_hyper + "\n\n"
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s += gpu_hyper + "\n\n"
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s += hyper_loop + "\n\n"
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s += """
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i=0 # we search from the first gpu
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while true; do
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gpu_id=${gpu[$i]}
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# nvidia-smi --query-gpu=utilization.gpu --format=csv -i 2 | grep -Eo "[0-9]+"
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gpu_u=$(nvidia-smi --query-gpu=utilization.gpu --format=csv -i $gpu_id | grep -Eo "[0-9]+")
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free_mem=$(nvidia-smi --query-gpu=memory.free --format=csv -i $gpu_id | grep -Eo "[0-9]+")
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if [[ $free_mem -lt $allow_gpu_memory_threshold || $gpu_u -ge ${gpu_threshold} ]]; then
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i=`expr $i + 1`
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i=`expr $i % $gpunum`
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echo "gpu id ${gpu[$i]} is full loaded, skip"
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if [ "$i" == "0" ]; then
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sleep ${all_full_sleep_time}
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echo "all the gpus are full, sleep 1m"
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fi
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else
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break
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fi
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done
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gpu_id=${gpu[$i]}
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free_mem=$(nvidia-smi --query-gpu=memory.free --format=csv -i $gpu_id | grep -Eo "[0-9]+")
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gpu_u=$(nvidia-smi --query-gpu=utilization.gpu --format=csv -i $gpu_id | grep -Eo "[0-9]+")
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export CUDA_VISIBLE_DEVICES=$gpu_id
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echo "use gpu id is ${gpu[$i]}, free memory is $free_mem, it utilization is ${gpu_u}%"
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"""
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s += f"""com="{python_cmd}"\n"""
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s += "echo $com\n"
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s += "echo ==========================================================================================\n"
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if debug:
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s += "$com\n"
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else:
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s += "mkdir -p ./logs/\n"
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s += "nohup $com > ./logs/$exp_name-$RANDOM.log 2>&1 &\n"
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s += """echo "sleep for $sleep_time_after_loading_task to wait the task loaded"
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sleep $sleep_time_after_loading_task\n"""
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s += end_loop
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st.success("Finished")
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st.code(s, language="shell")
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requirements.txt
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numpy
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streamlit
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pandas
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setup.sh
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mkdir -p ~/.streamlit/
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echo "\
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[general]\n\
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email = \"yila22@lehigh.edu\"\n\
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" > ~/.streamlit/credentials.toml
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echo "\
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[server]\n\
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headless = true\n\
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enableCORS=false\n\
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port = $PORT\n\
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" > ~/.streamlit/config.toml
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