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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"with open(\n",
" \"/Users/apple/Desktop/workspace/UsefulTool/exp-command-generator/test.txt\", 'r'\n",
") as f:\n",
" contents = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# find all \"#####\" indexes\n",
"import re\n",
"indexes = [m.start() for m in re.finditer('#####', contents)]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"assert len(indexes) % 2 == 0"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# split to span\n",
"spans = []\n",
"# spans.append(contents[:indexes[0]])\n",
"for i in range(len(indexes)):\n",
" if i != len(indexes) - 1:\n",
" spans.append(contents[indexes[i]:indexes[i+1]])\n",
"# spans.append(contents[indexes[-1]:])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"spans_with_type = [\n",
" \n",
"]\n",
"for span in spans:\n",
" if \"setup\" in span:\n",
" spans_with_type.append((span, \"setup\"))\n",
" elif \"loop\" in span:\n",
" spans_with_type.append((span, \"loop\"))\n",
" elif \"main\" in span:\n",
" spans_with_type.append((span, \"command\"))\n",
" else:\n",
" spans_with_type.append((span, \"other\"))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('##### setup\\n#!/bin/bash\\nfile_name=$(basename $0)\\ncurrent_path=$(pwd)\\ncd /data/yixin/workspace/unl-graph-usenix\\nsource activate /data/yixin/anaconda/unlg\\ndatasets=(\"IMDB-BINARY\" \"MUTAG\" \"ENZYMES\" \"IMDB-MULTI\" )\\nmodels=( \"gcn\" \"gin\" \"sage\" )\\nentity=\"mib-nlp\"\\nexp_name=\"adv-run-v3\"\\nbatch_size=8\\nmethods=( \"clean\" \"rand\" \"feat\" \"grad\" \"inject\" \"adv\")\\nwd=1e-5\\nadv_train_budgets=( 0.07 0.09 0.11 )\\ngen_exp_name=\"main-results-v2\"\\nlr=0.01\\nes_patience=40\\nseed_default=0\\noptimizer=\"adam\"\\nbudget=0.05\\ntotal_epoch=300\\nmax_steps=5000\\nseeds=(\"402\")\\n# mkdir $current_path/logs/ if not exist\\nmkdir -p $current_path/logs/\\nmkdir -p $current_path/logs/$exp_name\\n',\n",
" 'setup'),\n",
" ('#####\\n\\n\\n\\n', 'other'),\n",
" ('##### loop\\nfor adv_train_budget in \"${adv_train_budgets[@]}\"; do\\nfor dataset in \"${datasets[@]}\"; do\\nfor model in \"${models[@]}\"; do\\nfor method in \"${methods[@]}\"; do\\n',\n",
" 'loop'),\n",
" ('##### \\n\\n ', 'other'),\n",
" ('##### main\\n comb_command=\"for seed in ${seeds[@]} ; do nohup python eval.py --dataset $dataset --model ${model} --method ${method} --lr $lr --exp_name $exp_name --entity $entity --batch_size $batch_size --seed \\\\$seed --early_stop --num_epochs $total_epoch --wd $wd --device $device --es_patience $es_patience --optimizer $optimizer --max_steps $max_steps --adv_train --adv_train_budget $adv_train_budget --gen_exp_name $gen_exp_name > $current_path/logs/$exp_name/$dataset.$model.$method-\\\\$seed-$RANDOM$RANDOM.log 2>&1 ; done; \"\\n eval $comb_command & \\n \\n ',\n",
" 'command'),\n",
" ('##### \\n\\n', 'other'),\n",
" ('##### \\ndone;\\ndone;\\ndone;\\ndone;\\n', 'other')]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spans_with_type"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"gpu_env = \"\"\"\n",
"username_mine=root\n",
"max_gpu_utilization=90\n",
"total_aviable=24564\n",
"max_gpu_memory_gap=5000\n",
"available_devices=( 0 1 2 3 4 5 6 7 8 9 )\n",
"current_device_idx=-1\n",
"sleeptime=30\n",
"cpu_mean_max=77\n",
"memory_rate_max=80\n",
"constrain_total=true\n",
"constrain_mine=false\n",
"constrain_rate=2\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"update_device_func = \"\"\"\n",
"function update_device_idx {\n",
" sleep $sleeptime\n",
" if [ $constrain_total = true ]; then\n",
" # check total cpu usage\n",
" while true; do\n",
" cpu_mean_1=$(mpstat -P ALL 1 1 | awk '/Average:/ && $2 ~ /[0-9]/ { cpu_usage=100-$NF; total+=cpu_usage; count++ } END { print total/count }')\n",
" sleep 1\n",
" cpu_mean_2=$(mpstat -P ALL 1 1 | awk '/Average:/ && $2 ~ /[0-9]/ { cpu_usage=100-$NF; total+=cpu_usage; count++ } END { print total/count }')\n",
" sleep 1\n",
" cpu_mean_3=$(mpstat -P ALL 1 1 | awk '/Average:/ && $2 ~ /[0-9]/ { cpu_usage=100-$NF; total+=cpu_usage; count++ } END { print total/count }')\n",
" cpu_mean=$(echo \"scale=2; ($cpu_mean_1+$cpu_mean_2+$cpu_mean_3)/3\" | bc)\n",
"\n",
" # if currently cpu usage is less than the threshold, then break\n",
" if [ $(echo \"$cpu_mean < $cpu_mean_max\" | bc) -eq 1 ]; then\n",
" echo \"total cpu mean: $cpu_mean is less than $cpu_mean_max, continue to check total memory usage\"\n",
" break\n",
" else\n",
" echo \"total cpu mean: $cpu_mean is greater than $cpu_mean_max, sleep 10 seconds\"\n",
" sleep 10\n",
" fi\n",
" done;\n",
"\n",
" # check total memory usage\n",
" while true; do\n",
" # get memory usage of whole system\n",
" mem_used_1=$(free -m | awk '/Mem:/ {print $3}')\n",
" sleep 1\n",
" mem_used_2=$(free -m | awk '/Mem:/ {print $3}')\n",
" sleep 1\n",
" mem_used_3=$(free -m | awk '/Mem:/ {print $3}')\n",
" mem_used=$(echo \"scale=2; ($mem_used_1+$mem_used_2+$mem_used_3)/3\" | bc)\n",
" \n",
" # echo $mem_used\n",
" # get rate of memory usage\n",
" mem_rate=$(echo \"scale=2; $mem_used/$(free -m | awk '/Mem:/ {print $2}')*100\" | bc)\n",
" # echo $mem_rate\n",
" if [ $(echo \"$mem_rate < $memory_rate_max\" | bc) -eq 1 ]; then\n",
" echo \"total memory rate: $mem_rate is less than $memory_rate_max, continue to check my own cpu and memory usage\"\n",
" break\n",
" else\n",
" echo \"total memory rate: $mem_rate is greater than $memory_rate_max, sleep 10 seconds\"\n",
" sleep 10\n",
" fi\n",
" done;\n",
" fi;\n",
"\n",
" # if constrain_mine\n",
" if [ $constrain_mine = true ]; then\n",
"\n",
" # check my own cpu and memory usage, it should be less than 1/$constrain_rate of the given cpu_mean_max / memory_rate_max\n",
" while true; do\n",
" username=$username_mine\n",
" cpu_usage_user_sum=$(ps -u $username -o %cpu | awk '{sum+=$1} END {print sum}')\n",
" # echo $cpu_usage_user_sum\n",
" total_aviable_cpu=$(nproc)\n",
" total_aviable_cpu=$(echo \"$total_aviable_cpu*100\" | bc)\n",
" # echo $total_aviable_cpu\n",
" cpu_usage_user_ratio=$(echo \"scale=2; $cpu_usage_user_sum/$total_aviable_cpu*100\" | bc)\n",
" # echo $cpu_usage_user_ratio\n",
"\n",
" memory_usage_user_sum=$(ps -u $username -o rss | awk '{sum+=$1} END {print sum/1024}')\n",
" # echo $memory_usage_user_sum\n",
" memory_usage_total=$(free -m | awk '/Mem:/ {print $2}')\n",
" # echo $memory_usage_total\n",
" memory_usage_user_ratio=$(echo \"scale=2; $memory_usage_user_sum/$memory_usage_total*100\" | bc)\n",
" # echo $memory_usage_user_ratio\n",
"\n",
" # so my ratio should be less than 1/$constrain_rate of the given threshold\n",
" cpu_mean_max_mine=$(echo \"$cpu_mean_max/$constrain_rate\" | bc)\n",
" memory_rate_max_mine=$(echo \"$memory_rate_max/$constrain_rate\" | bc)\n",
" if [ $(echo \"$cpu_usage_user_ratio < $cpu_mean_max_mine\" | bc) -eq 1 ] && [ $(echo \"$memory_usage_user_ratio < $memory_rate_max_mine\" | bc) -eq 1 ]; then\n",
" echo \"my cpu usage: $cpu_usage_user_ratio, memory usage: $memory_usage_user_ratio is less than half of the given threshold for cpu: $cpu_mean_max_mine and memory: $memory_rate_max_mine, ready to take off\"\n",
" break\n",
" else\n",
" echo \"my cpu usage: $cpu_usage_user_ratio, memory usage: $memory_usage_user_ratio is greater than half of the given threshold, sleep 10 seconds\"\n",
" sleep 10\n",
" fi\n",
" done;\n",
" fi;\n",
"\n",
" # so all the conditions are satisfied, we can update the device idx and run the next experiment\n",
" while true; do\n",
" current_device_idx=$((current_device_idx+1))\n",
" if [ $current_device_idx -ge ${#available_devices[@]} ]; then\n",
" # reset \n",
" current_device_idx=0\n",
" fi\n",
" # check whether this device is fully booked using nvidia-smi\n",
" # get the gpu current memory usage \n",
" useage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i ${available_devices[$current_device_idx]})\n",
" utilization=$(nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader,nounits -i ${available_devices[$current_device_idx]})\n",
" \n",
" if [ $useage -ge $((total_aviable-max_gpu_memory_gap)) ] || [ $utilization -ge $max_gpu_utilization ]; then\n",
" echo \"device ${available_devices[$current_device_idx]} is fully booked, try next one\"\n",
" sleep 3\n",
" continue\n",
" else\n",
" break\n",
" fi\n",
" done\n",
" echo \"current device: ${available_devices[$current_device_idx]}\"\n",
" device=${available_devices[$current_device_idx]}\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"update_device_command = \"update_device_idx;\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"backend_run = False"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"gpu_utility = \"\"\n",
"gpu_utility = gpu_env + \"\\n\\n\" + update_device_func \n",
"with open(\"gpu_utility.sh\", 'w') as f:\n",
" f.write(gpu_utility)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"spans_with_type_added_device_control = []\n",
"\n",
"for span, type_ in spans_with_type:\n",
" if type_ == \"setup\":\n",
" spans_with_type_added_device_control.append((\n",
" \"\"\"cd $(cd \"$(dirname \"$0\")\";pwd); source gpu_utility.sh\\n\\n\"\"\"\n",
" , \"device_control\"))\n",
" spans_with_type_added_device_control.append((span, type_))\n",
" # spans_with_type_added_device_control.append((gpu_env, \"device_control\"))\n",
" # spans_with_type_added_device_control.append((update_device_func, \"device_control\"))\n",
" elif type_ == \"loop\":\n",
" spans_with_type_added_device_control.append((span, type_))\n",
" elif type_ == \"command\":\n",
" spans_with_type_added_device_control.append((\"\\n\"+update_device_command, \"device_control\"))\n",
" span_remove_the_first_part = span[span.index(\"\\n\"):]\n",
" spans_with_type_added_device_control.append((f\"\\n\\ncommand=\\\"\\\"\\\"{span_remove_the_first_part}\\\"\\\"\\\"\\n\", type_))\n",
" run_command = \"eval $command\"\n",
" if backend_run:\n",
" run_command += \" &\"\n",
" run_command += \"\\n\\n\\n\"\n",
" spans_with_type_added_device_control.append((run_command, type_))\n",
" else:\n",
" spans_with_type_added_device_control.append((span, type_))\n",
"spans_without_type = [span for span, type_ in spans_with_type_added_device_control]\n",
"spans_without_type_str = \"\".join(spans_without_type)\n",
"with open(\"./output.sh\", 'w') as f:\n",
" f.write(spans_without_type_str)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
}
|