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April 8, 2020 06:14
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data_visualization_test
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{ | |
"cells": [ | |
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"cell_type": "code", | |
"execution_count": 34, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Using matplotlib backend: GTK3Agg\n", | |
"Populating the interactive namespace from numpy and matplotlib\n" | |
] | |
}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/usr/local/lib/python3.6/dist-packages/IPython/core/magics/pylab.py:160: UserWarning:\n", | |
"\n", | |
"pylab import has clobbered these variables: ['f', 'title']\n", | |
"`%matplotlib` prevents importing * from pylab and numpy\n", | |
"\n" | |
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"data": { | |
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" <script type=\"text/javascript\">\n", | |
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n", | |
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" if (typeof require !== 'undefined') {\n", | |
" require.undef(\"plotly\");\n", | |
" requirejs.config({\n", | |
" paths: {\n", | |
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n", | |
" }\n", | |
" });\n", | |
" require(['plotly'], function(Plotly) {\n", | |
" window._Plotly = Plotly;\n", | |
" });\n", | |
" }\n", | |
" </script>\n", | |
" " | |
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"metadata": {}, | |
"output_type": "display_data" | |
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"data": { | |
"text/html": [ | |
" <script type=\"text/javascript\">\n", | |
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n", | |
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", | |
" if (typeof require !== 'undefined') {\n", | |
" require.undef(\"plotly\");\n", | |
" requirejs.config({\n", | |
" paths: {\n", | |
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n", | |
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"source": [ | |
"# import library\n", | |
"%pylab\n", | |
"%matplotlib inline\n", | |
"import pandas as pd\n", | |
"import os,glob,re\n", | |
"import cufflinks as cf\n", | |
"import plotly.offline\n", | |
"cf.go_offline()\n", | |
"cf.set_config_file(offline=True, world_readable=True)\n", | |
"import time" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# define path\n", | |
"result_folder_path='/home/jaehun/results'\n", | |
"llvm_result_folder_path=os.path.join(result_folder_path,'llvm')\n", | |
"cuda_result_folder_path=os.path.join(result_folder_path,'cuda')\n", | |
"llvm_result_folder_list=glob.glob(llvm_result_folder_path+'/*/*.out')\n", | |
"cuda_result_folder_list=glob.glob(cuda_result_folder_path+'/*/*.out')\n", | |
"# sort list\n", | |
"llvm_result_folder_list.sort()\n", | |
"cuda_result_folder_list.sort()\n", | |
"columns=['network','batchs','threads','algorithm','trials','mean_inference_time(ms)','std_inference_time(ms)']" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"results_llvm=[]\n", | |
"\n", | |
"for i in llvm_result_folder_list:\n", | |
" with open(i, 'r') as f:\n", | |
" for line in f.readlines():\n", | |
" if 'Mean' in line:\n", | |
" times=re.findall('\\d+.\\d+',line)\n", | |
" break\n", | |
" temp=i.split('/')[-1].split('.')[1].split('_')\n", | |
" if len(temp)<6:\n", | |
" temp+=i.split('/')[-1].split('.')[2].split('_')\n", | |
"# print(temp)\n", | |
" results_llvm.append([temp[1][:-1],temp[-5][0],temp[-5][1],temp[-3],temp[-1],float(times[0]),float(times[1])])\n", | |
" \n", | |
"# print('network: ',temp[1][:-1],'batch: ',temp[-5][0],'threads: ',temp[-5][1],\\\n", | |
"# 'algorithm: ',temp[-3],'trial',temp[-1],'\\nmean_inference_time: ',times[0],'std_inference_time: ',times[1],'\\n')\n", | |
" " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 37, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"llvm=pd.DataFrame(data=results_llvm,columns=columns)\n", | |
"llvm.to_csv('~/llvm.csv',index=False)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"ename": "AttributeError", | |
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"output_type": "error", | |
"traceback": [ | |
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"\u001b[0;31mAttributeError\u001b[0m: 'DataFrameGroupBy' object has no attribute 'iplot'" | |
] | |
} | |
], | |
"source": [ | |
"llvm.groupby(level=0,axis=1).iplot()" | |
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