{"cells":[
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  "source": "# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n#from subprocess import check_output\n#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.\nfrom joblib import Parallel, delayed\nimport multiprocessing\n# what are your inputs, and what operation do you want to\n# perform on each input. For example...\ninputs = range(10)\ndef processInput(i):\n    return i * i\nnum_cores = multiprocessing.cpu_count()\nresults = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs)\n"
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