Using numba for GPU acceleration with large Excel file

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I've minimal experience of using GPUs and after looking online, I came up with this:

import pandas as pd

from haversine import haversine

import numpy as np

from pandas import ExcelWriter

import numba as nb



np.set_printoptions(precision=20)





path = 'distance.xlsx'

df = pd.read_excel(path)

df = df.assign(Dist=pd.Series(np.zeros(27055)).values);

#df = df.assign(Facility=pd.Series(np.zeros(27055)).values);

df = df.assign(Facility=pd.Series(np.zeros((27055,),dtype='float,float')).values);

df["Facility_city"] = ""



#idx = np.asarray(df.loc[df["lat1"] != '.'].ix[:,0].index)

#temp1 = 1e10

#j = 0



idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][0])].index)



@nb.jit(nopython=True)

def f(df):

    temp1 = 1e10

    j = 0

    for i in range(0, len(df)):



        if df['state'][i+1] != df['state'][i]:

            idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][i+1])].index)



        #while (df.iloc[idx[j]]['state'] == df.iloc[i]['state']):     

        while (j!=len(idx)):



            p1 = (df.iloc[idx[j]]['lat1'],df.iloc[idx[j]]['long1'])

            p2 = (df.iloc[i]['lat2'],df.iloc[i]['long2'])

            df.Dist.iloc[i] = min(temp1,haversine(p1, p2, miles=True))



            if df.Dist.iloc[i] < temp1:

                #df.Facility.iloc[i] = idx[j]

                df.Facility.iloc[i] = (p1[0],p1[1])

                df.Facility_city.iloc[i] = df.city.iloc[idx[j]]



            temp1 = df.Dist.iloc[i]

            j+=1



        j = 0            

        temp1 = 1e10

    return df



if __name__ == "__main__":



    df = f(df)

    writer = ExcelWriter('Results.xlsx')

    df.to_excel(writer,'Sheet1')

    writer.save()

The for loop runs close to 30k times. Hence, my inclination to use a GPU (via Floydhub) I'm struggling to configure my code for the same.

When I execute this, I get an error.

Traceback (most recent call last):



  File "<ipython-input-8-1a4900ad9325>", line 1, in <module>

    runfile('/Users/deepayanbhadra/Downloads/Chotka /Chotka.py', wdir='/Users/deepayanbhadra/Downloads/Chotka ')



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile

    execfile(filename, namespace)



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile

    exec(compile(f.read(), filename, 'exec'), namespace)



  File "/Users/deepayanbhadra/Downloads/Chotka /Chotka.py", line 60, in <module>

    df = f(df)



  File "/anaconda3/lib/python3.6/site-packages/numba/dispatcher.py", line 344, in _compile_for_args

    reraise(type(e), e, None)



  File "/anaconda3/lib/python3.6/site-packages/numba/six.py", line 658, in reraise

    raise value.with_traceback(tb)



TypingError: cannot determine Numba type of <class 'function'>

How do I go about circumventing this? I think numba is the best framework from the references but any other (like PyCUDA) is also okay.

asked Nov 21 at 18:16

db18

1417

pandas is not (yet) supported by numba. You need to extract the df values with arr = df.values and then pass the array to numba. This means that you have to use numpy indexing (integers, lists, slices etc.) within the numba jitted function. Furthermore your code can easily be improved without numba. It looks like all of it can easily be replaced by vectorized approaches, yielding a performance nearly as good as numba but with the possibility to use pandas.
– Scotty1-
Nov 23 at 8:45

add a comment |

up vote
0
down vote

favorite

I've minimal experience of using GPUs and after looking online, I came up with this:

import pandas as pd

from haversine import haversine

import numpy as np

from pandas import ExcelWriter

import numba as nb



np.set_printoptions(precision=20)





path = 'distance.xlsx'

df = pd.read_excel(path)

df = df.assign(Dist=pd.Series(np.zeros(27055)).values);

#df = df.assign(Facility=pd.Series(np.zeros(27055)).values);

df = df.assign(Facility=pd.Series(np.zeros((27055,),dtype='float,float')).values);

df["Facility_city"] = ""



#idx = np.asarray(df.loc[df["lat1"] != '.'].ix[:,0].index)

#temp1 = 1e10

#j = 0



idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][0])].index)



@nb.jit(nopython=True)

def f(df):

    temp1 = 1e10

    j = 0

    for i in range(0, len(df)):



        if df['state'][i+1] != df['state'][i]:

            idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][i+1])].index)



        #while (df.iloc[idx[j]]['state'] == df.iloc[i]['state']):     

        while (j!=len(idx)):



            p1 = (df.iloc[idx[j]]['lat1'],df.iloc[idx[j]]['long1'])

            p2 = (df.iloc[i]['lat2'],df.iloc[i]['long2'])

            df.Dist.iloc[i] = min(temp1,haversine(p1, p2, miles=True))



            if df.Dist.iloc[i] < temp1:

                #df.Facility.iloc[i] = idx[j]

                df.Facility.iloc[i] = (p1[0],p1[1])

                df.Facility_city.iloc[i] = df.city.iloc[idx[j]]



            temp1 = df.Dist.iloc[i]

            j+=1



        j = 0            

        temp1 = 1e10

    return df



if __name__ == "__main__":



    df = f(df)

    writer = ExcelWriter('Results.xlsx')

    df.to_excel(writer,'Sheet1')

    writer.save()

The for loop runs close to 30k times. Hence, my inclination to use a GPU (via Floydhub) I'm struggling to configure my code for the same.

When I execute this, I get an error.

Traceback (most recent call last):



  File "<ipython-input-8-1a4900ad9325>", line 1, in <module>

    runfile('/Users/deepayanbhadra/Downloads/Chotka /Chotka.py', wdir='/Users/deepayanbhadra/Downloads/Chotka ')



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile

    execfile(filename, namespace)



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile

    exec(compile(f.read(), filename, 'exec'), namespace)



  File "/Users/deepayanbhadra/Downloads/Chotka /Chotka.py", line 60, in <module>

    df = f(df)



  File "/anaconda3/lib/python3.6/site-packages/numba/dispatcher.py", line 344, in _compile_for_args

    reraise(type(e), e, None)



  File "/anaconda3/lib/python3.6/site-packages/numba/six.py", line 658, in reraise

    raise value.with_traceback(tb)



TypingError: cannot determine Numba type of <class 'function'>

How do I go about circumventing this? I think numba is the best framework from the references but any other (like PyCUDA) is also okay.

asked Nov 21 at 18:16

db18

1417

pandas is not (yet) supported by numba. You need to extract the df values with arr = df.values and then pass the array to numba. This means that you have to use numpy indexing (integers, lists, slices etc.) within the numba jitted function. Furthermore your code can easily be improved without numba. It looks like all of it can easily be replaced by vectorized approaches, yielding a performance nearly as good as numba but with the possibility to use pandas.
– Scotty1-
Nov 23 at 8:45

add a comment |

up vote
0
down vote

favorite

I've minimal experience of using GPUs and after looking online, I came up with this:

import pandas as pd

from haversine import haversine

import numpy as np

from pandas import ExcelWriter

import numba as nb



np.set_printoptions(precision=20)





path = 'distance.xlsx'

df = pd.read_excel(path)

df = df.assign(Dist=pd.Series(np.zeros(27055)).values);

#df = df.assign(Facility=pd.Series(np.zeros(27055)).values);

df = df.assign(Facility=pd.Series(np.zeros((27055,),dtype='float,float')).values);

df["Facility_city"] = ""



#idx = np.asarray(df.loc[df["lat1"] != '.'].ix[:,0].index)

#temp1 = 1e10

#j = 0



idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][0])].index)



@nb.jit(nopython=True)

def f(df):

    temp1 = 1e10

    j = 0

    for i in range(0, len(df)):



        if df['state'][i+1] != df['state'][i]:

            idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][i+1])].index)



        #while (df.iloc[idx[j]]['state'] == df.iloc[i]['state']):     

        while (j!=len(idx)):



            p1 = (df.iloc[idx[j]]['lat1'],df.iloc[idx[j]]['long1'])

            p2 = (df.iloc[i]['lat2'],df.iloc[i]['long2'])

            df.Dist.iloc[i] = min(temp1,haversine(p1, p2, miles=True))



            if df.Dist.iloc[i] < temp1:

                #df.Facility.iloc[i] = idx[j]

                df.Facility.iloc[i] = (p1[0],p1[1])

                df.Facility_city.iloc[i] = df.city.iloc[idx[j]]



            temp1 = df.Dist.iloc[i]

            j+=1



        j = 0            

        temp1 = 1e10

    return df



if __name__ == "__main__":



    df = f(df)

    writer = ExcelWriter('Results.xlsx')

    df.to_excel(writer,'Sheet1')

    writer.save()

The for loop runs close to 30k times. Hence, my inclination to use a GPU (via Floydhub) I'm struggling to configure my code for the same.

When I execute this, I get an error.

Traceback (most recent call last):



  File "<ipython-input-8-1a4900ad9325>", line 1, in <module>

    runfile('/Users/deepayanbhadra/Downloads/Chotka /Chotka.py', wdir='/Users/deepayanbhadra/Downloads/Chotka ')



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile

    execfile(filename, namespace)



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile

    exec(compile(f.read(), filename, 'exec'), namespace)



  File "/Users/deepayanbhadra/Downloads/Chotka /Chotka.py", line 60, in <module>

    df = f(df)



  File "/anaconda3/lib/python3.6/site-packages/numba/dispatcher.py", line 344, in _compile_for_args

    reraise(type(e), e, None)



  File "/anaconda3/lib/python3.6/site-packages/numba/six.py", line 658, in reraise

    raise value.with_traceback(tb)



TypingError: cannot determine Numba type of <class 'function'>

How do I go about circumventing this? I think numba is the best framework from the references but any other (like PyCUDA) is also okay.

asked Nov 21 at 18:16

db18

1417

I've minimal experience of using GPUs and after looking online, I came up with this:

import pandas as pd

from haversine import haversine

import numpy as np

from pandas import ExcelWriter

import numba as nb



np.set_printoptions(precision=20)





path = 'distance.xlsx'

df = pd.read_excel(path)

df = df.assign(Dist=pd.Series(np.zeros(27055)).values);

#df = df.assign(Facility=pd.Series(np.zeros(27055)).values);

df = df.assign(Facility=pd.Series(np.zeros((27055,),dtype='float,float')).values);

df["Facility_city"] = ""



#idx = np.asarray(df.loc[df["lat1"] != '.'].ix[:,0].index)

#temp1 = 1e10

#j = 0



idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][0])].index)



@nb.jit(nopython=True)

def f(df):

    temp1 = 1e10

    j = 0

    for i in range(0, len(df)):



        if df['state'][i+1] != df['state'][i]:

            idx = np.asarray(df[(df['lat1']!='.') & (df['state']== df['state'][i+1])].index)



        #while (df.iloc[idx[j]]['state'] == df.iloc[i]['state']):     

        while (j!=len(idx)):



            p1 = (df.iloc[idx[j]]['lat1'],df.iloc[idx[j]]['long1'])

            p2 = (df.iloc[i]['lat2'],df.iloc[i]['long2'])

            df.Dist.iloc[i] = min(temp1,haversine(p1, p2, miles=True))



            if df.Dist.iloc[i] < temp1:

                #df.Facility.iloc[i] = idx[j]

                df.Facility.iloc[i] = (p1[0],p1[1])

                df.Facility_city.iloc[i] = df.city.iloc[idx[j]]



            temp1 = df.Dist.iloc[i]

            j+=1



        j = 0            

        temp1 = 1e10

    return df



if __name__ == "__main__":



    df = f(df)

    writer = ExcelWriter('Results.xlsx')

    df.to_excel(writer,'Sheet1')

    writer.save()

The for loop runs close to 30k times. Hence, my inclination to use a GPU (via Floydhub) I'm struggling to configure my code for the same.

When I execute this, I get an error.

Traceback (most recent call last):



  File "<ipython-input-8-1a4900ad9325>", line 1, in <module>

    runfile('/Users/deepayanbhadra/Downloads/Chotka /Chotka.py', wdir='/Users/deepayanbhadra/Downloads/Chotka ')



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile

    execfile(filename, namespace)



  File "/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile

    exec(compile(f.read(), filename, 'exec'), namespace)



  File "/Users/deepayanbhadra/Downloads/Chotka /Chotka.py", line 60, in <module>

    df = f(df)



  File "/anaconda3/lib/python3.6/site-packages/numba/dispatcher.py", line 344, in _compile_for_args

    reraise(type(e), e, None)



  File "/anaconda3/lib/python3.6/site-packages/numba/six.py", line 658, in reraise

    raise value.with_traceback(tb)



TypingError: cannot determine Numba type of <class 'function'>

How do I go about circumventing this? I think numba is the best framework from the references but any other (like PyCUDA) is also okay.

python for-loop gpu numba

asked Nov 21 at 18:16

db18

1417

asked Nov 21 at 18:16

db18

1417

asked Nov 21 at 18:16

db18

1417

asked Nov 21 at 18:16

db18

1417

asked Nov 21 at 18:16

db18

1417

pandas is not (yet) supported by numba. You need to extract the df values with arr = df.values and then pass the array to numba. This means that you have to use numpy indexing (integers, lists, slices etc.) within the numba jitted function. Furthermore your code can easily be improved without numba. It looks like all of it can easily be replaced by vectorized approaches, yielding a performance nearly as good as numba but with the possibility to use pandas.
– Scotty1-
Nov 23 at 8:45

add a comment |

pandas is not (yet) supported by numba. You need to extract the df values with arr = df.values and then pass the array to numba. This means that you have to use numpy indexing (integers, lists, slices etc.) within the numba jitted function. Furthermore your code can easily be improved without numba. It looks like all of it can easily be replaced by vectorized approaches, yielding a performance nearly as good as numba but with the possibility to use pandas.
– Scotty1-
Nov 23 at 8:45

pandas is not (yet) supported by numba. You need to extract the df values with arr = df.values and then pass the array to numba. This means that you have to use numpy indexing (integers, lists, slices etc.) within the numba jitted function. Furthermore your code can easily be improved without numba. It looks like all of it can easily be replaced by vectorized approaches, yielding a performance nearly as good as numba but with the possibility to use pandas.
– Scotty1-
Nov 23 at 8:45

add a comment |

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