@ -76,10 +76,11 @@ class Components :
partition = args [ ' partition ' ]
log_folder = os . sep . join ( [ log_folder , args [ ' context ' ] , str ( partition ) ] )
_args = { " batch_size " : 10 000, " logs " : log_folder , " context " : args [ ' context ' ] , " max_epochs " : 150 , " column " : args [ ' columns ' ] , " id " : " person_id " , " logger " : logger }
_args = { " batch_size " : 2 000, " logs " : log_folder , " context " : args [ ' context ' ] , " max_epochs " : 150 , " column " : args [ ' columns ' ] , " id " : " person_id " , " logger " : logger }
_args [ ' max_epochs ' ] = 150 if ' max_epochs ' not in args else int ( args [ ' max_epochs ' ] )
if ' batch_size ' in args :
_args [ ' batch_size ' ] = int ( args [ ' batch_size ' ] )
#
# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
#
@ -143,7 +144,7 @@ class Components :
# columns = args['columns']
# df = np.array_split(df[columns].values,PART_SIZE)
# df = pd.DataFrame(df[ int (partition) ],columns = columns)
info = { " parition " : int ( partition ) , " gpu " : _args [ " gpu " ] , " rows " : str ( df . shape [ 0 ] ) , " cols " : str ( df . shape [ 1 ] ) , " part_size " : int ( PART_SIZE ) }
info = { " parition " : int ( partition ) , " gpu " : _args [ " gpu " ] , " rows " : int ( df . shape [ 0 ] ) , " cols " : int ( df . shape [ 1 ] ) , " part_size " : int ( PART_SIZE ) }
logger . write ( { " module " : " generate " , " action " : " partition " , " input " : info } )
_args [ ' partition ' ] = int ( partition )
_args [ ' continuous ' ] = args [ ' continuous ' ] if ' continuous ' in args else [ ]
@ -163,7 +164,6 @@ class Components :
data_comp = _args [ ' data ' ] [ args [ ' columns ' ] ] . join ( _dc [ args [ ' columns ' ] ] , rsuffix = ' _io ' ) #-- will be used for comparison (store this in big query)
base_cols = list ( set ( _args [ ' data ' ] . columns ) - set ( args [ ' columns ' ] ) ) #-- rebuilt the dataset (and store it)
for name in cols :
_args [ ' data ' ] [ name ] = _dc [ name ]
info = { " module " : " generate " , " action " : " io " , " input " : { " rows " : _dc [ name ] . shape [ 0 ] , " name " : name } }
@ -193,8 +193,12 @@ class Components :
_fname = table . replace ( ' _io ' , ' _full_io ' )
partial = ' . ' . join ( [ ' io ' , args [ ' context ' ] + ' _partial_io ' ] )
complete = ' . ' . join ( [ ' io ' , args [ ' context ' ] + ' _full_io ' ] )
data_comp . to_gbq ( if_exists = ' append ' , destination_table = partial , credentials = credentials , chunksize = 50000 )
data_comp . to_csv ( _pname , index = False )
if ' dump ' in args :
print ( _args [ ' data ' ] . head ( ) )
else :
data_comp . to_gbq ( if_exists = ' append ' , destination_table = partial , credentials = credentials , chunksize = 50000 )
INSERT_FLAG = ' replace ' if ' partition ' not in args or ' segment ' not in args else ' append '
_args [ ' data ' ] . to_gbq ( if_exists = ' append ' , destination_table = complete , credentials = credentials , chunksize = 50000 )
_id = ' dataset '
@ -247,6 +251,7 @@ if __name__ == '__main__' :
args = dict ( args , * * SYS_ARGS )
args [ ' logs ' ] = args [ ' logs ' ] if ' logs ' in args else ' logs '
args [ ' batch_size ' ] = 2000 if ' batch_size ' not in args else int ( args [ ' batch_size ' ] )
if ' dataset ' not in args :
args [ ' dataset ' ] = ' combined20191004v2_deid '
PART_SIZE = int ( args [ ' part_size ' ] ) if ' part_size ' in args else 8
@ -350,10 +355,7 @@ if __name__ == '__main__' :
continue
args [ ' part_size ' ] = PART_SIZE
args [ ' partition ' ] = index
# _df = pd.DataFrame(DATA[index],columns=args['columns'])
args [ ' data ' ] = DATA [ index ]
# args['data'].to_csv('aou-'+str(index)+'csv',index=False)
# args['reader'] = lambda: _df
if int ( args [ ' num_gpu ' ] ) > 1 :
args [ ' gpu ' ] = index
else :
