405 lines
14 KiB
Python
405 lines
14 KiB
Python
#!/usr/bin/env python3
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import json
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from transport import factory
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import numpy as np
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import time
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import os
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from multiprocessing import Process
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import pandas as pd
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from google.oauth2 import service_account
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import data.maker
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from data.params import SYS_ARGS
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#
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# The configuration array is now loaded and we will execute the pipe line as follows
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DATASET='combined20191004v2_deid'
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class Components :
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@staticmethod
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def get(args):
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"""
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This function returns a data-frame provided a bigquery sql statement with conditions (and limits for testing purposes)
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The function must be wrapped around a lambda this makes testing easier and changing data stores transparent to the rest of the code. (Vital when testing)
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:sql basic sql statement
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:condition optional condition and filters
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"""
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SQL = args['sql']
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if 'condition' in args :
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condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
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SQL = " ".join([SQL,'WHERE',condition])
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SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
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if 'limit' in args :
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SQL = SQL + 'LIMIT ' + args['limit']
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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df = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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return df
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# return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna()
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@staticmethod
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def split(X,MAX_ROWS=3,PART_SIZE=3):
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return list(pd.cut( np.arange(X.shape[0]+1),PART_SIZE).categories)
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def train(self,**args):
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"""
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This function will perform training on the basis of a given pointer that reads data
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"""
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#
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# @TODO: we need to log something here about the parameters being passed
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# pointer = args['reader'] if 'reader' in args else lambda: Components.get(**args)
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df = args['reader']()
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# if df.shape[0] == 0 :
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# print ("CAN NOT TRAIN EMPTY DATASET ")
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# return
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#
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# Now we can parse the arguments and submit the entire thing to training
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#
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = args['logs'] if 'logs' in args else 'logs'
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_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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_args['gpu'] = args['gpu'] if 'gpu' in args else 0
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# MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
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PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
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if 'partition' not in args:
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lbound = 0
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# bounds = list(pd.cut( np.arange(df.shape[0]+1),PART_SIZE).categories)
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# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
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columns = args['columns']
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df = np.array_split(df[columns].values,PART_SIZE)
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qwriter = factory.instance(type='queue.QueueWriter',args={'queue':'aou.io'})
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part_index = 0
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#
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# let's start n processes to listen & train this mother ...
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#
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#-- hopefully they learn as daemons
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for _df in df:
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# _args['logs'] = os.sep.join([log_folder,str(part_index)])
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_args['partition'] = str(part_index)
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_args['logger'] = {'args':{'dbname':'aou','doc':args['context']},'type':'mongo.MongoWriter'}
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#
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# We should post the the partitions to a queue server (at least the instructions on ):
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# - where to get the data
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# - and athe arguments to use (partition #,columns,gpu,epochs)
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#
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_df = pd.DataFrame(_df,columns=columns)
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# print (columns)
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info = {"rows":_df.shape[0],"cols":_df.shape[1], "partition":part_index,"logs":_args['logs'],"num_gpu":1,"part_size":PART_SIZE}
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p = {"args":_args,"data":_df.to_dict(orient="records"),"input":info}
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part_index += 1
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qwriter.write(p)
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#
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# @TODO:
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# - Notify that information was just posted to the queue
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# In case we want slow-mode, we can store the partitions in mongodb and process (Yes|No)?
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#
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logger.write({"module":"train","action":"setup-partition","input":info})
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pass
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else:
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print ('.....')
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partition = args['partition'] if 'partition' in args else ''
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log_folder = os.sep.join([log_folder,args['context'],str(partition)])
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_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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#
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# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
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#
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if int(args['num_gpu']) > 1 :
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_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)[0]
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else:
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_args['gpu'] = 0
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_args['num_gpu'] = 1
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
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_args['data'] = df
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#
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# @log :
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# Logging information about the training process for this partition (or not)
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#
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info = {"rows":df.shape[0],"cols":df.shape[1], "partition":int(partition),"logs":_args['logs']}
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logger.write({"module":"train","action":"train","input":info})
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data.maker.train(**_args)
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pass
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# @staticmethod
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def generate(self,args):
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"""
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This function will generate data and store it to a given,
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"""
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = args['logs'] if 'logs' in args else 'logs'
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partition = args['partition'] if 'partition' in args else ''
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log_folder = os.sep.join([log_folder,args['context'],str(partition)])
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_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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# _args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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if int(args['num_gpu']) > 1 :
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_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)[0]
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else:
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_args['gpu'] = 0
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_args['num_gpu'] = 1
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
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_args['no_value']= args['no_value']
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# MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
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PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
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# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
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# reader = args['reader']
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# df = reader()
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df = args['reader']() if 'reader' in args else args['data']
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# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
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# if partition != '' :
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# columns = args['columns']
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# df = np.array_split(df[columns].values,PART_SIZE)
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# df = pd.DataFrame(df[ int (partition) ],columns = columns)
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info = {"parition":int(partition),"gpu":_args["gpu"],"rows":df.shape[0],"cols":df.shape[1],"part_size":PART_SIZE}
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logger.write({"module":"generate","action":"partition","input":info})
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_args['data'] = df
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# _args['data'] = reader()
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#_args['data'] = _args['data'].astype(object)
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# _args['num_gpu'] = 1
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_dc = data.maker.generate(**_args)
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#
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# We need to post the generate the data in order to :
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# 1. compare immediately
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# 2. synthetic copy
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#
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cols = _dc.columns.tolist()
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data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io') #-- will be used for comparison (store this in big query)
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base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
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for name in cols :
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_args['data'][name] = _dc[name]
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info = {"module":"generate","action":"io","input":{"rows":_dc[name].shape[0],"name":name}}
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if partition != '' :
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info['partition'] = int(partition)
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logger.write(info)
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# filename = os.sep.join([log_folder,'output',name+'.csv'])
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# data_comp[[name]].to_csv(filename,index=False)
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#
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#-- Let us store all of this into bigquery
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prefix = args['notify']+'.'+_args['context']
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table = '_'.join([prefix,partition,'io']).replace('__','_')
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folder = os.sep.join([args['logs'],args['context'],partition,'output'])
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if 'file' in args :
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_fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
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_pname = os.sep.join([folder,table])+'.csv'
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data_comp.to_csv( _pname,index=False)
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_args['data'].to_csv(_fname,index=False)
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else:
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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_pname = os.sep.join([folder,table+'.csv'])
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_fname = table.replace('_io','_full_io')
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data_comp.to_gbq(if_exists='replace',destination_table=_pname,credentials='credentials',chunk_size=50000)
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data_comp.to_csv(_pname,index=False)
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INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append'
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_args['data'].to_gbq(if_exists=INSERT_FLAG,destination_table=_fname,credentials='credentials',chunk_size=50000)
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info = {"full":{"path":_fname,"rows":_args['data'].shape[0]},"compare":{"name":_pname,"rows":data_comp.shape[0]} }
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if partition :
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info ['partition'] = int(partition)
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logger.write({"module":"generate","action":"write","input":info} )
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@staticmethod
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def callback(channel,method,header,stream):
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if stream.decode('utf8') in ['QUIT','EXIT','END'] :
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channel.close()
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channel.connection.close()
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info = json.loads(stream)
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':SYS_ARGS['context']})
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logger.write({'module':'process','action':'read-partition','input':info['input']})
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df = pd.DataFrame(info['data'])
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args = info['args']
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if args['num_gpu'] > 1 :
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args['gpu'] = int(info['input']['partition']) if info['input']['partition'] < 8 else np.random.choice(np.arange(8),1).astype(int)[0]
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else:
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args['gpu'] = 0
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args['num_gpu'] = 1
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# if int(args['num_gpu']) > 1 and args['gpu'] > 0:
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# args['gpu'] = args['gpu'] + args['num_gpu'] if args['gpu'] + args['num_gpu'] < 8 else args['gpu'] #-- 8 max gpus
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args['reader'] = lambda: df
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#
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# @TODO: Fix
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# There is an inconsistency in column/columns ... fix this shit!
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#
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channel.close()
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channel.connection.close()
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args['columns'] = args['column']
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(Components()).train(**args)
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logger.write({"module":"process","action":"exit","input":info["input"]})
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pass
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if __name__ == '__main__' :
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filename = SYS_ARGS['config'] if 'config' in SYS_ARGS else 'config.json'
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f = open (filename)
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PIPELINE = json.loads(f.read())
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f.close()
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index = int(SYS_ARGS['index']) if 'index' in SYS_ARGS else 0
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args = (PIPELINE[index])
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args = dict(args,**SYS_ARGS)
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args['logs'] = args['logs'] if 'logs' in args else 'logs'
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if 'dataset' not in args :
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args['dataset'] = 'combined20191004v2_deid'
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#
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# @TODO:
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# Log what was initiated so we have context of this processing ...
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#
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if 'listen' not in SYS_ARGS :
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if 'file' in args :
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reader = lambda: pd.read_csv(args['file']) ;
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else:
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DATA = Components().get(args)
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reader = lambda: DATA
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args['reader'] = reader
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if 'generate' in SYS_ARGS :
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#
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# Let us see if we have partitions given the log folder
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content = os.listdir( os.sep.join([args['logs'],args['context']]))
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generator = Components()
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DATA = reader()
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if ''.join(content).isnumeric() :
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#
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# we have partitions we are working with
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jobs = []
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del args['reader']
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columns = DATA.columns.tolist()
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DATA = np.array_split(DATA[args['columns']],len(content))
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for id in ''.join(content) :
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if 'focus' in args and int(args['focus']) != int(id) :
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#
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# This handles failures/recoveries for whatever reason
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# If we are only interested in generating data for a given partition
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continue
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args['partition'] = id
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args['data'] = pd.DataFrame(DATA[(int(id))],columns=args['columns'])
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if int(args['num_gpu']) > 1 :
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args['gpu'] = id
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else:
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args['gpu']=0
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make = lambda _args: (Components()).generate(_args)
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job = Process(target=make,args=(args,))
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job.name = 'generator # '+str(id)
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job.start()
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jobs.append(job)
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print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
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while len(jobs)> 0 :
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jobs = [job for job in jobs if job.is_alive()]
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time.sleep(2)
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# generator.generate(args)
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else:
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generator.generate(args)
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# Components.generate(args)
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elif 'listen' in args :
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#
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# This will start a worker just in case to listen to a queue
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SYS_ARGS = dict(args) #-- things get lost in context
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if 'read' in SYS_ARGS :
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QUEUE_TYPE = 'queue.QueueReader'
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pointer = lambda qreader: qreader.read()
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else:
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QUEUE_TYPE = 'queue.QueueListener'
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pointer = lambda qlistener: qlistener.listen()
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N = int(SYS_ARGS['jobs']) if 'jobs' in SYS_ARGS else 1
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qhandlers = [factory.instance(type=QUEUE_TYPE,args={'queue':'aou.io'}) for i in np.arange(N)]
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jobs = []
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for qhandler in qhandlers :
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qhandler.callback = Components.callback
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job = Process(target=pointer,args=(qhandler,))
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job.start()
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jobs.append(job)
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#
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# let us wait for the jobs
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print (["Started ",len(jobs)," trainers"])
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while len(jobs) > 0 :
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jobs = [job for job in jobs if job.is_alive()]
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time.sleep(2)
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# pointer(qhandler)
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# qreader.read(1)
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pass
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else:
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PART_SIZE = int(args['jobs']) if 'jobs' in args else 8
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DATA = reader()
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DATA = np.array_split(DATA[args['columns']],PART_SIZE)
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jobs = []
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for index in range(0,int(args['jobs'])) :
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if 'focus' in args and int(args['focus']) != index :
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continue
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args['partition'] = index
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_df = pd.DataFrame(DATA[index],columns=args['columns'])
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args['reader'] = lambda: _df
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make = lambda _args: (Components()).train(**_args)
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job = Process(target=make,args=(args,))
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job.name = 'Trainer # ' + str(index)
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job.start()
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jobs.append(job)
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# args['gpu']
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print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
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while len(jobs)> 0 :
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jobs = [job for job in jobs if job.is_alive()]
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time.sleep(2)
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# trainer = Components()
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# trainer.train(**args)
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# Components.train(**args)
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#for args in PIPELINE :
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#args['dataset'] = 'combined20190510'
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#process = Process(target=Components.train,args=(args,))
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#process.name = args['context']
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#process.start()
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# Components.train(args)
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