diff --git a/pipeline.py b/pipeline.py
index 15d562a..59745a9 100644
--- a/pipeline.py
+++ b/pipeline.py
@@ -143,29 +143,36 @@ class Components :
 		
 		_args = {"batch_size":2000,"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'])
-		_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1 
-		os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
+		# _args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1 
+		
+		if args['num_gpu'] > 1 :
+			_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)[0]
+		else:
+			_args['gpu']  = 0
+		_args['num_gpu'] 	= 1
+		
 		_args['no_value']= args['no_value']
 		# MAX_ROWS = args['max_rows']  	if 'max_rows' in args else 0
 		PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
 		
 		# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
 		# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
-		reader = args['reader']
-		df = reader()
+		# reader = args['reader']
+		# df = reader()
+		df = args['reader']() if 'reader' in args else args['data']
 		# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
-		if partition != '' :
-			columns = args['columns']
-			df = np.array_split(df[columns].values,PART_SIZE)
-			df = pd.DataFrame(df[ int (partition) ],columns = columns)
-			info = {"parition":int(partition),"rows":df.shape[0],"cols":df.shape[0],"part_size":PART_SIZE}
-			logger.write({"module":"generate","action":"partition","input":info})
-
+		# if partition != '' :
+		# 	columns = args['columns']
+		# 	df = np.array_split(df[columns].values,PART_SIZE)
+		# 	df = pd.DataFrame(df[ int (partition) ],columns = columns)
+		info = {"parition":int(partition),"rows":df.shape[0],"cols":df.shape[0],"part_size":PART_SIZE}
+		logger.write({"module":"generate","action":"partition","input":info})
+		
 		_args['data'] = df
 			# _args['data'] = reader()
 		#_args['data'] = _args['data'].astype(object)
-		_args['num_gpu'] = 1
-		_args['gpu'] = partition
+		# _args['num_gpu'] = 1
+		
 		_dc = data.maker.generate(**_args) 
 		#
 		# We need to post the generate the data in order to :
@@ -226,7 +233,7 @@ class Components :
 		df = pd.DataFrame(info['data'])
 		args = info['args']
 		if args['num_gpu'] > 1 :
-			args['gpu'] = int(info['info']['partition']) if info['input']['partition'] == 0 else info['input']['partition'] + 2
+			args['gpu'] = int(info['input']['partition']) if info['input']['partition'] < 8  else np.random.choice(np.arange(8),1).astype(int)[0]
 		
 		else:
 			args['gpu'] 	= 0
@@ -269,8 +276,8 @@ if __name__ == '__main__' :
 		if 'file' in args :
 			reader = lambda: pd.read_csv(args['file']) ;
 		else:
-			_df = Components().get(args)
-			reader = lambda: _df
+			DATA = Components().get(args)
+			reader = lambda: DATA
 		args['reader'] = reader
 	
 	if 'generate' in SYS_ARGS :
@@ -279,15 +286,23 @@ if __name__ == '__main__' :
 		
 		content = os.listdir( os.sep.join([args['logs'],args['context']]))
 		generator = Components()
-		
+		DATA = reader()
 		if ''.join(content).isnumeric() :
 			#
 			# we have partitions we are working with
 			make = lambda _args: (Components()).generate(_args)
 			jobs = []
-			
+			del args['reader']
+			columns = DATA.columns.tolist()
+			DATA  = np.array_split(DATA[args['columns']],len(content))
 			for id in ''.join(content) :
 				args['partition'] = id
+				args['data'] = pd.DataFrame(DATA[(int(id))],columns=args['columns'])
+				if args['num_gpu'] > 0 :
+					args['gpu'] = id
+				else:
+					args['gpu']=0
+				args['num_gpu']=1
 				job = Process(target=make,args=(args,))
 				job.name = 'generator # '+str(id)
 				job.start()