diff --git a/data/gan.py b/data/gan.py
index 1df26a3..898d4ea 100644
--- a/data/gan.py
+++ b/data/gan.py
@@ -581,7 +581,6 @@ class Predict(GNet):
                                 
                                 df =   pd.DataFrame(np.round(f)).astype(np.int32)
                                 
-                                
                                 p = 0 not in df.sum(axis=1).values
                                 x = df.sum(axis=1).values
                                 
@@ -599,7 +598,8 @@ class Predict(GNet):
                         #
                         # In case we are dealing with actual values like diagnosis codes we can perform 
                         #
-                        _index = [found.index(item) for item in found if item.shape[1] == len(self.values)]
+                        N = len(found)
+                        _index = [i for i in range(0,N) if found[i].shape[1] == len(self.values)]
                         if not _index :
                                 INDEX = np.random.choice(np.arange(len(found)),1)[0]
                                 INDEX = ratio.index(np.max(ratio))
diff --git a/data/maker/__init__.py b/data/maker/__init__.py
index 080939c..f4bce16 100644
--- a/data/maker/__init__.py
+++ b/data/maker/__init__.py
@@ -37,11 +37,14 @@ class ContinuousToDiscrete :
                     index = BOUNDS.index(row)
                     x_[index]  = 1
                     break
-
+        #
+        # for items in BOUNDS :
+        #   index = BOUNDS.index(items)
         return _matrix
     
     @staticmethod
     def bounds(x,n):
+        # return np.array_split(x,n)
         return list(pd.cut(np.array( np.round(x,ContinuousToDiscrete.ROUND_UP) ),n).categories)
         
 
@@ -175,7 +178,8 @@ def generate(**args):
         handler.load_meta(col)
         r           =  handler.apply()                
         BIN_SIZE    = 4 if 'bin_size' not in args else int(args['bin_size'])
-        _df[col]    = ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if 'float' in df[col].dtypes.name or col in CONTINUOUS else r[col]
+        _df[col]    = ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if col in CONTINUOUS else r[col]
+        # _df[col]    = r[col]
         #
         # @TODO: log basic stats about the synthetic attribute
         #
diff --git a/pipeline.py b/pipeline.py
index 9d9c097..6234c26 100644
--- a/pipeline.py
+++ b/pipeline.py
@@ -50,11 +50,12 @@ class Components :
 		"""
 		#
 		# @TODO: we need to log something here about the parameters being passed
-		pointer  = args['reader'] if 'reader' in args else lambda: Components.get(**args)
-		df = pointer()
-		if df.shape[0] == 0 :
-			print ("CAN NOT TRAIN EMPTY DATASET ")
-			return 
+		# pointer  = args['reader'] if 'reader' in args else lambda: Components.get(**args)
+		df = args['reader']()
+		
+		# if df.shape[0] == 0 :
+		# 	print ("CAN NOT TRAIN EMPTY DATASET ")
+		# 	return 
 		#
 		# Now we can parse the arguments and submit the entire thing to training
 		#
@@ -113,18 +114,29 @@ class Components :
 			
 			pass
 		else:
+			print ('.....')
 			partition = args['partition'] if 'partition' in args else ''
-			log_folder = os.sep.join([log_folder,args['context'],partition])
+			log_folder = os.sep.join([log_folder,args['context'],str(partition)])
 			_args = {"batch_size":10000,"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'
+
+			#
+			# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
+			#
+			if int(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
+			os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) 
+			
 			
 			_args['data'] = df
 			#
 			# @log :
 			#	Logging information about the training process for this partition (or not)
 			#
+			
 			info = {"rows":df.shape[0],"cols":df.shape[1], "partition":int(partition),"logs":_args['logs']}
 			
 			logger.write({"module":"train","action":"train","input":info})
@@ -291,7 +303,7 @@ if __name__ == '__main__' :
 		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()
@@ -310,13 +322,13 @@ if __name__ == '__main__' :
 					args['gpu'] = id
 				else:
 					args['gpu']=0
-				
+				make = lambda _args: (Components()).generate(_args)
 				job = Process(target=make,args=(args,))
 				job.name = 'generator # '+str(id)
 				job.start()
 				jobs.append(job)
 
-			print (["Started ",len(jobs),"generator"+"s" if len(jobs)>1 else "" ])
+			print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
 			while len(jobs)> 0 :
 				jobs = [job for job in jobs if job.is_alive()]
 				time.sleep(2)
@@ -358,9 +370,31 @@ if __name__ == '__main__' :
 		# qreader.read(1)
 		pass
 	else:
+		PART_SIZE = int(args['jobs']) if 'jobs' in args else 8
+		DATA = reader()
+		DATA  = np.array_split(DATA[args['columns']],PART_SIZE)
+		jobs = []
+		for index in range(0,int(args['jobs'])) :
+			if 'focus' in args and int(args['focus']) != index :
+				continue
+			args['partition'] = index
+			_df = pd.DataFrame(DATA[index],columns=args['columns'])
+			args['reader'] = lambda: _df
+			make = lambda _args: (Components()).train(**_args)
+			job = Process(target=make,args=(args,))
+			job.name = 'Trainer # ' + str(index)
+			job.start()
+			jobs.append(job)
+			# args['gpu']
+		print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
+		while len(jobs)> 0 :
+			jobs = [job for job in jobs if job.is_alive()]
+			time.sleep(2)
+
+		# trainer = Components()
+		# trainer.train(**args)
+		
 		
-		trainer = Components()
-		trainer.train(**args)
 		# Components.train(**args)
 #for args in PIPELINE :
 	#args['dataset'] = 'combined20190510'