diff --git a/data/gan.py b/data/gan.py
index e349018..3391b78 100644
--- a/data/gan.py
+++ b/data/gan.py
@@ -14,6 +14,7 @@ import sys
 from data.params import SYS_ARGS
 from data.bridge import Binary
 import json
+import pickle
 
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 os.environ['CUDA_VISIBLE_DEVICES'] = "0"
@@ -38,7 +39,7 @@ class GNet :
         self.layers.normalize = self.normalize
 
 
-        self.NUM_GPUS = 1
+        self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
        
 
         self.X_SPACE_SIZE = args['real'].shape[1] if 'real' in args else 854
@@ -64,8 +65,8 @@ class GNet :
 
         self.get = void()
         self.get.variables = self._variable_on_cpu
-
         self.get.suffix = lambda : "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
+        self.logger = args['logger'] if 'logger' in args and args['logger'] else None
         self.init_logs(**args)
 
     def init_logs(self,**args):
@@ -98,7 +99,7 @@ class GNet :
                 
             
     def log_meta(self,**args) :
-        object = {
+        _object = {
             'CONTEXT':self.CONTEXT,
             'ATTRIBUTES':self.ATTRIBUTES,
             'BATCHSIZE_PER_GPU':self.BATCHSIZE_PER_GPU,
@@ -120,7 +121,8 @@ class GNet :
         _name = os.sep.join([self.out_dir,'meta-'+suffix])
         
         f = open(_name+'.json','w')
-        f.write(json.dumps(object))
+        f.write(json.dumps(_object))
+        return _object
     def mkdir (self,path):
         if not os.path.exists(path) :
             os.mkdir(path)        
@@ -295,7 +297,7 @@ class Train (GNet):
         self.column = args['column']
         # print ([" *** ",self.BATCHSIZE_PER_GPU])
         
-        self.log_meta()
+        self.meta = self.log_meta()
     def load_meta(self, column):
         """
         This function will delegate the calls to load meta data to it's dependents
@@ -393,7 +395,7 @@ class Train (GNet):
             # saver = tf.train.Saver()
             saver   = tf.compat.v1.train.Saver()
             init    = tf.global_variables_initializer()
-
+            logs = []
             with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
                 sess.run(init)
                 sess.run(iterator_d.initializer,
@@ -415,6 +417,10 @@ class Train (GNet):
 
                     format_str = 'epoch: %d, w_distance = %f (%.1f)'
                     print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
+                    # print (dir (w_distance))
+
+                    logs.append({"epoch":epoch,"distance":-w_sum/(self.STEPS_PER_EPOCH*2) })
+
                     if epoch % self.MAX_EPOCHS == 0:
                         # suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
                         suffix = self.get.suffix()
@@ -423,6 +429,10 @@ class Train (GNet):
                         saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
                         #
                         #
+                        if self.logger :
+                            row = {"logs":logs} #,"model":pickle.dump(sess)}
+                            
+                            self.logger.write(row=row)
 
 class Predict(GNet):
     """
diff --git a/data/maker/__init__.py b/data/maker/__init__.py
index 7a441f8..075bfd3 100644
--- a/data/maker/__init__.py
+++ b/data/maker/__init__.py
@@ -11,7 +11,7 @@ This package is designed to generate synthetic data from a dataset from an origi
 import pandas as pd
 import numpy as np
 from data import gan
-
+from transport import factory
 def train (**args) :
     """
     This function is intended to train the GAN in order to learn about the distribution of the features
@@ -21,17 +21,24 @@ def train (**args) :
     :data       data-frame to be synthesized
     :context    label of what we are synthesizing
     """
-    column = args['column']
+    column      = args['column']
     
-    column_id  = args['id']
-    df  = args['data']
-    logs    = args['logs']
-    real    = pd.get_dummies(df[column]).astype(np.float32).values
+    column_id   = args['id']
+    df          = args['data']
+    logs        = args['logs']
+    real        = pd.get_dummies(df[column]).astype(np.float32).values
+    labels      = pd.get_dummies(df[column_id]).astype(np.float32).values
     
-    labels  = pd.get_dummies(df[column_id]).astype(np.float32).values
-    max_epochs = 10
-    context = args['context']
-    trainer = gan.Train(context=context,max_epochs=max_epochs,real=real,label=labels,column=column,column_id=column_id)
+    max_epochs  = 10 if 'max_epochs' not in args else args['max_epochs']
+    context     = args['context']
+    if 'store' in args :
+        args['store']['args']['doc'] = context
+        logger = factory.instance(**args['store'])
+        
+    else:
+        logger = None
+        
+    trainer     = gan.Train(context=context,max_epochs=max_epochs,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
     return trainer.apply()
 
 def generate(**args):