bug fixes

data/gan.py

@@ -58,7 +58,14 @@ class GNet :
                 self.layers.normalize = self.normalize
                 self.logs = {}
 
-                self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
+                # self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
+                self.GPU_CHIPS = None if 'gpu' not in args else args['gpu']
+                if self.GPU_CHIPS is None:
+                        self.GPU_CHIPS = [0]
+                        if 'CUDA_VISIBLE_DEVICES' in os.environ :
+                                os.environ.pop('CUDA_VISIBLE_DEVICES')
+                self.NUM_GPUS = len(self.GPU_CHIPS)
+                
                 self.PARTITION = args['partition']
                 # if self.NUM_GPUS > 1 :
                 #     os.environ['CUDA_VISIBLE_DEVICES'] = "4"
@@ -150,6 +157,7 @@ class GNet :
                         "D_STRUCTURE":self.D_STRUCTURE,
                         "G_STRUCTURE":self.G_STRUCTURE,
                         "NUM_GPUS":self.NUM_GPUS,
+                        "GPU_CHIPS":self.GPU_CHIPS,
                         "NUM_LABELS":self.NUM_LABELS,
                         "MAX_EPOCHS":self.MAX_EPOCHS,
                         "ROW_COUNT":self.ROW_COUNT
@@ -443,7 +451,7 @@ class Train (GNet):
                         #       - abstract hardware specification
                         #       - determine if the GPU/CPU are busy
                         #
-                        for i in range(self.NUM_GPUS):
+                        for i in self.GPU_CHIPS : #range(self.NUM_GPUS):
                                 with tf.device('/gpu:%d' % i):
                                         with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
                                                 if self._LABEL is not None :

data/maker/__init__.py

@@ -90,16 +90,16 @@ def train (**_args):
     #
     # Let us prepare the data by calling the utility function
     #
-    if 'file' in _args :
-        #
-        # We are reading data from a file
-        _args['data'] = pd.read_csv(_args['file'])
-    else:
-        #
-        # data will be read from elsewhere (a data-store)...
-        pass        
+    # if 'file' in _args :
+    #     #
+    #     # We are reading data from a file
+    #     _args['data'] = pd.read_csv(_args['file'])
+    # else:
+    #     #
+    #     # data will be read from elsewhere (a data-store)...
+    #     pass        
     # if 'ignore' in _args and 'columns' in _args['ignore']:
-        
+    
     _inputhandler = prepare.Input(**_args)
     values,_matrix = _inputhandler.convert()
     args  = {"real":_matrix,"context":_args['context']}
@@ -107,6 +107,7 @@ def train (**_args):
     if 'store' in _args :
         #
         # This 
+        
         args['store'] = copy.deepcopy(_args['store']['logs'])
         args['store']['args']['doc'] = _args['context']
         logger = factory.instance(**args['store'])

data/maker/prepare/__init__.py

@@ -13,7 +13,7 @@ import transport
 import json
 import pandas as pd
 import numpy as np
-import cupy as cp
+# import cupy as cp
 import sys
 import os
 # from multiprocessing import Process, Queue
@@ -62,7 +62,7 @@ class Input :
         self._schema = _args['schema'] if 'schema' in _args else {}
         self.df = _args['data']
         if 'sql' not in _args :
-            # self._initdata(**_args)   
+            self._initdata(**_args)   
             # 
             pass  
         else:
@@ -70,12 +70,12 @@ class Input :
         self._map = {} if 'map' not in _args else _args['map']
         # self._metadf = pd.DataFrame(self.df[self._columns].dtypes.values.astype(str)).T  #,self._columns]
         # self._metadf.columns = self._columns
-        if 'gpu' in _args and 'GPU' in os.environ:
+        # if 'gpu' in _args and 'GPU' in os.environ:
             
-            np = cp
-            index = int(_args['gpu'])
-            np.cuda.Device(index).use()            
-            print(['..:: GPU ',index])
+        #     np = cp
+        #     index = int(_args['gpu'])
+        #     np.cuda.Device(index).use()            
+        #     print(['..:: GPU ',index])
         
     def _initsql(self,**_args):
         """
@@ -107,6 +107,8 @@ class Input :
         row_count = self.df.shape[0]
         cols    = None if 'columns' not in _args else _args['columns']
         self.columns = self.df.columns.tolist()
+        self._io = []
+
         if 'columns' in _args :
             self._columns = _args['columns']
         else:
@@ -115,6 +117,8 @@ class Input :
             _df = pd.DataFrame(self.df.apply(lambda col: col.dropna().unique().size )).T
             MIN_SPACE_SIZE = 2
             self._columns  = cols if cols else _df.apply(lambda col:None if col[0] == row_count or col[0] < MIN_SPACE_SIZE else col.name).dropna().tolist()
+            self._io = _df.to_dict(orient='records')
+            
     def _initdata(self,**_args):
         """
         This function will initialize the class with a data-frame and columns of interest (if any)

setup.py

@@ -4,7 +4,7 @@ import sys
 
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
-args = {"name":"data-maker","version":"1.4.0","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.4.1","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
         "packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
 args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo']
 args['url'] = 'https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git'