gpu indexing

data/gan.py

@@ -64,7 +64,9 @@ class GNet :
                         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.NUM_GPUS = 0
+                else:
+                        self.NUM_GPUS = len(self.GPU_CHIPS)
                 
                 self.PARTITION = args['partition']
                 # if self.NUM_GPUS > 1 :

data/maker/__init__.py

@@ -86,18 +86,6 @@ def train (**_args):
     :params sql
     :params store
     """
-    #
-    # 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 'ignore' in _args and 'columns' in _args['ignore']:
     
     _inputhandler = prepare.Input(**_args)
     values,_matrix = _inputhandler.convert()
@@ -125,6 +113,8 @@ def train (**_args):
     args['matrix_size'] = _matrix.shape[0]
     args['batch_size'] = 2000
     args['partition'] = 0 if 'partition' not in _args else _args['partition']
+    if 'gpu' in _args :
+        args['gpu'] = _args['gpu']
     # os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
     
     trainer = gan.Train(**args)   
@@ -137,50 +127,7 @@ def train (**_args):
 
     trainer.apply()
     pass    
-def _train (**args) :
-    """
-    This function is intended to train the GAN in order to learn about the distribution of the features
-    :column     columns that need to be synthesized (discrete)
-    :logs       where the output of the (location on disk)
-    :id         identifier of the dataset
-    :data       data-frame to be synthesized
-    :context    label of what we are synthesizing
-    """
-    column      = args['column'] if (isinstance(args['column'],list)) else [args['column']]
-    # CONTINUOUS  = args['continuous'] if 'continuous' in args else []
-    # column_id   = args['id']
-    df          = args['data'] if not isinstance(args['data'],str) else pd.read_csv(args['data'])
-    df.columns = [name.lower() for name in df.columns]
-    #
-    # @TODO:
-    # Consider sequential training of sub population for extremely large datasets
-    #
-    
-    #
-    # If we have several columns we will proceed one at a time (it could be done in separate threads)
-    # @TODO : Consider performing this task on several threads/GPUs simulataneously
-    # 
-    for col in column : 
-        msize = args['matrix_size'] if 'matrix_size' in args else -1        
-        args['real'] = (Binary()).apply(df[col],msize)
-
-        context     = args['context']
-        if 'store' in args :
-            args['store']['args']['doc'] = context
-            logger = factory.instance(**args['store'])
-            args['logger'] = logger
-            info = {"rows":args['real'].shape[0],"cols":args['real'].shape[1],"name":col,"partition":args['partition']}
-            logger.write({"module":"gan-train","action":"data-prep","input":info})
-            
-        else:
-            logger = None
-        args['column']  = col
-        args['context'] = col
 
-        #
-        # If the s
-        trainer = gan.Train(**args)        
-        trainer.apply()
 def get(**args):
     """
     This function will restore a checkpoint from a persistant storage on to disk
@@ -214,6 +161,8 @@ def generate(**_args):
     _inputhandler = prepare.Input(**_args)
     values,_matrix = _inputhandler.convert()    
     args['values'] = np.array(values)
+    if 'gpu' in _args :
+        args['gpu'] = _args['gpu']
        
     handler     = gan.Predict (**args)
     handler.load_meta(None)

pipeline.py

@@ -87,6 +87,8 @@ class Components :
 			_index = str(gpu[0])
 			os.environ['CUDA_VISIBLE_DEVICES'] = _index
 			return gpu
+		else :
+			return None
 	def train(self,**args):
 		"""
 		This function will perform training on the basis of a given pointer that reads data