--- a
+++ b/modules/Admin/Admin.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+import os
+import sys
+#import django
+sys.path.insert(0, "/home/skjena/cancerTherapy/modules")
+#os.environ.setdefault("DJANGO_SETTINGS_MODULE", "RESTAPI.mutationDnnWeb.mutationDnnWeb.settings")
+#os.environ['DJANGO_SETTINGS_MODULE'] = 'RESTAPI.mutationDnnWeb.mutationDnnWeb.settings'
+from multiprocessing import Process, Queue
+#sys.path.insert(0, "/home/skjena/cancerTherapy/modules/RESTAPI")
+from RESTAPI.RAPIManager import RAPIManager
+#sys.path.insert(0, "/home/skjena/cancerTherapy/modules")
+from CrossValidation.CVManager import CVManager
+from DataDispatcher.DDManager import DDManager
+from IndexedDB.IDBManager import IDBManager
+from RawDB.RDBManager import RDBManager
+from NeuralNet.management.NNManager import NNManager
+from StatManip.SMManager import SMManager
+from VectorSpace.mds.mds import MDS
+import time
+from Status.Status import Status
+
+class Admin:
+    def __init__(self):
+        self.status = Status("Admin")
+        self.numFolds = 10
+        self.restApi = RAPIManager()
+        while self.restApi.djangostatus == False:
+            self.restApi.isRunning()
+        if self.restApi.networkstate == False:
+            self.restApi.populate()
+
+        self.rawDb = RDBManager("fm_mutations_independent")
+        self.statmanip = SMManager()
+        self.mds = MDS(self.rawDb.dataframe,self.restApi.network.state.noise)
+        self.statmanip.writeToFile(self.statmanip.adjoin(self.mds.scale(2),
+            self.statmanip.skewLabels(self.rawDb.dataframe, "0")),
+                self.rawDb.scaledpath)
+        self.indexedDb = IDBManager(self.rawDb.scaledpath, self.numFolds)
+        #self.foldList is a list of 10 nodes where each node holds a path to a fold.csv file.
+        self.foldList = self.indexedDb.createFolds()
+
+        #These queues will be used by multiple processes to produce/use different train and test paths
+        self.trainPaths = Queue()
+        self.testPaths = Queue()
+        self.crossValidate = CVManager()
+        #Create process to run CrossValidation on all folds in self.foldList
+        self.crossValidate = Process(target=self.crossValidate.CrossValidate, args=(self.foldList,self.numFolds,self.testPaths,self.trainPaths))
+        self.crossValidate.start()
+
+        self.DD = DDManager()
+        #self.dataDispatcherQ will hold data which DDManager deemed ready to be passed to NN (train path first then test path)
+        self.dataDispatcherQ = Queue()
+        for j in range(0, self.numFolds):
+            self.dispatchData = Process(target=self.DD.DispatchData, args=(self.trainPaths,self.testPaths,self.dataDispatcherQ))
+            self.dispatchData.start()
+
+        #self.neuralNets Queue will hold all NNManager instances
+        self.neuralNets = Queue()
+        for i in range(0, self.numFolds):
+            while (self.dataDispatcherQ.empty()==True):
+                #if DD does not have data ready to send to NN, wait until it does.
+                time.sleep(1)
+            train = self.dataDispatcherQ.get()
+            test = self.dataDispatcherQ.get()
+            self.NNProcess = NNManager(train,test,self.restApi.network,self.restApi.network.arguments.problemType)
+            self.neuralNets.put(self.NNProcess)
+
+        for j in range(0, self.numFolds):
+            NeuralNetInstance = self.neuralNets.get()
+            self.NNProcess = Process(target=NeuralNetInstance.modelZero)
+            self.NNProcess.start()
+
+
+    def initialize(self):
+        self.status.message(1,"initialize()")
+
+        self.status.message(0,"initialize()")
+        return
+
+    def build(self):
+        self.status.message(1, "build(self)")
+