fix bug and refine smoke models for supporting CE

fluid/ce_demo/kpi.py

-# this file is copied from CE
-# it can also shared to models from CE in some other ways, such as wget.
-# it can only take effect in CE environment.

fluid/ce_demo/mnist_directory_in_ce/run.xsh

-#!/bin/bash
-
-cd $mnist_model_in_models
-
-./.run_ce.sh

fluid/ce_demo/mnist/_ce.py → fluid/mnist/_ce.py

@@ -35,11 +35,13 @@ def parse_log(log):
     train_acc\t1.2
     "
     '''
-    for line in log.split():
+    #kpi_map = {}
+    for line in log.split('\n'):
         fs = line.strip().split('\t')
-        if len(fs) == 2:
-            kpi_name = fs[0]
-            kpi_value = float(fs[1])
+        print (fs)
+        if len(fs) == 3 and fs[0] == 'kpis':
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
             yield kpi_name, kpi_value
 
 
@@ -49,7 +51,9 @@ def log_to_ce(log):
         kpi_tracker[kpi.name] = kpi
 
     for (kpi_name, kpi_value) in parse_log(log):
+        print (kpi_name, kpi_value)
         kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
 
 
 if __name__ == '__main__':

fluid/ce_demo/mnist/model.py → fluid/mnist/model.py

@@ -181,15 +181,10 @@ def run_benchmark(model, args):
             "pass=%d, train_avg_acc=%f,train_avg_loss=%f, test_avg_acc=%f, elapse=%f"
             % (pass_id, train_avg_acc, train_avg_loss, test_avg_acc,
                (pass_end - pass_start)))
-
-        with open("train_acc_factor.txt", 'a+') as f:
-            f.write("%s\n" % train_avg_acc)
-        with open("train_cost_factor.txt", 'a+') as f:
-            f.write('%s\n' % [train_avg_loss])
-        with open("test_acc_factor.txt", 'a+') as f:
-            f.write("%s\n" % test_avg_acc)
-        with open("train_duration_factor.txt", 'a+') as f:
-            f.write('%s\n' % [pass_end - pass_start])
+        print ("kpis	train_acc	%f" % train_avg_acc)
+        print ("kpis	train_cost	%f" % train_avg_loss)  
+        print ("kpis	test_acc	%f" % test_avg_acc)  
+        print ("kpis	train_duration	%f" % (pass_end - pass_start))  
 
 
 if __name__ == '__main__':