Merge branch 'develop' of upstream into sequence_tagging_for_ner_ce

fluid/language_model/train.py

@@ -145,7 +145,7 @@ def train(train_reader,
         if pass_idx == pass_num - 1 and args.enable_ce:
             #Note: The following logs are special for CE monitoring.
             #Other situations do not need to care about these logs.
-            gpu_num = get_cards()
+            gpu_num = get_cards(args.enable_ce)
             if gpu_num == 1:
                 print("kpis	imikolov_20_pass_duration	%s" %
                       (total_time / epoch_idx))

fluid/text_classification/infer.py

@@ -4,8 +4,8 @@ import unittest
 import contextlib
 import numpy as np
 
+import paddle
 import paddle.fluid as fluid
-import paddle.v2 as paddle
 
 import utils
 

fluid/text_classification/train.py

@@ -4,8 +4,8 @@ import time
 import unittest
 import contextlib
 
+import paddle
 import paddle.fluid as fluid
-import paddle.v2 as paddle
 
 import utils
 from nets import bow_net
@@ -55,7 +55,7 @@ def train(train_reader,
     feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
 
     # For internal continuous evaluation
-    if 'CE_MODE_X' in os.environ:
+    if "CE_MODE_X" in os.environ:
         fluid.default_startup_program().random_seed = 110
     exe.run(fluid.default_startup_program())
     for pass_id in xrange(pass_num):
@@ -80,7 +80,7 @@ def train(train_reader,
 
         pass_end = time.time()
         # For internal continuous evaluation
-        if 'CE_MODE_X' in os.environ:
+        if "CE_MODE_X" in os.environ:
             print("kpis	train_acc	%f" % avg_acc)
             print("kpis	train_cost	%f" % avg_cost)
             print("kpis	train_duration	%f" % (pass_end - pass_start))

fluid/text_classification/utils.py

@@ -65,7 +65,7 @@ def prepare_data(data_type="imdb",
             raise RuntimeError("No such dataset")
 
     if data_type == "imdb":
-        if 'CE_MODE_X' in os.environ:
+        if "CE_MODE_X" in os.environ:
             train_reader = paddle.batch(
                 paddle.dataset.imdb.train(word_dict), batch_size=batch_size)