update ngraph (#2045)

* update ngraph

* fix typo

PaddleCV/image_classification/README_ngraph.md

@@ -35,9 +35,8 @@ export KMP_AFFINITY=granularity=fine,compact,1,0
 ```
 
 ## 3. How the benchmark script might be run.
-If everything built successfully, you can run command in ResNet50 nGraph session in script [run.sh](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleCV/image_classification/run.sh) to start the benchmark job locally. You will need to uncomment the `#ResNet50 nGraph` part of script.
+If everything built successfully, you can run command in ResNet50 nGraph session in script [run.sh](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/run.sh) to start the benchmark job locally. You will need to uncomment the `#ResNet50 nGraph` part of script.
 
 Above is training job using the nGraph, to run the inference job using the nGraph:
 
 Please download the pre-trained resnet50 model from [supported models](https://github.com/PaddlePaddle/models/tree/72dcc7c1a8d5de9d19fbd65b4143bd0d661eee2c/fluid/PaddleCV/image_classification#supported-models-and-performances) for inference script.
-

PaddleCV/image_classification/train.py

@@ -323,6 +323,7 @@ def train(args):
     train_py_reader.decorate_paddle_reader(train_reader)
     test_py_reader.decorate_paddle_reader(test_reader)
 
+    # use_ngraph is for CPU only, please refer to README_ngraph.md for details
     use_ngraph = os.getenv('FLAGS_use_ngraph')
     if not use_ngraph:
         train_exe = fluid.ParallelExecutor(