Minor fixes to comments and logging

Author: bpawlik

sagemaker_neo_compilation_jobs/gluoncv_yolo_darknet/gluoncv_yolo_darknet_neo.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "This is an end-to-end example of GluonCV YoloV3 model training inside of Amazon SageMaker notebook and then compile the trained model using Neo runtime. In this demo, we will demonstrate how to train and to host a darknet53 model on the [Pascal VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) using the YoloV3 algorithm. We will also demonstrate how to optimize this trained model using Neo.\n",
     "\n",
-    "***This is notebook is for demostration purpose only. Please fine tune the training parameters based on your own dataset.***"
+    "***This notebook is for demonstration purpose only. Please fine tune the training parameters based on your own dataset.***"
    ]
   },
   {

sagemaker_neo_compilation_jobs/gluoncv_yolo_darknet/train_yolo.py

@@ -434,9 +434,9 @@ def model_fn(model_dir):
     """
     logging.info('Invoking user-defined model_fn')
 
-    import neomxnet
+    import neomxnet # noqa: F401
 
-    #check if GPUs area available
+    #select GPU context
     ctx = mx.gpu()
 
     net = gluon.SymbolBlock.imports(
@@ -457,6 +457,8 @@ def transform_fn(net, data, content_type, output_content_type):
     """
     Transform incoming requests.
     """
+    logging.info('Invoking user-defined transform_fn')
+
     import gluoncv as gcv
 
     #decode json string into numpy array
@@ -465,7 +467,7 @@ def transform_fn(net, data, content_type, output_content_type):
     #preprocess image   
     x, image = gcv.data.transforms.presets.yolo.transform_test(mx.nd.array(data), 320)
 
-    #check if GPUs area available
+    #select GPU context
     ctx = mx.gpu()
 
     #load image onto right context