Merge pull request #115310 from areddish/areddish/python-quickstart-update

Updates to match version 2.0.0 of custom vision SDK

Author: PRMerger18

articles/cognitive-services/Custom-Vision-Service/includes/quickstarts/python-tutorial-od.md

@@ -40,6 +40,7 @@ See the [create_project](https://docs.microsoft.com/python/api/azure-cognitivese
 ```Python
 from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
 from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateEntry, Region
+from msrest.authentication import ApiKeyCredentials
 
 ENDPOINT = "<your API endpoint>"
 
@@ -50,7 +51,8 @@ prediction_resource_id = "<your prediction resource id>"
 
 publish_iteration_name = "detectModel"
 
-trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT)
+credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
+trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
 # Find the object detection domain
 obj_detection_domain = next(domain for domain in trainer.get_domains() if domain.type == "ObjectDetection" and domain.name == "General")
@@ -188,10 +190,11 @@ To send an image to the prediction endpoint and retrieve the prediction, add the
 
 ```Python
 from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
 
 # Now there is a trained endpoint that can be used to make a prediction
-
-predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(ENDPOINT, prediction_credentials)
 
 # Open the sample image and get back the prediction results.
 with open(base_image_url + "images/Test/test_od_image.jpg", mode="rb") as test_data:

articles/cognitive-services/Custom-Vision-Service/includes/quickstarts/python-tutorial.md

@@ -38,6 +38,7 @@ See the [create_project](https://docs.microsoft.com/python/api/azure-cognitivese
 ```Python
 from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
 from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateEntry
+from msrest.authentication import ApiKeyCredentials
 
 ENDPOINT = "<your API endpoint>"
 
@@ -48,7 +49,8 @@ prediction_resource_id = "<your prediction resource id>"
 
 publish_iteration_name = "classifyModel"
 
-trainer = CustomVisionTrainingClient(training_key, endpoint=ENDPOINT)
+credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
+trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 
 # Create a new project
 print ("Creating project...")
@@ -122,9 +124,11 @@ To send an image to the prediction endpoint and retrieve the prediction, add the
 
 ```python
 from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
 
 # Now there is a trained endpoint that can be used to make a prediction
-predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
+prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+predictor = CustomVisionPredictionClient(ENDPOINT, prediction_credentials)
 
 with open(base_image_url + "images/Test/test_image.jpg", "rb") as image_contents:
     results = predictor.classify_image(