breaking: rename image to image_uri (#1670)

Author: laurenyu

src/sagemaker/amazon/amazon_estimator.py

@@ -156,7 +156,7 @@ class constructor
                     init_params[attribute] = init_params["hyperparameters"][value.name]
 
         del init_params["hyperparameters"]
-        del init_params["image"]
+        del init_params["image_uri"]
         return init_params
 
     def prepare_workflow_for_training(self, records=None, mini_batch_size=None, job_name=None):

src/sagemaker/amazon/factorization_machines.py

@@ -312,9 +312,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
-        image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
+        image_uri = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(FactorizationMachinesModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=FactorizationMachinesPredictor,

src/sagemaker/amazon/ipinsights.py

@@ -218,12 +218,12 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(IPInsights.repo_name, IPInsights.repo_version)
-        image = "{}/{}".format(
+        image_uri = "{}/{}".format(
             registry(sagemaker_session.boto_session.region_name, IPInsights.repo_name), repo
         )
 
         super(IPInsightsModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=IPInsightsPredictor,

src/sagemaker/amazon/kmeans.py

@@ -243,9 +243,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
-        image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
+        image_uri = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(KMeansModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=KMeansPredictor,

src/sagemaker/amazon/lda.py

@@ -215,11 +215,11 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(LDA.repo_name, LDA.repo_version)
-        image = "{}/{}".format(
+        image_uri = "{}/{}".format(
             registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
         )
         super(LDAModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=LDAPredictor,

src/sagemaker/amazon/linear_learner.py

@@ -476,9 +476,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
-        image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
+        image_uri = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(LinearLearnerModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=LinearLearnerPredictor,

src/sagemaker/amazon/ntm.py

@@ -245,11 +245,11 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(NTM.repo_name, NTM.repo_version)
-        image = "{}/{}".format(
+        image_uri = "{}/{}".format(
             registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
         )
         super(NTMModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=NTMPredictor,

src/sagemaker/amazon/object2vec.py

@@ -351,11 +351,11 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(Object2Vec.repo_name, Object2Vec.repo_version)
-        image = "{}/{}".format(
+        image_uri = "{}/{}".format(
             registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
         )
         super(Object2VecModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=Predictor,

src/sagemaker/amazon/pca.py

@@ -227,9 +227,9 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
-        image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
+        image_uri = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(PCAModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=PCAPredictor,

src/sagemaker/amazon/randomcutforest.py

@@ -206,11 +206,11 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         """
         sagemaker_session = sagemaker_session or Session()
         repo = "{}:{}".format(RandomCutForest.repo_name, RandomCutForest.repo_version)
-        image = "{}/{}".format(
+        image_uri = "{}/{}".format(
             registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
         )
         super(RandomCutForestModel, self).__init__(
-            image,
+            image_uri,
             model_data,
             role,
             predictor_cls=RandomCutForestPredictor,