Additional fixes for SKLearn and Tensorflow Estimators

* Removed sagemaker_session for SKLearn
* Moved checkpoint_path into hyper parameters (https://sagemaker.readthedocs.io/en/v2.0.0.rc0/frameworks/tensorflow/upgrade_from_legacy.html)
* Added framework_version and py_version
* Update entry_point and renamed image_name to image_uri for TensorFlow

Author: brightsparc

tests/integ/test_inference_pipeline.py

@@ -43,6 +43,8 @@ def sklearn_preprocessor(sagemaker_role_arn, sagemaker_session):
                                'one_p_mnist',
                                'sklearn_mnist_preprocessor.py')
     sklearn_preprocessor = SKLearn(
+        framework_version='0.20.0',
+        py_version='py3',
         entry_point=script_path,
         role=sagemaker_role_arn,
         instance_type="ml.m5.large",
@@ -58,6 +60,8 @@ def sklearn_estimator(sagemaker_role_arn, sagemaker_session):
                                'one_p_mnist',
                                'sklearn_mnist_estimator.py')
     sklearn_estimator = SKLearn(
+        framework_version='0.20.0',
+        py_version='py3',
         entry_point=script_path,
         role=sagemaker_role_arn,
         instance_type="ml.m5.large",

tests/integ/test_training_pipeline_framework_estimator.py

@@ -33,6 +33,7 @@
 def torch_estimator(sagemaker_role_arn):
     script_path = os.path.join(DATA_DIR, "pytorch_mnist", "mnist.py")   
     return PyTorch(
+        py_version='py3',
         entry_point=script_path,
         role=sagemaker_role_arn,
         framework_version='1.2.0',
@@ -48,11 +49,12 @@ def torch_estimator(sagemaker_role_arn):
 def sklearn_estimator(sagemaker_role_arn):
     script_path = os.path.join(DATA_DIR, "sklearn_mnist", "mnist.py")   
     return SKLearn(
+        framework_version='0.20.0',
+        py_version='py3',
         entry_point=script_path,
         role=sagemaker_role_arn,
         instance_count=1,
         instance_type='ml.m5.large',
-        framework_version='0.20.0',
         hyperparameters={
             "epochs": 1
         }

tests/unit/test_pipeline.py

@@ -60,11 +60,12 @@ def sklearn_preprocessor():
     sagemaker_session.boto_region_name = 'us-east-1'
 
     sklearn_preprocessor = SKLearn(
+        framework_version='0.20.0',
+        py_version='py3',
         entry_point=script_path,
         role=SAGEMAKER_EXECUTION_ROLE,
         instance_type="ml.c4.xlarge",
         source_dir=source_dir,
-        sagemaker_session=sagemaker_session
     )
 
     sklearn_preprocessor.debugger_hook_config = DebuggerHookConfig(

tests/unit/test_sagemaker_steps.py

@@ -135,17 +135,21 @@ def tensorflow_estimator():
     s3_output_location = 's3://sagemaker/models'
     s3_source_location = 's3://sagemaker/source'
 
-    estimator = TensorFlow(entry_point='tf_train.py',
+    estimator = TensorFlow(
+        entry_point='tf_train.py',
         role=EXECUTION_ROLE,
         framework_version='1.13',
-        training_steps=1000,
-        evaluation_steps=100,
         instance_count=1,
         instance_type='ml.p2.xlarge',
         output_path=s3_output_location,
         source_dir=s3_source_location,
-        image_name=TENSORFLOW_IMAGE,
-        checkpoint_path='s3://sagemaker/models/sagemaker-tensorflow/checkpoints'
+        image_uri=TENSORFLOW_IMAGE,
+        model_dir=False,
+        hyperparameters={
+            'training_steps': 1000,
+            'evaluation_steps': 100,
+            'checkpoint_path': 's3://sagemaker/models/sagemaker-tensorflow/checkpoints',
+        }
     )
 
     estimator.debugger_hook_config = DebuggerHookConfig(