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(