Merging changes from upstream master. Targeting v2+

Author: brightsparc

src/stepfunctions/steps/sagemaker.py

@@ -67,7 +67,7 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
         else:
             parameters = training_config(estimator=estimator, inputs=data, mini_batch_size=mini_batch_size)
 
-        if estimator.debugger_hook_config != None:
+        if estimator.debugger_hook_config != None and estimator.debugger_hook_config is not False:
             parameters['DebugHookConfig'] = estimator.debugger_hook_config._to_request_dict()
 
         if estimator.rules != None:

tests/unit/test_sagemaker_steps.py

@@ -111,6 +111,34 @@ def pca_estimator_with_debug_hook():
 
     return pca
 
+
+@pytest.fixture
+def pca_estimator_with_falsy_debug_hook():
+    s3_output_location = 's3://sagemaker/models'
+
+    pca = sagemaker.estimator.Estimator(
+        PCA_IMAGE,
+        role=EXECUTION_ROLE,
+        train_instance_count=1,
+        train_instance_type='ml.c4.xlarge',
+        output_path=s3_output_location,
+        debugger_hook_config = False
+    )
+
+    pca.set_hyperparameters(
+        feature_dim=50000,
+        num_components=10,
+        subtract_mean=True,
+        algorithm_mode='randomized',
+        mini_batch_size=200
+    )
+
+    pca.sagemaker_session = MagicMock()
+    pca.sagemaker_session.boto_region_name = 'us-east-1'
+    pca.sagemaker_session._default_bucket = 'sagemaker'
+
+    return pca
+
 @pytest.fixture
 def pca_model():
     model_data = 's3://sagemaker/models/pca.tar.gz'
@@ -287,6 +315,43 @@ def test_training_step_creation_with_debug_hook(pca_estimator_with_debug_hook):
         'End': True
     }
 
+@patch('botocore.client.BaseClient._make_api_call', new=mock_boto_api_call)
+def test_training_step_creation_with_falsy_debug_hook(pca_estimator_with_falsy_debug_hook):
+    step = TrainingStep('Training',
+        estimator=pca_estimator_with_falsy_debug_hook,
+        job_name='TrainingJob')
+    assert step.to_dict() == {
+        'Type': 'Task',
+        'Parameters': {
+            'AlgorithmSpecification': {
+                'TrainingImage': PCA_IMAGE,
+                'TrainingInputMode': 'File'
+            },
+            'OutputDataConfig': {
+                'S3OutputPath': 's3://sagemaker/models'
+            },
+            'StoppingCondition': {
+                'MaxRuntimeInSeconds': 86400
+            },
+            'ResourceConfig': {
+                'InstanceCount': 1,
+                'InstanceType': 'ml.c4.xlarge',
+                'VolumeSizeInGB': 30
+            },
+            'RoleArn': EXECUTION_ROLE,
+            'HyperParameters': {
+                'feature_dim': '50000',
+                'num_components': '10',
+                'subtract_mean': 'True',
+                'algorithm_mode': 'randomized',
+                'mini_batch_size': '200'
+            },
+            'TrainingJobName': 'TrainingJob'
+        },
+        'Resource': 'arn:aws:states:::sagemaker:createTrainingJob.sync',
+        'End': True
+    }
+
 @patch('botocore.client.BaseClient._make_api_call', new=mock_boto_api_call)
 def test_training_step_creation_with_model(pca_estimator):
     training_step = TrainingStep('Training', estimator=pca_estimator, job_name='TrainingJob')