Update docker images and minor refactoring

components/aws/sagemaker/batch_transform/component.yaml

@@ -71,7 +71,7 @@ outputs:
   - {name: output_location,    description: 'S3 URI of the transform job results.'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       batch_transform.py,

components/aws/sagemaker/common/_utils.py

@@ -80,7 +80,7 @@ def create_training_job_request(args):
         request['AlgorithmSpecification']['TrainingImage'] = args['image']
         request['AlgorithmSpecification'].pop('AlgorithmName')
     else:
-        # TODO: determine if users can make custom algorithm resources that have the same name as built-in algorithm names
+        # TODO: Adjust this implementation to account for custom algorithm resources names that are the same as built-in algorithm names
         algo_name = args['algorithm_name'].lower().strip()
         if algo_name in built_in_algos.keys():
             request['AlgorithmSpecification']['TrainingImage'] = get_image_uri(args['region'], built_in_algos[algo_name])
@@ -114,7 +114,7 @@ def create_training_job_request(args):
         request['InputDataConfig'] = args['channels']
         # Max number of input channels/data locations is 20, but currently only 8 data location parameters are exposed separately.
         #   Source: Input data configuration description in the SageMaker create training job form
-        for i in range(1, len(args['channels'] + 1)):
+        for i in range(1, len(args['channels']) + 1):
             if args['data_location_' + str(i)]:
                 request['InputDataConfig'][i-1]['DataSource']['S3DataSource']['S3Uri'] = args['data_location_' + str(i)]
     else:
@@ -514,7 +514,7 @@ def create_hyperparameter_tuning_job_request(args):
         request['TrainingJobDefinition']['InputDataConfig'] = args['channels']
         # Max number of input channels/data locations is 20, but currently only 8 data location parameters are exposed separately.
         #   Source: Input data configuration description in the SageMaker create hyperparameter tuning job form
-        for i in range(1, len(args['channels'] + 1):
+        for i in range(1, len(args['channels']) + 1):
             if args['data_location_' + str(i)]:
                 request['InputDataConfig'][i-1]['DataSource']['S3DataSource']['S3Uri'] = args['data_location_' + str(i)]
     else:

components/aws/sagemaker/deploy/component.yaml

@@ -76,7 +76,7 @@ outputs:
   - {name: endpoint_name,          description: 'Endpoint name'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       deploy.py,

components/aws/sagemaker/ground_truth/component.yaml

@@ -85,7 +85,7 @@ outputs:
   - {name: active_learning_model_arn, description: 'The ARN for the most recent Amazon SageMaker model trained as part of automated data labeling.'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       ground_truth.py,

components/aws/sagemaker/hyperparameter_tuning/component.yaml

@@ -125,7 +125,7 @@ outputs:
     description: 'The registry path of the Docker image that contains the training algorithm'
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       hyperparameter_tuning.py,

components/aws/sagemaker/model/component.yaml

@@ -42,7 +42,7 @@ outputs:
   - {name: model_name,          description: 'The model name Sagemaker created'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       create_model.py,

components/aws/sagemaker/train/component.yaml

@@ -91,7 +91,7 @@ outputs:
   - {name: training_image,         description: 'The registry path of the Docker image that contains the training algorithm'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       train.py,

components/aws/sagemaker/workteam/component.yaml

@@ -24,7 +24,7 @@ outputs:
   - {name: workteam_arn, description: 'The ARN of the workteam.'}
 implementation:
   container:
-    image: carowang/kubeflow-pipeline-aws-sm:20190801-01
+    image: carowang/kubeflow-pipeline-aws-sm:latest
     command: ['python']
     args: [
       workteam.py,

samples/contrib/aws-samples/titanic-survival-prediction/titanic-survival-prediction.py

@@ -6,9 +6,9 @@
 from kfp import gcp
 from kfp.aws import use_aws_secret
 
-emr_create_cluster_op     = components.load_component_from_file('../../../components/aws/emr/create_cluster/component.yaml')
-emr_submit_spark_job_op = components.load_component_from_file('../../../components/aws/emr/submit_spark_job/component.yaml')
-emr_delete_cluster_op     = components.load_component_from_file('../../../components/aws/emr/delete_cluster/component.yaml')
+emr_create_cluster_op     = components.load_component_from_file('../../../../components/aws/emr/create_cluster/component.yaml')
+emr_submit_spark_job_op = components.load_component_from_file('../../../../components/aws/emr/submit_spark_job/component.yaml')
+emr_delete_cluster_op     = components.load_component_from_file('../../../../components/aws/emr/delete_cluster/component.yaml')
 
 @dsl.pipeline(
   name='Titanic Suvival Prediction Pipeline',