Make endpoint_url None (#3374)
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parent
c0fb46aa1b
commit
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@ -74,7 +74,7 @@ outputs:
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- {name: output_location, description: 'S3 URI of the transform job results.'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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batch_transform.py,
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@ -52,9 +52,16 @@ built_in_algos = {
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# Get current directory to open templates
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__cwd__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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def nullable_string_argument(value):
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value = value.strip()
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if not value:
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return None
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return value
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def add_default_client_arguments(parser):
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parser.add_argument('--region', type=str.strip, required=True, help='The region where the training job launches.')
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parser.add_argument('--endpoint_url', type=str.strip, required=False, help='The URL to use when communicating with the Sagemaker service.')
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parser.add_argument('--endpoint_url', type=nullable_string_argument, required=False, help='The URL to use when communicating with the Sagemaker service.')
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def get_sagemaker_client(region, endpoint_url=None):
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"""Builds a client to the AWS SageMaker API."""
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@ -79,7 +79,7 @@ outputs:
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- {name: endpoint_name, description: 'Endpoint name'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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deploy.py,
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@ -88,7 +88,7 @@ outputs:
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- {name: active_learning_model_arn, description: 'The ARN for the most recent Amazon SageMaker model trained as part of automated data labeling.'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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ground_truth.py,
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@ -139,7 +139,7 @@ outputs:
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description: 'The registry path of the Docker image that contains the training algorithm'
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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hyperparameter_tuning.py,
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@ -45,7 +45,7 @@ outputs:
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- {name: model_name, description: 'The model name Sagemaker created'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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create_model.py,
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@ -103,7 +103,7 @@ outputs:
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- {name: training_image, description: 'The registry path of the Docker image that contains the training algorithm'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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train.py,
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@ -27,7 +27,7 @@ outputs:
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- {name: workteam_arn, description: 'The ARN of the workteam.'}
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implementation:
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container:
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image: redbackthomson/aws-kubeflow-sagemaker:20191003
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image: redbackthomson/aws-kubeflow-sagemaker:20200402
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command: ['python']
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args: [
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workteam.py,
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