@@ -341,7 +341,9 @@ def register(
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"MACHINE_LEARNING" (default: None).
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Returns:
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- A `sagemaker.model.ModelPackage` instance.
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+ A `sagemaker.model.ModelPackage` instance or pipeline step arguments
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+ in case the Model instance is built with
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+ :class:`~sagemaker.workflow.pipeline_context.PipelineSession`
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"""
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if self .model_data is None :
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raise ValueError ("SageMaker Model Package cannot be created without model data." )
@@ -398,15 +400,22 @@ def create(
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attach to an endpoint for model loading and inference, for
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example, 'ml.eia1.medium'. If not specified, no Elastic
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Inference accelerator will be attached to the endpoint (default: None).
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- serverless_inference_config (sagemaker.serverless. ServerlessInferenceConfig):
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+ serverless_inference_config (ServerlessInferenceConfig):
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Specifies configuration related to serverless endpoint. Instance type is
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not provided in serverless inference. So this is used to find image URIs
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(default: None).
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tags (List[Dict[str, str]]): The list of tags to add to
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- the model (default: None). Example: >>> tags = [{'Key': 'tagname', 'Value':
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- 'tagvalue'}] For more information about tags, see
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- https://boto3.amazonaws.com/v1/documentation
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- /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
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+ the model (default: None). Example::
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+
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+ tags = [{'Key': 'tagname', 'Value':'tagvalue'}]
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+
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+ For more information about tags, see
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+ `boto3 documentation <https://boto3.amazonaws.com/v1/documentation/\
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+ api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags>`_
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+
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+ Returns:
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+ None or pipeline step arguments in case the Model instance is built with
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+ :class:`~sagemaker.workflow.pipeline_context.PipelineSession`
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"""
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# TODO: we should replace _create_sagemaker_model() with create()
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self ._create_sagemaker_model (
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