src/sagemaker/mxnet/model.py [156:256]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs ) self.model_server_workers = model_server_workers def register( self, content_types: List[Union[str, PipelineVariable]] = None, response_types: List[Union[str, PipelineVariable]] = None, inference_instances: Optional[List[Union[str, PipelineVariable]]] = None, transform_instances: Optional[List[Union[str, PipelineVariable]]] = None, model_package_name: Optional[Union[str, PipelineVariable]] = None, model_package_group_name: Optional[Union[str, PipelineVariable]] = None, image_uri: Optional[Union[str, PipelineVariable]] = None, model_metrics: Optional[ModelMetrics] = None, metadata_properties: Optional[MetadataProperties] = None, marketplace_cert: bool = False, approval_status: Optional[Union[str, PipelineVariable]] = None, description: Optional[str] = None, drift_check_baselines: Optional[DriftCheckBaselines] = None, customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None, domain: Optional[Union[str, PipelineVariable]] = None, sample_payload_url: Optional[Union[str, PipelineVariable]] = None, task: Optional[Union[str, PipelineVariable]] = None, framework: Optional[Union[str, PipelineVariable]] = None, framework_version: Optional[Union[str, PipelineVariable]] = None, nearest_model_name: Optional[Union[str, PipelineVariable]] = None, data_input_configuration: Optional[Union[str, PipelineVariable]] = None, skip_model_validation: Optional[Union[str, PipelineVariable]] = None, source_uri: Optional[Union[str, PipelineVariable]] = None, model_card: Optional[Union[ModelPackageModelCard, ModelCard]] = None, model_life_cycle: Optional[ModelLifeCycle] = None, ): """Creates a model package for creating SageMaker models or listing on Marketplace. Args: content_types (list[str] or list[PipelineVariable]): The supported MIME types for the input data. response_types (list[str] or list[PipelineVariable]): The supported MIME types for the output data. inference_instances (list[str] or list[PipelineVariable]): A list of the instance types that are used to generate inferences in real-time (default: None). transform_instances (list[str] or list[PipelineVariable]): A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None). model_package_name (str or PipelineVariable): Model Package name, exclusive to `model_package_group_name`, using `model_package_name` makes the Model Package un-versioned (default: None). model_package_group_name (str or PipelineVariable): Model Package Group name, exclusive to `model_package_name`, using `model_package_group_name` makes the Model Package versioned (default: None). image_uri (str or PipelineVariable): Inference image uri for the container. Model class' self.image will be used if it is None (default: None). model_metrics (ModelMetrics): ModelMetrics object (default: None). metadata_properties (MetadataProperties): MetadataProperties (default: None). marketplace_cert (bool): A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False). approval_status (str or PipelineVariable): Model Approval Status, values can be "Approved", "Rejected", or "PendingManualApproval" (default: "PendingManualApproval"). description (str): Model Package description (default: None). drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None). customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]): A dictionary of key-value paired metadata properties (default: None). domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION", "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None). sample_payload_url (str or PipelineVariable): The S3 path where the sample payload is stored (default: None). task (str or PipelineVariable): Task values which are supported by Inference Recommender are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None). framework (str or PipelineVariable): Machine learning framework of the model package container image (default: None). framework_version (str or PipelineVariable): Framework version of the Model Package Container Image (default: None). nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None). data_input_configuration (str or PipelineVariable): Input object for the model (default: None). skip_model_validation (str or PipelineVariable): Indicates if you want to skip model validation. Values can be "All" or "None" (default: None). source_uri (str or PipelineVariable): The URI of the source for the model package (default: None). model_card (ModeCard or ModelPackageModelCard): document contains qualitative and quantitative information about a model (default: None). model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None). Returns: A `sagemaker.model.ModelPackage` instance. """ instance_type = inference_instances[0] if inference_instances else None self._init_sagemaker_session_if_does_not_exist(instance_type) if image_uri: self.image_uri = image_uri if not self.image_uri: self.image_uri = self.serving_image_uri( region_name=self.sagemaker_session.boto_session.region_name, instance_type=instance_type, ) if not is_pipeline_variable(framework): framework = (framework or self._framework_name).upper() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - src/sagemaker/pytorch/model.py [157:258]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs ) self.model_server_workers = model_server_workers def register( self, content_types: List[Union[str, PipelineVariable]] = None, response_types: List[Union[str, PipelineVariable]] = None, inference_instances: Optional[List[Union[str, PipelineVariable]]] = None, transform_instances: Optional[List[Union[str, PipelineVariable]]] = None, model_package_name: Optional[Union[str, PipelineVariable]] = None, model_package_group_name: Optional[Union[str, PipelineVariable]] = None, image_uri: Optional[Union[str, PipelineVariable]] = None, model_metrics: Optional[ModelMetrics] = None, metadata_properties: Optional[MetadataProperties] = None, marketplace_cert: bool = False, approval_status: Optional[Union[str, PipelineVariable]] = None, description: Optional[str] = None, drift_check_baselines: Optional[DriftCheckBaselines] = None, customer_metadata_properties: Optional[Dict[str, Union[str, PipelineVariable]]] = None, domain: Optional[Union[str, PipelineVariable]] = None, sample_payload_url: Optional[Union[str, PipelineVariable]] = None, task: Optional[Union[str, PipelineVariable]] = None, framework: Optional[Union[str, PipelineVariable]] = None, framework_version: Optional[Union[str, PipelineVariable]] = None, nearest_model_name: Optional[Union[str, PipelineVariable]] = None, data_input_configuration: Optional[Union[str, PipelineVariable]] = None, skip_model_validation: Optional[Union[str, PipelineVariable]] = None, source_uri: Optional[Union[str, PipelineVariable]] = None, model_card: Optional[Union[ModelPackageModelCard, ModelCard]] = None, model_life_cycle: Optional[ModelLifeCycle] = None, ): """Creates a model package for creating SageMaker models or listing on Marketplace. Args: content_types (list[str] or list[PipelineVariable]): The supported MIME types for the input data. response_types (list[str] or list[PipelineVariable]): The supported MIME types for the output data. inference_instances (list[str] or list[PipelineVariable]): A list of the instance types that are used to generate inferences in real-time (default: None). transform_instances (list[str] or list[PipelineVariable]): A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None). model_package_name (str or PipelineVariable): Model Package name, exclusive to `model_package_group_name`, using `model_package_name` makes the Model Package un-versioned (default: None). model_package_group_name (str or PipelineVariable): Model Package Group name, exclusive to `model_package_name`, using `model_package_group_name` makes the Model Package versioned (default: None). image_uri (str or PipelineVariable): Inference image uri for the container. Model class' self.image will be used if it is None (default: None). model_metrics (ModelMetrics): ModelMetrics object (default: None). metadata_properties (MetadataProperties): MetadataProperties object (default: None). marketplace_cert (bool): A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False). approval_status (str or PipelineVariable): Model Approval Status, values can be "Approved", "Rejected", or "PendingManualApproval" (default: "PendingManualApproval"). description (str): Model Package description (default: None). drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None). customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]): A dictionary of key-value paired metadata properties (default: None). domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION", "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None). sample_payload_url (str or PipelineVariable): The S3 path where the sample payload is stored (default: None). task (str or PipelineVariable): Task values which are supported by Inference Recommender are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None). framework (str or PipelineVariable): Machine learning framework of the model package container image (default: None). framework_version (str or PipelineVariable): Framework version of the Model Package Container Image (default: None). nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None). data_input_configuration (str or PipelineVariable): Input object for the model (default: None). skip_model_validation (str or PipelineVariable): Indicates if you want to skip model validation. Values can be "All" or "None" (default: None). source_uri (str or PipelineVariable): The URI of the source for the model package (default: None). model_card (ModeCard or ModelPackageModelCard): document contains qualitative and quantitative information about a model (default: None). model_life_cycle (ModelLifeCycle): ModelLifeCycle object (default: None). Returns: A `sagemaker.model.ModelPackage` instance. """ instance_type = inference_instances[0] if inference_instances else None self._init_sagemaker_session_if_does_not_exist(instance_type) if image_uri: self.image_uri = image_uri if not self.image_uri: self.image_uri = self.serving_image_uri( region_name=self.sagemaker_session.boto_session.region_name, instance_type=instance_type, ) if not is_pipeline_variable(framework): framework = (framework or self._framework_name).upper() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -