sagemaker.core.model_registry

sagemaker.core.model_registry#

Functions

create_model_package_from_algorithm(self, ...)

Create a SageMaker Model Package from the results of training with an Algorithm Package.

create_model_package_from_containers(...[, ...])

Get request dictionary for CreateModelPackage API.

get_create_model_package_request([...])

get_model_package_args([content_types, ...])

sagemaker.core.model_registry.create_model_package_from_algorithm(self, name, description, algorithm_arn, model_data)[source]#

Create a SageMaker Model Package from the results of training with an Algorithm Package.

Parameters:

  • name (str) – ModelPackage name

  • description (str) – Model Package description

  • algorithm_arn (str) – arn or name of the algorithm used for training.

  • model_data (str or dict[str, Any]) – s3 URI or a dictionary representing a

  • training (ModelDataSource to the model artifacts produced by)

sagemaker.core.model_registry.create_model_package_from_containers(sagemaker_session, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status='PendingManualApproval', description=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None, skip_model_validation='None', source_uri=None, model_card=None, model_life_cycle=None)[source]#

Get request dictionary for CreateModelPackage API.

Parameters:

  • containers (list) – A list of inference containers that can be used for inference specifications of Model Package (default: None).

  • content_types (list) – The supported MIME types for the input data (default: None).

  • response_types (list) – The supported MIME types for the output data (default: None).

  • inference_instances (list) – A list of the instance types that are used to generate inferences in real-time (default: None).

  • transform_instances (list) – 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) – 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) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (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) – 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]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str) – The S3 path where the sample payload is stored (default: None).

  • task (str) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

  • skip_model_validation (str) – Indicates if you want to skip model validation. Values can be “All” or “None” (default: None).

  • source_uri (str) – 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).

sagemaker.core.model_registry.get_create_model_package_request(model_package_name=None, model_package_group_name=None, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status='PendingManualApproval', description=None, tags=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None, skip_model_validation='None', source_uri=None, model_card=None, model_life_cycle=None)[source]#
sagemaker.core.model_registry.get_model_package_args(content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, model_data=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, tags=None, container_def_list=None, drift_check_baselines=None, customer_metadata_properties=None, validation_specification=None, domain=None, sample_payload_url=None, task=None, skip_model_validation=None, source_uri=None, model_card=None, model_life_cycle=None)[source]#
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