Added register step in Jumpstart

Author: Keshav Chandak

src/sagemaker/jumpstart/factory/model.py

@@ -34,14 +34,19 @@
     JUMPSTART_DEFAULT_REGION_NAME,
     JUMPSTART_LOGGER,
 )
+from sagemaker.model_metrics import ModelMetrics
+from sagemaker.metadata_properties import MetadataProperties
+from sagemaker.drift_check_baselines import DriftCheckBaselines
 from sagemaker.jumpstart.enums import JumpStartScriptScope
 from sagemaker.jumpstart.types import (
     JumpStartModelDeployKwargs,
     JumpStartModelInitKwargs,
+    JumpStartModelRegisterKwargs,
 )
 from sagemaker.jumpstart.utils import (
     update_dict_if_key_not_present,
     resolve_model_sagemaker_config_field,
+    verify_model_region_and_return_specs,
 )
 
 from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
@@ -506,6 +511,87 @@ def get_deploy_kwargs(
     return deploy_kwargs
 
 
+def get_register_kwargs(
+    model_id: str,
+    model_version: Optional[str] = None,
+    region: Optional[str] = None,
+    tolerate_deprecated_model: Optional[bool] = None,
+    tolerate_vulnerable_model: Optional[bool] = None,
+    sagemaker_session: Optional[Any] = None,
+    supported_content_types: List[str] = None,
+    response_types: List[str] = None,
+    inference_instances: Optional[List[str]] = None,
+    transform_instances: Optional[List[str]] = None,
+    model_package_group_name: Optional[str] = None,
+    image_uri: Optional[str] = None,
+    model_metrics: Optional[ModelMetrics] = None,
+    metadata_properties: Optional[MetadataProperties] = None,
+    approval_status: Optional[str] = None,
+    description: Optional[str] = None,
+    drift_check_baselines: Optional[DriftCheckBaselines] = None,
+    customer_metadata_properties: Optional[Dict[str, str]] = None,
+    validation_specification: Optional[str] = None,
+    domain: Optional[str] = None,
+    task: Optional[str] = None,
+    sample_payload_url: Optional[str] = None,
+    framework: Optional[str] = None,
+    framework_version: Optional[str] = None,
+    nearest_model_name: Optional[str] = None,
+    data_input_configuration: Optional[str] = None,
+    skip_model_validation: Optional[str] = None,
+) -> JumpStartModelRegisterKwargs:
+    """Returns kwargs required to call `register` on `sagemaker.estimator.Model` object."""
+
+    register_kwargs = JumpStartModelRegisterKwargs(
+        model_id=model_id,
+        model_version=model_version,
+        region=region,
+        tolerate_deprecated_model=tolerate_deprecated_model,
+        tolerate_vulnerable_model=tolerate_vulnerable_model,
+        sagemaker_session=sagemaker_session,
+        content_types=supported_content_types,
+        response_types=response_types,
+        inference_instances=inference_instances,
+        transform_instances=transform_instances,
+        model_package_group_name=model_package_group_name,
+        image_uri=image_uri,
+        model_metrics=model_metrics,
+        metadata_properties=metadata_properties,
+        approval_status=approval_status,
+        description=description,
+        drift_check_baselines=drift_check_baselines,
+        customer_metadata_properties=customer_metadata_properties,
+        validation_specification=validation_specification,
+        domain=domain,
+        task=task,
+        sample_payload_url=sample_payload_url,
+        framework=framework,
+        framework_version=framework_version,
+        nearest_model_name=nearest_model_name,
+        data_input_configuration=data_input_configuration,
+        skip_model_validation=skip_model_validation,
+    )
+
+    model_specs = verify_model_region_and_return_specs(
+        model_id=model_id,
+        version=model_version,
+        region=region,
+        scope=JumpStartScriptScope.INFERENCE,
+        sagemaker_session=sagemaker_session,
+        tolerate_deprecated_model=tolerate_deprecated_model,
+        tolerate_vulnerable_model=tolerate_vulnerable_model,
+    )
+
+    register_kwargs.content_types = (
+        register_kwargs.content_types or model_specs.predictor_specs.supported_content_types
+    )
+    register_kwargs.response_types = (
+        register_kwargs.response_types or model_specs.predictor_specs.supported_accept_types
+    )
+
+    return register_kwargs
+
+
 def get_init_kwargs(
     model_id: str,
     model_from_estimator: bool = False,

src/sagemaker/jumpstart/model.py

@@ -13,7 +13,6 @@
 """This module stores JumpStart implementation of Model class."""
 
 from __future__ import absolute_import
-import re
 
 from typing import Dict, List, Optional, Union
 from sagemaker import payloads
@@ -28,16 +27,23 @@
     get_default_predictor,
     get_deploy_kwargs,
     get_init_kwargs,
+    get_register_kwargs,
 )
 from sagemaker.jumpstart.types import JumpStartSerializablePayload
 from sagemaker.jumpstart.utils import is_valid_model_id
 from sagemaker.utils import stringify_object
-from sagemaker.model import MODEL_PACKAGE_ARN_PATTERN, Model
+from sagemaker.model import (
+    Model,
+    ModelPackage,
+)
 from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
 from sagemaker.predictor import PredictorBase
 from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
 from sagemaker.session import Session
 from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.model_metrics import ModelMetrics
+from sagemaker.metadata_properties import MetadataProperties
+from sagemaker.drift_check_baselines import DriftCheckBaselines
 
 
 class JumpStartModel(Model):
@@ -390,29 +396,21 @@ def _create_sagemaker_model(
         # inference endpoint.
         if self.model_package_arn and not self._model_data_is_set:
             # When a ModelPackageArn is provided we just create the Model
-            match = re.match(MODEL_PACKAGE_ARN_PATTERN, self.model_package_arn)
-            if match:
-                model_package_name = match.group(3)
-            else:
-                # model_package_arn can be just the name if your account owns the Model Package
-                model_package_name = self.model_package_arn
-            container_def = {"ModelPackageName": self.model_package_arn}
-
-            if self.env != {}:
-                container_def["Environment"] = self.env
-
-            if self.name is None:
-                self._base_name = model_package_name
-
-            self._set_model_name_if_needed()
-
-            self.sagemaker_session.create_model(
-                self.name,
-                self.role,
-                container_def,
+            model_package = ModelPackage(
+                role=self.role,
+                model_data=self.model_data,
+                model_package_arn=self.model_package_arn,
+                sagemaker_session=self.sagemaker_session,
+                predictor_cls=self.predictor_cls,
                 vpc_config=self.vpc_config,
-                enable_network_isolation=self.enable_network_isolation(),
+            )
+            model_package.name = self.name
+            model_package._create_sagemaker_model(
+                instance_type=instance_type,
+                accelerator_type=accelerator_type,
                 tags=tags,
+                serverless_inference_config=serverless_inference_config,
+                **kwargs,
             )
         else:
             super(JumpStartModel, self)._create_sagemaker_model(
@@ -565,6 +563,116 @@ def deploy(
         # If a predictor class was passed, do not mutate predictor
         return predictor
 
+    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_group_name: Optional[Union[str, PipelineVariable]] = None,
+        image_uri: Optional[Union[str, PipelineVariable]] = None,
+        model_metrics: Optional[ModelMetrics] = None,
+        metadata_properties: Optional[MetadataProperties] = None,
+        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,
+        validation_specification: Optional[Union[str, PipelineVariable]] = None,
+        domain: Optional[Union[str, PipelineVariable]] = None,
+        task: Optional[Union[str, PipelineVariable]] = None,
+        sample_payload_url: 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,
+    ):
+        """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_group_name (str or PipelineVariable): Model Package Group name,
+                exclusive to `model_package_name`, using `model_package_group_name` makes the
+                Model Package versioned. Defaults to ``None``.
+            image_uri (str or PipelineVariable): Inference image URI for the container. Model class'
+                self.image will be used if it is None. Defaults to ``None``.
+            model_metrics (ModelMetrics): ModelMetrics object. Defaults to ``None``.
+            metadata_properties (MetadataProperties): MetadataProperties object.
+                Defaults to ``None``.
+            approval_status (str or PipelineVariable): Model Approval Status, values can be
+                "Approved", "Rejected", or "PendingManualApproval". Defaults to
+                ``PendingManualApproval``.
+            description (str): Model Package description. Defaults to ``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).
+
+        Returns:
+            A `sagemaker.model.ModelPackage` instance.
+        """
+
+        register_kwargs = get_register_kwargs(
+            model_id=self.model_id,
+            model_version=self.model_version,
+            region=self.region,
+            tolerate_deprecated_model=self.tolerate_deprecated_model,
+            tolerate_vulnerable_model=self.tolerate_vulnerable_model,
+            sagemaker_session=self.sagemaker_session,
+            supported_content_types=content_types,
+            response_types=response_types,
+            inference_instances=inference_instances,
+            transform_instances=transform_instances,
+            model_package_group_name=model_package_group_name,
+            image_uri=image_uri,
+            model_metrics=model_metrics,
+            metadata_properties=metadata_properties,
+            approval_status=approval_status,
+            description=description,
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+            validation_specification=validation_specification,
+            domain=domain,
+            task=task,
+            sample_payload_url=sample_payload_url,
+            framework=framework,
+            framework_version=framework_version,
+            nearest_model_name=nearest_model_name,
+            data_input_configuration=data_input_configuration,
+            skip_model_validation=skip_model_validation,
+        )
+
+        model_package = super(JumpStartModel, self).register(**register_kwargs.to_kwargs_dict())
+        self.model_package_arn = model_package.model_package_arn
+
+        model_package.deploy = self.deploy
+
+        return model_package
+
     def __str__(self) -> str:
         """Overriding str(*) method to make more human-readable."""
         return stringify_object(self)