feature: Add ModelStep for SageMaker Model Building Pipeline

Author: Dewen Qi

src/sagemaker/model.py

@@ -19,6 +19,7 @@
 import os
 import re
 import copy
+from typing import List, Dict
 
 import sagemaker
 from sagemaker import (
@@ -38,6 +39,7 @@
 from sagemaker.async_inference import AsyncInferenceConfig
 from sagemaker.predictor_async import AsyncPredictor
 from sagemaker.workflow import is_pipeline_variable
+from sagemaker.workflow.pipeline_context import runnable_by_pipeline, PipelineSession
 
 LOGGER = logging.getLogger("sagemaker")
 
@@ -289,6 +291,7 @@ def __init__(
         self.uploaded_code = None
         self.repacked_model_data = None
 
+    @runnable_by_pipeline
     def register(
         self,
         content_types,
@@ -309,12 +312,12 @@ def register(
         """Creates a model package for creating SageMaker models or listing on Marketplace.
 
         Args:
-            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).
+            content_types (list): The supported MIME types for the input data.
+            response_types (list): The supported MIME types for the output data.
             inference_instances (list): A list of the instance types that are used to
-                generate inferences in real-time (default: None).
+                generate inferences in real-time.
             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).
+                job can be run or on which an endpoint can be deployed.
             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
@@ -364,12 +367,49 @@ def register(
         model_package = self.sagemaker_session.create_model_package_from_containers(
             **model_pkg_args
         )
+        if isinstance(self.sagemaker_session, PipelineSession):
+            return None
         return ModelPackage(
             role=self.role,
             model_data=self.model_data,
             model_package_arn=model_package.get("ModelPackageArn"),
         )
 
+    @runnable_by_pipeline
+    def create(
+        self,
+        instance_type: str = None,
+        accelerator_type: str = None,
+        serverless_inference_config: ServerlessInferenceConfig = None,
+        tags: List[Dict[str, str]] = None,
+    ):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not (default: None).
+            accelerator_type (str): Type of Elastic Inference accelerator to
+                attach to an endpoint for model loading and inference, for
+                example, 'ml.eia1.medium'. If not specified, no Elastic
+                Inference accelerator will be attached to the endpoint (default: None).
+            serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
+                Specifies configuration related to serverless endpoint. Instance type is
+                not provided in serverless inference. So this is used to find image URIs
+                (default: None).
+            tags (List[Dict[str, str]]): The list of tags to add to
+                the model (default: None). Example: >>> tags = [{'Key': 'tagname', 'Value':
+                'tagvalue'}] For more information about tags, see
+                https://boto3.amazonaws.com/v1/documentation
+                /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
+        """
+        self._create_sagemaker_model(
+            instance_type=instance_type,
+            accelerator_type=accelerator_type,
+            tags=tags,
+            serverless_inference_config=serverless_inference_config,
+        )
+
     def _init_sagemaker_session_if_does_not_exist(self, instance_type=None):
         """Set ``self.sagemaker_session`` to ``LocalSession`` or ``Session`` if it's not already.
 
@@ -453,6 +493,24 @@ def _upload_code(self, key_prefix: str, repack: bool = False) -> None:
         if repack and self.model_data is not None and self.entry_point is not None:
             if is_pipeline_variable(self.model_data):
                 # model is not yet there, defer repacking to later during pipeline execution
+                if not isinstance(self.sagemaker_session, PipelineSession):
+                    # TODO: link the doc in the warning once ready
+                    logging.warning(
+                        "The model_data is a Pipeline variable of type %s, "
+                        "which should be used under `PipelineSession` and "
+                        "leverage `ModelStep` to create or register model. "
+                        "Otherwise some functionalities e.g. "
+                        "runtime repack may be missing",
+                        type(self.model_data),
+                    )
+                    return
+                self.sagemaker_session.context.need_runtime_repack.add(id(self))
+                # Add the uploaded_code and repacked_model_data to update the container env
+                self.repacked_model_data = self.model_data
+                self.uploaded_code = fw_utils.UploadedCode(
+                    s3_prefix=self.repacked_model_data,
+                    script_name=os.path.basename(self.entry_point),
+                )
                 return
 
             bucket = self.bucket or self.sagemaker_session.default_bucket()
@@ -534,22 +592,29 @@ def _create_sagemaker_model(
             serverless_inference_config=serverless_inference_config,
         )
 
-        self._ensure_base_name_if_needed(
-            image_uri=container_def["Image"], script_uri=self.source_dir, model_uri=self.model_data
-        )
-        self._set_model_name_if_needed()
+        if not isinstance(self.sagemaker_session, PipelineSession):
+            # _base_name, model_name are not needed under PipelineSession.
+            # the model_data may be Pipeline variable
+            # which may break the _base_name generation
+            self._ensure_base_name_if_needed(
+                image_uri=container_def["Image"],
+                script_uri=self.source_dir,
+                model_uri=self.model_data,
+            )
+            self._set_model_name_if_needed()
 
         enable_network_isolation = self.enable_network_isolation()
 
         self._init_sagemaker_session_if_does_not_exist(instance_type)
-        self.sagemaker_session.create_model(
-            self.name,
-            self.role,
-            container_def,
+        create_model_args = dict(
+            name=self.name,
+            role=self.role,
+            container_defs=container_def,
             vpc_config=self.vpc_config,
             enable_network_isolation=enable_network_isolation,
             tags=tags,
         )
+        self.sagemaker_session.create_model(**create_model_args)
 
     def _ensure_base_name_if_needed(self, image_uri, script_uri, model_uri):
         """Create a base name from the image URI if there is no model name provided.

src/sagemaker/pipeline.py

@@ -13,10 +13,16 @@
 """Placeholder docstring"""
 from __future__ import absolute_import
 
+from typing import Optional, Dict
+
 import sagemaker
+from sagemaker import ModelMetrics
+from sagemaker.drift_check_baselines import DriftCheckBaselines
+from sagemaker.metadata_properties import MetadataProperties
 from sagemaker.session import Session
 from sagemaker.utils import name_from_image
 from sagemaker.transformer import Transformer
+from sagemaker.workflow.pipeline_context import runnable_by_pipeline
 
 
 class PipelineModel(object):
@@ -221,6 +227,17 @@ def deploy(
             return predictor
         return None
 
+    @runnable_by_pipeline
+    def create(self, instance_type: str):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be
+                used for, this is only used to determine if the image needs GPU
+                support or not.
+        """
+        self._create_sagemaker_pipeline_model(instance_type)
+
     def _create_sagemaker_pipeline_model(self, instance_type):
         """Create a SageMaker Model Entity
 
@@ -235,13 +252,92 @@ def _create_sagemaker_pipeline_model(self, instance_type):
         containers = self.pipeline_container_def(instance_type)
 
         self.name = self.name or name_from_image(containers[0]["Image"])
-        self.sagemaker_session.create_model(
-            self.name,
-            self.role,
-            containers,
+        create_model_args = dict(
+            name=self.name,
+            role=self.role,
+            container_defs=containers,
             vpc_config=self.vpc_config,
             enable_network_isolation=self.enable_network_isolation,
         )
+        self.sagemaker_session.create_model(**create_model_args)
+
+    @runnable_by_pipeline
+    def register(
+        self,
+        content_types: list,
+        response_types: list,
+        inference_instances: list,
+        transform_instances: list,
+        model_package_name: Optional[str] = None,
+        model_package_group_name: Optional[str] = None,
+        image_uri: Optional[str] = None,
+        model_metrics: Optional[ModelMetrics] = None,
+        metadata_properties: Optional[MetadataProperties] = None,
+        marketplace_cert: bool = False,
+        approval_status: Optional[str] = None,
+        description: Optional[str] = None,
+        drift_check_baselines: Optional[DriftCheckBaselines] = None,
+        customer_metadata_properties: Optional[Dict[str, str]] = None,
+    ):
+        """Creates a model package for creating SageMaker models or listing on Marketplace.
+
+        Args:
+            content_types (list): The supported MIME types for the input data.
+            response_types (list): The supported MIME types for the output data.
+            inference_instances (list): A list of the instance types that are used to
+                generate inferences in real-time.
+            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.
+            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).
+            image_uri (str): 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): 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).
+
+        Returns:
+            A `sagemaker.model.ModelPackage` instance.
+        """
+        for model in self.models:
+            if model.model_data is None:
+                raise ValueError("SageMaker Model Package cannot be created without model data.")
+        if model_package_group_name is not None:
+            container_def = self.pipeline_container_def(inference_instances[0])
+        else:
+            container_def = [
+                {"Image": image_uri or model.image_uri, "ModelDataUrl": model.model_data}
+                for model in self.models
+            ]
+
+        model_pkg_args = sagemaker.get_model_package_args(
+            content_types,
+            response_types,
+            inference_instances,
+            transform_instances,
+            model_package_name=model_package_name,
+            model_package_group_name=model_package_group_name,
+            model_metrics=model_metrics,
+            metadata_properties=metadata_properties,
+            marketplace_cert=marketplace_cert,
+            approval_status=approval_status,
+            description=description,
+            container_def_list=container_def,
+            drift_check_baselines=drift_check_baselines,
+            customer_metadata_properties=customer_metadata_properties,
+        )
+
+        self.sagemaker_session.create_model_package_from_containers(**model_pkg_args)
 
     def transformer(
         self,

src/sagemaker/session.py

@@ -2679,23 +2679,24 @@ def create_model(
             primary_container=primary_container,
             tags=tags,
         )
-        LOGGER.info("Creating model with name: %s", name)
-        LOGGER.debug("CreateModel request: %s", json.dumps(create_model_request, indent=4))
 
-        try:
-            self.sagemaker_client.create_model(**create_model_request)
-        except ClientError as e:
-            error_code = e.response["Error"]["Code"]
-            message = e.response["Error"]["Message"]
-
-            if (
-                error_code == "ValidationException"
-                and "Cannot create already existing model" in message
-            ):
-                LOGGER.warning("Using already existing model: %s", name)
-            else:
-                raise
+        def submit(request):
+            LOGGER.info("Creating model with name: %s", name)
+            LOGGER.debug("CreateModel request: %s", json.dumps(request, indent=4))
+            try:
+                self.sagemaker_client.create_model(**request)
+            except ClientError as e:
+                error_code = e.response["Error"]["Code"]
+                message = e.response["Error"]["Message"]
+                if (
+                    error_code == "ValidationException"
+                    and "Cannot create already existing model" in message
+                ):
+                    LOGGER.warning("Using already existing model: %s", name)
+                else:
+                    raise
 
+        self._intercept_create_request(create_model_request, submit, self.create_model.__name__)
         return name
 
     def create_model_from_job(
@@ -2828,10 +2829,9 @@ def create_model_package_from_containers(
             drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
             customer_metadata_properties (dict[str, str]): A dictionary of key-value paired
                 metadata properties (default: None).
-
         """
 
-        request = get_create_model_package_request(
+        model_pkg_request = get_create_model_package_request(
             model_package_name,
             model_package_group_name,
             containers,
@@ -2847,16 +2847,22 @@ def create_model_package_from_containers(
             drift_check_baselines=drift_check_baselines,
             customer_metadata_properties=customer_metadata_properties,
         )
-        if model_package_group_name is not None:
-            try:
-                self.sagemaker_client.describe_model_package_group(
-                    ModelPackageGroupName=request["ModelPackageGroupName"]
-                )
-            except ClientError:
-                self.sagemaker_client.create_model_package_group(
-                    ModelPackageGroupName=request["ModelPackageGroupName"]
-                )
-        return self.sagemaker_client.create_model_package(**request)
+
+        def submit(request):
+            if model_package_group_name is not None:
+                try:
+                    self.sagemaker_client.describe_model_package_group(
+                        ModelPackageGroupName=request["ModelPackageGroupName"]
+                    )
+                except ClientError:
+                    self.sagemaker_client.create_model_package_group(
+                        ModelPackageGroupName=request["ModelPackageGroupName"]
+                    )
+            return self.sagemaker_client.create_model_package(**request)
+
+        return self._intercept_create_request(
+            model_pkg_request, submit, self.create_model_package_from_containers.__name__
+        )
 
     def wait_for_model_package(self, model_package_name, poll=5):
         """Wait for an Amazon SageMaker endpoint deployment to complete.
@@ -4175,7 +4181,9 @@ def account_id(self) -> str:
         )
         return sts_client.get_caller_identity()["Account"]
 
-    def _intercept_create_request(self, request: typing.Dict, create):
+    def _intercept_create_request(
+        self, request: typing.Dict, create, func_name: str = None  # pylint: disable=unused-argument
+    ):
         """This function intercepts the create job request.
 
         PipelineSession inherits this Session class and will override
@@ -4184,8 +4192,9 @@ def _intercept_create_request(self, request: typing.Dict, create):
         Args:
             request (dict): the create job request
             create (functor): a functor calls the sagemaker client create method
+            func_name (str): the name of the function needed intercepting
         """
-        create(request)
+        return create(request)
 
 
 def get_model_package_args(