# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Placeholder docstring""" from __future__ import absolute_import import logging from typing import Union, Optional, List, Dict import sagemaker from sagemaker import image_uris, ModelMetrics from sagemaker.deserializers import NumpyDeserializer from sagemaker.drift_check_baselines import DriftCheckBaselines from sagemaker.fw_utils import model_code_key_prefix, validate_version_or_image_args from sagemaker.metadata_properties import MetadataProperties from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME from sagemaker.predictor import Predictor from sagemaker.serializers import NumpySerializer from sagemaker.sklearn import defaults from sagemaker.utils import to_string from sagemaker.workflow import is_pipeline_variable from sagemaker.workflow.entities import PipelineVariable logger = logging.getLogger("sagemaker") class SKLearnPredictor(Predictor): """A Predictor for inference against Scikit-learn Endpoints. This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Scikit-learn inference. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=NumpySerializer(), deserializer=NumpyDeserializer(), ): """Initialize an ``SKLearnPredictor``. Args: endpoint_name (str): The name of the endpoint to perform inference on. sagemaker_session (sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain. serializer (sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to .npy format. Handles lists and numpy arrays. deserializer (sagemaker.deserializers.BaseDeserializer): Optional. Default parses the response from .npy format to numpy array. """ super(SKLearnPredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) class SKLearnModel(FrameworkModel): """An Scikit-learn SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``.""" _framework_name = defaults.SKLEARN_NAME def __init__( self, model_data: Union[str, PipelineVariable], role: Optional[str] = None, entry_point: Optional[str] = None, framework_version: Optional[str] = None, py_version: str = "py3", image_uri: Optional[Union[str, PipelineVariable]] = None, predictor_cls: callable = SKLearnPredictor, model_server_workers: Optional[Union[int, PipelineVariable]] = None, **kwargs ): """Initialize an SKLearnModel. Args: model_data (str or PipelineVariable): The S3 location of a SageMaker model data ``.tar.gz`` file. role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource. entry_point (str): Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If ``source_dir`` is specified, then ``entry_point`` must point to a file located at the root of ``source_dir``. framework_version (str): Scikit-learn version you want to use for executing your model training code. Defaults to ``None``. Required unless ``image_uri`` is provided. py_version (str): Python version you want to use for executing your model training code (default: 'py3'). Currently, 'py3' is the only supported version. If ``None`` is passed in, ``image_uri`` must be provided. image_uri (str or PipelineVariable): A Docker image URI (default: None). If not specified, a default image for Scikit-learn will be used. If ``framework_version`` or ``py_version`` are ``None``, then ``image_uri`` is required. If ``image_uri`` is also ``None``, then a ``ValueError`` will be raised. predictor_cls (callable[str, sagemaker.session.Session]): A function to call to create a predictor with an endpoint name and SageMaker ``Session``. If specified, ``deploy()`` returns the result of invoking this function on the created endpoint name. model_server_workers (int or PipelineVariable): Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU. **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer. .. tip:: You can find additional parameters for initializing this class at :class:`~sagemaker.model.FrameworkModel` and :class:`~sagemaker.model.Model`. """ validate_version_or_image_args(framework_version, py_version, image_uri) if py_version and py_version != "py3": raise AttributeError( "Scikit-learn image only supports Python 3. Please use 'py3' for py_version." ) self.framework_version = framework_version self.py_version = py_version super(SKLearnModel, self).__init__( 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]], response_types: List[Union[str, PipelineVariable]], 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, ): """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). 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() return super(SKLearnModel, self).register( content_types, response_types, inference_instances, transform_instances, model_package_name, model_package_group_name, image_uri, model_metrics, metadata_properties, marketplace_cert, approval_status, description, drift_check_baselines=drift_check_baselines, customer_metadata_properties=customer_metadata_properties, domain=domain, sample_payload_url=sample_payload_url, task=task, framework=framework, framework_version=framework_version, nearest_model_name=nearest_model_name, data_input_configuration=data_input_configuration, ) def prepare_container_def( self, instance_type=None, accelerator_type=None, serverless_inference_config=None ): """Container definition with framework configuration set in model environment variables. Args: instance_type (str): The EC2 instance type to deploy this Model to. This parameter is unused because Scikit-learn supports only CPU. accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. This parameter is unused because accelerator types are not supported by SKLearnModel. 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. Returns: dict[str, str]: A container definition object usable with the CreateModel API. """ if accelerator_type: raise ValueError("Accelerator types are not supported for Scikit-Learn.") deploy_image = self.image_uri if not deploy_image: deploy_image = self.serving_image_uri( self.sagemaker_session.boto_region_name, instance_type ) deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image) self._upload_code(key_prefix=deploy_key_prefix, repack=self.enable_network_isolation()) deploy_env = dict(self.env) deploy_env.update(self._script_mode_env_vars()) if self.model_server_workers: deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = to_string( self.model_server_workers ) model_data_uri = ( self.repacked_model_data if self.enable_network_isolation() else self.model_data ) return sagemaker.container_def(deploy_image, model_data_uri, deploy_env) def serving_image_uri(self, region_name, instance_type, serverless_inference_config=None): """Create a URI for the serving image. Args: region_name (str): AWS region where the image is uploaded. instance_type (str): SageMaker instance type. 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 determine device type. Returns: str: The appropriate image URI based on the given parameters. """ return image_uris.retrieve( self._framework_name, region_name, version=self.framework_version, py_version=self.py_version, instance_type=instance_type, serverless_inference_config=serverless_inference_config, )