# 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 Optional, Union, List, Dict import sagemaker from sagemaker import image_uris, ModelMetrics from sagemaker.deserializers import JSONDeserializer 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 JSONSerializer from sagemaker.session import Session from sagemaker.utils import to_string from sagemaker.workflow import is_pipeline_variable from sagemaker.workflow.entities import PipelineVariable logger = logging.getLogger("sagemaker") class HuggingFacePredictor(Predictor): """A Predictor for inference against Hugging Face Endpoints. This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Hugging Face inference. """ def __init__( self, endpoint_name, sagemaker_session=None, serializer=JSONSerializer(), deserializer=JSONDeserializer(), ): """Initialize an ``HuggingFacePredictor``. Args: endpoint_name (str): The name of the endpoint to perform inference on. sagemaker_session (sagemaker.session.Session): Session object that 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(HuggingFacePredictor, self).__init__( endpoint_name, sagemaker_session, serializer=serializer, deserializer=deserializer, ) def _validate_pt_tf_versions(pytorch_version, tensorflow_version, image_uri): """Placeholder docstring""" if image_uri is not None: return if tensorflow_version is not None and pytorch_version is not None: raise ValueError( "tensorflow_version and pytorch_version are both not None. " "Specify only tensorflow_version or pytorch_version." ) if tensorflow_version is None and pytorch_version is None: raise ValueError( "tensorflow_version and pytorch_version are both None. " "Specify either tensorflow_version or pytorch_version." ) def fetch_framework_and_framework_version(tensorflow_version, pytorch_version): """Function to check the framework used in HuggingFace class""" if tensorflow_version is not None: # pylint: disable=no-member return ("tensorflow", tensorflow_version) # pylint: disable=no-member return ("pytorch", pytorch_version) # pylint: disable=no-member class HuggingFaceModel(FrameworkModel): """A Hugging Face SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``.""" _framework_name = "huggingface" def __init__( self, role: Optional[str] = None, model_data: Optional[Union[str, PipelineVariable]] = None, entry_point: Optional[str] = None, transformers_version: Optional[str] = None, tensorflow_version: Optional[str] = None, pytorch_version: Optional[str] = None, py_version: Optional[str] = None, image_uri: Optional[Union[str, PipelineVariable]] = None, predictor_cls: callable = HuggingFacePredictor, model_server_workers: Optional[Union[int, PipelineVariable]] = None, **kwargs, ): """Initialize a HuggingFaceModel. Args: model_data (str or PipelineVariable): The Amazon S3 location of a SageMaker model data ``.tar.gz`` file. role (str): An AWS IAM role specified with either the 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): The absolute or relative path to the Python source file that 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``. Defaults to None. transformers_version (str): Transformers version you want to use for executing your model training code. Defaults to None. Required unless ``image_uri`` is provided. tensorflow_version (str): TensorFlow version you want to use for executing your inference code. Defaults to ``None``. Required unless ``pytorch_version`` is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators. pytorch_version (str): PyTorch version you want to use for executing your inference code. Defaults to ``None``. Required unless ``tensorflow_version`` is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators. py_version (str): Python version you want to use for executing your model training code. Defaults to ``None``. Required unless ``image_uri`` is provided. image_uri (str or PipelineVariable): A Docker image URI. Defaults to None. If not specified, a default image for PyTorch will be used. If ``framework_version`` or ``py_version`` are ``None``, then ``image_uri`` is required. If 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 superclass :class:`~sagemaker.model.FrameworkModel` and, subsequently, its superclass :class:`~sagemaker.model.Model`. .. 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(transformers_version, py_version, image_uri) _validate_pt_tf_versions( pytorch_version=pytorch_version, tensorflow_version=tensorflow_version, image_uri=image_uri, ) if py_version == "py2": raise ValueError("py2 is not supported with HuggingFace images") self.framework_version = transformers_version self.pytorch_version = pytorch_version self.tensorflow_version = tensorflow_version self.py_version = py_version super(HuggingFaceModel, self).__init__( model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs ) self.sagemaker_session = self.sagemaker_session or Session() self.model_server_workers = model_server_workers # TODO: Remove the following function # botocore needs to add hugginface to the list of valid neo compilable frameworks. # Ideally with inferentia framewrok, call to .compile( ... ) method will create the image_uri. # currently, call to compile( ... ) method is causing `ValidationException` def deploy( self, initial_instance_count=None, instance_type=None, serializer=None, deserializer=None, accelerator_type=None, endpoint_name=None, tags=None, kms_key=None, wait=True, data_capture_config=None, async_inference_config=None, serverless_inference_config=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None, inference_recommendation_id=None, explainer_config=None, **kwargs, ): """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``. Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``. If ``self.predictor_cls`` is not None, this method returns a the result of invoking ``self.predictor_cls`` on the created endpoint name. The name of the created model is accessible in the ``name`` field of this ``Model`` after deploy returns The name of the created endpoint is accessible in the ``endpoint_name`` field of this ``Model`` after deploy returns. Args: initial_instance_count (int): The initial number of instances to run in the ``Endpoint`` created from this ``Model``. If not using serverless inference, then it need to be a number larger or equals to 1 (default: None) instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge', or 'local' for local mode. If not using serverless inference, then it is required to deploy a model. (default: None) serializer (:class:`~sagemaker.serializers.BaseSerializer`): A serializer object, used to encode data for an inference endpoint (default: None). If ``serializer`` is not None, then ``serializer`` will override the default serializer. The default serializer is set by the ``predictor_cls``. deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A deserializer object, used to decode data from an inference endpoint (default: None). If ``deserializer`` is not None, then ``deserializer`` will override the default deserializer. The default deserializer is set by the ``predictor_cls``. accelerator_type (str): Type of Elastic Inference accelerator to deploy this model for model loading and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator will be attached to the endpoint. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html endpoint_name (str): The name of the endpoint to create (default: None). If not specified, a unique endpoint name will be created. tags (List[dict[str, str]]): The list of tags to attach to this specific endpoint. kms_key (str): The ARN of the KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint. wait (bool): Whether the call should wait until the deployment of this model completes (default: True). data_capture_config (sagemaker.model_monitor.DataCaptureConfig): Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None. async_inference_config (sagemaker.model_monitor.AsyncInferenceConfig): Specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference. If empty config object passed through, will use default config to deploy async endpoint. Deploy a real-time endpoint if it's None. (default: None) serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig): Specifies configuration related to serverless endpoint. Use this configuration when trying to create serverless endpoint and make serverless inference. If empty object passed through, will use pre-defined values in ``ServerlessInferenceConfig`` class to deploy serverless endpoint. Deploy an instance based endpoint if it's None. (default: None) volume_size (int): The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported. model_data_download_timeout (int): The timeout value, in seconds, to download and extract model data from Amazon S3 to the individual inference instance associated with this production variant. container_startup_health_check_timeout (int): The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check see: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests inference_recommendation_id (str): The recommendation id which specifies the recommendation you picked from inference recommendation job results and would like to deploy the model and endpoint with recommended parameters. explainer_config (sagemaker.explainer.ExplainerConfig): Specifies online explainability configuration for use with Amazon SageMaker Clarify. (default: None) Raises: ValueError: If arguments combination check failed in these circumstances: - If no role is specified or - If serverless inference config is not specified and instance type and instance count are also not specified or - If a wrong type of object is provided as serverless inference config or async inference config Returns: callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on the created endpoint name, if ``self.predictor_cls`` is not None. Otherwise, return None. """ if not self.image_uri and instance_type is not None and instance_type.startswith("ml.inf"): inference_tool = "neuron" if instance_type.startswith("ml.inf1") else "neuronx" self.image_uri = self.serving_image_uri( region_name=self.sagemaker_session.boto_session.region_name, instance_type=instance_type, inference_tool=inference_tool, ) return super(HuggingFaceModel, self).deploy( initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size=volume_size, model_data_download_timeout=model_data_download_timeout, container_startup_health_check_timeout=container_startup_health_check_timeout, inference_recommendation_id=inference_recommendation_id, explainer_config=explainer_config, ) 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. Defaults to ``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``. marketplace_cert (bool): A boolean value indicating if the Model Package is certified for AWS Marketplace. Defaults to ``False``. 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). 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 fetch_framework_and_framework_version( self.tensorflow_version, self.pytorch_version )[0] ).upper() return super(HuggingFaceModel, 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 or fetch_framework_and_framework_version(self.tensorflow_version, self.pytorch_version)[ 1 ], 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, inference_tool=None, ): """A container definition with framework configuration set in model environment variables. Args: instance_type (str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'. accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. 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. inference_tool (str): the tool that will be used to aid in the inference. Valid values: "neuron, neuronx, None" (default: None). Returns: dict[str, str]: A container definition object usable with the CreateModel API. """ deploy_image = self.image_uri if not deploy_image: if instance_type is None and serverless_inference_config is None: raise ValueError( "Must supply either an instance type (for choosing CPU vs GPU) or an image URI." ) region_name = self.sagemaker_session.boto_session.region_name deploy_image = self.serving_image_uri( region_name, instance_type, accelerator_type=accelerator_type, serverless_inference_config=serverless_inference_config, inference_tool=inference_tool, ) deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image) self._upload_code(deploy_key_prefix, repack=True) 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 ) return sagemaker.container_def( deploy_image, self.repacked_model_data or self.model_data, deploy_env ) def serving_image_uri( self, region_name, instance_type=None, accelerator_type=None, serverless_inference_config=None, inference_tool=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. Used to determine device type (cpu/gpu/family-specific optimized). accelerator_type (str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig): Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used used to determine device type. inference_tool (str): the tool that will be used to aid in the inference. Valid values: "neuron, neuronx, None" (default: None). Returns: str: The appropriate image URI based on the given parameters. """ if self.tensorflow_version is not None: # pylint: disable=no-member base_framework_version = ( f"tensorflow{self.tensorflow_version}" # pylint: disable=no-member ) else: base_framework_version = f"pytorch{self.pytorch_version}" # pylint: disable=no-member return image_uris.retrieve( self._framework_name, region_name, version=self.framework_version, py_version=self.py_version, instance_type=instance_type, accelerator_type=accelerator_type, image_scope="inference", base_framework_version=base_framework_version, serverless_inference_config=serverless_inference_config, inference_tool=inference_tool, )