TensorFlowModel: TensorFlowModel Class
| TensorFlowModel | R Documentation |
TensorFlowModel Class
Description
A “FrameworkModel“ implementation for inference with TensorFlow Serving.
Super classes
sagemaker.mlcore::ModelBase -> sagemaker.mlcore::Model -> sagemaker.mlcore::FrameworkModel -> TensorFlowModel
Public fields
LOG_LEVEL_PARAM_NAMElogging level
LOG_LEVEL_MAPlogging level map
LATEST_EIA_VERSIONlatest eia version supported
Methods
Public methods
Inherited methods
Method new()
Initialize a Model.
Usage
TensorFlowModel$new( model_data, role, entry_point = NULL, image_uri = NULL, framework_version = NULL, container_log_level = NULL, predictor_cls = TensorFlowPredictor, ... )
Arguments
model_data(str): The S3 location of a SageMaker model data “.tar.gz“ file.
role(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.
entry_point(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.
image_uri(str): A Docker image URI (default: None). If not specified, a default image for TensorFlow Serving will be used. If “framework_version“ is “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.
framework_version(str): Optional. TensorFlow Serving version you want to use. Defaults to “None“. Required unless “image_uri“ is provided.
container_log_level(int): Log level to use within the container (default: logging.ERROR). Valid values are defined in the Python logging module.
predictor_cls(callable[str, sagemaker.session.Session]): A function to call to create a predictor with an endpoint name and SageMaker “Session“. If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.
...: Keyword arguments passed to the superclass :class:'~sagemaker.model.FrameworkModel' and, subsequently, its superclass :class:'~sagemaker.model.Model'. .. tip:: You can find additional parameters for initializing this class at :class:'~sagemaker.model.FrameworkModel' and :class:'~sagemaker.model.Model'.
Method register()
Creates a model package for creating SageMaker models or listing on Marketplace.
Usage
TensorFlowModel$register( content_types, response_types, inference_instances, transform_instances, model_package_name = NULL, model_package_group_name = NULL, image_uri = NULL, model_metrics = NULL, metadata_properties = NULL, marketplace_cert = FALSE, approval_status = NULL, description = NULL )
Arguments
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).
Returns
str: A string of SageMaker Model Package ARN.
Method deploy()
Deploy a Tensorflow “Model“ to a SageMaker “Endpoint“.
Usage
TensorFlowModel$deploy( initial_instance_count = NULL, instance_type = NULL, serializer = NULL, deserializer = NULL, accelerator_type = NULL, endpoint_name = NULL, tags = NULL, kms_key = NULL, wait = TRUE, data_capture_config = NULL, update_endpoint = NULL, serverless_inference_config = NULL )
Arguments
initial_instance_count(int): The initial number of instances to run in the “Endpoint“ created from this “Model“.
instance_type(str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge', or 'local' for local mode.
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: NULL). 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.
update_endpoint: Placeholder
serverless_inference_config(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, we will use pre-defined values in “ServerlessInferenceConfig“ class to deploy serverless endpoint (default: None)
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.
Method prepare_container_def()
Prepare the container definition.
Usage
TensorFlowModel$prepare_container_def( instance_type = NULL, accelerator_type = NULL )
Arguments
instance_type: Instance type of the container.
accelerator_type: Accelerator type, if applicable.
Returns
A container definition for deploying a “Model“ to an “Endpoint“.
Method serving_image_uri()
Create a URI for the serving image.
Usage
TensorFlowModel$serving_image_uri()
Arguments
region_name(str): AWS region where the image is uploaded.
instance_type(str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).
accelerator_type(str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the
model(default: None). For example, 'ml.eia1.medium'.
Returns
str: The appropriate image URI based on the given parameters.
Method clone()
The objects of this class are cloneable with this method.
Usage
TensorFlowModel$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
