PipelineModel — sagemaker 2.247.0 documentation (original) (raw)

class sagemaker.pipeline.PipelineModel(models, role=None, predictor_cls=None, name=None, vpc_config=None, sagemaker_session=None, enable_network_isolation=None)

Bases: object

A pipeline of SageMaker Model instances.

This pipeline can be deployed as an Endpoint on SageMaker.

Initialize a SageMaker Model instance.

The Model can be used to build an Inference Pipeline comprising of multiple model containers.

Parameters:

• models (list[ sagemaker.Model ]) – For using multiple containers to build an inference pipeline, you can pass a list ofsagemaker.Model objects in the order you want the inference to happen.
• 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.
• predictor_cls (Callable _[_ _[_ _string_ _,_ sagemaker.session.Session] , Any ]) – A function to call to create a predictor (default: None). If not None, deploy will return the result of invoking this function on the created endpoint name.
• name (str) – The model name. If None, a default model name will be selected on each deploy.
• vpc_config (dict[_str,_ list[_str] ] or dict[_str, list_[_PipelineVariable] ]) – The VpcConfig set on the model (default: None) * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.
• sagemaker_session (sagemaker.session.Session) – A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.
• enable_network_isolation (bool or PipelineVariable) – Default False. if True, enables network isolation in the endpoint, isolating the model container. No inbound or outbound network calls can be made to or from the model container.Boolean

pipeline_container_def(instance_type=None)

The pipeline definition for deploying this model.

This is the dict created by sagemaker.pipeline_container_def().

The instance type to be used may be specified.

Subclasses can override this to provide custom container definitions for deployment to a specific instance type. Called by deploy().

Parameters:

instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.

Returns:

A list of container definition objects usable with the CreateModel API in the scenario of multiple containers (Inference Pipeline).

Return type:

list[dict[str, str]]

deploy(initial_instance_count, instance_type, serializer=None, deserializer=None, endpoint_name=None, tags=None, wait=True, update_endpoint=False, data_capture_config=None, kms_key=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None)

Deploy the Model to an Endpoint.

It optionally return a Predictor.

Create a SageMaker Model and EndpointConfig, and deploy anEndpoint 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 theendpoint_name field of this Model after deploy returns.

Parameters:

• 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’.
• serializer (BaseSerializer) – A serializer object, used to encode data for an inference endpoint (default: None). If serializer is not None, thenserializer will override the default serializer. The default serializer is set by the predictor_cls.
• deserializer (BaseDeserializer) – A deserializer object, used to decode data from an inference endpoint (default: None). If deserializer is not None, thendeserializer will override the default deserializer. The default deserializer is set by the predictor_cls.
• 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.
• wait (bool) – Whether the call should wait until the deployment of model completes (default: True).
• update_endpoint (bool) – Flag to update the model in an existing Amazon SageMaker endpoint. If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources corresponding to the previous EndpointConfig. If False, a new endpoint will be created. Default: False
• data_capture_config (sagemaker.model_monitor.DataCaptureConfig) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.
• kms_key (str) – The ARN, Key ID or Alias of the KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.
• 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

Returns:

Invocation ofself.predictor_cls on the created endpoint name, if self.predictor_clsis not None. Otherwise, return None.

Return type:

Optional[Callable[[string, sagemaker.session.Session], Any]]

create(instance_type)

Create a SageMaker Model Entity

Parameters:

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.

register(content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, drift_check_baselines=None, customer_metadata_properties=None, domain=None, sample_payload_url=None, task=None, framework=None, framework_version=None, nearest_model_name=None, data_input_configuration=None, skip_model_validation=None, source_uri=None, model_card=None)

Creates a model package for creating SageMaker models or listing on Marketplace.

Parameters:

• 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 tomodel_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).
• skip_model_validation (str or PipelineVariable) – Indicates if you want to skip model validation. Values can be “All” or “None” (default: None).
• source_uri (str or PipelineVariable) – The URI of the source for the model package (default: None).
• model_card (ModeCard or ModelPackageModelCard) – document contains qualitative and quantitative information about a model (default: None).

Returns:

If sagemaker_session is a PipelineSession instance, returns pipeline step

arguments. Otherwise, returns None

transformer(instance_count, instance_type, strategy=None, assemble_with=None, output_path=None, output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None, max_payload=None, tags=None, volume_kms_key=None)

Return a Transformer that uses this Model.

Parameters:

• instance_count (int) – Number of EC2 instances to use.
• instance_type (str) – Type of EC2 instance to use, for example, ‘ml.c4.xlarge’.
• strategy (str) – The strategy used to decide how to batch records in a single request (default: None). Valid values: ‘MultiRecord’ and ‘SingleRecord’.
• assemble_with (str) – How the output is assembled (default: None). Valid values: ‘Line’ or ‘None’.
• output_path (str) – S3 location for saving the transform result. If not specified, results are stored to a default bucket.
• output_kms_key (str) – Optional. KMS key ID for encrypting the transform output (default: None).
• accept (str) – The accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.
• env (dict) – Environment variables to be set for use during the transform job (default: None).
• max_concurrent_transforms (int) – The maximum number of HTTP requests to be made to each individual transform container at one time.
• max_payload (int) – Maximum size of the payload in a single HTTP request to the container in MB.
• tags (list_[_dict]) – List of tags for labeling a transform job. If none specified, then the tags used for the training job are used for the transform job.
• volume_kms_key (str) – Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: None).

delete_model()

Delete the SageMaker model backing this pipeline model.

This does not delete the list of SageMaker models used in multiple containers to build the inference pipeline.