PipelineModel¶
- 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 of
sagemaker.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]) – 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()
.
- 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 anEndpoint
.It optionally return a
Predictor
.Create a SageMaker
Model
andEndpointConfig
, and deploy anEndpoint
from thisModel
. Ifself.predictor_cls
is not None, this method returns a the result of invokingself.predictor_cls
on the created endpoint name.The name of the created model is accessible in the
name
field of thisModel
after deploy returnsThe name of the created endpoint is accessible in the
endpoint_name
field of thisModel
after deploy returns.- Parameters
initial_instance_count (int) – The initial number of instances to run in the
Endpoint
created from thisModel
.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). Ifserializer
is not None, thenserializer
will override the default serializer. The default serializer is set by thepredictor_cls
.deserializer (
BaseDeserializer
) – A deserializer object, used to decode data from an inference endpoint (default: None). Ifdeserializer
is not None, thendeserializer
will override the default deserializer. The default deserializer is set by thepredictor_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 of
self.predictor_cls
on the created endpoint name, ifself.predictor_cls
is not None. Otherwise, return None.- Return type
callable[string, sagemaker.session.Session] or None
- 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 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).
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 aPipelineSession
instance, returns pipeline step arguments. Otherwise, returns
None
- If
- 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.