PipelineModel

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

Bases: object

A pipeline of SageMaker Model``s that can be deployed to an ``Endpoint.

Initialize an SageMaker Model which 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]]) – 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.
pipeline_container_def(instance_type)

Return a dict created by sagemaker.pipeline_container_def() for deploying this model to a specified instance type.

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, endpoint_name=None, tags=None, wait=True, update_endpoint=False, data_capture_config=None)

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.

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’.
  • 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.
Returns:

Invocation of self.predictor_cls on the created endpoint name, if self.predictor_cls is not None. Otherwise, return None.
Return type:

callable[string, sagemaker.session.Session] or 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: ‘MULTI_RECORD’ and ‘SINGLE_RECORD’.
  • 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.