PipelineModel: A pipeline of SageMaker 'Model' instances.

In DyfanJones/sagemaker-r-mlcore: sagemaker machine learning core classes and methods

A pipeline of SageMaker 'Model' instances.

Description

This pipeline can be deployed as an 'Endpoint' on SageMaker.

Methods

Public methods

• PipelineModel$new()

• PipelineModel$pipeline_container_def()

• PipelineModel$deploy()

• PipelineModel$transformer()

• PipelineModel$delete_model()

• PipelineModel$format()

• PipelineModel$clone()

---

Method new()

Initialize an SageMaker “Model“ which can be used to build an Inference Pipeline comprising of multiple model containers.

Usage

PipelineModel$new(
  models,
  role,
  predictor_cls = NULL,
  name = NULL,
  vpc_config = NULL,
  sagemaker_session = NULL,
  enable_network_isolation = FALSE
)

Arguments

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.

enable_network_isolation

(bool): 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

---

Method pipeline_container_def()

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()“.

Usage

PipelineModel$pipeline_container_def(instance_type)

Arguments

instance_type

(str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.

Returns

list[dict[str, str]]: A list of container definition objects usable with the CreateModel API in the scenario of multiple containers (Inference Pipeline).

---

Method deploy()

Deploy the “Model“ to an “Endpoint“. It 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.

Usage

PipelineModel$deploy(
  initial_instance_count,
  instance_type,
  serializer = NULL,
  deserializer = NULL,
  endpoint_name = NULL,
  tags = NULL,
  wait = TRUE,
  update_endpoint = FALSE,
  data_capture_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'.

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_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.

endpoint

(default: None). If “deserializer“ is not None, then “deserializer“ will override the default deserializer. The default deserializer is set by the “predictor_cls“.

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 transformer()

Return a “Transformer“ that uses this Model.

Usage

PipelineModel$transformer(
  instance_count,
  instance_type,
  strategy = NULL,
  assemble_with = NULL,
  output_path = NULL,
  output_kms_key = NULL,
  accept = NULL,
  env = NULL,
  max_concurrent_transforms = NULL,
  max_payload = NULL,
  tags = NULL,
  volume_kms_key = NULL
)

Arguments

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).

---

Method 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.

Usage

PipelineModel$delete_model()

---

Method format()

format class

Usage

PipelineModel$format()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipelineModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlcore documentation built on May 3, 2022, 10:08 a.m.