RegisterModel: Workflow RegisterModel class

In DyfanJones/sagemaker-r-workflow: sagemaker pipeline and workflows

Workflow RegisterModel class

Description

Register Model step collection for workflow.

Super class

sagemaker.workflow::StepCollection -> RegisterModel

Methods

Public methods

• RegisterModel$new()

• RegisterModel$clone()

Inherited methods

• sagemaker.workflow::StepCollection$format()

• sagemaker.workflow::StepCollection$request_list()

---

Method new()

Construct steps '_RepackModelStep' and '_RegisterModelStep' based on the estimator.

Usage

RegisterModel$new(
  name,
  content_types,
  response_types,
  inference_instances,
  transform_instances,
  estimator = NULL,
  model_data = NULL,
  depends_on = NULL,
  repack_model_step_retry_policies = NULL,
  register_model_step_retry_policies = NULL,
  model_package_group_name = NULL,
  model_metrics = NULL,
  approval_status = NULL,
  image_uri = NULL,
  compile_model_family = NULL,
  display_name = NULL,
  description = NULL,
  tags = NULL,
  model = NULL,
  drift_check_baselines = NULL,
  ...
)

Arguments

name

(str): The name of the training step.

content_types

(list): The supported MIME types for the input data (default: None).

response_types

(list): The supported MIME types for the output data (default: None).

inference_instances

(list): A list of the instance types that are used to generate inferences in real-time (default: None).

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 (default: None).

estimator

The estimator instance.

model_data

The S3 uri to the model data from training.

depends_on

(List[str] or List[Step]): The list of step names or step instances the first step in the collection depends on

repack_model_step_retry_policies

(List[RetryPolicy]): The list of retry policies for the repack model step

register_model_step_retry_policies

(List[RetryPolicy]): The list of retry policies for register model step

model_package_group_name

(str): The Model Package Group name, exclusive to 'model_package_name', using 'model_package_group_name' makes the Model Package versioned (default: None).

model_metrics

(ModelMetrics): ModelMetrics object (default: None).

approval_status

(str): Model Approval Status, values can be "Approved", "Rejected", or "PendingManualApproval" (default: "PendingManualApproval").

image_uri

(str): The container image uri for Model Package, if not specified, Estimator's training container image is used (default: None).

compile_model_family

(str): The instance family for the compiled model. If specified, a compiled model is used (default: None).

display_name

(str): The display name of the step.

description

(str): Model Package description (default: None).

tags

(List[dict[str, str]]): The list of tags to attach to the model package group. Note that tags will only be applied to newly created model package groups; if the name of an existing group is passed to "model_package_group_name", tags will not be applied.

model

(object or Model): A PipelineModel object that comprises a list of models which gets executed as a serial inference pipeline or a Model object.

drift_check_baselines

(DriftCheckBaselines): DriftCheckBaselines object (default: None).

...

: additional arguments to 'create_model'.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

RegisterModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-workflow documentation built on April 3, 2022, 11:28 p.m.