Get Scaling Configuration Recommendation

sagemaker_get_scaling_configuration_recommendation

R Documentation

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job

Description

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.

Usage

sagemaker_get_scaling_configuration_recommendation(
  InferenceRecommendationsJobName, RecommendationId, EndpointName,
  TargetCpuUtilizationPerCore, ScalingPolicyObjective)

Arguments

InferenceRecommendationsJobName

[required] The name of a previously completed Inference Recommender job.

RecommendationId

The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.

Specify either this field or the EndpointName field.

EndpointName

The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.

Specify either this field or the RecommendationId field.

TargetCpuUtilizationPerCore

The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.

ScalingPolicyObjective

An object where you specify the anticipated traffic pattern for an endpoint.

Value

A list with the following syntax:

list(
  InferenceRecommendationsJobName = "string",
  RecommendationId = "string",
  EndpointName = "string",
  TargetCpuUtilizationPerCore = 123,
  ScalingPolicyObjective = list(
    MinInvocationsPerMinute = 123,
    MaxInvocationsPerMinute = 123
  ),
  Metric = list(
    InvocationsPerInstance = 123,
    ModelLatency = 123
  ),
  DynamicScalingConfiguration = list(
    MinCapacity = 123,
    MaxCapacity = 123,
    ScaleInCooldown = 123,
    ScaleOutCooldown = 123,
    ScalingPolicies = list(
      list(
        TargetTracking = list(
          MetricSpecification = list(
            Predefined = list(
              PredefinedMetricType = "string"
            ),
            Customized = list(
              MetricName = "string",
              Namespace = "string",
              Statistic = "Average"|"Minimum"|"Maximum"|"SampleCount"|"Sum"
            )
          ),
          TargetValue = 123.0
        )
      )
    )
  )
)

Request syntax

svc$get_scaling_configuration_recommendation(
  InferenceRecommendationsJobName = "string",
  RecommendationId = "string",
  EndpointName = "string",
  TargetCpuUtilizationPerCore = 123,
  ScalingPolicyObjective = list(
    MinInvocationsPerMinute = 123,
    MaxInvocationsPerMinute = 123
  )
)