Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration¶

Description¶

Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration. The benchmark job measures metrics such as latency, throughput, and cost for your generative AI inference endpoints.

Usage¶

sagemaker_create_ai_benchmark_job(AIBenchmarkJobName, BenchmarkTarget,
  OutputConfig, AIWorkloadConfigIdentifier, RoleArn, NetworkConfig, Tags)

Arguments¶

• AIBenchmarkJobName

  [required] The name of the AI benchmark job. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.

• BenchmarkTarget

  [required] The target endpoint to benchmark. Specify a SageMaker endpoint by providing its name or Amazon Resource Name (ARN).

• OutputConfig

  [required] The output configuration for the benchmark job, including the Amazon S3 location where benchmark results are stored.

• AIWorkloadConfigIdentifier

  [required] The name or Amazon Resource Name (ARN) of the AI workload configuration to use for this benchmark job.

• RoleArn

  [required] The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

• NetworkConfig

  The network configuration for the benchmark job, including VPC settings.

• Tags

  The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define.

Value¶

A list with the following syntax:

list(
  AIBenchmarkJobArn = "string"
)

Request syntax¶

svc$create_ai_benchmark_job(
  AIBenchmarkJobName = "string",
  BenchmarkTarget = list(
    Endpoint = list(
      Identifier = "string",
      TargetContainerHostname = "string",
      InferenceComponents = list(
        list(
          Identifier = "string"
        )
      )
    )
  ),
  OutputConfig = list(
    S3OutputLocation = "string",
    MlflowConfig = list(
      MlflowResourceArn = "string",
      MlflowExperimentName = "string",
      MlflowRunName = "string"
    )
  ),
  AIWorkloadConfigIdentifier = "string",
  RoleArn = "string",
  NetworkConfig = list(
    VpcConfig = list(
      SecurityGroupIds = list(
        "string"
      ),
      Subnets = list(
        "string"
      )
    )
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)