Create Model Explainability Job Definition

sagemaker_create_model_explainability_job_definition

R Documentation

Creates the definition for a model explainability job¶

Description¶

Creates the definition for a model explainability job.

Usage¶

sagemaker_create_model_explainability_job_definition(JobDefinitionName,
  ModelExplainabilityBaselineConfig, ModelExplainabilityAppSpecification,
  ModelExplainabilityJobInput, ModelExplainabilityJobOutputConfig,
  JobResources, NetworkConfig, RoleArn, StoppingCondition, Tags)

Arguments¶

JobDefinitionName

[required] The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

ModelExplainabilityBaselineConfig

The baseline configuration for a model explainability job.

ModelExplainabilityAppSpecification

[required] Configures the model explainability job to run a specified Docker container image.

ModelExplainabilityJobInput

[required] Inputs for the model explainability job.

ModelExplainabilityJobOutputConfig

[required] The output configuration for monitoring jobs.

JobResources

[required] Identifies the resources to deploy for a monitoring job.

NetworkConfig

Networking options for a model explainability job.

RoleArn

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

StoppingCondition

Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with create_model.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Value¶

A list with the following syntax:

list(
  JobDefinitionArn = "string"
)

Request syntax¶

svc$create_model_explainability_job_definition(
  JobDefinitionName = "string",
  ModelExplainabilityBaselineConfig = list(
    BaseliningJobName = "string",
    ConstraintsResource = list(
      S3Uri = "string"
    )
  ),
  ModelExplainabilityAppSpecification = list(
    ImageUri = "string",
    ConfigUri = "string",
    Environment = list(
      "string"
    )
  ),
  ModelExplainabilityJobInput = list(
    EndpointInput = list(
      EndpointName = "string",
      LocalPath = "string",
      S3InputMode = "Pipe"|"File",
      S3DataDistributionType = "FullyReplicated"|"ShardedByS3Key",
      FeaturesAttribute = "string",
      InferenceAttribute = "string",
      ProbabilityAttribute = "string",
      ProbabilityThresholdAttribute = 123.0,
      StartTimeOffset = "string",
      EndTimeOffset = "string",
      ExcludeFeaturesAttribute = "string"
    ),
    BatchTransformInput = list(
      DataCapturedDestinationS3Uri = "string",
      DatasetFormat = list(
        Csv = list(
          Header = TRUE|FALSE
        ),
        Json = list(
          Line = TRUE|FALSE
        ),
        Parquet = list()
      ),
      LocalPath = "string",
      S3InputMode = "Pipe"|"File",
      S3DataDistributionType = "FullyReplicated"|"ShardedByS3Key",
      FeaturesAttribute = "string",
      InferenceAttribute = "string",
      ProbabilityAttribute = "string",
      ProbabilityThresholdAttribute = 123.0,
      StartTimeOffset = "string",
      EndTimeOffset = "string",
      ExcludeFeaturesAttribute = "string"
    )
  ),
  ModelExplainabilityJobOutputConfig = list(
    MonitoringOutputs = list(
      list(
        S3Output = list(
          S3Uri = "string",
          LocalPath = "string",
          S3UploadMode = "Continuous"|"EndOfJob"
        )
      )
    ),
    KmsKeyId = "string"
  ),
  JobResources = list(
    ClusterConfig = list(
      InstanceCount = 123,
      InstanceType = "ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.r5d.large"|"ml.r5d.xlarge"|"ml.r5d.2xlarge"|"ml.r5d.4xlarge"|"ml.r5d.8xlarge"|"ml.r5d.12xlarge"|"ml.r5d.16xlarge"|"ml.r5d.24xlarge"|"ml.g6.xlarge"|"ml.g6.2xlarge"|"ml.g6.4xlarge"|"ml.g6.8xlarge"|"ml.g6.12xlarge"|"ml.g6.16xlarge"|"ml.g6.24xlarge"|"ml.g6.48xlarge"|"ml.g6e.xlarge"|"ml.g6e.2xlarge"|"ml.g6e.4xlarge"|"ml.g6e.8xlarge"|"ml.g6e.12xlarge"|"ml.g6e.16xlarge"|"ml.g6e.24xlarge"|"ml.g6e.48xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|"ml.p5.4xlarge"|"ml.g7e.2xlarge"|"ml.g7e.4xlarge"|"ml.g7e.8xlarge"|"ml.g7e.12xlarge"|"ml.g7e.24xlarge"|"ml.g7e.48xlarge",
      VolumeSizeInGB = 123,
      VolumeKmsKeyId = "string"
    )
  ),
  NetworkConfig = list(
    EnableInterContainerTrafficEncryption = TRUE|FALSE,
    EnableNetworkIsolation = TRUE|FALSE,
    VpcConfig = list(
      SecurityGroupIds = list(
        "string"
      ),
      Subnets = list(
        "string"
      )
    )
  ),
  RoleArn = "string",
  StoppingCondition = list(
    MaxRuntimeInSeconds = 123
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)