Describe Auto Ml Job

sagemaker_describe_auto_ml_job

R Documentation

Returns information about an AutoML job created by calling CreateAutoMLJob

Description

Returns information about an AutoML job created by calling create_auto_ml_job.

AutoML jobs created by calling create_auto_ml_job_v2 cannot be described by describe_auto_ml_job.

Usage

sagemaker_describe_auto_ml_job(AutoMLJobName)

Arguments

AutoMLJobName

[required] Requests information about an AutoML job using its unique name.

Value

A list with the following syntax:

list(
  AutoMLJobName = "string",
  AutoMLJobArn = "string",
  InputDataConfig = list(
    list(
      DataSource = list(
        S3DataSource = list(
          S3DataType = "ManifestFile"|"S3Prefix"|"AugmentedManifestFile",
          S3Uri = "string"
        )
      ),
      CompressionType = "None"|"Gzip",
      TargetAttributeName = "string",
      ContentType = "string",
      ChannelType = "training"|"validation",
      SampleWeightAttributeName = "string"
    )
  ),
  OutputDataConfig = list(
    KmsKeyId = "string",
    S3OutputPath = "string"
  ),
  RoleArn = "string",
  AutoMLJobObjective = list(
    MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"
  ),
  ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression",
  AutoMLJobConfig = list(
    CompletionCriteria = list(
      MaxCandidates = 123,
      MaxRuntimePerTrainingJobInSeconds = 123,
      MaxAutoMLJobRuntimeInSeconds = 123
    ),
    SecurityConfig = list(
      VolumeKmsKeyId = "string",
      EnableInterContainerTrafficEncryption = TRUE|FALSE,
      VpcConfig = list(
        SecurityGroupIds = list(
          "string"
        ),
        Subnets = list(
          "string"
        )
      )
    ),
    CandidateGenerationConfig = list(
      FeatureSpecificationS3Uri = "string",
      AlgorithmsConfig = list(
        list(
          AutoMLAlgorithms = list(
            "xgboost"|"linear-learner"|"mlp"|"lightgbm"|"catboost"|"randomforest"|"extra-trees"|"nn-torch"|"fastai"|"cnn-qr"|"deepar"|"prophet"|"npts"|"arima"|"ets"
          )
        )
      )
    ),
    DataSplitConfig = list(
      ValidationFraction = 123.0
    ),
    Mode = "AUTO"|"ENSEMBLING"|"HYPERPARAMETER_TUNING"
  ),
  CreationTime = as.POSIXct(
    "2015-01-01"
  ),
  EndTime = as.POSIXct(
    "2015-01-01"
  ),
  LastModifiedTime = as.POSIXct(
    "2015-01-01"
  ),
  FailureReason = "string",
  PartialFailureReasons = list(
    list(
      PartialFailureMessage = "string"
    )
  ),
  BestCandidate = list(
    CandidateName = "string",
    FinalAutoMLJobObjectiveMetric = list(
      Type = "Maximize"|"Minimize",
      MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss",
      Value = 123.0,
      StandardMetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"
    ),
    ObjectiveStatus = "Succeeded"|"Pending"|"Failed",
    CandidateSteps = list(
      list(
        CandidateStepType = "AWS::SageMaker::TrainingJob"|"AWS::SageMaker::TransformJob"|"AWS::SageMaker::ProcessingJob",
        CandidateStepArn = "string",
        CandidateStepName = "string"
      )
    ),
    CandidateStatus = "Completed"|"InProgress"|"Failed"|"Stopped"|"Stopping",
    InferenceContainers = list(
      list(
        Image = "string",
        ModelDataUrl = "string",
        Environment = list(
          "string"
        )
      )
    ),
    CreationTime = as.POSIXct(
      "2015-01-01"
    ),
    EndTime = as.POSIXct(
      "2015-01-01"
    ),
    LastModifiedTime = as.POSIXct(
      "2015-01-01"
    ),
    FailureReason = "string",
    CandidateProperties = list(
      CandidateArtifactLocations = list(
        Explainability = "string",
        ModelInsights = "string",
        BacktestResults = "string"
      ),
      CandidateMetrics = list(
        list(
          MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss",
          Value = 123.0,
          Set = "Train"|"Validation"|"Test",
          StandardMetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"MAE"|"R2"|"BalancedAccuracy"|"Precision"|"PrecisionMacro"|"Recall"|"RecallMacro"|"LogLoss"|"InferenceLatency"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"|"Rouge1"|"Rouge2"|"RougeL"|"RougeLSum"|"Perplexity"|"ValidationLoss"|"TrainingLoss"
        )
      )
    ),
    InferenceContainerDefinitions = list(
      list(
        list(
          Image = "string",
          ModelDataUrl = "string",
          Environment = list(
            "string"
          )
        )
      )
    )
  ),
  AutoMLJobStatus = "Completed"|"InProgress"|"Failed"|"Stopped"|"Stopping",
  AutoMLJobSecondaryStatus = "Starting"|"MaxCandidatesReached"|"Failed"|"Stopped"|"MaxAutoMLJobRuntimeReached"|"Stopping"|"CandidateDefinitionsGenerated"|"Completed"|"ExplainabilityError"|"DeployingModel"|"ModelDeploymentError"|"GeneratingModelInsightsReport"|"ModelInsightsError"|"AnalyzingData"|"FeatureEngineering"|"ModelTuning"|"GeneratingExplainabilityReport"|"TrainingModels"|"PreTraining",
  GenerateCandidateDefinitionsOnly = TRUE|FALSE,
  AutoMLJobArtifacts = list(
    CandidateDefinitionNotebookLocation = "string",
    DataExplorationNotebookLocation = "string"
  ),
  ResolvedAttributes = list(
    AutoMLJobObjective = list(
      MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"
    ),
    ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression",
    CompletionCriteria = list(
      MaxCandidates = 123,
      MaxRuntimePerTrainingJobInSeconds = 123,
      MaxAutoMLJobRuntimeInSeconds = 123
    )
  ),
  ModelDeployConfig = list(
    AutoGenerateEndpointName = TRUE|FALSE,
    EndpointName = "string"
  ),
  ModelDeployResult = list(
    EndpointName = "string"
  )
)

Request syntax

svc$describe_auto_ml_job(
  AutoMLJobName = "string"
)