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Class: Aws::SageMaker::Types::CreateAutoMLJobRequest

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreateAutoMLJobRequest as input to an Aws::Client method, you can use a vanilla Hash:

{
  auto_ml_job_name: "AutoMLJobName", # required
  input_data_config: [ # required
    {
      data_source: { # required
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
          s3_uri: "S3Uri", # required
        },
      },
      compression_type: "None", # accepts None, Gzip
      target_attribute_name: "TargetAttributeName", # required
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
  auto_ml_job_objective: {
    metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro
  },
  auto_ml_job_config: {
    completion_criteria: {
      max_candidates: 1,
      max_runtime_per_training_job_in_seconds: 1,
      max_auto_ml_job_runtime_in_seconds: 1,
    },
    security_config: {
      volume_kms_key_id: "KmsKeyId",
      enable_inter_container_traffic_encryption: false,
      vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
    },
  },
  role_arn: "RoleArn", # required
  generate_candidate_definitions_only: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
}

Instance Attribute Summary collapse

Instance Attribute Details

#auto_ml_job_configTypes::AutoMLJobConfig

Contains CompletionCriteria and SecurityConfig.

Returns:

#auto_ml_job_nameString

Identifies an AutoPilot job. Must be unique to your account and is case-insensitive.

Returns:

  • (String)

    Identifies an AutoPilot job.

#auto_ml_job_objectiveTypes::AutoMLJobObjective

Defines the job\'s objective. You provide a MetricName and AutoML will infer minimize or maximize. If this is not provided, the most commonly used ObjectiveMetric for problem type will be selected.

Returns:

#generate_candidate_definitions_onlyBoolean

This will generate possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

  • (Boolean)

    This will generate possible candidates without training a model.

#input_data_configArray<Types::AutoMLChannel>

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 1000 rows.

Returns:

#output_data_configTypes::AutoMLOutputDataConfig

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

Returns:

#problem_typeString

Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Possible values:

  • BinaryClassification
  • MulticlassClassification
  • Regression

Returns:

  • (String)

    Defines the kind of preprocessing and algorithms intended for the candidates.

#role_arnString

The ARN of the role that will be used to access the data.

Returns:

  • (String)

    The ARN of the role that will be used to access the data.

#tagsArray<Types::Tag>

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Returns:

  • (Array<Types::Tag>)

    Each tag consists of a key and an optional value.