Class: Aws::SageMaker::Types::CreateAutoMLJobV2Request

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::CreateAutoMLJobV2Request

Defined in:

gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #auto_ml_compute_config ⇒ Types::AutoMLComputeConfig

  Specifies the compute configuration for the AutoML job V2.

• #auto_ml_job_input_data_config ⇒ Array<Types::AutoMLJobChannel>

  An array of channel objects describing the input data and their location.

• #auto_ml_job_name ⇒ String

  Identifies an Autopilot job.

• #auto_ml_job_objective ⇒ Types::AutoMLJobObjective

  Specifies a metric to minimize or maximize as the objective of a job.

• #auto_ml_problem_type_config ⇒ Types::AutoMLProblemTypeConfig

  Defines the configuration settings of one of the supported problem types.

• #data_split_config ⇒ Types::AutoMLDataSplitConfig

  This structure specifies how to split the data into train and validation datasets.

• #model_deploy_config ⇒ Types::ModelDeployConfig

  Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

• #output_data_config ⇒ Types::AutoMLOutputDataConfig

  Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

• #role_arn ⇒ String

  The ARN of the role that is used to access the data.

• #security_config ⇒ Types::AutoMLSecurityConfig

  The security configuration for traffic encryption or Amazon VPC settings.

• #tags ⇒ Array<Types::Tag>

  An array of key-value pairs.

Instance Attribute Details

#auto_ml_compute_config ⇒ Types::AutoMLComputeConfig

Specifies the compute configuration for the AutoML job V2.

Returns:

• (Types::AutoMLComputeConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_input_data_config ⇒ Array<Types::AutoMLJobChannel>

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

• For tabular problem types: S3Prefix, ManifestFile.

• For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

• For text classification: S3Prefix.

• For time-series forecasting: S3Prefix.

• For text generation (LLMs fine-tuning): S3Prefix.

Returns:

• (Array<Types::AutoMLJobChannel>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_name ⇒ String

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

Returns:

• (String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_objective ⇒ Types::AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

Returns:

• (Types::AutoMLJobObjective)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_problem_type_config ⇒ Types::AutoMLProblemTypeConfig

Defines the configuration settings of one of the supported problem types.

Returns:

• (Types::AutoMLProblemTypeConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#data_split_config ⇒ Types::AutoMLDataSplitConfig

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

Returns:

• (Types::AutoMLDataSplitConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#model_deploy_config ⇒ Types::ModelDeployConfig

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Returns:

• (Types::ModelDeployConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#output_data_config ⇒ Types::AutoMLOutputDataConfig

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

Returns:

• (Types::AutoMLOutputDataConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#role_arn ⇒ String

The ARN of the role that is used to access the data.

Returns:

• (String)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#security_config ⇒ Types::AutoMLSecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

Returns:

• (Types::AutoMLSecurityConfig)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end

#tags ⇒ Array<Types::Tag>

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

Returns:

• (Array<Types::Tag>)

# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6128

class CreateAutoMLJobV2Request < Struct.new(
  :auto_ml_job_name,
  :auto_ml_job_input_data_config,
  :output_data_config,
  :auto_ml_problem_type_config,
  :role_arn,
  :tags,
  :security_config,
  :auto_ml_job_objective,
  :model_deploy_config,
  :data_split_config,
  :auto_ml_compute_config)
  SENSITIVE = []
  include Aws::Structure
end