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

Inherits:
Struct
  • Object
show all
Includes:
Aws::Structure
Defined in:
lib/aws-sdk-sagemaker/types.rb

Overview

See Also:

Constant Summary collapse

SENSITIVE = 
[]

Instance Attribute Summary collapse

Instance Attribute Details

#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][1

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`.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJob.html#sagemaker-CreateAutoMLJob-request-InputDataConfig

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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].

<note markdown=“1”> * 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].

</note>

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/llms-finetuning-models.html

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_problem_type_config ⇒ Types::AutoMLProblemTypeConfig

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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.

<note markdown=“1”> 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.

</note>

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  SENSITIVE = []
  include Aws::Structure
end

#role_arn ⇒ String

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

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  SENSITIVE = []
  include Aws::Structure
end

#security_config ⇒ Types::AutoMLSecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  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.

[1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 5005

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)
  SENSITIVE = []
  include Aws::Structure
end