Package software.amazon.awssdk.services.sagemaker.model

Interface CreateAutoMlJobV2Request.Builder

All Superinterfaces:
AwsRequest.Builder, Buildable, CopyableBuilder<CreateAutoMlJobV2Request.Builder,CreateAutoMlJobV2Request>, SageMakerRequest.Builder, SdkBuilder<CreateAutoMlJobV2Request.Builder,CreateAutoMlJobV2Request>, SdkPojo, SdkRequest.Builder
Enclosing class:
CreateAutoMlJobV2Request
public static interface CreateAutoMlJobV2Request.Builder extends SageMakerRequest.Builder, SdkPojo, CopyableBuilder<CreateAutoMlJobV2Request.Builder,CreateAutoMlJobV2Request>

  • Method Details

    • autoMLJobName

      CreateAutoMlJobV2Request.Builder autoMLJobName(String autoMLJobName)

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

      Parameters:
      autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLJobInputDataConfig

      CreateAutoMlJobV2Request.Builder autoMLJobInputDataConfig(Collection<AutoMLJobChannel> autoMLJobInputDataConfig)

      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.

      Parameters:
      autoMLJobInputDataConfig - 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:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLJobInputDataConfig

      CreateAutoMlJobV2Request.Builder autoMLJobInputDataConfig(AutoMLJobChannel... autoMLJobInputDataConfig)

      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.

      Parameters:
      autoMLJobInputDataConfig - 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:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLJobInputDataConfig

      CreateAutoMlJobV2Request.Builder autoMLJobInputDataConfig(Consumer<AutoMLJobChannel.Builder>... autoMLJobInputDataConfig)

      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.

      This is a convenience method that creates an instance of the AutoMLJobChannel.Builder avoiding the need to create one manually via AutoMLJobChannel.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to autoMLJobInputDataConfig(List<AutoMLJobChannel>).

      Parameters:
      autoMLJobInputDataConfig - a consumer that will call methods on AutoMLJobChannel.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • outputDataConfig

      CreateAutoMlJobV2Request.Builder outputDataConfig(AutoMLOutputDataConfig outputDataConfig)

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

      Parameters:
      outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • outputDataConfig

      default CreateAutoMlJobV2Request.Builder outputDataConfig(Consumer<AutoMLOutputDataConfig.Builder> outputDataConfig)

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

      This is a convenience method that creates an instance of the AutoMLOutputDataConfig.Builder avoiding the need to create one manually via AutoMLOutputDataConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to outputDataConfig(AutoMLOutputDataConfig).

      Parameters:
      outputDataConfig - a consumer that will call methods on AutoMLOutputDataConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • autoMLProblemTypeConfig

      CreateAutoMlJobV2Request.Builder autoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig)

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

      Parameters:
      autoMLProblemTypeConfig - Defines the configuration settings of one of the supported problem types.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLProblemTypeConfig

      default CreateAutoMlJobV2Request.Builder autoMLProblemTypeConfig(Consumer<AutoMLProblemTypeConfig.Builder> autoMLProblemTypeConfig)

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

      This is a convenience method that creates an instance of the AutoMLProblemTypeConfig.Builder avoiding the need to create one manually via AutoMLProblemTypeConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to autoMLProblemTypeConfig(AutoMLProblemTypeConfig).

      Parameters:
      autoMLProblemTypeConfig - a consumer that will call methods on AutoMLProblemTypeConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • roleArn

      CreateAutoMlJobV2Request.Builder roleArn(String roleArn)

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

      Parameters:
      roleArn - The ARN of the role that is used to access the data.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • tags

      CreateAutoMlJobV2Request.Builder tags(Collection<Tag> tags)

      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.

      Parameters:
      tags - 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:
      Returns a reference to this object so that method calls can be chained together.

    • tags

      CreateAutoMlJobV2Request.Builder tags(Tag... tags)

      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.

      Parameters:
      tags - 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:
      Returns a reference to this object so that method calls can be chained together.

    • tags

      CreateAutoMlJobV2Request.Builder tags(Consumer<Tag.Builder>... tags)

      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.

      This is a convenience method that creates an instance of the Tag.Builder avoiding the need to create one manually via Tag.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to tags(List<Tag>).

      Parameters:
      tags - a consumer that will call methods on Tag.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • securityConfig

      CreateAutoMlJobV2Request.Builder securityConfig(AutoMLSecurityConfig securityConfig)

      The security configuration for traffic encryption or Amazon VPC settings.

      Parameters:
      securityConfig - The security configuration for traffic encryption or Amazon VPC settings.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • securityConfig

      default CreateAutoMlJobV2Request.Builder securityConfig(Consumer<AutoMLSecurityConfig.Builder> securityConfig)

      The security configuration for traffic encryption or Amazon VPC settings.

      This is a convenience method that creates an instance of the AutoMLSecurityConfig.Builder avoiding the need to create one manually via AutoMLSecurityConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to securityConfig(AutoMLSecurityConfig).

      Parameters:
      securityConfig - a consumer that will call methods on AutoMLSecurityConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • autoMLJobObjective

      CreateAutoMlJobV2Request.Builder autoMLJobObjective(AutoMLJobObjective 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.

      Parameters:
      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:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLJobObjective

      default CreateAutoMlJobV2Request.Builder autoMLJobObjective(Consumer<AutoMLJobObjective.Builder> 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.

      This is a convenience method that creates an instance of the AutoMLJobObjective.Builder avoiding the need to create one manually via AutoMLJobObjective.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to autoMLJobObjective(AutoMLJobObjective).

      Parameters:
      autoMLJobObjective - a consumer that will call methods on AutoMLJobObjective.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • modelDeployConfig

      CreateAutoMlJobV2Request.Builder modelDeployConfig(ModelDeployConfig modelDeployConfig)

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

      Parameters:
      modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • modelDeployConfig

      default CreateAutoMlJobV2Request.Builder modelDeployConfig(Consumer<ModelDeployConfig.Builder> modelDeployConfig)

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

      This is a convenience method that creates an instance of the ModelDeployConfig.Builder avoiding the need to create one manually via ModelDeployConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to modelDeployConfig(ModelDeployConfig).

      Parameters:
      modelDeployConfig - a consumer that will call methods on ModelDeployConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • dataSplitConfig

      CreateAutoMlJobV2Request.Builder dataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)

      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.

      Parameters:
      dataSplitConfig - 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:
      Returns a reference to this object so that method calls can be chained together.

    • dataSplitConfig

      default CreateAutoMlJobV2Request.Builder dataSplitConfig(Consumer<AutoMLDataSplitConfig.Builder> dataSplitConfig)

      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.

      This is a convenience method that creates an instance of the AutoMLDataSplitConfig.Builder avoiding the need to create one manually via AutoMLDataSplitConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to dataSplitConfig(AutoMLDataSplitConfig).

      Parameters:
      dataSplitConfig - a consumer that will call methods on AutoMLDataSplitConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • autoMLComputeConfig

      CreateAutoMlJobV2Request.Builder autoMLComputeConfig(AutoMLComputeConfig autoMLComputeConfig)

      Specifies the compute configuration for the AutoML job V2.

      Parameters:
      autoMLComputeConfig - Specifies the compute configuration for the AutoML job V2.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • autoMLComputeConfig

      default CreateAutoMlJobV2Request.Builder autoMLComputeConfig(Consumer<AutoMLComputeConfig.Builder> autoMLComputeConfig)

      Specifies the compute configuration for the AutoML job V2.

      This is a convenience method that creates an instance of the AutoMLComputeConfig.Builder avoiding the need to create one manually via AutoMLComputeConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to autoMLComputeConfig(AutoMLComputeConfig).

      Parameters:
      autoMLComputeConfig - a consumer that will call methods on AutoMLComputeConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • overrideConfiguration

      CreateAutoMlJobV2Request.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      overrideConfiguration - The override configuration.
      Returns:
      This object for method chaining.

    • overrideConfiguration

      CreateAutoMlJobV2Request.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      builderConsumer - A Consumer to which an empty AwsRequestOverrideConfiguration.Builder will be given.
      Returns:
      This object for method chaining.