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

Interface CreateAutoMlJobV2Request.Builder

    • Method Detail

      • 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).

        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:
        #autoMLJobInputDataConfig(java.util.Collection)

      • 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.

      • 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.

      • 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).

        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:
        #tags(java.util.Collection)

      • 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.

      • 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:
        autoMLJobObjective(AutoMLJobObjective)

      • 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.

      • 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.