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

Class CreateAutoMlJobV2Request

    • Method Detail

      • autoMLJobName

        public final String autoMLJobName()

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

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

      • hasAutoMLJobInputDataConfig

        public final boolean hasAutoMLJobInputDataConfig()
        For responses, this returns true if the service returned a value for the AutoMLJobInputDataConfig property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • autoMLJobInputDataConfig

        public final List<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.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasAutoMLJobInputDataConfig() method.

        Returns:
        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.

      • outputDataConfig

        public final AutoMLOutputDataConfig outputDataConfig()

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

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

      • autoMLProblemTypeConfig

        public final AutoMLProblemTypeConfig autoMLProblemTypeConfig()

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

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

      • roleArn

        public final String roleArn()

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

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

      • hasTags

        public final boolean hasTags()
        For responses, this returns true if the service returned a value for the Tags property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • tags

        public final List<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.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasTags() method.

        Returns:
        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.

      • securityConfig

        public final AutoMLSecurityConfig securityConfig()

        The security configuration for traffic encryption or Amazon VPC settings.

        Returns:
        The security configuration for traffic encryption or Amazon VPC settings.

      • autoMLJobObjective

        public final 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.

        Returns:
        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.

      • modelDeployConfig

        public final ModelDeployConfig modelDeployConfig()

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

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

      • dataSplitConfig

        public final 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.

        Returns:
        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.

      • toString

        public final String toString()
        Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
        Overrides:
        toString in class Object