aws_sdk_sagemaker::operation::create_auto_ml_job_v2::builders

Struct CreateAutoMlJobV2InputBuilder

#[non_exhaustive]
pub struct CreateAutoMlJobV2InputBuilder { /* private fields */ }

Expand description

A builder for CreateAutoMlJobV2Input.

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impl CreateAutoMlJobV2InputBuilder

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pub fn auto_ml_job_name(self, input: impl Into<String>) -> Self

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

This field is required.

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pub fn set_auto_ml_job_name(self, input: Option<String>) -> Self

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

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pub fn get_auto_ml_job_name(&self) -> &Option<String>

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

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pub fn auto_ml_job_input_data_config(self, input: AutoMlJobChannel) -> Self

Appends an item to auto_ml_job_input_data_config.

To override the contents of this collection use set_auto_ml_job_input_data_config.

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.

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pub fn set_auto_ml_job_input_data_config( self, input: Option<Vec<AutoMlJobChannel>>, ) -> Self

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.

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pub fn get_auto_ml_job_input_data_config( &self, ) -> &Option<Vec<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.

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pub fn output_data_config(self, input: AutoMlOutputDataConfig) -> Self

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

This field is required.

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pub fn set_output_data_config( self, input: Option<AutoMlOutputDataConfig>, ) -> Self

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

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pub fn get_output_data_config(&self) -> &Option<AutoMlOutputDataConfig>

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

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pub fn auto_ml_problem_type_config(self, input: AutoMlProblemTypeConfig) -> Self

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

This field is required.

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pub fn set_auto_ml_problem_type_config( self, input: Option<AutoMlProblemTypeConfig>, ) -> Self

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

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pub fn get_auto_ml_problem_type_config( &self, ) -> &Option<AutoMlProblemTypeConfig>

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

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pub fn role_arn(self, input: impl Into<String>) -> Self

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

This field is required.

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pub fn set_role_arn(self, input: Option<String>) -> Self

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

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pub fn get_role_arn(&self) -> &Option<String>

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

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pub fn tags(self, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_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.

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pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

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pub fn get_tags(&self) -> &Option<Vec<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.

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pub fn security_config(self, input: AutoMlSecurityConfig) -> Self

The security configuration for traffic encryption or Amazon VPC settings.

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pub fn set_security_config(self, input: Option<AutoMlSecurityConfig>) -> Self

The security configuration for traffic encryption or Amazon VPC settings.

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pub fn get_security_config(&self) -> &Option<AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

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pub fn auto_ml_job_objective(self, input: AutoMlJobObjective) -> Self

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.

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pub fn set_auto_ml_job_objective( self, input: Option<AutoMlJobObjective>, ) -> Self

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.

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pub fn get_auto_ml_job_objective(&self) -> &Option<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.

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pub fn model_deploy_config(self, input: ModelDeployConfig) -> Self

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

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pub fn set_model_deploy_config(self, input: Option<ModelDeployConfig>) -> Self

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

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pub fn get_model_deploy_config(&self) -> &Option<ModelDeployConfig>

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

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pub fn data_split_config(self, input: AutoMlDataSplitConfig) -> Self

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.

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pub fn set_data_split_config(self, input: Option<AutoMlDataSplitConfig>) -> Self

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.

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pub fn get_data_split_config(&self) -> &Option<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.

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pub fn auto_ml_compute_config(self, input: AutoMlComputeConfig) -> Self

Specifies the compute configuration for the AutoML job V2.

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pub fn set_auto_ml_compute_config( self, input: Option<AutoMlComputeConfig>, ) -> Self

Specifies the compute configuration for the AutoML job V2.

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pub fn get_auto_ml_compute_config(&self) -> &Option<AutoMlComputeConfig>

Specifies the compute configuration for the AutoML job V2.

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pub fn build(self) -> Result<CreateAutoMlJobV2Input, BuildError>

Consumes the builder and constructs a CreateAutoMlJobV2Input.

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impl CreateAutoMlJobV2InputBuilder

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pub async fn send_with( self, client: &Client, ) -> Result<CreateAutoMlJobV2Output, SdkError<CreateAutoMLJobV2Error, HttpResponse>>

Sends a request with this input using the given client.

Trait Implementations§

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impl Clone for CreateAutoMlJobV2InputBuilder

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fn clone(&self) -> CreateAutoMlJobV2InputBuilder

Returns a duplicate of the value. Read more

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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more

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impl Debug for CreateAutoMlJobV2InputBuilder

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more

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impl Default for CreateAutoMlJobV2InputBuilder

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fn default() -> CreateAutoMlJobV2InputBuilder

Returns the “default value” for a type. Read more

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impl PartialEq for CreateAutoMlJobV2InputBuilder

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fn eq(&self, other: &CreateAutoMlJobV2InputBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.

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fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

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impl StructuralPartialEq for CreateAutoMlJobV2InputBuilder

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impl UnwindSafe for CreateAutoMlJobV2InputBuilder

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impl<T> WithSubscriber for T

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fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

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fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more

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Struct CreateAutoMlJobV2InputBuilderCopy item path

Implementations§

impl CreateAutoMlJobV2InputBuilder

pub fn auto_ml_job_name(self, input: impl Into<String>) -> Self

pub fn set_auto_ml_job_name(self, input: Option<String>) -> Self

pub fn get_auto_ml_job_name(&self) -> &Option<String>

pub fn auto_ml_job_input_data_config(self, input: AutoMlJobChannel) -> Self

pub fn set_auto_ml_job_input_data_config( self, input: Option<Vec<AutoMlJobChannel>>, ) -> Self

pub fn get_auto_ml_job_input_data_config( &self, ) -> &Option<Vec<AutoMlJobChannel>>

pub fn output_data_config(self, input: AutoMlOutputDataConfig) -> Self

pub fn set_output_data_config( self, input: Option<AutoMlOutputDataConfig>, ) -> Self

pub fn get_output_data_config(&self) -> &Option<AutoMlOutputDataConfig>

pub fn auto_ml_problem_type_config(self, input: AutoMlProblemTypeConfig) -> Self

pub fn set_auto_ml_problem_type_config( self, input: Option<AutoMlProblemTypeConfig>, ) -> Self

pub fn get_auto_ml_problem_type_config( &self, ) -> &Option<AutoMlProblemTypeConfig>

pub fn role_arn(self, input: impl Into<String>) -> Self

pub fn set_role_arn(self, input: Option<String>) -> Self

pub fn get_role_arn(&self) -> &Option<String>

pub fn tags(self, input: Tag) -> Self

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

pub fn get_tags(&self) -> &Option<Vec<Tag>>

pub fn security_config(self, input: AutoMlSecurityConfig) -> Self

pub fn set_security_config(self, input: Option<AutoMlSecurityConfig>) -> Self

pub fn get_security_config(&self) -> &Option<AutoMlSecurityConfig>

pub fn auto_ml_job_objective(self, input: AutoMlJobObjective) -> Self

pub fn set_auto_ml_job_objective( self, input: Option<AutoMlJobObjective>, ) -> Self

pub fn get_auto_ml_job_objective(&self) -> &Option<AutoMlJobObjective>

pub fn model_deploy_config(self, input: ModelDeployConfig) -> Self

pub fn set_model_deploy_config(self, input: Option<ModelDeployConfig>) -> Self

pub fn get_model_deploy_config(&self) -> &Option<ModelDeployConfig>

pub fn data_split_config(self, input: AutoMlDataSplitConfig) -> Self

pub fn set_data_split_config(self, input: Option<AutoMlDataSplitConfig>) -> Self

pub fn get_data_split_config(&self) -> &Option<AutoMlDataSplitConfig>

pub fn auto_ml_compute_config(self, input: AutoMlComputeConfig) -> Self

pub fn set_auto_ml_compute_config( self, input: Option<AutoMlComputeConfig>, ) -> Self

pub fn get_auto_ml_compute_config(&self) -> &Option<AutoMlComputeConfig>

pub fn build(self) -> Result<CreateAutoMlJobV2Input, BuildError>

impl CreateAutoMlJobV2InputBuilder

pub async fn send_with( self, client: &Client, ) -> Result<CreateAutoMlJobV2Output, SdkError<CreateAutoMLJobV2Error, HttpResponse>>

Trait Implementations§

impl Clone for CreateAutoMlJobV2InputBuilder

fn clone(&self) -> CreateAutoMlJobV2InputBuilder

fn clone_from(&mut self, source: &Self)

impl Debug for CreateAutoMlJobV2InputBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

impl Default for CreateAutoMlJobV2InputBuilder

fn default() -> CreateAutoMlJobV2InputBuilder

impl PartialEq for CreateAutoMlJobV2InputBuilder

fn eq(&self, other: &CreateAutoMlJobV2InputBuilder) -> bool

fn ne(&self, other: &Rhs) -> bool

impl StructuralPartialEq for CreateAutoMlJobV2InputBuilder

Auto Trait Implementations§

impl Freeze for CreateAutoMlJobV2InputBuilder

impl RefUnwindSafe for CreateAutoMlJobV2InputBuilder

impl Send for CreateAutoMlJobV2InputBuilder

impl Sync for CreateAutoMlJobV2InputBuilder

impl Unpin for CreateAutoMlJobV2InputBuilder

impl UnwindSafe for CreateAutoMlJobV2InputBuilder

Blanket Implementations§

impl<T> Any for Twhere T: 'static + ?Sized,

fn type_id(&self) -> TypeId

impl<T> Borrow<T> for Twhere T: ?Sized,

fn borrow(&self) -> &T

impl<T> BorrowMut<T> for Twhere T: ?Sized,

fn borrow_mut(&mut self) -> &mut T

impl<T> CloneToUninit for Twhere T: Clone,

unsafe fn clone_to_uninit(&self, dest: *mut u8)

impl<T> From<T> for T

fn from(t: T) -> T

impl<T> Instrument for T

fn instrument(self, span: Span) -> Instrumented<Self>

fn in_current_span(self) -> Instrumented<Self>

impl<T, U> Into<U> for Twhere U: From<T>,

fn into(self) -> U

impl<T> IntoEither for T

fn into_either(self, into_left: bool) -> Either<Self, Self>

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>where F: FnOnce(&Self) -> bool,

impl<Unshared, Shared> IntoShared<Shared> for Unsharedwhere Shared: FromUnshared<Unshared>,

fn into_shared(self) -> Shared

impl<T> Paint for Twhere T: ?Sized,

Struct CreateAutoMlJobV2InputBuilder

impl<T> Any for T
where T: 'static + ?Sized,

impl<T> Borrow<T> for T
where T: ?Sized,

impl<T> BorrowMut<T> for T
where T: ?Sized,

impl<T> CloneToUninit for T
where T: Clone,

impl<T, U> Into<U> for T
where U: From<T>,

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

impl<Unshared, Shared> IntoShared<Shared> for Unshared
where Shared: FromUnshared<Unshared>,

impl<T> Paint for T
where T: ?Sized,