Struct aws_sdk_sagemaker::operation::create_auto_ml_job::builders::CreateAutoMlJobInputBuilder

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

A builder for CreateAutoMlJobInput.

Implementations

impl CreateAutoMlJobInputBuilder

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.

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.

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.

pub fn input_data_config(self, input: AutoMlChannel) -> Self

Appends an item to input_data_config.

To override the contents of this collection use set_input_data_config.

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

pub fn set_input_data_config(self, input: Option<Vec<AutoMlChannel>>) -> Self

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

pub fn get_input_data_config(&self) -> &Option<Vec<AutoMlChannel>>

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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. Format(s) supported: CSV.

This field is required.

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. Format(s) supported: CSV.

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. Format(s) supported: CSV.

pub fn problem_type(self, input: ProblemType) -> Self

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

pub fn set_problem_type(self, input: Option<ProblemType>) -> Self

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

pub fn get_problem_type(&self) -> &Option<ProblemType>

Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

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. See AutoMLJobObjective for the default values.

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. See AutoMLJobObjective for the default values.

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. See AutoMLJobObjective for the default values.

pub fn auto_ml_job_config(self, input: AutoMlJobConfig) -> Self

A collection of settings used to configure an AutoML job.

pub fn set_auto_ml_job_config(self, input: Option<AutoMlJobConfig>) -> Self

A collection of settings used to configure an AutoML job.

pub fn get_auto_ml_job_config(&self) -> &Option<AutoMlJobConfig>

A collection of settings used to configure an AutoML job.

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.

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

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

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

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

pub fn generate_candidate_definitions_only(self, input: bool) -> Self

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

pub fn set_generate_candidate_definitions_only( self, input: Option<bool> ) -> Self

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

pub fn get_generate_candidate_definitions_only(&self) -> &Option<bool>

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

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, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

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, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

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, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

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

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

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.

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

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

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

Consumes the builder and constructs a CreateAutoMlJobInput.

impl CreateAutoMlJobInputBuilder

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

Sends a request with this input using the given client.

Trait Implementations

impl Clone for CreateAutoMlJobInputBuilder

fn clone(&self) -> CreateAutoMlJobInputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMlJobInputBuilder

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

Formats the value using the given formatter.

impl Default for CreateAutoMlJobInputBuilder

fn default() -> CreateAutoMlJobInputBuilder

Returns the “default value” for a type.

impl PartialEq for CreateAutoMlJobInputBuilder

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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for CreateAutoMlJobInputBuilder