Struct aws_sdk_sagemaker::operation::create_auto_ml_job::CreateAutoMlJobInput

#[non_exhaustive]
pub struct CreateAutoMlJobInput {
    pub auto_ml_job_name: Option<String>,
    pub input_data_config: Option<Vec<AutoMlChannel>>,
    pub output_data_config: Option<AutoMlOutputDataConfig>,
    pub problem_type: Option<ProblemType>,
    pub auto_ml_job_objective: Option<AutoMlJobObjective>,
    pub auto_ml_job_config: Option<AutoMlJobConfig>,
    pub role_arn: Option<String>,
    pub generate_candidate_definitions_only: Option<bool>,
    pub tags: Option<Vec<Tag>>,
    pub model_deploy_config: Option<ModelDeployConfig>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

auto_ml_job_name: Option<String>

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

input_data_config: 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.

output_data_config: Option<AutoMlOutputDataConfig>

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

problem_type: Option<ProblemType>

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

auto_ml_job_objective: 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.

auto_ml_job_config: Option<AutoMlJobConfig>

A collection of settings used to configure an AutoML job.

role_arn: Option<String>

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

generate_candidate_definitions_only: Option<bool>

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

tags: 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.

model_deploy_config: Option<ModelDeployConfig>

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

Implementations

impl CreateAutoMlJobInput

pub fn auto_ml_job_name(&self) -> Option<&str>

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

pub fn input_data_config(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .input_data_config.is_none().

pub fn 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) -> 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) -> 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) -> Option<&AutoMlJobConfig>

A collection of settings used to configure an AutoML job.

pub fn role_arn(&self) -> Option<&str>

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

pub fn 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) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

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

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

impl CreateAutoMlJobInput

pub fn builder() -> CreateAutoMlJobInputBuilder

Creates a new builder-style object to manufacture CreateAutoMlJobInput.

Trait Implementations

impl Clone for CreateAutoMlJobInput

fn clone(&self) -> CreateAutoMlJobInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMlJobInput

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

Formats the value using the given formatter.

impl PartialEq for CreateAutoMlJobInput

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

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

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

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

impl StructuralPartialEq for CreateAutoMlJobInput