Struct aws_sdk_sagemaker::operation::create_auto_ml_job_v2::CreateAutoMlJobV2Input

#[non_exhaustive]
pub struct CreateAutoMlJobV2Input { /* private fields */ }

Implementations

impl CreateAutoMlJobV2Input

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 auto_ml_job_input_data_config(&self) -> Option<&[AutoMlJobChannel]>

An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to InputDataConfig supported by CreateAutoMLJob. The supported formats depend on the problem type:

• ImageClassification: S3Prefix, ManifestFile, AugmentedManifestFile

• TextClassification: S3Prefix

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.

pub fn auto_ml_problem_type_config(&self) -> Option<&AutoMlProblemTypeConfig>

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

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

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

pub fn tags(&self) -> Option<&[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.

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

The security configuration for traffic encryption or Amazon VPC settings.

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

Specifies a metric to minimize or maximize as the objective of a job. For CreateAutoMLJobV2, only Accuracy is supported.

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

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

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

This structure specifies how to split the data into train and validation datasets.

If you are using the V1 API (for example CreateAutoMLJob) or the V2 API for Natural Language Processing problems (for example CreateAutoMLJobV2 with a TextClassificationJobConfig problem type), the validation and training datasets must contain the same headers. Also, for V1 API jobs, the validation dataset must be less than 2 GB in size.

impl CreateAutoMlJobV2Input

pub fn builder() -> CreateAutoMlJobV2InputBuilder

Creates a new builder-style object to manufacture CreateAutoMlJobV2Input.

impl CreateAutoMlJobV2Input

pub async fn make_operation( &self, _config: &Config ) -> Result<Operation<CreateAutoMLJobV2, AwsResponseRetryClassifier>, BuildError>

Consumes the builder and constructs an Operation<CreateAutoMLJobV2>

Trait Implementations

impl Clone for CreateAutoMlJobV2Input

fn clone(&self) -> CreateAutoMlJobV2Input

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMlJobV2Input

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

Formats the value using the given formatter.

impl PartialEq<CreateAutoMlJobV2Input> for CreateAutoMlJobV2Input

fn eq(&self, other: &CreateAutoMlJobV2Input) -> 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 CreateAutoMlJobV2Input