Struct aws_sdk_sagemaker::types::LabelingJobAlgorithmsConfig

#[non_exhaustive]
pub struct LabelingJobAlgorithmsConfig {
    pub labeling_job_algorithm_specification_arn: Option<String>,
    pub initial_active_learning_model_arn: Option<String>,
    pub labeling_job_resource_config: Option<LabelingJobResourceConfig>,
}

Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.

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.

labeling_job_algorithm_specification_arn: Option<String>

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

• Image classification

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification

• Text classification

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification

• Object detection

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection

• Semantic Segmentation

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation

initial_active_learning_model_arn: Option<String>

At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

labeling_job_resource_config: Option<LabelingJobResourceConfig>

Provides configuration information for a labeling job.

Implementations

impl LabelingJobAlgorithmsConfig

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

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

• Image classification

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification

• Text classification

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification

• Object detection

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection

• Semantic Segmentation

  arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation

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

At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

pub fn labeling_job_resource_config(&self) -> Option<&LabelingJobResourceConfig>

Provides configuration information for a labeling job.

impl LabelingJobAlgorithmsConfig

pub fn builder() -> LabelingJobAlgorithmsConfigBuilder

Creates a new builder-style object to manufacture LabelingJobAlgorithmsConfig.

Trait Implementations

impl Clone for LabelingJobAlgorithmsConfig

fn clone(&self) -> LabelingJobAlgorithmsConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for LabelingJobAlgorithmsConfig

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

Formats the value using the given formatter.

impl PartialEq for LabelingJobAlgorithmsConfig

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