Struct aws_sdk_sagemaker::operation::create_algorithm::CreateAlgorithmInput

#[non_exhaustive]
pub struct CreateAlgorithmInput {
    pub algorithm_name: Option<String>,
    pub algorithm_description: Option<String>,
    pub training_specification: Option<TrainingSpecification>,
    pub inference_specification: Option<InferenceSpecification>,
    pub validation_specification: Option<AlgorithmValidationSpecification>,
    pub certify_for_marketplace: Option<bool>,
    pub tags: Option<Vec<Tag>>,
}

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.

algorithm_name: Option<String>

The name of the algorithm.

algorithm_description: Option<String>

A description of the algorithm.

training_specification: Option<TrainingSpecification>

Specifies details about training jobs run by this algorithm, including the following:

• The Amazon ECR path of the container and the version digest of the algorithm.

• The hyperparameters that the algorithm supports.

• The instance types that the algorithm supports for training.

• Whether the algorithm supports distributed training.

• The metrics that the algorithm emits to Amazon CloudWatch.

• Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

• The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

inference_specification: Option<InferenceSpecification>

Specifies details about inference jobs that the algorithm runs, including the following:

• The Amazon ECR paths of containers that contain the inference code and model artifacts.

• The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

• The input and output content formats that the algorithm supports for inference.

validation_specification: Option<AlgorithmValidationSpecification>

Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.

certify_for_marketplace: Option<bool>

Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

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 Services Resources.

Implementations

impl CreateAlgorithmInput

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

The name of the algorithm.

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

A description of the algorithm.

pub fn training_specification(&self) -> Option<&TrainingSpecification>

Specifies details about training jobs run by this algorithm, including the following:

• The Amazon ECR path of the container and the version digest of the algorithm.

• The hyperparameters that the algorithm supports.

• The instance types that the algorithm supports for training.

• Whether the algorithm supports distributed training.

• The metrics that the algorithm emits to Amazon CloudWatch.

• Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

• The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

pub fn inference_specification(&self) -> Option<&InferenceSpecification>

Specifies details about inference jobs that the algorithm runs, including the following:

• The Amazon ECR paths of containers that contain the inference code and model artifacts.

• The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

• The input and output content formats that the algorithm supports for inference.

pub fn validation_specification( &self, ) -> Option<&AlgorithmValidationSpecification>

Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.

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

Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

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 Services Resources.

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().

impl CreateAlgorithmInput

pub fn builder() -> CreateAlgorithmInputBuilder

Creates a new builder-style object to manufacture CreateAlgorithmInput.

Trait Implementations

impl Clone for CreateAlgorithmInput

fn clone(&self) -> CreateAlgorithmInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAlgorithmInput

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

Formats the value using the given formatter.

impl PartialEq for CreateAlgorithmInput

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