Struct CreateAlgorithmInput

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

Fields

algorithm_description: Option<String>

A description of the algorithm.

algorithm_name: String

The name of the algorithm.

certify_for_marketplace: Option<bool>

Whether to certify the algorithm so that it can be listed in AWS Marketplace.

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.

tags: Option<Vec<Tag>>

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

training_specification: 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.

validation_specification: Option<AlgorithmValidationSpecification>

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

Trait Implementations

impl Clone for CreateAlgorithmInput

fn clone(&self) -> CreateAlgorithmInput

Returns a duplicate 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 Default for CreateAlgorithmInput

fn default() -> CreateAlgorithmInput

Returns the “default value” for a type.

impl PartialEq for CreateAlgorithmInput

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

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreateAlgorithmInput