Struct aws_sdk_sagemaker::client::fluent_builders::CreateAlgorithm

pub struct CreateAlgorithm { /* private fields */ }

Fluent builder constructing a request to CreateAlgorithm.

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Implementations

impl CreateAlgorithm

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreateAlgorithm, AwsResponseRetryClassifier>, SdkError<CreateAlgorithmError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

pub async fn send(
    self
) -> Result<CreateAlgorithmOutput, SdkError<CreateAlgorithmError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn algorithm_name(self, input: impl Into<String>) -> Self

The name of the algorithm.

pub fn set_algorithm_name(self, input: Option<String>) -> Self

The name of the algorithm.

pub fn algorithm_description(self, input: impl Into<String>) -> Self

A description of the algorithm.

pub fn set_algorithm_description(self, input: Option<String>) -> Self

A description of the algorithm.

pub fn training_specification(self, input: TrainingSpecification) -> Self

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 set_training_specification(
    self,
    input: Option<TrainingSpecification>
) -> Self

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, input: InferenceSpecification) -> Self

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 set_inference_specification(
    self,
    input: Option<InferenceSpecification>
) -> Self

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,
    input: AlgorithmValidationSpecification
) -> Self

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 set_validation_specification(
    self,
    input: Option<AlgorithmValidationSpecification>
) -> Self

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, input: bool) -> Self

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

pub fn set_certify_for_marketplace(self, input: Option<bool>) -> Self

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

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

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.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

Trait Implementations

impl Clone for CreateAlgorithm

fn clone(&self) -> CreateAlgorithm

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAlgorithm

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

Formats the value using the given formatter.