Struct aws_sdk_sagemaker::client::fluent_builders::CreateModelPackage

pub struct CreateModelPackage<C = DynConnector, M = DefaultMiddleware, R = Standard> { /* private fields */ }

Fluent builder constructing a request to CreateModelPackage.

Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

There are two types of model packages:

• Versioned - a model that is part of a model group in the model registry.

• Unversioned - a model package that is not part of a model group.

Implementations

impl<C, M, R> CreateModelPackage<C, M, R> where
    C: SmithyConnector,
    M: SmithyMiddleware<C>,
    R: NewRequestPolicy, 

pub async fn send(
    self
) -> Result<CreateModelPackageOutput, SdkError<CreateModelPackageError>> where
    R::Policy: SmithyRetryPolicy<CreateModelPackageInputOperationOutputAlias, CreateModelPackageOutput, CreateModelPackageError, CreateModelPackageInputOperationRetryAlias>, 

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 model_package_name(self, input: impl Into<String>) -> Self

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

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

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

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

A description of the model package.

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

A description of the model package.

pub fn inference_specification(self, input: InferenceSpecification) -> Self

Specifies details about inference jobs that can be run with models based on this model package, including the following:

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

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

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

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

Specifies details about inference jobs that can be run with models based on this model package, including the following:

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

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

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

pub fn validation_specification(
    self,
    input: ModelPackageValidationSpecification
) -> Self

Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

pub fn set_validation_specification(
    self,
    input: Option<ModelPackageValidationSpecification>
) -> Self

Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

pub fn source_algorithm_specification(
    self,
    input: SourceAlgorithmSpecification
) -> Self

Details about the algorithm that was used to create the model package.

pub fn set_source_algorithm_specification(
    self,
    input: Option<SourceAlgorithmSpecification>
) -> Self

Details about the algorithm that was used to create the model package.

pub fn certify_for_marketplace(self, input: bool) -> Self

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

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

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

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

Appends an item to Tags.

To override the contents of this collection use set_tags.

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

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

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

pub fn model_approval_status(self, input: ModelApprovalStatus) -> Self

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

pub fn set_model_approval_status(
    self,
    input: Option<ModelApprovalStatus>
) -> Self

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

pub fn metadata_properties(self, input: MetadataProperties) -> Self

Metadata properties of the tracking entity, trial, or trial component.

pub fn set_metadata_properties(self, input: Option<MetadataProperties>) -> Self

Metadata properties of the tracking entity, trial, or trial component.

pub fn model_metrics(self, input: ModelMetrics) -> Self

A structure that contains model metrics reports.

pub fn set_model_metrics(self, input: Option<ModelMetrics>) -> Self

A structure that contains model metrics reports.

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

A unique token that guarantees that the call to this API is idempotent.

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

A unique token that guarantees that the call to this API is idempotent.

pub fn customer_metadata_properties(
    self,
    k: impl Into<String>,
    v: impl Into<String>
) -> Self

Adds a key-value pair to CustomerMetadataProperties.

To override the contents of this collection use set_customer_metadata_properties.

The metadata properties associated with the model package versions.

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

The metadata properties associated with the model package versions.

pub fn drift_check_baselines(self, input: DriftCheckBaselines) -> Self

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

pub fn set_drift_check_baselines(
    self,
    input: Option<DriftCheckBaselines>
) -> Self

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

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

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

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

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

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

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.

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

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

pub fn additional_inference_specifications(
    self,
    input: AdditionalInferenceSpecificationDefinition
) -> Self

Appends an item to AdditionalInferenceSpecifications.

To override the contents of this collection use set_additional_inference_specifications.

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

pub fn set_additional_inference_specifications(
    self,
    input: Option<Vec<AdditionalInferenceSpecificationDefinition>>
) -> Self

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

Trait Implementations

impl<C: Clone, M: Clone, R: Clone> Clone for CreateModelPackage<C, M, R>

fn clone(&self) -> CreateModelPackage<C, M, R>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<C: Debug, M: Debug, R: Debug> Debug for CreateModelPackage<C, M, R>

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

Formats the value using the given formatter.