Struct aws_sdk_sagemaker::input::CreateModelPackageInput

#[non_exhaustive]
pub struct CreateModelPackageInput {

    pub model_package_name: Option<String>,
    pub model_package_group_name: Option<String>,
    pub model_package_description: Option<String>,
    pub inference_specification: Option<InferenceSpecification>,
    pub validation_specification: Option<ModelPackageValidationSpecification>,
    pub source_algorithm_specification: Option<SourceAlgorithmSpecification>,
    pub certify_for_marketplace: bool,
    pub tags: Option<Vec<Tag>>,
    pub model_approval_status: Option<ModelApprovalStatus>,
    pub metadata_properties: Option<MetadataProperties>,
    pub model_metrics: Option<ModelMetrics>,
    pub client_token: Option<String>,
    pub customer_metadata_properties: Option<HashMap<String, String>>,
    pub drift_check_baselines: Option<DriftCheckBaselines>,
    pub domain: Option<String>,
    pub task: Option<String>,
    pub sample_payload_url: Option<String>,
    pub additional_inference_specifications: Option<Vec<AdditionalInferenceSpecificationDefinition>>,
}

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.

model_package_name: Option<String>

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.

model_package_group_name: Option<String>

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.

model_package_description: Option<String>

A description of the model package.

inference_specification: Option<InferenceSpecification>

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.

validation_specification: Option<ModelPackageValidationSpecification>

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

source_algorithm_specification: Option<SourceAlgorithmSpecification>

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

certify_for_marketplace: bool

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.

tags: Option<Vec<Tag>>

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.

model_approval_status: Option<ModelApprovalStatus>

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.

metadata_properties: Option<MetadataProperties>

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

model_metrics: Option<ModelMetrics>

A structure that contains model metrics reports.

client_token: Option<String>

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

customer_metadata_properties: Option<HashMap<String, String>>

The metadata properties associated with the model package versions.

drift_check_baselines: Option<DriftCheckBaselines>

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.

domain: Option<String>

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

task: Option<String>

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

sample_payload_url: Option<String>

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).

additional_inference_specifications: Option<Vec<AdditionalInferenceSpecificationDefinition>>

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.

Implementations

impl CreateModelPackageInput

pub async fn make_operation(
    self,
    _config: &Config
) -> Result<Operation<CreateModelPackage, AwsErrorRetryPolicy>, BuildError>

Consumes the builder and constructs an Operation<CreateModelPackage>

pub fn builder() -> Builder

Creates a new builder-style object to manufacture CreateModelPackageInput

impl CreateModelPackageInput

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

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) -> Option<&str>

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) -> Option<&str>

A description of the model package.

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

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
) -> Option<&ModelPackageValidationSpecification>

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

pub fn source_algorithm_specification(
    &self
) -> Option<&SourceAlgorithmSpecification>

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

pub fn certify_for_marketplace(&self) -> bool

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) -> Option<&[Tag]>

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) -> Option<&ModelApprovalStatus>

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) -> Option<&MetadataProperties>

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

pub fn model_metrics(&self) -> Option<&ModelMetrics>

A structure that contains model metrics reports.

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

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

pub fn customer_metadata_properties(&self) -> Option<&HashMap<String, String>>

The metadata properties associated with the model package versions.

pub fn drift_check_baselines(&self) -> Option<&DriftCheckBaselines>

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) -> Option<&str>

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) -> Option<&str>

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

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

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
) -> Option<&[AdditionalInferenceSpecificationDefinition]>

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 Clone for CreateModelPackageInput

fn clone(&self) -> CreateModelPackageInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateModelPackageInput

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

Formats the value using the given formatter.

impl PartialEq<CreateModelPackageInput> for CreateModelPackageInput

fn eq(&self, other: &CreateModelPackageInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &CreateModelPackageInput) -> bool

This method tests for !=.

impl StructuralPartialEq for CreateModelPackageInput