Struct 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: Option<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 domain: Option<String>,
    pub task: Option<String>,
    pub sample_payload_url: Option<String>,
    pub customer_metadata_properties: Option<HashMap<String, String>>,
    pub drift_check_baselines: Option<DriftCheckBaselines>,
    pub additional_inference_specifications: Option<Vec<AdditionalInferenceSpecificationDefinition>>,
    pub skip_model_validation: Option<SkipModelValidation>,
    pub source_uri: Option<String>,
    pub security_config: Option<ModelPackageSecurityConfig>,
    pub model_card: Option<ModelPackageModelCard>,
    pub model_life_cycle: Option<ModelLifeCycle>,
}

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 you can run with models based on this model package, including the following information:

• 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 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: Option<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.

If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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.

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. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

sample_payload_url: Option<String>

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

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.

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.

skip_model_validation: Option<SkipModelValidation>

Indicates if you want to skip model validation.

source_uri: Option<String>

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

security_config: Option<ModelPackageSecurityConfig>

The KMS Key ID (KMSKeyId) used for encryption of model package information.

model_card: Option<ModelPackageModelCard>

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

model_life_cycle: Option<ModelLifeCycle>

A structure describing the current state of the model in its life cycle.

Implementations

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 you can run with models based on this model package, including the following information:

• 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 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) -> Option<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) -> &[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.

If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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().

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 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. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

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 additional_inference_specifications( &self, ) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .additional_inference_specifications.is_none().

pub fn skip_model_validation(&self) -> Option<&SkipModelValidation>

Indicates if you want to skip model validation.

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

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

pub fn security_config(&self) -> Option<&ModelPackageSecurityConfig>

The KMS Key ID (KMSKeyId) used for encryption of model package information.

pub fn model_card(&self) -> Option<&ModelPackageModelCard>

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

pub fn model_life_cycle(&self) -> Option<&ModelLifeCycle>

A structure describing the current state of the model in its life cycle.

impl CreateModelPackageInput

pub fn builder() -> CreateModelPackageInputBuilder

Creates a new builder-style object to manufacture CreateModelPackageInput.

Trait Implementations

impl Clone for CreateModelPackageInput

fn clone(&self) -> CreateModelPackageInput

Returns a duplicate 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 for CreateModelPackageInput

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