Struct aws_sdk_sagemaker::output::DescribeModelPackageOutput

#[non_exhaustive]
pub struct DescribeModelPackageOutput {

    pub model_package_name: Option<String>,
    pub model_package_group_name: Option<String>,
    pub model_package_version: Option<i32>,
    pub model_package_arn: Option<String>,
    pub model_package_description: Option<String>,
    pub creation_time: Option<DateTime>,
    pub inference_specification: Option<InferenceSpecification>,
    pub source_algorithm_specification: Option<SourceAlgorithmSpecification>,
    pub validation_specification: Option<ModelPackageValidationSpecification>,
    pub model_package_status: Option<ModelPackageStatus>,
    pub model_package_status_details: Option<ModelPackageStatusDetails>,
    pub certify_for_marketplace: bool,
    pub model_approval_status: Option<ModelApprovalStatus>,
    pub created_by: Option<UserContext>,
    pub metadata_properties: Option<MetadataProperties>,
    pub model_metrics: Option<ModelMetrics>,
    pub last_modified_time: Option<DateTime>,
    pub last_modified_by: Option<UserContext>,
    pub approval_description: 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 being described.

model_package_group_name: Option<String>

If the model is a versioned model, the name of the model group that the versioned model belongs to.

model_package_version: Option<i32>

The version of the model package.

model_package_arn: Option<String>

The Amazon Resource Name (ARN) of the model package.

model_package_description: Option<String>

A brief summary of the model package.

creation_time: Option<DateTime>

A timestamp specifying when the model package was created.

inference_specification: Option<InferenceSpecification>

Details about inference jobs that can be run with models based on this model package.

source_algorithm_specification: Option<SourceAlgorithmSpecification>

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

validation_specification: Option<ModelPackageValidationSpecification>

Configurations for one or more transform jobs that SageMaker runs to test the model package.

model_package_status: Option<ModelPackageStatus>

The current status of the model package.

model_package_status_details: Option<ModelPackageStatusDetails>

Details about the current status of the model package.

certify_for_marketplace: bool

Whether the model package is certified for listing on Amazon Web Services Marketplace.

model_approval_status: Option<ModelApprovalStatus>

The approval status of the model package.

created_by: Option<UserContext>

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

metadata_properties: Option<MetadataProperties>

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

model_metrics: Option<ModelMetrics>

Metrics for the model.

last_modified_time: Option<DateTime>

The last time the model package was modified.

last_modified_by: Option<UserContext>

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

approval_description: Option<String>

A description provided for the model approval.

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 the model package you specified. Common machine learning domains include computer vision and natural language processing.

task: Option<String>

The machine learning task you specified that 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 points 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 DescribeModelPackageOutput

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

The name of the model package being described.

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

If the model is a versioned model, the name of the model group that the versioned model belongs to.

pub fn model_package_version(&self) -> Option<i32>

The version of the model package.

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

The Amazon Resource Name (ARN) of the model package.

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

A brief summary of the model package.

pub fn creation_time(&self) -> Option<&DateTime>

A timestamp specifying when the model package was created.

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

Details about inference jobs that can be run with models based on this model package.

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

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

pub fn validation_specification(
    &self
) -> Option<&ModelPackageValidationSpecification>

Configurations for one or more transform jobs that SageMaker runs to test the model package.

pub fn model_package_status(&self) -> Option<&ModelPackageStatus>

The current status of the model package.

pub fn model_package_status_details(&self) -> Option<&ModelPackageStatusDetails>

Details about the current status of the model package.

pub fn certify_for_marketplace(&self) -> bool

Whether the model package is certified for listing on Amazon Web Services Marketplace.

pub fn model_approval_status(&self) -> Option<&ModelApprovalStatus>

The approval status of the model package.

pub fn created_by(&self) -> Option<&UserContext>

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

pub fn metadata_properties(&self) -> Option<&MetadataProperties>

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

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

Metrics for the model.

pub fn last_modified_time(&self) -> Option<&DateTime>

The last time the model package was modified.

pub fn last_modified_by(&self) -> Option<&UserContext>

Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

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

A description provided for the model approval.

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 the model package you specified. Common machine learning domains include computer vision and natural language processing.

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

The machine learning task you specified that 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 points 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.

impl DescribeModelPackageOutput

pub fn builder() -> Builder

Creates a new builder-style object to manufacture DescribeModelPackageOutput

Trait Implementations

impl Clone for DescribeModelPackageOutput

fn clone(&self) -> DescribeModelPackageOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeModelPackageOutput

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

Formats the value using the given formatter.

impl PartialEq<DescribeModelPackageOutput> for DescribeModelPackageOutput

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

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

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

This method tests for !=.

impl StructuralPartialEq for DescribeModelPackageOutput