Struct CreateInferenceExperimentInput

#[non_exhaustive]
pub struct CreateInferenceExperimentInput {
    pub name: Option<String>,
    pub type: Option<InferenceExperimentType>,
    pub schedule: Option<InferenceExperimentSchedule>,
    pub description: Option<String>,
    pub role_arn: Option<String>,
    pub endpoint_name: Option<String>,
    pub model_variants: Option<Vec<ModelVariantConfig>>,
    pub data_storage_config: Option<InferenceExperimentDataStorageConfig>,
    pub shadow_mode_config: Option<ShadowModeConfig>,
    pub kms_key: Option<String>,
    pub tags: Option<Vec<Tag>>,
}

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.

name: Option<String>

The name for the inference experiment.

type: Option<InferenceExperimentType>

The type of the inference experiment that you want to run. The following types of experiments are possible:

• ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.

schedule: Option<InferenceExperimentSchedule>

The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.

description: Option<String>

A description for the inference experiment.

role_arn: Option<String>

The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

endpoint_name: Option<String>

The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

model_variants: Option<Vec<ModelVariantConfig>>

An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.

data_storage_config: Option<InferenceExperimentDataStorageConfig>

The Amazon S3 location and configuration for storing inference request and response data.

This is an optional parameter that you can use for data capture. For more information, see Capture data.

shadow_mode_config: Option<ShadowModeConfig>

The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

kms_key: Option<String>

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:

• KMS key ID

  "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS key

  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

• KMS key Alias

  "alias/ExampleAlias"

• Amazon Resource Name (ARN) of a KMS key Alias

  "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

tags: Option<Vec<Tag>>

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 your Amazon Web Services Resources.

Implementations

impl CreateInferenceExperimentInput

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

The name for the inference experiment.

pub fn type(&self) -> Option<&InferenceExperimentType>

The type of the inference experiment that you want to run. The following types of experiments are possible:

• ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.

pub fn schedule(&self) -> Option<&InferenceExperimentSchedule>

The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.

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

A description for the inference experiment.

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

The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

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

The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

pub fn model_variants(&self) -> &[ModelVariantConfig]

An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.

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

pub fn data_storage_config( &self, ) -> Option<&InferenceExperimentDataStorageConfig>

The Amazon S3 location and configuration for storing inference request and response data.

This is an optional parameter that you can use for data capture. For more information, see Capture data.

pub fn shadow_mode_config(&self) -> Option<&ShadowModeConfig>

The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

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

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:

• KMS key ID

  "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS key

  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

• KMS key Alias

  "alias/ExampleAlias"

• Amazon Resource Name (ARN) of a KMS key Alias

  "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.

pub fn tags(&self) -> &[Tag]

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 your Amazon Web Services Resources.

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().

impl CreateInferenceExperimentInput

pub fn builder() -> CreateInferenceExperimentInputBuilder

Creates a new builder-style object to manufacture CreateInferenceExperimentInput.

Trait Implementations

impl Clone for CreateInferenceExperimentInput

fn clone(&self) -> CreateInferenceExperimentInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateInferenceExperimentInput

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

Formats the value using the given formatter.

impl PartialEq for CreateInferenceExperimentInput

fn eq(&self, other: &CreateInferenceExperimentInput) -> 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 CreateInferenceExperimentInput