Struct aws_sdk_sagemaker::operation::create_inference_experiment::builders::CreateInferenceExperimentInputBuilder

#[non_exhaustive]
pub struct CreateInferenceExperimentInputBuilder { /* private fields */ }

A builder for CreateInferenceExperimentInput.

Implementations

impl CreateInferenceExperimentInputBuilder

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

The name for the inference experiment.

This field is required.

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

The name for the inference experiment.

pub fn get_name(&self) -> &Option<String>

The name for the inference experiment.

pub fn type(self, input: InferenceExperimentType) -> Self

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.

This field is required.

pub fn set_type(self, input: Option<InferenceExperimentType>) -> Self

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 get_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, input: InferenceExperimentSchedule) -> Self

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 set_schedule(self, input: Option<InferenceExperimentSchedule>) -> Self

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 get_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, input: impl Into<String>) -> Self

A description for the inference experiment.

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

A description for the inference experiment.

pub fn get_description(&self) -> &Option<String>

A description for the inference experiment.

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

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.

This field is required.

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

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 get_role_arn(&self) -> &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.

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

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

This field is required.

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

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

pub fn get_endpoint_name(&self) -> &Option<String>

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

pub fn model_variants(self, input: ModelVariantConfig) -> Self

Appends an item to model_variants.

To override the contents of this collection use set_model_variants.

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.

pub fn set_model_variants(self, input: Option<Vec<ModelVariantConfig>>) -> Self

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.

pub fn get_model_variants(&self) -> &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.

pub fn data_storage_config( self, input: InferenceExperimentDataStorageConfig ) -> Self

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 set_data_storage_config( self, input: Option<InferenceExperimentDataStorageConfig> ) -> Self

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 get_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, input: ShadowModeConfig) -> Self

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.

This field is required.

pub fn set_shadow_mode_config(self, input: Option<ShadowModeConfig>) -> Self

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 get_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, input: impl Into<String>) -> Self

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 set_kms_key(self, input: Option<String>) -> Self

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 get_kms_key(&self) -> &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.

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

Appends an item to tags.

To override the contents of this collection use set_tags.

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.

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

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.

pub fn get_tags(&self) -> &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.

pub fn build(self) -> Result<CreateInferenceExperimentInput, BuildError>

Consumes the builder and constructs a CreateInferenceExperimentInput.

impl CreateInferenceExperimentInputBuilder

pub async fn send_with( self, client: &Client ) -> Result<CreateInferenceExperimentOutput, SdkError<CreateInferenceExperimentError, HttpResponse>>

Sends a request with this input using the given client.

Trait Implementations

impl Clone for CreateInferenceExperimentInputBuilder

fn clone(&self) -> CreateInferenceExperimentInputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateInferenceExperimentInputBuilder

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

Formats the value using the given formatter.

impl Default for CreateInferenceExperimentInputBuilder

fn default() -> CreateInferenceExperimentInputBuilder

Returns the “default value” for a type.

impl PartialEq for CreateInferenceExperimentInputBuilder

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateInferenceExperimentInputBuilder