aws_sdk_cleanroomsml::operation::start_trained_model_inference_job::builders

Struct StartTrainedModelInferenceJobInputBuilder

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

Expand description

A builder for StartTrainedModelInferenceJobInput.

Implementations§

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impl StartTrainedModelInferenceJobInputBuilder

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

The membership ID of the membership that contains the trained model inference job.

This field is required.

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

The membership ID of the membership that contains the trained model inference job.

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

The membership ID of the membership that contains the trained model inference job.

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

The name of the trained model inference job.

This field is required.

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

The name of the trained model inference job.

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

The name of the trained model inference job.

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

The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job.

This field is required.

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

The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job.

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

The Amazon Resource Name (ARN) of the trained model that is used for this trained model inference job.

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

The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.

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

The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.

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

The version identifier of the trained model to use for inference. This specifies which version of the trained model should be used to generate predictions on the input data.

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

The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job.

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

The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job.

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

The Amazon Resource Name (ARN) of the configured model algorithm association that is used for this trained model inference job.

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pub fn resource_config(self, input: InferenceResourceConfig) -> Self

Defines the resource configuration for the trained model inference job.

This field is required.

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pub fn set_resource_config(self, input: Option<InferenceResourceConfig>) -> Self

Defines the resource configuration for the trained model inference job.

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pub fn get_resource_config(&self) -> &Option<InferenceResourceConfig>

Defines the resource configuration for the trained model inference job.

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pub fn output_configuration(self, input: InferenceOutputConfiguration) -> Self

Defines the output configuration information for the trained model inference job.

This field is required.

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pub fn set_output_configuration( self, input: Option<InferenceOutputConfiguration>, ) -> Self

Defines the output configuration information for the trained model inference job.

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pub fn get_output_configuration(&self) -> &Option<InferenceOutputConfiguration>

Defines the output configuration information for the trained model inference job.

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pub fn data_source(self, input: ModelInferenceDataSource) -> Self

Defines the data source that is used for the trained model inference job.

This field is required.

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pub fn set_data_source(self, input: Option<ModelInferenceDataSource>) -> Self

Defines the data source that is used for the trained model inference job.

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pub fn get_data_source(&self) -> &Option<ModelInferenceDataSource>

Defines the data source that is used for the trained model inference job.

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

The description of the trained model inference job.

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

The description of the trained model inference job.

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

The description of the trained model inference job.

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pub fn container_execution_parameters( self, input: InferenceContainerExecutionParameters, ) -> Self

The execution parameters for the container.

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pub fn set_container_execution_parameters( self, input: Option<InferenceContainerExecutionParameters>, ) -> Self

The execution parameters for the container.

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pub fn get_container_execution_parameters( &self, ) -> &Option<InferenceContainerExecutionParameters>

The execution parameters for the container.

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pub fn environment(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to environment.

To override the contents of this collection use set_environment.

The environment variables to set in the Docker container.

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

The environment variables to set in the Docker container.

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

The environment variables to set in the Docker container.

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

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.

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

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.

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

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the ML inference job and associated data.

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pub fn tags(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to tags.

To override the contents of this collection use set_tags.

The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

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

Consumes the builder and constructs a StartTrainedModelInferenceJobInput.

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impl StartTrainedModelInferenceJobInputBuilder

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

Sends a request with this input using the given client.

Trait Implementations§

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impl Clone for StartTrainedModelInferenceJobInputBuilder

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fn clone(&self) -> StartTrainedModelInferenceJobInputBuilder

Returns a duplicate of the value. Read more

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

Performs copy-assignment from source. Read more

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impl Debug for StartTrainedModelInferenceJobInputBuilder

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

Formats the value using the given formatter. Read more

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impl Default for StartTrainedModelInferenceJobInputBuilder

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fn default() -> StartTrainedModelInferenceJobInputBuilder

Returns the “default value” for a type. Read more

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impl PartialEq for StartTrainedModelInferenceJobInputBuilder

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

Tests for self and other values to be equal, and is used by ==.

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

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

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impl StructuralPartialEq for StartTrainedModelInferenceJobInputBuilder

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impl UnwindSafe for StartTrainedModelInferenceJobInputBuilder

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StartTrainedModelInferenceJobInputBuilder

Struct StartTrainedModelInferenceJobInputBuilder Copy item path

Implementations§

impl StartTrainedModelInferenceJobInputBuilder

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pub fn resource_config(self, input: InferenceResourceConfig) -> Self

pub fn set_resource_config(self, input: Option<InferenceResourceConfig>) -> Self

pub fn get_resource_config(&self) -> &Option<InferenceResourceConfig>

pub fn output_configuration(self, input: InferenceOutputConfiguration) -> Self

pub fn set_output_configuration( self, input: Option<InferenceOutputConfiguration>, ) -> Self

pub fn get_output_configuration(&self) -> &Option<InferenceOutputConfiguration>

pub fn data_source(self, input: ModelInferenceDataSource) -> Self

pub fn set_data_source(self, input: Option<ModelInferenceDataSource>) -> Self

pub fn get_data_source(&self) -> &Option<ModelInferenceDataSource>

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

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

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

pub fn container_execution_parameters( self, input: InferenceContainerExecutionParameters, ) -> Self

pub fn set_container_execution_parameters( self, input: Option<InferenceContainerExecutionParameters>, ) -> Self

pub fn get_container_execution_parameters( &self, ) -> &Option<InferenceContainerExecutionParameters>

pub fn environment(self, k: impl Into<String>, v: impl Into<String>) -> Self

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

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

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

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

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

pub fn tags(self, k: impl Into<String>, v: impl Into<String>) -> Self

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

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

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

impl StartTrainedModelInferenceJobInputBuilder

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

Trait Implementations§

impl Clone for StartTrainedModelInferenceJobInputBuilder

fn clone(&self) -> StartTrainedModelInferenceJobInputBuilder

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

impl Debug for StartTrainedModelInferenceJobInputBuilder

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

impl Default for StartTrainedModelInferenceJobInputBuilder

fn default() -> StartTrainedModelInferenceJobInputBuilder

impl PartialEq for StartTrainedModelInferenceJobInputBuilder

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

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

impl StructuralPartialEq for StartTrainedModelInferenceJobInputBuilder

Auto Trait Implementations§

impl Freeze for StartTrainedModelInferenceJobInputBuilder

impl RefUnwindSafe for StartTrainedModelInferenceJobInputBuilder

impl Send for StartTrainedModelInferenceJobInputBuilder

impl Sync for StartTrainedModelInferenceJobInputBuilder

impl Unpin for StartTrainedModelInferenceJobInputBuilder

impl UnwindSafe for StartTrainedModelInferenceJobInputBuilder

Blanket Implementations§

impl<T> Any for Twhere T: 'static + ?Sized,

fn type_id(&self) -> TypeId

impl<T> Borrow<T> for Twhere T: ?Sized,

fn borrow(&self) -> &T

impl<T> BorrowMut<T> for Twhere T: ?Sized,

fn borrow_mut(&mut self) -> &mut T

impl<T> CloneToUninit for Twhere T: Clone,

unsafe fn clone_to_uninit(&self, dest: *mut u8)

impl<T> From<T> for T

fn from(t: T) -> T

impl<T> Instrument for T

fn instrument(self, span: Span) -> Instrumented<Self>

fn in_current_span(self) -> Instrumented<Self>

impl<T, U> Into<U> for Twhere U: From<T>,

Struct StartTrainedModelInferenceJobInputBuilder

impl<T> Any for T
where T: 'static + ?Sized,

impl<T> Borrow<T> for T
where T: ?Sized,

impl<T> BorrowMut<T> for T
where T: ?Sized,

impl<T> CloneToUninit for T
where T: Clone,

impl<T, U> Into<U> for T
where U: From<T>,

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

impl<Unshared, Shared> IntoShared<Shared> for Unshared
where Shared: FromUnshared<Unshared>,

impl<T> Paint for T
where T: ?Sized,