GetTrainedModelOutputBuilder

aws_sdk_cleanroomsml::operation::get_trained_model::builders

Struct GetTrainedModelOutputBuilder 

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#[non_exhaustive]
pub struct GetTrainedModelOutputBuilder { /* private fields */ }
Expand description

A builder for GetTrainedModelOutput.

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impl GetTrainedModelOutputBuilder

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

The membership ID of the member that created the trained model.

This field is required.
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pub fn set_membership_identifier(self, input: Option<String>) -> Self

The membership ID of the member that created the trained model.

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

The membership ID of the member that created the trained model.

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

The collaboration ID of the collaboration that contains the trained model.

This field is required.
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pub fn set_collaboration_identifier(self, input: Option<String>) -> Self

The collaboration ID of the collaboration that contains the trained model.

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

The collaboration ID of the collaboration that contains the trained model.

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

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

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.

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

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

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

The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.

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

The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.

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

The version identifier of the trained model. This unique identifier distinguishes this version from other versions of the same trained model.

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pub fn incremental_training_data_channels( self, input: IncrementalTrainingDataChannelOutput, ) -> Self

Appends an item to incremental_training_data_channels.

To override the contents of this collection use set_incremental_training_data_channels.

Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.

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pub fn set_incremental_training_data_channels( self, input: Option<Vec<IncrementalTrainingDataChannelOutput>>, ) -> Self

Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.

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pub fn get_incremental_training_data_channels( &self, ) -> &Option<Vec<IncrementalTrainingDataChannelOutput>>

Information about the incremental training data channels used to create this version of the trained model. This includes details about the base model that was used for incremental training and the channel configuration.

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

The name of the trained model.

This field is required.
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pub fn set_name(self, input: Option<String>) -> Self

The name of the trained model.

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

The name of the trained model.

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

The description of the trained model.

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

The description of the trained model.

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

The description of the trained model.

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pub fn status(self, input: TrainedModelStatus) -> Self

The status of the trained model.

This field is required.
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pub fn set_status(self, input: Option<TrainedModelStatus>) -> Self

The status of the trained model.

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pub fn get_status(&self) -> &Option<TrainedModelStatus>

The status of the trained model.

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pub fn status_details(self, input: StatusDetails) -> Self

Details about the status of a resource.

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pub fn set_status_details(self, input: Option<StatusDetails>) -> Self

Details about the status of a resource.

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pub fn get_status_details(&self) -> &Option<StatusDetails>

Details about the status of a resource.

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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 was used to create the trained model.

This field is required.
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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 was used to create the trained model.

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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 was used to create the trained model.

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

The EC2 resource configuration that was used to create the trained model.

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

The EC2 resource configuration that was used to create the trained model.

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

The EC2 resource configuration that was used to create the trained model.

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pub fn training_input_mode(self, input: TrainingInputMode) -> Self

The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.

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pub fn set_training_input_mode(self, input: Option<TrainingInputMode>) -> Self

The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.

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pub fn get_training_input_mode(&self) -> &Option<TrainingInputMode>

The input mode that was used for accessing the training data when this trained model was created. This indicates how the training data was made available to the training algorithm.

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pub fn stopping_condition(self, input: StoppingCondition) -> Self

The stopping condition that was used to terminate model training.

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pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self

The stopping condition that was used to terminate model training.

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pub fn get_stopping_condition(&self) -> &Option<StoppingCondition>

The stopping condition that was used to terminate model training.

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pub fn metrics_status(self, input: MetricsStatus) -> Self

The status of the model metrics.

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pub fn set_metrics_status(self, input: Option<MetricsStatus>) -> Self

The status of the model metrics.

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pub fn get_metrics_status(&self) -> &Option<MetricsStatus>

The status of the model metrics.

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

Details about the metrics status for the trained model.

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

Details about the metrics status for the trained model.

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

Details about the metrics status for the trained model.

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pub fn logs_status(self, input: LogsStatus) -> Self

The logs status for the trained model.

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pub fn set_logs_status(self, input: Option<LogsStatus>) -> Self

The logs status for the trained model.

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pub fn get_logs_status(&self) -> &Option<LogsStatus>

The logs status for the trained model.

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

Details about the logs status for the trained model.

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

Details about the logs status for the trained model.

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

Details about the logs status for the trained model.

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

Information about the training image container.

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

Information about the training image container.

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

Information about the training image container.

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pub fn create_time(self, input: DateTime) -> Self

The time at which the trained model was created.

This field is required.
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pub fn set_create_time(self, input: Option<DateTime>) -> Self

The time at which the trained model was created.

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

The time at which the trained model was created.

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pub fn update_time(self, input: DateTime) -> Self

The most recent time at which the trained model was updated.

This field is required.
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pub fn set_update_time(self, input: Option<DateTime>) -> Self

The most recent time at which the trained model was updated.

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

The most recent time at which the trained model was updated.

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

Adds a key-value pair to hyperparameters.

To override the contents of this collection use set_hyperparameters.

The hyperparameters that were used to create the trained model.

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

The hyperparameters that were used to create the trained model.

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

The hyperparameters that were used to create the trained model.

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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 EC2 environment that was used to create the trained model.

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

The EC2 environment that was used to create the trained model.

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

The EC2 environment that was used to create the trained model.

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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 trained ML model 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 trained ML model 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 trained ML model 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 applied 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 applied 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 applied 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 data_channels(self, input: ModelTrainingDataChannel) -> Self

Appends an item to data_channels.

To override the contents of this collection use set_data_channels.

The data channels that were used for the trained model.

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pub fn set_data_channels( self, input: Option<Vec<ModelTrainingDataChannel>>, ) -> Self

The data channels that were used for the trained model.

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pub fn get_data_channels(&self) -> &Option<Vec<ModelTrainingDataChannel>>

The data channels that were used for the trained model.

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

Consumes the builder and constructs a GetTrainedModelOutput. This method will fail if any of the following fields are not set:

Trait Implementations§

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impl Clone for GetTrainedModelOutputBuilder

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fn clone(&self) -> GetTrainedModelOutputBuilder

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 GetTrainedModelOutputBuilder

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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 GetTrainedModelOutputBuilder

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fn default() -> GetTrainedModelOutputBuilder

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

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

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👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

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Enable styling painted only when both stdout and stderr are TTYs:

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