Struct Client 

pub struct Client { /* private fields */ }

Client for AWS Clean Rooms ML

Client for invoking operations on AWS Clean Rooms ML. Each operation on AWS Clean Rooms ML is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_cleanroomsml::Client::new(&config);

Occasionally, SDKs may have additional service-specific values that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Builder struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_cleanroomsml::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the CancelTrainedModel operation has a Client::cancel_trained_model, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.cancel_trained_model()
    .membership_identifier("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn cancel_trained_model(&self) -> CancelTrainedModelFluentBuilder

Constructs a fluent builder for the CancelTrainedModel operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the trained model job that you want to cancel.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model job that you want to cancel.

    
    
  • version_identifier(impl Into<String>) / set_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to cancel. This parameter allows you to specify which version of the trained model you want to cancel when multiple versions exist.

    If versionIdentifier is not specified, the base model will be cancelled.

    
    
• On success, responds with CancelTrainedModelOutput
• On failure, responds with SdkError<CancelTrainedModelError>

impl Client

pub fn cancel_trained_model_inference_job( &self, ) -> CancelTrainedModelInferenceJobFluentBuilder

Constructs a fluent builder for the CancelTrainedModelInferenceJob operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the trained model inference job that you want to cancel.

    
    
  • trained_model_inference_job_arn(impl Into<String>) / set_trained_model_inference_job_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model inference job that you want to cancel.

    
    
• On success, responds with CancelTrainedModelInferenceJobOutput
• On failure, responds with SdkError<CancelTrainedModelInferenceJobError>

impl Client

pub fn create_audience_model(&self) -> CreateAudienceModelFluentBuilder

Constructs a fluent builder for the CreateAudienceModel operation.

• The fluent builder is configurable:
  • training_data_start_time(DateTime) / set_training_data_start_time(Option<DateTime>):
    required: false
    

    The start date and time of the training window.

    
    
  • training_data_end_time(DateTime) / set_training_data_end_time(Option<DateTime>):
    required: false
    

    The end date and time of the training window.

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the audience model resource.

    
    
  • training_dataset_arn(impl Into<String>) / set_training_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the training dataset for this audience model.

    
    
  • kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
    required: false
    

    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 the associated data.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the audience model.

    
    
• On success, responds with CreateAudienceModelOutput with field(s):
  • audience_model_arn(String):

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

• On failure, responds with SdkError<CreateAudienceModelError>

impl Client

pub fn create_configured_audience_model( &self, ) -> CreateConfiguredAudienceModelFluentBuilder

Constructs a fluent builder for the CreateConfiguredAudienceModel operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the configured audience model.

    
    
  • audience_model_arn(impl Into<String>) / set_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience model to use for the configured audience model.

    
    
  • output_config(ConfiguredAudienceModelOutputConfig) / set_output_config(Option<ConfiguredAudienceModelOutputConfig>):
    required: true
    

    Configure the Amazon S3 location and IAM Role for audiences created using this configured audience model. Each audience will have a unique location. The IAM Role must have s3:PutObject permission on the destination Amazon S3 location. If the destination is protected with Amazon S3 KMS-SSE, then the Role must also have the required KMS permissions.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the configured audience model.

    
    
  • shared_audience_metrics(SharedAudienceMetrics) / set_shared_audience_metrics(Option<Vec::<SharedAudienceMetrics>>):
    required: true
    

    Whether audience metrics are shared.

    
    
  • min_matching_seed_size(i32) / set_min_matching_seed_size(Option<i32>):
    required: false
    

    The minimum number of users from the seed audience that must match with users in the training data of the audience model. The default value is 500.

    
    
  • audience_size_config(AudienceSizeConfig) / set_audience_size_config(Option<AudienceSizeConfig>):
    required: false
    

    Configure the list of output sizes of audiences that can be created using this configured audience model. A request to StartAudienceGenerationJob that uses this configured audience model must have an audienceSize selected from this list. You can use the ABSOLUTE AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the Percentage AudienceSize to configure sizes in the range 1-100 percent.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • child_resource_tag_on_create_policy(TagOnCreatePolicy) / set_child_resource_tag_on_create_policy(Option<TagOnCreatePolicy>):
    required: false
    

    Configure how the service tags audience generation jobs created using this configured audience model. If you specify NONE, the tags from the StartAudienceGenerationJob request determine the tags of the audience generation job. If you specify FROM_PARENT_RESOURCE, the audience generation job inherits the tags from the configured audience model, by default. Tags in the StartAudienceGenerationJob will override the default.

    When the client is in a different account than the configured audience model, the tags from the client are never applied to a resource in the caller’s account.

    
    
• On success, responds with CreateConfiguredAudienceModelOutput with field(s):
  • configured_audience_model_arn(String):

    The Amazon Resource Name (ARN) of the configured audience model.

• On failure, responds with SdkError<CreateConfiguredAudienceModelError>

impl Client

pub fn create_configured_model_algorithm( &self, ) -> CreateConfiguredModelAlgorithmFluentBuilder

Constructs a fluent builder for the CreateConfiguredModelAlgorithm operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the configured model algorithm.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the configured model algorithm.

    
    
  • role_arn(impl Into<String>) / set_role_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the role that is used to access the repository.

    
    
  • training_container_config(ContainerConfig) / set_training_container_config(Option<ContainerConfig>):
    required: false
    

    Configuration information for the training container, including entrypoints and arguments.

    
    
  • inference_container_config(InferenceContainerConfig) / set_inference_container_config(Option<InferenceContainerConfig>):
    required: false
    

    Configuration information for the inference container that is used when you run an inference job on a configured model algorithm.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
    required: false
    

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

    
    
• On success, responds with CreateConfiguredModelAlgorithmOutput with field(s):
  • configured_model_algorithm_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm.

• On failure, responds with SdkError<CreateConfiguredModelAlgorithmError>

impl Client

pub fn create_configured_model_algorithm_association( &self, ) -> CreateConfiguredModelAlgorithmAssociationFluentBuilder

Constructs a fluent builder for the CreateConfiguredModelAlgorithmAssociation operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member who is associating this configured model algorithm.

    
    
  • configured_model_algorithm_arn(impl Into<String>) / set_configured_model_algorithm_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm that you want to associate.

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the configured model algorithm association.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the configured model algorithm association.

    
    
  • privacy_configuration(PrivacyConfiguration) / set_privacy_configuration(Option<PrivacyConfiguration>):
    required: false
    

    Specifies the privacy configuration information for the configured model algorithm association. This information includes the maximum data size that can be exported.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
• On success, responds with CreateConfiguredModelAlgorithmAssociationOutput with field(s):
  • configured_model_algorithm_association_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association.

• On failure, responds with SdkError<CreateConfiguredModelAlgorithmAssociationError>

impl Client

pub fn create_ml_input_channel(&self) -> CreateMLInputChannelFluentBuilder

Constructs a fluent builder for the CreateMLInputChannel operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is creating the ML input channel.

    
    
  • configured_model_algorithm_associations(impl Into<String>) / set_configured_model_algorithm_associations(Option<Vec::<String>>):
    required: true
    

    The associated configured model algorithms that are necessary to create this ML input channel.

    
    
  • input_channel(InputChannel) / set_input_channel(Option<InputChannel>):
    required: true
    

    The input data that is used to create this ML input channel.

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the ML input channel.

    
    
  • retention_in_days(i32) / set_retention_in_days(Option<i32>):
    required: true
    

    The number of days that the data in the ML input channel is retained.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the ML input channel.

    
    
  • kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the KMS key that is used to access the input channel.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
• On success, responds with CreateMlInputChannelOutput with field(s):
  • ml_input_channel_arn(String):

    The Amazon Resource Name (ARN) of the ML input channel.

• On failure, responds with SdkError<CreateMLInputChannelError>

impl Client

pub fn create_trained_model(&self) -> CreateTrainedModelFluentBuilder

Constructs a fluent builder for the CreateTrainedModel operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is creating the trained model.

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the trained model.

    
    
  • configured_model_algorithm_association_arn(impl Into<String>) / set_configured_model_algorithm_association_arn(Option<String>):
    required: true
    

    The associated configured model algorithm used to train this model.

    
    
  • hyperparameters(impl Into<String>, impl Into<String>) / set_hyperparameters(Option<HashMap::<String, String>>):
    required: false
    

    Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.

    
    
  • environment(impl Into<String>, impl Into<String>) / set_environment(Option<HashMap::<String, String>>):
    required: false
    

    The environment variables to set in the Docker container.

    
    
  • resource_config(ResourceConfig) / set_resource_config(Option<ResourceConfig>):
    required: true
    

    Information about the EC2 resources that are used to train this model.

    
    
  • stopping_condition(StoppingCondition) / set_stopping_condition(Option<StoppingCondition>):
    required: false
    

    The criteria that is used to stop model training.

    
    
  • incremental_training_data_channels(IncrementalTrainingDataChannel) / set_incremental_training_data_channels(Option<Vec::<IncrementalTrainingDataChannel>>):
    required: false
    

    Specifies the incremental training data channels for the trained model.

    Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.

    Limit: Maximum of 20 channels total (including both incrementalTrainingDataChannels and dataChannels).

    
    
  • data_channels(ModelTrainingDataChannel) / set_data_channels(Option<Vec::<ModelTrainingDataChannel>>):
    required: true
    

    Defines the data channels that are used as input for the trained model request.

    Limit: Maximum of 20 channels total (including both dataChannels and incrementalTrainingDataChannels).

    
    
  • training_input_mode(TrainingInputMode) / set_training_input_mode(Option<TrainingInputMode>):
    required: false
    

    The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:

    • File - The training data is downloaded to the training instance and made available as files.

    • FastFile - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.

    • Pipe - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the trained model.

    
    
  • kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
    required: false
    

    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 the associated data.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
• On success, responds with CreateTrainedModelOutput with field(s):
  • trained_model_arn(String):

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

  • version_identifier(Option<String>):

    The unique version identifier assigned to the newly created trained model. This identifier can be used to reference this specific version of the trained model in subsequent operations such as inference jobs or incremental training.

    The initial version identifier for the base version of the trained model is “NULL”.

• On failure, responds with SdkError<CreateTrainedModelError>

impl Client

pub fn create_training_dataset(&self) -> CreateTrainingDatasetFluentBuilder

Constructs a fluent builder for the CreateTrainingDataset operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the training dataset. This name must be unique in your account and region.

    
    
  • role_arn(impl Into<String>) / set_role_arn(Option<String>):
    required: true
    

    The ARN of the IAM role that Clean Rooms ML can assume to read the data referred to in the dataSource field of each dataset.

    Passing a role across AWS accounts is not allowed. If you pass a role that isn’t in your account, you get an AccessDeniedException error.

    
    
  • training_data(Dataset) / set_training_data(Option<Vec::<Dataset>>):
    required: true
    

    An array of information that lists the Dataset objects, which specifies the dataset type and details on its location and schema. You must provide a role that has read access to these tables.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the training dataset.

    
    
• On success, responds with CreateTrainingDatasetOutput with field(s):
  • training_dataset_arn(String):

    The Amazon Resource Name (ARN) of the training dataset resource.

• On failure, responds with SdkError<CreateTrainingDatasetError>

impl Client

pub fn delete_audience_generation_job( &self, ) -> DeleteAudienceGenerationJobFluentBuilder

Constructs a fluent builder for the DeleteAudienceGenerationJob operation.

• The fluent builder is configurable:
  • audience_generation_job_arn(impl Into<String>) / set_audience_generation_job_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience generation job that you want to delete.

    
    
• On success, responds with DeleteAudienceGenerationJobOutput
• On failure, responds with SdkError<DeleteAudienceGenerationJobError>

impl Client

pub fn delete_audience_model(&self) -> DeleteAudienceModelFluentBuilder

Constructs a fluent builder for the DeleteAudienceModel operation.

• The fluent builder is configurable:
  • audience_model_arn(impl Into<String>) / set_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience model that you want to delete.

    
    
• On success, responds with DeleteAudienceModelOutput
• On failure, responds with SdkError<DeleteAudienceModelError>

impl Client

pub fn delete_configured_audience_model( &self, ) -> DeleteConfiguredAudienceModelFluentBuilder

Constructs a fluent builder for the DeleteConfiguredAudienceModel operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that you want to delete.

    
    
• On success, responds with DeleteConfiguredAudienceModelOutput
• On failure, responds with SdkError<DeleteConfiguredAudienceModelError>

impl Client

pub fn delete_configured_audience_model_policy( &self, ) -> DeleteConfiguredAudienceModelPolicyFluentBuilder

Constructs a fluent builder for the DeleteConfiguredAudienceModelPolicy operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model policy that you want to delete.

    
    
• On success, responds with DeleteConfiguredAudienceModelPolicyOutput
• On failure, responds with SdkError<DeleteConfiguredAudienceModelPolicyError>

impl Client

pub fn delete_configured_model_algorithm( &self, ) -> DeleteConfiguredModelAlgorithmFluentBuilder

Constructs a fluent builder for the DeleteConfiguredModelAlgorithm operation.

• The fluent builder is configurable:
  • configured_model_algorithm_arn(impl Into<String>) / set_configured_model_algorithm_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm that you want to delete.

    
    
• On success, responds with DeleteConfiguredModelAlgorithmOutput
• On failure, responds with SdkError<DeleteConfiguredModelAlgorithmError>

impl Client

pub fn delete_configured_model_algorithm_association( &self, ) -> DeleteConfiguredModelAlgorithmAssociationFluentBuilder

Constructs a fluent builder for the DeleteConfiguredModelAlgorithmAssociation operation.

• The fluent builder is configurable:
  • configured_model_algorithm_association_arn(impl Into<String>) / set_configured_model_algorithm_association_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm association that you want to delete.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is deleting the configured model algorithm association.

    
    
• On success, responds with DeleteConfiguredModelAlgorithmAssociationOutput
• On failure, responds with SdkError<DeleteConfiguredModelAlgorithmAssociationError>

impl Client

pub fn delete_ml_configuration(&self) -> DeleteMLConfigurationFluentBuilder

Constructs a fluent builder for the DeleteMLConfiguration operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the of the member that is deleting the ML modeling configuration.

    
    
• On success, responds with DeleteMlConfigurationOutput
• On failure, responds with SdkError<DeleteMLConfigurationError>

impl Client

pub fn delete_ml_input_channel_data( &self, ) -> DeleteMLInputChannelDataFluentBuilder

Constructs a fluent builder for the DeleteMLInputChannelData operation.

• The fluent builder is configurable:
  • ml_input_channel_arn(impl Into<String>) / set_ml_input_channel_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the ML input channel that you want to delete.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the membership that contains the ML input channel you want to delete.

    
    
• On success, responds with DeleteMlInputChannelDataOutput
• On failure, responds with SdkError<DeleteMLInputChannelDataError>

impl Client

pub fn delete_trained_model_output( &self, ) -> DeleteTrainedModelOutputFluentBuilder

Constructs a fluent builder for the DeleteTrainedModelOutput operation.

• The fluent builder is configurable:
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model whose output you want to delete.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is deleting the trained model output.

    
    
  • version_identifier(impl Into<String>) / set_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to delete. If not specified, the operation will delete the base version of the trained model. When specified, only the particular version will be deleted.

    
    
• On success, responds with DeleteTrainedModelOutputOutput
• On failure, responds with SdkError<DeleteTrainedModelOutputError>

impl Client

pub fn delete_training_dataset(&self) -> DeleteTrainingDatasetFluentBuilder

Constructs a fluent builder for the DeleteTrainingDataset operation.

• The fluent builder is configurable:
  • training_dataset_arn(impl Into<String>) / set_training_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the training dataset that you want to delete.

    
    
• On success, responds with DeleteTrainingDatasetOutput
• On failure, responds with SdkError<DeleteTrainingDatasetError>

impl Client

pub fn get_audience_generation_job( &self, ) -> GetAudienceGenerationJobFluentBuilder

Constructs a fluent builder for the GetAudienceGenerationJob operation.

• The fluent builder is configurable:
  • audience_generation_job_arn(impl Into<String>) / set_audience_generation_job_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience generation job that you are interested in.

    
    
• On success, responds with GetAudienceGenerationJobOutput with field(s):
  • create_time(DateTime):

    The time at which the audience generation job was created.

  • update_time(DateTime):

    The most recent time at which the audience generation job was updated.

  • audience_generation_job_arn(String):

    The Amazon Resource Name (ARN) of the audience generation job.

  • name(String):

    The name of the audience generation job.

  • description(Option<String>):

    The description of the audience generation job.

  • status(AudienceGenerationJobStatus):

    The status of the audience generation job.

  • status_details(Option<StatusDetails>):

    Details about the status of the audience generation job.

  • configured_audience_model_arn(String):

    The Amazon Resource Name (ARN) of the configured audience model used for this audience generation job.

  • seed_audience(Option<AudienceGenerationJobDataSource>):

    The seed audience that was used for this audience generation job. This field will be null if the account calling the API is the account that started this audience generation job.

  • include_seed_in_output(Option<bool>):

    Configure whether the seed users are included in the output audience. By default, Clean Rooms ML removes seed users from the output audience. If you specify TRUE, the seed users will appear first in the output. Clean Rooms ML does not explicitly reveal whether a user was in the seed, but the recipient of the audience will know that the first minimumSeedSize count of users are from the seed.

  • collaboration_id(Option<String>):

    The identifier of the collaboration that this audience generation job is associated with.

  • metrics(Option<AudienceQualityMetrics>):

    The relevance scores for different audience sizes and the recall score of the generated audience.

  • started_by(Option<String>):

    The AWS account that started this audience generation job.

  • tags(Option<HashMap::<String, String>>):

    The tags that are associated to this audience generation job.

  • protected_query_identifier(Option<String>):

    The unique identifier of the protected query for this audience generation job.

• On failure, responds with SdkError<GetAudienceGenerationJobError>

impl Client

pub fn get_audience_model(&self) -> GetAudienceModelFluentBuilder

Constructs a fluent builder for the GetAudienceModel operation.

• The fluent builder is configurable:
  • audience_model_arn(impl Into<String>) / set_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience model that you are interested in.

    
    
• On success, responds with GetAudienceModelOutput with field(s):
  • create_time(DateTime):

    The time at which the audience model was created.

  • update_time(DateTime):

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

  • training_data_start_time(Option<DateTime>):

    The start date specified for the training window.

  • training_data_end_time(Option<DateTime>):

    The end date specified for the training window.

  • audience_model_arn(String):

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

  • name(String):

    The name of the audience model.

  • training_dataset_arn(String):

    The Amazon Resource Name (ARN) of the training dataset that was used for this audience model.

  • status(AudienceModelStatus):

    The status of the audience model.

  • status_details(Option<StatusDetails>):

    Details about the status of the audience model.

  • kms_key_arn(Option<String>):

    The KMS key ARN used for the audience model.

  • tags(Option<HashMap::<String, String>>):

    The tags that are assigned to the audience model.

  • description(Option<String>):

    The description of the audience model.

• On failure, responds with SdkError<GetAudienceModelError>

impl Client

pub fn get_collaboration_configured_model_algorithm_association( &self, ) -> GetCollaborationConfiguredModelAlgorithmAssociationFluentBuilder

Constructs a fluent builder for the GetCollaborationConfiguredModelAlgorithmAssociation operation.

• The fluent builder is configurable:
  • configured_model_algorithm_association_arn(impl Into<String>) / set_configured_model_algorithm_association_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm association that you want to return information about.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID for the collaboration that contains the configured model algorithm association that you want to return information about.

    
    
• On success, responds with GetCollaborationConfiguredModelAlgorithmAssociationOutput with field(s):
  • create_time(DateTime):

    The time at which the configured model algorithm association was created.

  • update_time(DateTime):

    The most recent time at which the configured model algorithm association was updated.

  • configured_model_algorithm_association_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association.

  • membership_identifier(String):

    The membership ID of the member that created the configured model algorithm association.

  • collaboration_identifier(String):

    The collaboration ID of the collaboration that contains the configured model algorithm association.

  • configured_model_algorithm_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association.

  • name(String):

    The name of the configured model algorithm association.

  • description(Option<String>):

    The description of the configured model algorithm association.

  • creator_account_id(String):

    The account ID of the member that created the configured model algorithm association.

  • privacy_configuration(Option<PrivacyConfiguration>):

    Information about the privacy configuration for a configured model algorithm association.

• On failure, responds with SdkError<GetCollaborationConfiguredModelAlgorithmAssociationError>

impl Client

pub fn get_collaboration_ml_input_channel( &self, ) -> GetCollaborationMLInputChannelFluentBuilder

Constructs a fluent builder for the GetCollaborationMLInputChannel operation.

• The fluent builder is configurable:
  • ml_input_channel_arn(impl Into<String>) / set_ml_input_channel_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the ML input channel that you want to get.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the ML input channel that you want to get.

    
    
• On success, responds with GetCollaborationMlInputChannelOutput with field(s):
  • membership_identifier(String):

    The membership ID of the membership that contains the ML input channel.

  • collaboration_identifier(String):

    The collaboration ID of the collaboration that contains the ML input channel.

  • ml_input_channel_arn(String):

    The Amazon Resource Name (ARN) of the ML input channel.

  • name(String):

    The name of the ML input channel.

  • configured_model_algorithm_associations(Vec::<String>):

    The configured model algorithm associations that were used to create the ML input channel.

  • status(MlInputChannelStatus):

    The status of the ML input channel.

  • status_details(Option<StatusDetails>):

    Details about the status of a resource.

  • retention_in_days(i32):

    The number of days to retain the data for the ML input channel.

  • number_of_records(Option<i64>):

    The number of records in the ML input channel.

  • privacy_budgets(Option<PrivacyBudgets>):

    Returns the privacy budgets that control access to this Clean Rooms ML input channel. Use these budgets to monitor and limit resource consumption over specified time periods.

  • description(Option<String>):

    The description of the ML input channel.

  • synthetic_data_configuration(Option<SyntheticDataConfiguration>):

    The synthetic data configuration for this ML input channel, including parameters for generating privacy-preserving synthetic data and evaluation scores for measuring the privacy of the generated data.

  • create_time(DateTime):

    The time at which the ML input channel was created.

  • update_time(DateTime):

    The most recent time at which the ML input channel was updated.

  • creator_account_id(String):

    The account ID of the member who created the ML input channel.

• On failure, responds with SdkError<GetCollaborationMLInputChannelError>

impl Client

pub fn get_collaboration_trained_model( &self, ) -> GetCollaborationTrainedModelFluentBuilder

Constructs a fluent builder for the GetCollaborationTrainedModel operation.

• The fluent builder is configurable:
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model that you want to return information about.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID that contains the trained model that you want to return information about.

    
    
  • version_identifier(impl Into<String>) / set_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model.

    
    
• On success, responds with GetCollaborationTrainedModelOutput with field(s):
  • membership_identifier(String):

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

  • collaboration_identifier(String):

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

  • trained_model_arn(String):

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

  • version_identifier(Option<String>):

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

  • incremental_training_data_channels(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.

  • name(String):

    The name of the trained model.

  • description(Option<String>):

    The description of the trained model.

  • status(TrainedModelStatus):

    The status of the trained model.

  • status_details(Option<StatusDetails>):

    Details about the status of a resource.

  • configured_model_algorithm_association_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create this trained model.

  • resource_config(Option<ResourceConfig>):

    The EC2 resource configuration that was used to train this model.

  • training_input_mode(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.

  • stopping_condition(Option<StoppingCondition>):

    The stopping condition that determined when model training ended.

  • metrics_status(Option<MetricsStatus>):

    The status of the model metrics.

  • metrics_status_details(Option<String>):

    Details about the status information for the model metrics.

  • logs_status(Option<LogsStatus>):

    Status information for the logs.

  • logs_status_details(Option<String>):

    Details about the status information for the logs.

  • training_container_image_digest(Option<String>):

    Information about the training container image.

  • create_time(DateTime):

    The time at which the trained model was created.

  • update_time(DateTime):

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

  • creator_account_id(String):

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

• On failure, responds with SdkError<GetCollaborationTrainedModelError>

impl Client

pub fn get_configured_audience_model( &self, ) -> GetConfiguredAudienceModelFluentBuilder

Constructs a fluent builder for the GetConfiguredAudienceModel operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that you are interested in.

    
    
• On success, responds with GetConfiguredAudienceModelOutput with field(s):
  • create_time(DateTime):

    The time at which the configured audience model was created.

  • update_time(DateTime):

    The most recent time at which the configured audience model was updated.

  • configured_audience_model_arn(String):

    The Amazon Resource Name (ARN) of the configured audience model.

  • name(String):

    The name of the configured audience model.

  • audience_model_arn(String):

    The Amazon Resource Name (ARN) of the audience model used for this configured audience model.

  • output_config(Option<ConfiguredAudienceModelOutputConfig>):

    The output configuration of the configured audience model

  • description(Option<String>):

    The description of the configured audience model.

  • status(ConfiguredAudienceModelStatus):

    The status of the configured audience model.

  • shared_audience_metrics(Vec::<SharedAudienceMetrics>):

    Whether audience metrics are shared.

  • min_matching_seed_size(Option<i32>):

    The minimum number of users from the seed audience that must match with users in the training data of the audience model.

  • audience_size_config(Option<AudienceSizeConfig>):

    The list of output sizes of audiences that can be created using this configured audience model. A request to StartAudienceGenerationJob that uses this configured audience model must have an audienceSize selected from this list. You can use the ABSOLUTE AudienceSize to configure out audience sizes using the count of identifiers in the output. You can use the Percentage AudienceSize to configure sizes in the range 1-100 percent.

  • tags(Option<HashMap::<String, String>>):

    The tags that are associated to this configured audience model.

  • child_resource_tag_on_create_policy(Option<TagOnCreatePolicy>):

    Provides the childResourceTagOnCreatePolicy that was used for this configured audience model.

• On failure, responds with SdkError<GetConfiguredAudienceModelError>

impl Client

pub fn get_configured_audience_model_policy( &self, ) -> GetConfiguredAudienceModelPolicyFluentBuilder

Constructs a fluent builder for the GetConfiguredAudienceModelPolicy operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that you are interested in.

    
    
• On success, responds with GetConfiguredAudienceModelPolicyOutput with field(s):
  • configured_audience_model_arn(String):

    The Amazon Resource Name (ARN) of the configured audience model.

  • configured_audience_model_policy(String):

    The configured audience model policy. This is a JSON IAM resource policy.

  • policy_hash(String):

    A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy.

• On failure, responds with SdkError<GetConfiguredAudienceModelPolicyError>

impl Client

pub fn get_configured_model_algorithm( &self, ) -> GetConfiguredModelAlgorithmFluentBuilder

Constructs a fluent builder for the GetConfiguredModelAlgorithm operation.

• The fluent builder is configurable:
  • configured_model_algorithm_arn(impl Into<String>) / set_configured_model_algorithm_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm that you want to return information about.

    
    
• On success, responds with GetConfiguredModelAlgorithmOutput with field(s):
  • create_time(DateTime):

    The time at which the configured model algorithm was created.

  • update_time(DateTime):

    The most recent time at which the configured model algorithm was updated.

  • configured_model_algorithm_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm.

  • name(String):

    The name of the configured model algorithm.

  • training_container_config(Option<ContainerConfig>):

    The configuration information of the training container for the configured model algorithm.

  • inference_container_config(Option<InferenceContainerConfig>):

    Configuration information for the inference container.

  • role_arn(String):

    The Amazon Resource Name (ARN) of the service role that was used to create the configured model algorithm.

  • description(Option<String>):

    The description of the configured model algorithm.

  • tags(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.

  • kms_key_arn(Option<String>):

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

• On failure, responds with SdkError<GetConfiguredModelAlgorithmError>

impl Client

pub fn get_configured_model_algorithm_association( &self, ) -> GetConfiguredModelAlgorithmAssociationFluentBuilder

Constructs a fluent builder for the GetConfiguredModelAlgorithmAssociation operation.

• The fluent builder is configurable:
  • configured_model_algorithm_association_arn(impl Into<String>) / set_configured_model_algorithm_association_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured model algorithm association that you want to return information about.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that created the configured model algorithm association.

    
    
• On success, responds with GetConfiguredModelAlgorithmAssociationOutput with field(s):
  • create_time(DateTime):

    The time at which the configured model algorithm association was created.

  • update_time(DateTime):

    The most recent time at which the configured model algorithm association was updated.

  • configured_model_algorithm_association_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association.

  • membership_identifier(String):

    The membership ID of the member that created the configured model algorithm association.

  • collaboration_identifier(String):

    The collaboration ID of the collaboration that contains the configured model algorithm association.

  • configured_model_algorithm_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm that was associated to the collaboration.

  • name(String):

    The name of the configured model algorithm association.

  • privacy_configuration(Option<PrivacyConfiguration>):

    The privacy configuration information for the configured model algorithm association.

  • description(Option<String>):

    The description of the configured model algorithm association.

  • tags(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.

• On failure, responds with SdkError<GetConfiguredModelAlgorithmAssociationError>

impl Client

pub fn get_ml_configuration(&self) -> GetMLConfigurationFluentBuilder

Constructs a fluent builder for the GetMLConfiguration operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that owns the ML configuration you want to return information about.

    
    
• On success, responds with GetMlConfigurationOutput with field(s):
  • membership_identifier(String):

    The membership ID of the member that owns the ML configuration you requested.

  • default_output_location(Option<MlOutputConfiguration>):

    The Amazon S3 location where ML model output is stored.

  • create_time(DateTime):

    The time at which the ML configuration was created.

  • update_time(DateTime):

    The most recent time at which the ML configuration was updated.

• On failure, responds with SdkError<GetMLConfigurationError>

impl Client

pub fn get_ml_input_channel(&self) -> GetMLInputChannelFluentBuilder

Constructs a fluent builder for the GetMLInputChannel operation.

• The fluent builder is configurable:
  • ml_input_channel_arn(impl Into<String>) / set_ml_input_channel_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the ML input channel that you want to get.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the membership that contains the ML input channel that you want to get.

    
    
• On success, responds with GetMlInputChannelOutput with field(s):
  • membership_identifier(String):

    The membership ID of the membership that contains the ML input channel.

  • collaboration_identifier(String):

    The collaboration ID of the collaboration that contains the ML input channel.

  • ml_input_channel_arn(String):

    The Amazon Resource Name (ARN) of the ML input channel.

  • name(String):

    The name of the ML input channel.

  • configured_model_algorithm_associations(Vec::<String>):

    The configured model algorithm associations that were used to create the ML input channel.

  • status(MlInputChannelStatus):

    The status of the ML input channel.

  • status_details(Option<StatusDetails>):

    Details about the status of a resource.

  • retention_in_days(i32):

    The number of days to keep the data in the ML input channel.

  • number_of_records(Option<i64>):

    The number of records in the ML input channel.

  • privacy_budgets(Option<PrivacyBudgets>):

    Returns the privacy budgets that control access to this Clean Rooms ML input channel. Use these budgets to monitor and limit resource consumption over specified time periods.

  • description(Option<String>):

    The description of the ML input channel.

  • synthetic_data_configuration(Option<SyntheticDataConfiguration>):

    The synthetic data configuration for this ML input channel, including parameters for generating privacy-preserving synthetic data and evaluation scores for measuring the privacy of the generated data.

  • create_time(DateTime):

    The time at which the ML input channel was created.

  • update_time(DateTime):

    The most recent time at which the ML input channel was updated.

  • input_channel(Option<InputChannel>):

    The input channel that was used to create the ML input channel.

  • protected_query_identifier(Option<String>):

    The ID of the protected query that was used to create the ML input channel.

  • number_of_files(Option<f64>):

    The number of files in the ML input channel.

  • size_in_gb(Option<f64>):

    The size, in GB, of the ML input channel.

  • kms_key_arn(Option<String>):

    The Amazon Resource Name (ARN) of the KMS key that was used to create the ML input channel.

  • tags(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.

• On failure, responds with SdkError<GetMLInputChannelError>

impl Client

pub fn get_trained_model(&self) -> GetTrainedModelFluentBuilder

Constructs a fluent builder for the GetTrainedModel operation.

• The fluent builder is configurable:
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model that you are interested in.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that created the trained model that you are interested in.

    
    
  • version_identifier(impl Into<String>) / set_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to retrieve. If not specified, the operation returns information about the latest version of the trained model.

    
    
• On success, responds with GetTrainedModelOutput with field(s):
  • membership_identifier(String):

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

  • collaboration_identifier(String):

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

  • trained_model_arn(String):

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

  • version_identifier(Option<String>):

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

  • incremental_training_data_channels(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.

  • name(String):

    The name of the trained model.

  • description(Option<String>):

    The description of the trained model.

  • status(TrainedModelStatus):

    The status of the trained model.

  • status_details(Option<StatusDetails>):

    Details about the status of a resource.

  • configured_model_algorithm_association_arn(String):

    The Amazon Resource Name (ARN) of the configured model algorithm association that was used to create the trained model.

  • resource_config(Option<ResourceConfig>):

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

  • training_input_mode(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.

  • stopping_condition(Option<StoppingCondition>):

    The stopping condition that was used to terminate model training.

  • metrics_status(Option<MetricsStatus>):

    The status of the model metrics.

  • metrics_status_details(Option<String>):

    Details about the metrics status for the trained model.

  • logs_status(Option<LogsStatus>):

    The logs status for the trained model.

  • logs_status_details(Option<String>):

    Details about the logs status for the trained model.

  • training_container_image_digest(Option<String>):

    Information about the training image container.

  • create_time(DateTime):

    The time at which the trained model was created.

  • update_time(DateTime):

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

  • hyperparameters(Option<HashMap::<String, String>>):

    The hyperparameters that were used to create the trained model.

  • environment(Option<HashMap::<String, String>>):

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

  • kms_key_arn(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.

  • tags(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.

  • data_channels(Vec::<ModelTrainingDataChannel>):

    The data channels that were used for the trained model.

• On failure, responds with SdkError<GetTrainedModelError>

impl Client

pub fn get_trained_model_inference_job( &self, ) -> GetTrainedModelInferenceJobFluentBuilder

Constructs a fluent builder for the GetTrainedModelInferenceJob operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    Provides the membership ID of the membership that contains the trained model inference job that you are interested in.

    
    
  • trained_model_inference_job_arn(impl Into<String>) / set_trained_model_inference_job_arn(Option<String>):
    required: true
    

    Provides the Amazon Resource Name (ARN) of the trained model inference job that you are interested in.

    
    
• On success, responds with GetTrainedModelInferenceJobOutput with field(s):
  • create_time(DateTime):

    The time at which the trained model inference job was created.

  • update_time(DateTime):

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

  • trained_model_inference_job_arn(String):

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

  • configured_model_algorithm_association_arn(Option<String>):

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

  • name(String):

    The name of the trained model inference job.

  • status(TrainedModelInferenceJobStatus):

    The status of the trained model inference job.

  • trained_model_arn(String):

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

  • trained_model_version_identifier(Option<String>):

    The version identifier of the trained model used for this inference job. This identifies the specific version of the trained model that was used to generate the inference results.

  • resource_config(Option<InferenceResourceConfig>):

    The resource configuration information for the trained model inference job.

  • output_configuration(Option<InferenceOutputConfiguration>):

    The output configuration information for the trained model inference job.

  • membership_identifier(String):

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

  • data_source(Option<ModelInferenceDataSource>):

    The data source that was used for the trained model inference job.

  • container_execution_parameters(Option<InferenceContainerExecutionParameters>):

    The execution parameters for the model inference job container.

  • status_details(Option<StatusDetails>):

    Details about the status of a resource.

  • description(Option<String>):

    The description of the trained model inference job.

  • inference_container_image_digest(Option<String>):

    Information about the training container image.

  • environment(Option<HashMap::<String, String>>):

    The environment variables to set in the Docker container.

  • kms_key_arn(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.

  • metrics_status(Option<MetricsStatus>):

    The metrics status for the trained model inference job.

  • metrics_status_details(Option<String>):

    Details about the metrics status for the trained model inference job.

  • logs_status(Option<LogsStatus>):

    The logs status for the trained model inference job.

  • logs_status_details(Option<String>):

    Details about the logs status for the trained model inference job.

  • tags(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.

• On failure, responds with SdkError<GetTrainedModelInferenceJobError>

impl Client

pub fn get_training_dataset(&self) -> GetTrainingDatasetFluentBuilder

Constructs a fluent builder for the GetTrainingDataset operation.

• The fluent builder is configurable:
  • training_dataset_arn(impl Into<String>) / set_training_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the training dataset that you are interested in.

    
    
• On success, responds with GetTrainingDatasetOutput with field(s):
  • create_time(DateTime):

    The time at which the training dataset was created.

  • update_time(DateTime):

    The most recent time at which the training dataset was updated.

  • training_dataset_arn(String):

    The Amazon Resource Name (ARN) of the training dataset.

  • name(String):

    The name of the training dataset.

  • training_data(Vec::<Dataset>):

    Metadata about the requested training data.

  • status(TrainingDatasetStatus):

    The status of the training dataset.

  • role_arn(String):

    The IAM role used to read the training data.

  • tags(Option<HashMap::<String, String>>):

    The tags that are assigned to this training dataset.

  • description(Option<String>):

    The description of the training dataset.

• On failure, responds with SdkError<GetTrainingDatasetError>

impl Client

pub fn list_audience_export_jobs(&self) -> ListAudienceExportJobsFluentBuilder

Constructs a fluent builder for the ListAudienceExportJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • audience_generation_job_arn(impl Into<String>) / set_audience_generation_job_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the audience generation job that you are interested in.

    
    
• On success, responds with ListAudienceExportJobsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • audience_export_jobs(Vec::<AudienceExportJobSummary>):

    The audience export jobs that match the request.

• On failure, responds with SdkError<ListAudienceExportJobsError>

impl Client

pub fn list_audience_generation_jobs( &self, ) -> ListAudienceGenerationJobsFluentBuilder

Constructs a fluent builder for the ListAudienceGenerationJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the configured audience model that was used for the audience generation jobs that you are interested in.

    
    
  • collaboration_id(impl Into<String>) / set_collaboration_id(Option<String>):
    required: false
    

    The identifier of the collaboration that contains the audience generation jobs that you are interested in.

    
    
• On success, responds with ListAudienceGenerationJobsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • audience_generation_jobs(Vec::<AudienceGenerationJobSummary>):

    The audience generation jobs that match the request.

• On failure, responds with SdkError<ListAudienceGenerationJobsError>

impl Client

pub fn list_audience_models(&self) -> ListAudienceModelsFluentBuilder

Constructs a fluent builder for the ListAudienceModels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
• On success, responds with ListAudienceModelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • audience_models(Vec::<AudienceModelSummary>):

    The audience models that match the request.

• On failure, responds with SdkError<ListAudienceModelsError>

impl Client

pub fn list_collaboration_configured_model_algorithm_associations( &self, ) -> ListCollaborationConfiguredModelAlgorithmAssociationsFluentBuilder

Constructs a fluent builder for the ListCollaborationConfiguredModelAlgorithmAssociations operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the configured model algorithm associations that you are interested in.

    
    
• On success, responds with ListCollaborationConfiguredModelAlgorithmAssociationsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • collaboration_configured_model_algorithm_associations(Vec::<CollaborationConfiguredModelAlgorithmAssociationSummary>):

    The configured model algorithm associations that belong to this collaboration.

• On failure, responds with SdkError<ListCollaborationConfiguredModelAlgorithmAssociationsError>

impl Client

pub fn list_collaboration_ml_input_channels( &self, ) -> ListCollaborationMLInputChannelsFluentBuilder

Constructs a fluent builder for the ListCollaborationMLInputChannels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of results to return.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the ML input channels that you want to list.

    
    
• On success, responds with ListCollaborationMlInputChannelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • collaboration_ml_input_channels_list(Vec::<CollaborationMlInputChannelSummary>):

    The list of ML input channels that you wanted.

• On failure, responds with SdkError<ListCollaborationMLInputChannelsError>

impl Client

pub fn list_collaboration_trained_model_export_jobs( &self, ) -> ListCollaborationTrainedModelExportJobsFluentBuilder

Constructs a fluent builder for the ListCollaborationTrainedModelExportJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the trained model export jobs that you are interested in.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model that was used to create the export jobs that you are interested in.

    
    
  • trained_model_version_identifier(impl Into<String>) / set_trained_model_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to filter export jobs by. When specified, only export jobs for this specific version of the trained model are returned.

    
    
• On success, responds with ListCollaborationTrainedModelExportJobsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • collaboration_trained_model_export_jobs(Vec::<CollaborationTrainedModelExportJobSummary>):

    The exports jobs that exist for the requested trained model in the requested collaboration.

• On failure, responds with SdkError<ListCollaborationTrainedModelExportJobsError>

impl Client

pub fn list_collaboration_trained_model_inference_jobs( &self, ) -> ListCollaborationTrainedModelInferenceJobsFluentBuilder

Constructs a fluent builder for the ListCollaborationTrainedModelInferenceJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the trained model inference jobs that you are interested in.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the trained model that was used to create the trained model inference jobs that you are interested in.

    
    
  • trained_model_version_identifier(impl Into<String>) / set_trained_model_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.

    
    
• On success, responds with ListCollaborationTrainedModelInferenceJobsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • collaboration_trained_model_inference_jobs(Vec::<CollaborationTrainedModelInferenceJobSummary>):

    The trained model inference jobs that you are interested in.

• On failure, responds with SdkError<ListCollaborationTrainedModelInferenceJobsError>

impl Client

pub fn list_collaboration_trained_models( &self, ) -> ListCollaborationTrainedModelsFluentBuilder

Constructs a fluent builder for the ListCollaborationTrainedModels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • collaboration_identifier(impl Into<String>) / set_collaboration_identifier(Option<String>):
    required: true
    

    The collaboration ID of the collaboration that contains the trained models you are interested in.

    
    
• On success, responds with ListCollaborationTrainedModelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • collaboration_trained_models(Vec::<CollaborationTrainedModelSummary>):

    The trained models in the collaboration that you requested.

• On failure, responds with SdkError<ListCollaborationTrainedModelsError>

impl Client

pub fn list_configured_audience_models( &self, ) -> ListConfiguredAudienceModelsFluentBuilder

Constructs a fluent builder for the ListConfiguredAudienceModels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
• On success, responds with ListConfiguredAudienceModelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • configured_audience_models(Vec::<ConfiguredAudienceModelSummary>):

    The configured audience models.

• On failure, responds with SdkError<ListConfiguredAudienceModelsError>

impl Client

pub fn list_configured_model_algorithm_associations( &self, ) -> ListConfiguredModelAlgorithmAssociationsFluentBuilder

Constructs a fluent builder for the ListConfiguredModelAlgorithmAssociations operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that created the configured model algorithm associations you are interested in.

    
    
• On success, responds with ListConfiguredModelAlgorithmAssociationsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • configured_model_algorithm_associations(Vec::<ConfiguredModelAlgorithmAssociationSummary>):

    The list of configured model algorithm associations.

• On failure, responds with SdkError<ListConfiguredModelAlgorithmAssociationsError>

impl Client

pub fn list_configured_model_algorithms( &self, ) -> ListConfiguredModelAlgorithmsFluentBuilder

Constructs a fluent builder for the ListConfiguredModelAlgorithms operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
• On success, responds with ListConfiguredModelAlgorithmsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • configured_model_algorithms(Vec::<ConfiguredModelAlgorithmSummary>):

    The list of configured model algorithms.

• On failure, responds with SdkError<ListConfiguredModelAlgorithmsError>

impl Client

pub fn list_ml_input_channels(&self) -> ListMLInputChannelsFluentBuilder

Constructs a fluent builder for the ListMLInputChannels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of ML input channels to return.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the membership that contains the ML input channels that you want to list.

    
    
• On success, responds with ListMlInputChannelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • ml_input_channels_list(Vec::<MlInputChannelSummary>):

    The list of ML input channels that you wanted.

• On failure, responds with SdkError<ListMLInputChannelsError>

impl Client

pub fn list_tags_for_resource(&self) -> ListTagsForResourceFluentBuilder

Constructs a fluent builder for the ListTagsForResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the resource that you are interested in.

    
    
• On success, responds with ListTagsForResourceOutput with field(s):
  • tags(HashMap::<String, String>):

    The tags that are associated with the resource.

• On failure, responds with SdkError<ListTagsForResourceError>

impl Client

pub fn list_trained_model_inference_jobs( &self, ) -> ListTrainedModelInferenceJobsFluentBuilder

Constructs a fluent builder for the ListTrainedModelInferenceJobs operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of a trained model that was used to create the trained model inference jobs that you are interested in.

    
    
  • trained_model_version_identifier(impl Into<String>) / set_trained_model_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to filter inference jobs by. When specified, only inference jobs that used this specific version of the trained model are returned.

    
    
• On success, responds with ListTrainedModelInferenceJobsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • trained_model_inference_jobs(Vec::<TrainedModelInferenceJobSummary>):

    Returns the requested trained model inference jobs.

• On failure, responds with SdkError<ListTrainedModelInferenceJobsError>

impl Client

pub fn list_trained_model_versions( &self, ) -> ListTrainedModelVersionsFluentBuilder

Constructs a fluent builder for the ListTrainedModelVersions operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The pagination token from a previous ListTrainedModelVersions request. Use this token to retrieve the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of trained model versions to return in a single page. The default value is 10, and the maximum value is 100.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership identifier for the collaboration that contains the trained model.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model for which to list versions.

    
    
  • status(TrainedModelStatus) / set_status(Option<TrainedModelStatus>):
    required: false
    

    Filter the results to only include trained model versions with the specified status. Valid values include CREATE_PENDING, CREATE_IN_PROGRESS, ACTIVE, CREATE_FAILED, and others.

    
    
• On success, responds with ListTrainedModelVersionsOutput with field(s):
  • next_token(Option<String>):

    The pagination token to use in a subsequent ListTrainedModelVersions request to retrieve the next page of results. This value is null when there are no more results to return.

  • trained_models(Vec::<TrainedModelSummary>):

    A list of trained model versions that match the specified criteria. Each entry contains summary information about a trained model version, including its version identifier, status, and creation details.

• On failure, responds with SdkError<ListTrainedModelVersionsError>

impl Client

pub fn list_trained_models(&self) -> ListTrainedModelsFluentBuilder

Constructs a fluent builder for the ListTrainedModels operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that created the trained models you are interested in.

    
    
• On success, responds with ListTrainedModelsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • trained_models(Vec::<TrainedModelSummary>):

    The list of trained models.

• On failure, responds with SdkError<ListTrainedModelsError>

impl Client

pub fn list_training_datasets(&self) -> ListTrainingDatasetsFluentBuilder

Constructs a fluent builder for the ListTrainingDatasets operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The token value retrieved from a previous call to access the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum size of the results that is returned per call.

    
    
• On success, responds with ListTrainingDatasetsOutput with field(s):
  • next_token(Option<String>):

    The token value used to access the next page of results.

  • training_datasets(Vec::<TrainingDatasetSummary>):

    The training datasets that match the request.

• On failure, responds with SdkError<ListTrainingDatasetsError>

impl Client

pub fn put_configured_audience_model_policy( &self, ) -> PutConfiguredAudienceModelPolicyFluentBuilder

Constructs a fluent builder for the PutConfiguredAudienceModelPolicy operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that the resource policy will govern.

    
    
  • configured_audience_model_policy(impl Into<String>) / set_configured_audience_model_policy(Option<String>):
    required: true
    

    The IAM resource policy.

    
    
  • previous_policy_hash(impl Into<String>) / set_previous_policy_hash(Option<String>):
    required: false
    

    A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy.

    
    
  • policy_existence_condition(PolicyExistenceCondition) / set_policy_existence_condition(Option<PolicyExistenceCondition>):
    required: false
    

    Use this to prevent unexpected concurrent modification of the policy.

    
    
• On success, responds with PutConfiguredAudienceModelPolicyOutput with field(s):
  • configured_audience_model_policy(String):

    The IAM resource policy.

  • policy_hash(String):

    A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy.

• On failure, responds with SdkError<PutConfiguredAudienceModelPolicyError>

impl Client

pub fn put_ml_configuration(&self) -> PutMLConfigurationFluentBuilder

Constructs a fluent builder for the PutMLConfiguration operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is being configured.

    
    
  • default_output_location(MlOutputConfiguration) / set_default_output_location(Option<MlOutputConfiguration>):
    required: true
    

    The default Amazon S3 location where ML output is stored for the specified member.

    
    
• On success, responds with PutMlConfigurationOutput
• On failure, responds with SdkError<PutMLConfigurationError>

impl Client

pub fn start_audience_export_job(&self) -> StartAudienceExportJobFluentBuilder

Constructs a fluent builder for the StartAudienceExportJob operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the audience export job.

    
    
  • audience_generation_job_arn(impl Into<String>) / set_audience_generation_job_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the audience generation job that you want to export.

    
    
  • audience_size(AudienceSize) / set_audience_size(Option<AudienceSize>):
    required: true
    

    The size of the generated audience. Must match one of the sizes in the configured audience model.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the audience export job.

    
    
• On success, responds with StartAudienceExportJobOutput
• On failure, responds with SdkError<StartAudienceExportJobError>

impl Client

pub fn start_audience_generation_job( &self, ) -> StartAudienceGenerationJobFluentBuilder

Constructs a fluent builder for the StartAudienceGenerationJob operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the audience generation job.

    
    
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that is used for this audience generation job.

    
    
  • seed_audience(AudienceGenerationJobDataSource) / set_seed_audience(Option<AudienceGenerationJobDataSource>):
    required: true
    

    The seed audience that is used to generate the audience.

    
    
  • include_seed_in_output(bool) / set_include_seed_in_output(Option<bool>):
    required: false
    

    Whether the seed audience is included in the audience generation output.

    
    
  • collaboration_id(impl Into<String>) / set_collaboration_id(Option<String>):
    required: false
    

    The identifier of the collaboration that contains the audience generation job.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the audience generation job.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
• On success, responds with StartAudienceGenerationJobOutput with field(s):
  • audience_generation_job_arn(String):

    The Amazon Resource Name (ARN) of the audience generation job.

• On failure, responds with SdkError<StartAudienceGenerationJobError>

impl Client

pub fn start_trained_model_export_job( &self, ) -> StartTrainedModelExportJobFluentBuilder

Constructs a fluent builder for the StartTrainedModelExportJob operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the trained model export job.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model that you want to export.

    
    
  • trained_model_version_identifier(impl Into<String>) / set_trained_model_version_identifier(Option<String>):
    required: false
    

    The version identifier of the trained model to export. This specifies which version of the trained model should be exported to the specified destination.

    
    
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

    The membership ID of the member that is receiving the exported trained model artifacts.

    
    
  • output_configuration(TrainedModelExportOutputConfiguration) / set_output_configuration(Option<TrainedModelExportOutputConfiguration>):
    required: true
    

    The output configuration information for the trained model export job.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the trained model export job.

    
    
• On success, responds with StartTrainedModelExportJobOutput
• On failure, responds with SdkError<StartTrainedModelExportJobError>

impl Client

pub fn start_trained_model_inference_job( &self, ) -> StartTrainedModelInferenceJobFluentBuilder

Constructs a fluent builder for the StartTrainedModelInferenceJob operation.

• The fluent builder is configurable:
  • membership_identifier(impl Into<String>) / set_membership_identifier(Option<String>):
    required: true
    

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

    
    
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the trained model inference job.

    
    
  • trained_model_arn(impl Into<String>) / set_trained_model_arn(Option<String>):
    required: true
    

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

    
    
  • trained_model_version_identifier(impl Into<String>) / set_trained_model_version_identifier(Option<String>):
    required: false
    

    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.

    
    
  • configured_model_algorithm_association_arn(impl Into<String>) / set_configured_model_algorithm_association_arn(Option<String>):
    required: false
    

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

    
    
  • resource_config(InferenceResourceConfig) / set_resource_config(Option<InferenceResourceConfig>):
    required: true
    

    Defines the resource configuration for the trained model inference job.

    
    
  • output_configuration(InferenceOutputConfiguration) / set_output_configuration(Option<InferenceOutputConfiguration>):
    required: true
    

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

    
    
  • data_source(ModelInferenceDataSource) / set_data_source(Option<ModelInferenceDataSource>):
    required: true
    

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

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The description of the trained model inference job.

    
    
  • container_execution_parameters(InferenceContainerExecutionParameters) / set_container_execution_parameters(Option<InferenceContainerExecutionParameters>):
    required: false
    

    The execution parameters for the container.

    
    
  • environment(impl Into<String>, impl Into<String>) / set_environment(Option<HashMap::<String, String>>):
    required: false
    

    The environment variables to set in the Docker container.

    
    
  • kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
    required: false
    

    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.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
• On success, responds with StartTrainedModelInferenceJobOutput with field(s):
  • trained_model_inference_job_arn(String):

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

• On failure, responds with SdkError<StartTrainedModelInferenceJobError>

impl Client

pub fn tag_resource(&self) -> TagResourceFluentBuilder

Constructs a fluent builder for the TagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the resource that you want to assign tags.

    
    
  • tags(impl Into<String>, impl Into<String>) / set_tags(Option<HashMap::<String, String>>):
    required: true
    

    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 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.

    
    
• On success, responds with TagResourceOutput
• On failure, responds with SdkError<TagResourceError>

impl Client

pub fn untag_resource(&self) -> UntagResourceFluentBuilder

Constructs a fluent builder for the UntagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the resource that you want to remove tags from.

    
    
  • tag_keys(impl Into<String>) / set_tag_keys(Option<Vec::<String>>):
    required: true
    

    The key values of tags that you want to remove.

    
    
• On success, responds with UntagResourceOutput
• On failure, responds with SdkError<UntagResourceError>

impl Client

pub fn update_configured_audience_model( &self, ) -> UpdateConfiguredAudienceModelFluentBuilder

Constructs a fluent builder for the UpdateConfiguredAudienceModel operation.

• The fluent builder is configurable:
  • configured_audience_model_arn(impl Into<String>) / set_configured_audience_model_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the configured audience model that you want to update.

    
    
  • output_config(ConfiguredAudienceModelOutputConfig) / set_output_config(Option<ConfiguredAudienceModelOutputConfig>):
    required: false
    

    The new output configuration.

    
    
  • audience_model_arn(impl Into<String>) / set_audience_model_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the new audience model that you want to use.

    
    
  • shared_audience_metrics(SharedAudienceMetrics) / set_shared_audience_metrics(Option<Vec::<SharedAudienceMetrics>>):
    required: false
    

    The new value for whether to share audience metrics.

    
    
  • min_matching_seed_size(i32) / set_min_matching_seed_size(Option<i32>):
    required: false
    

    The minimum number of users from the seed audience that must match with users in the training data of the audience model.

    
    
  • audience_size_config(AudienceSizeConfig) / set_audience_size_config(Option<AudienceSizeConfig>):
    required: false
    

    The new audience size configuration.

    
    
  • description(impl Into<String>) / set_description(Option<String>):
    required: false
    

    The new description of the configured audience model.

    
    
• On success, responds with UpdateConfiguredAudienceModelOutput with field(s):
  • configured_audience_model_arn(String):

    The Amazon Resource Name (ARN) of the configured audience model that was updated.

• On failure, responds with SdkError<UpdateConfiguredAudienceModelError>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

• Retries or timeouts are enabled without a sleep_impl configured.
• Identity caching is enabled without a sleep_impl and time_source configured.
• No behavior_version is provided.

The panic message for each of these will have instructions on how to resolve them.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.
• This method will panic if no BehaviorVersion is provided. If you experience this panic, set behavior_version on the Config or enable the behavior-version-latest Cargo feature.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Client

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

Formats the value using the given formatter.