Struct aws_sdk_sagemaker::client::Client

pub struct Client { /* private fields */ }

Client for Amazon SageMaker Service

Client for invoking operations on Amazon SageMaker Service. Each operation on Amazon SageMaker Service is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Examples

Constructing a client and invoking an operation

    // create a shared configuration. This can be used & shared between multiple service clients.
    let shared_config = aws_config::load_from_env().await;
    let client = aws_sdk_sagemaker::Client::new(&shared_config);
    // invoke an operation
    /* let rsp = client
        .<operation_name>().
        .<param>("some value")
        .send().await; */

Constructing a client with custom configuration

use aws_config::RetryConfig;
    let shared_config = aws_config::load_from_env().await;
    let config = aws_sdk_sagemaker::config::Builder::from(&shared_config)
        .retry_config(RetryConfig::disabled())
        .build();
    let client = aws_sdk_sagemaker::Client::from_conf(config);

Implementations

impl Client

pub fn with_config(
    client: Client<DynConnector, DynMiddleware<DynConnector>>,
    conf: Config
) -> Self

Creates a client with the given service configuration.

pub fn conf(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn add_association(&self) -> AddAssociation

Constructs a fluent builder for the AddAssociation operation.

• The fluent builder is configurable:
  • source_arn(impl Into<String>) / set_source_arn(Option<String>):

    The ARN of the source.

  • destination_arn(impl Into<String>) / set_destination_arn(Option<String>):

    The Amazon Resource Name (ARN) of the destination.

  • association_type(AssociationEdgeType) / set_association_type(Option<AssociationEdgeType>):

    The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.

    • ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.

    • AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.

    • DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.

    • Produced - The source generated the destination. For example, a training job produced a model artifact.

• On success, responds with AddAssociationOutput with field(s):
  • source_arn(Option<String>):

    The ARN of the source.

  • destination_arn(Option<String>):

    The Amazon Resource Name (ARN) of the destination.

• On failure, responds with SdkError<AddAssociationError>

pub fn add_tags(&self) -> AddTags

Constructs a fluent builder for the AddTags operation.

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

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

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with AddTagsOutput with field(s):
  • tags(Option<Vec<Tag>>):

    A list of tags associated with the Amazon SageMaker resource.

• On failure, responds with SdkError<AddTagsError>

pub fn associate_trial_component(&self) -> AssociateTrialComponent

Constructs a fluent builder for the AssociateTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the component to associated with the trial.

  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial to associate with.

• On success, responds with AssociateTrialComponentOutput with field(s):
  • trial_component_arn(Option<String>):

    The ARN of the trial component.

  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<AssociateTrialComponentError>

pub fn batch_describe_model_package(&self) -> BatchDescribeModelPackage

Constructs a fluent builder for the BatchDescribeModelPackage operation.

• The fluent builder is configurable:
  • model_package_arn_list(Vec<String>) / set_model_package_arn_list(Option<Vec<String>>):

    The list of Amazon Resource Name (ARN) of the model package groups.

• On success, responds with BatchDescribeModelPackageOutput with field(s):
  • model_package_summaries(Option<HashMap<String, BatchDescribeModelPackageSummary>>):

    The summaries for the model package versions

  • batch_describe_model_package_error_map(Option<HashMap<String, BatchDescribeModelPackageError>>):

    A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

• On failure, responds with SdkError<BatchDescribeModelPackageError>

pub fn create_action(&self) -> CreateAction

Constructs a fluent builder for the CreateAction operation.

• The fluent builder is configurable:
  • action_name(impl Into<String>) / set_action_name(Option<String>):

    The name of the action. Must be unique to your account in an Amazon Web Services Region.

  • source(ActionSource) / set_source(Option<ActionSource>):

    The source type, ID, and URI.

  • action_type(impl Into<String>) / set_action_type(Option<String>):

    The action type.

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

    The description of the action.

  • status(ActionStatus) / set_status(Option<ActionStatus>):

    The status of the action.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    A list of properties to add to the action.

  • metadata_properties(MetadataProperties) / set_metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the action.

• On success, responds with CreateActionOutput with field(s):
  • action_arn(Option<String>):

    The Amazon Resource Name (ARN) of the action.

• On failure, responds with SdkError<CreateActionError>

pub fn create_algorithm(&self) -> CreateAlgorithm

Constructs a fluent builder for the CreateAlgorithm operation.

• The fluent builder is configurable:
  • algorithm_name(impl Into<String>) / set_algorithm_name(Option<String>):

    The name of the algorithm.

  • algorithm_description(impl Into<String>) / set_algorithm_description(Option<String>):

    A description of the algorithm.

  • training_specification(TrainingSpecification) / set_training_specification(Option<TrainingSpecification>):

    Specifies details about training jobs run by this algorithm, including the following:

    • The Amazon ECR path of the container and the version digest of the algorithm.

    • The hyperparameters that the algorithm supports.

    • The instance types that the algorithm supports for training.

    • Whether the algorithm supports distributed training.

    • The metrics that the algorithm emits to Amazon CloudWatch.

    • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

    • The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

  • inference_specification(InferenceSpecification) / set_inference_specification(Option<InferenceSpecification>):

    Specifies details about inference jobs that the algorithm runs, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the algorithm supports for inference.

  • validation_specification(AlgorithmValidationSpecification) / set_validation_specification(Option<AlgorithmValidationSpecification>):

    Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm’s training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm’s inference code.

  • certify_for_marketplace(bool) / set_certify_for_marketplace(bool):

    Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with CreateAlgorithmOutput with field(s):
  • algorithm_arn(Option<String>):

    The Amazon Resource Name (ARN) of the new algorithm.

• On failure, responds with SdkError<CreateAlgorithmError>

pub fn create_app(&self) -> CreateApp

Constructs a fluent builder for the CreateApp operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name.

  • app_type(AppType) / set_app_type(Option<AppType>):

    The type of app. Supported apps are JupyterServer and KernelGateway. TensorBoard is not supported.

  • app_name(impl Into<String>) / set_app_name(Option<String>):

    The name of the app.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • resource_spec(ResourceSpec) / set_resource_spec(Option<ResourceSpec>):

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

• On success, responds with CreateAppOutput with field(s):
  • app_arn(Option<String>):

    The Amazon Resource Name (ARN) of the app.

• On failure, responds with SdkError<CreateAppError>

pub fn create_app_image_config(&self) -> CreateAppImageConfig

Constructs a fluent builder for the CreateAppImageConfig operation.

• The fluent builder is configurable:
  • app_image_config_name(impl Into<String>) / set_app_image_config_name(Option<String>):

    The name of the AppImageConfig. Must be unique to your account.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the AppImageConfig.

  • kernel_gateway_image_config(KernelGatewayImageConfig) / set_kernel_gateway_image_config(Option<KernelGatewayImageConfig>):

    The KernelGatewayImageConfig.

• On success, responds with CreateAppImageConfigOutput with field(s):
  • app_image_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the AppImageConfig.

• On failure, responds with SdkError<CreateAppImageConfigError>

pub fn create_artifact(&self) -> CreateArtifact

Constructs a fluent builder for the CreateArtifact operation.

• The fluent builder is configurable:
  • artifact_name(impl Into<String>) / set_artifact_name(Option<String>):

    The name of the artifact. Must be unique to your account in an Amazon Web Services Region.

  • source(ArtifactSource) / set_source(Option<ArtifactSource>):

    The ID, ID type, and URI of the source.

  • artifact_type(impl Into<String>) / set_artifact_type(Option<String>):

    The artifact type.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    A list of properties to add to the artifact.

  • metadata_properties(MetadataProperties) / set_metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the artifact.

• On success, responds with CreateArtifactOutput with field(s):
  • artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact.

• On failure, responds with SdkError<CreateArtifactError>

pub fn create_auto_ml_job(&self) -> CreateAutoMLJob

Constructs a fluent builder for the CreateAutoMLJob operation.

• The fluent builder is configurable:
  • auto_ml_job_name(impl Into<String>) / set_auto_ml_job_name(Option<String>):

    Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.

  • input_data_config(Vec<AutoMlChannel>) / set_input_data_config(Option<Vec<AutoMlChannel>>):

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.

  • output_data_config(AutoMlOutputDataConfig) / set_output_data_config(Option<AutoMlOutputDataConfig>):

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

  • problem_type(ProblemType) / set_problem_type(Option<ProblemType>):

    Defines the type of supervised learning available for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

  • auto_ml_job_objective(AutoMlJobObjective) / set_auto_ml_job_objective(Option<AutoMlJobObjective>):

    Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

  • auto_ml_job_config(AutoMlJobConfig) / set_auto_ml_job_config(Option<AutoMlJobConfig>):

    Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

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

    The ARN of the role that is used to access the data.

  • generate_candidate_definitions_only(bool) / set_generate_candidate_definitions_only(bool):

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • model_deploy_config(ModelDeployConfig) / set_model_deploy_config(Option<ModelDeployConfig>):

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

• On success, responds with CreateAutoMlJobOutput with field(s):
  • auto_ml_job_arn(Option<String>):

    The unique ARN assigned to the AutoML job when it is created.

• On failure, responds with SdkError<CreateAutoMLJobError>

pub fn create_code_repository(&self) -> CreateCodeRepository

Constructs a fluent builder for the CreateCodeRepository operation.

• The fluent builder is configurable:
  • code_repository_name(impl Into<String>) / set_code_repository_name(Option<String>):

    The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

  • git_config(GitConfig) / set_git_config(Option<GitConfig>):

    Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with CreateCodeRepositoryOutput with field(s):
  • code_repository_arn(Option<String>):

    The Amazon Resource Name (ARN) of the new repository.

• On failure, responds with SdkError<CreateCodeRepositoryError>

pub fn create_compilation_job(&self) -> CreateCompilationJob

Constructs a fluent builder for the CreateCompilationJob operation.

• The fluent builder is configurable:
  • compilation_job_name(impl Into<String>) / set_compilation_job_name(Option<String>):

    A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

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

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

    During model compilation, Amazon SageMaker needs your permission to:

    • Read input data from an S3 bucket

    • Write model artifacts to an S3 bucket

    • Write logs to Amazon CloudWatch Logs

    • Publish metrics to Amazon CloudWatch

    You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

  • model_package_version_arn(impl Into<String>) / set_model_package_version_arn(Option<String>):

    The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

  • input_config(InputConfig) / set_input_config(Option<InputConfig>):

    Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

  • output_config(OutputConfig) / set_output_config(Option<OutputConfig>):

    Provides information about the output location for the compiled model and the target device the model runs on.

  • vpc_config(NeoVpcConfig) / set_vpc_config(Option<NeoVpcConfig>):

    A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

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

    Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with CreateCompilationJobOutput with field(s):
  • compilation_job_arn(Option<String>):

    If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

    • CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.

• On failure, responds with SdkError<CreateCompilationJobError>

pub fn create_context(&self) -> CreateContext

Constructs a fluent builder for the CreateContext operation.

• The fluent builder is configurable:
  • context_name(impl Into<String>) / set_context_name(Option<String>):

    The name of the context. Must be unique to your account in an Amazon Web Services Region.

  • source(ContextSource) / set_source(Option<ContextSource>):

    The source type, ID, and URI.

  • context_type(impl Into<String>) / set_context_type(Option<String>):

    The context type.

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

    The description of the context.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    A list of properties to add to the context.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the context.

• On success, responds with CreateContextOutput with field(s):
  • context_arn(Option<String>):

    The Amazon Resource Name (ARN) of the context.

• On failure, responds with SdkError<CreateContextError>

pub fn create_data_quality_job_definition(
    &self
) -> CreateDataQualityJobDefinition

Constructs a fluent builder for the CreateDataQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name for the monitoring job definition.

  • data_quality_baseline_config(DataQualityBaselineConfig) / set_data_quality_baseline_config(Option<DataQualityBaselineConfig>):

    Configures the constraints and baselines for the monitoring job.

  • data_quality_app_specification(DataQualityAppSpecification) / set_data_quality_app_specification(Option<DataQualityAppSpecification>):

    Specifies the container that runs the monitoring job.

  • data_quality_job_input(DataQualityJobInput) / set_data_quality_job_input(Option<DataQualityJobInput>):

    A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.

  • data_quality_job_output_config(MonitoringOutputConfig) / set_data_quality_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(MonitoringResources) / set_job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(MonitoringNetworkConfig) / set_network_config(Option<MonitoringNetworkConfig>):

    Specifies networking configuration for the monitoring job.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(MonitoringStoppingCondition) / set_stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateDataQualityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

    The Amazon Resource Name (ARN) of the job definition.

• On failure, responds with SdkError<CreateDataQualityJobDefinitionError>

pub fn create_device_fleet(&self) -> CreateDeviceFleet

Constructs a fluent builder for the CreateDeviceFleet operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet that the device belongs to.

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

    The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

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

    A description of the fleet.

  • output_config(EdgeOutputConfig) / set_output_config(Option<EdgeOutputConfig>):

    The output configuration for storing sample data collected by the fleet.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Creates tags for the specified fleet.

  • enable_iot_role_alias(bool) / set_enable_iot_role_alias(Option<bool>):

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName}”.

    For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

• On success, responds with CreateDeviceFleetOutput
• On failure, responds with SdkError<CreateDeviceFleetError>

pub fn create_domain(&self) -> CreateDomain

Constructs a fluent builder for the CreateDomain operation.

• The fluent builder is configurable:
  • domain_name(impl Into<String>) / set_domain_name(Option<String>):

    A name for the domain.

  • auth_mode(AuthMode) / set_auth_mode(Option<AuthMode>):

    The mode of authentication that members use to access the domain.

  • default_user_settings(UserSettings) / set_default_user_settings(Option<UserSettings>):

    The default settings to use to create a user profile when UserSettings isn’t specified in the call to the CreateUserProfile API.

    SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.

  • subnet_ids(Vec<String>) / set_subnet_ids(Option<Vec<String>>):

    The VPC subnets that Studio uses for communication.

  • vpc_id(impl Into<String>) / set_vpc_id(Option<String>):

    The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

    Tags that you specify for the Domain are also added to all Apps that the Domain launches.

  • app_network_access_type(AppNetworkAccessType) / set_app_network_access_type(Option<AppNetworkAccessType>):

    Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.

    • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access

    • VpcOnly - All Studio traffic is through the specified VPC and subnets

  • home_efs_file_system_kms_key_id(impl Into<String>) / set_home_efs_file_system_kms_key_id(Option<String>):

    Use KmsKeyId.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.

  • app_security_group_management(AppSecurityGroupManagement) / set_app_security_group_management(Option<AppSecurityGroupManagement>):

    The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.

  • domain_settings(DomainSettings) / set_domain_settings(Option<DomainSettings>):

    A collection of Domain settings.

• On success, responds with CreateDomainOutput with field(s):
  • domain_arn(Option<String>):

    The Amazon Resource Name (ARN) of the created domain.

  • url(Option<String>):

    The URL to the created domain.

• On failure, responds with SdkError<CreateDomainError>

pub fn create_edge_packaging_job(&self) -> CreateEdgePackagingJob

Constructs a fluent builder for the CreateEdgePackagingJob operation.

• The fluent builder is configurable:
  • edge_packaging_job_name(impl Into<String>) / set_edge_packaging_job_name(Option<String>):

    The name of the edge packaging job.

  • compilation_job_name(impl Into<String>) / set_compilation_job_name(Option<String>):

    The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.

  • model_name(impl Into<String>) / set_model_name(Option<String>):

    The name of the model.

  • model_version(impl Into<String>) / set_model_version(Option<String>):

    The version of the model.

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

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

  • output_config(EdgeOutputConfig) / set_output_config(Option<EdgeOutputConfig>):

    Provides information about the output location for the packaged model.

  • resource_key(impl Into<String>) / set_resource_key(Option<String>):

    The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Creates tags for the packaging job.

• On success, responds with CreateEdgePackagingJobOutput
• On failure, responds with SdkError<CreateEdgePackagingJobError>

pub fn create_endpoint(&self) -> CreateEndpoint

Constructs a fluent builder for the CreateEndpoint operation.

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in .

  • endpoint_config_name(impl Into<String>) / set_endpoint_config_name(Option<String>):

    The name of an endpoint configuration. For more information, see CreateEndpointConfig.

  • deployment_config(DeploymentConfig) / set_deployment_config(Option<DeploymentConfig>):

    The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with CreateEndpointOutput with field(s):
  • endpoint_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint.

• On failure, responds with SdkError<CreateEndpointError>

pub fn create_endpoint_config(&self) -> CreateEndpointConfig

Constructs a fluent builder for the CreateEndpointConfig operation.

• The fluent builder is configurable:
  • endpoint_config_name(impl Into<String>) / set_endpoint_config_name(Option<String>):

    The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

  • production_variants(Vec<ProductionVariant>) / set_production_variants(Option<Vec<ProductionVariant>>):

    An list of ProductionVariant objects, one for each model that you want to host at this endpoint.

  • data_capture_config(DataCaptureConfig) / set_data_capture_config(Option<DataCaptureConfig>):

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

    The KmsKeyId can be any of the following formats:

    • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

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

    • Alias name: alias/ExampleAlias

    • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

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

    Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can’t request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

    For a list of instance types that support local instance storage, see Instance Store Volumes.

    For more information about local instance storage encryption, see SSD Instance Store Volumes.

  • async_inference_config(AsyncInferenceConfig) / set_async_inference_config(Option<AsyncInferenceConfig>):

    Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.

• On success, responds with CreateEndpointConfigOutput with field(s):
  • endpoint_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint configuration.

• On failure, responds with SdkError<CreateEndpointConfigError>

pub fn create_experiment(&self) -> CreateExperiment

Constructs a fluent builder for the CreateExperiment operation.

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the experiment as displayed. The name doesn’t need to be unique. If you don’t specify DisplayName, the value in ExperimentName is displayed.

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

    The description of the experiment.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to associate with the experiment. You can use Search API to search on the tags.

• On success, responds with CreateExperimentOutput with field(s):
  • experiment_arn(Option<String>):

    The Amazon Resource Name (ARN) of the experiment.

• On failure, responds with SdkError<CreateExperimentError>

pub fn create_feature_group(&self) -> CreateFeatureGroup

Constructs a fluent builder for the CreateFeatureGroup operation.

• The fluent builder is configurable:
  • feature_group_name(impl Into<String>) / set_feature_group_name(Option<String>):

    The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:

    • Must start and end with an alphanumeric character.

    • Can only contain alphanumeric character and hyphens. Spaces are not allowed.

  • record_identifier_feature_name(impl Into<String>) / set_record_identifier_feature_name(Option<String>):

    The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore. Only the latest record per identifier value will be stored in the OnlineStore. RecordIdentifierFeatureName must be one of feature definitions’ names.

    You use the RecordIdentifierFeatureName to access data in a FeatureStore.

    This name:

    • Must start and end with an alphanumeric character.

    • Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.

  • event_time_feature_name(impl Into<String>) / set_event_time_feature_name(Option<String>):

    The name of the feature that stores the EventTime of a Record in a FeatureGroup.

    An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.

    An EventTime can be a String or Fractional.

    • Fractional: EventTime feature values must be a Unix timestamp in seconds.

    • String: EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd’T’HH:mm:ssZ and yyyy-MM-dd’T’HH:mm:ss.SSSZ where yyyy, MM, and dd represent the year, month, and day respectively and HH, mm, ss, and if applicable, SSS represent the hour, month, second and milliseconds respsectively. ‘T’ and Z are constants.

  • feature_definitions(Vec<FeatureDefinition>) / set_feature_definitions(Option<Vec<FeatureDefinition>>):

    A list of Feature names and types. Name and Type is compulsory per Feature.

    Valid feature FeatureTypes are Integral, Fractional and String.

    FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time

    You can create up to 2,500 FeatureDefinitions per FeatureGroup.

  • online_store_config(OnlineStoreConfig) / set_online_store_config(Option<OnlineStoreConfig>):

    You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig; the default value is False.

    You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the OnlineStore.

  • offline_store_config(OfflineStoreConfig) / set_offline_store_config(Option<OfflineStoreConfig>):

    Use this to configure an OfflineFeatureStore. This parameter allows you to specify:

    • The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore.

    • A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.

    • An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.

    To learn more about this parameter, see OfflineStoreConfig.

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

    The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.

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

    A free-form description of a FeatureGroup.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Tags used to identify Features in each FeatureGroup.

• On success, responds with CreateFeatureGroupOutput with field(s):
  • feature_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the FeatureGroup. This is a unique identifier for the feature group.

• On failure, responds with SdkError<CreateFeatureGroupError>

pub fn create_flow_definition(&self) -> CreateFlowDefinition

Constructs a fluent builder for the CreateFlowDefinition operation.

• The fluent builder is configurable:
  • flow_definition_name(impl Into<String>) / set_flow_definition_name(Option<String>):

    The name of your flow definition.

  • human_loop_request_source(HumanLoopRequestSource) / set_human_loop_request_source(Option<HumanLoopRequestSource>):

    Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

  • human_loop_activation_config(HumanLoopActivationConfig) / set_human_loop_activation_config(Option<HumanLoopActivationConfig>):

    An object containing information about the events that trigger a human workflow.

  • human_loop_config(HumanLoopConfig) / set_human_loop_config(Option<HumanLoopConfig>):

    An object containing information about the tasks the human reviewers will perform.

  • output_config(FlowDefinitionOutputConfig) / set_output_config(Option<FlowDefinitionOutputConfig>):

    An object containing information about where the human review results will be uploaded.

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

    The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

• On success, responds with CreateFlowDefinitionOutput with field(s):
  • flow_definition_arn(Option<String>):

    The Amazon Resource Name (ARN) of the flow definition you create.

• On failure, responds with SdkError<CreateFlowDefinitionError>

pub fn create_human_task_ui(&self) -> CreateHumanTaskUi

Constructs a fluent builder for the CreateHumanTaskUi operation.

• The fluent builder is configurable:
  • human_task_ui_name(impl Into<String>) / set_human_task_ui_name(Option<String>):

    The name of the user interface you are creating.

  • ui_template(UiTemplate) / set_ui_template(Option<UiTemplate>):

    The Liquid template for the worker user interface.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.

• On success, responds with CreateHumanTaskUiOutput with field(s):
  • human_task_ui_arn(Option<String>):

    The Amazon Resource Name (ARN) of the human review workflow user interface you create.

• On failure, responds with SdkError<CreateHumanTaskUiError>

pub fn create_hyper_parameter_tuning_job(&self) -> CreateHyperParameterTuningJob

Constructs a fluent builder for the CreateHyperParameterTuningJob operation.

• The fluent builder is configurable:
  • hyper_parameter_tuning_job_name(impl Into<String>) / set_hyper_parameter_tuning_job_name(Option<String>):

    The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

  • hyper_parameter_tuning_job_config(HyperParameterTuningJobConfig) / set_hyper_parameter_tuning_job_config(Option<HyperParameterTuningJobConfig>):

    The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

  • training_job_definition(HyperParameterTrainingJobDefinition) / set_training_job_definition(Option<HyperParameterTrainingJobDefinition>):

    The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

  • training_job_definitions(Vec<HyperParameterTrainingJobDefinition>) / set_training_job_definitions(Option<Vec<HyperParameterTrainingJobDefinition>>):

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

  • warm_start_config(HyperParameterTuningJobWarmStartConfig) / set_warm_start_config(Option<HyperParameterTuningJobWarmStartConfig>):

    Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

    All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

    All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

• On success, responds with CreateHyperParameterTuningJobOutput with field(s):
  • hyper_parameter_tuning_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.

• On failure, responds with SdkError<CreateHyperParameterTuningJobError>

pub fn create_image(&self) -> CreateImage

Constructs a fluent builder for the CreateImage operation.

• The fluent builder is configurable:
  • description(impl Into<String>) / set_description(Option<String>):

    The description of the image.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The display name of the image. If not provided, ImageName is displayed.

  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image. Must be unique to your account.

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

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the image.

• On success, responds with CreateImageOutput with field(s):
  • image_arn(Option<String>):

    The Amazon Resource Name (ARN) of the image.

• On failure, responds with SdkError<CreateImageError>

pub fn create_image_version(&self) -> CreateImageVersion

Constructs a fluent builder for the CreateImageVersion operation.

• The fluent builder is configurable:
  • base_image(impl Into<String>) / set_base_image(Option<String>):

    The registry path of the container image to use as the starting point for this version. The path is an Amazon Container Registry (ECR) URI in the following format:

    .dkr.ecr. .amazonaws.com/ <repo-name[:tag] or [@digest]></repo-name[:tag]>

  • client_token(impl Into<String>) / set_client_token(Option<String>):

    A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.

  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The ImageName of the Image to create a version of.

• On success, responds with CreateImageVersionOutput with field(s):
  • image_version_arn(Option<String>):

    The Amazon Resource Name (ARN) of the image version.

• On failure, responds with SdkError<CreateImageVersionError>

pub fn create_inference_recommendations_job(
    &self
) -> CreateInferenceRecommendationsJob

Constructs a fluent builder for the CreateInferenceRecommendationsJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):

    A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

  • job_type(RecommendationJobType) / set_job_type(Option<RecommendationJobType>):

    Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT) job.

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

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • input_config(RecommendationJobInputConfig) / set_input_config(Option<RecommendationJobInputConfig>):

    Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.

  • job_description(impl Into<String>) / set_job_description(Option<String>):

    Description of the recommendation job.

  • stopping_conditions(RecommendationJobStoppingConditions) / set_stopping_conditions(Option<RecommendationJobStoppingConditions>):

    A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.

• On success, responds with CreateInferenceRecommendationsJobOutput with field(s):
  • job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the recommendation job.

• On failure, responds with SdkError<CreateInferenceRecommendationsJobError>

pub fn create_labeling_job(&self) -> CreateLabelingJob

Constructs a fluent builder for the CreateLabelingJob operation.

• The fluent builder is configurable:
  • labeling_job_name(impl Into<String>) / set_labeling_job_name(Option<String>):

    The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.

  • label_attribute_name(impl Into<String>) / set_label_attribute_name(Option<String>):

    The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements.

    • The name can’t end with “-metadata”.

    • If you are using one of the following built-in task types, the attribute name must end with “-ref”. If the task type you are using is not listed below, the attribute name must not end with “-ref”.

      • Image semantic segmentation (SemanticSegmentation), and adjustment (AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation) labeling jobs for this task type.

      • Video frame object detection (VideoObjectDetection), and adjustment and verification (AdjustmentVideoObjectDetection) labeling jobs for this task type.

      • Video frame object tracking (VideoObjectTracking), and adjustment and verification (AdjustmentVideoObjectTracking) labeling jobs for this task type.

      • 3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.

      • 3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.

    If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.

  • input_config(LabelingJobInputConfig) / set_input_config(Option<LabelingJobInputConfig>):

    Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

    You must specify at least one of the following: S3DataSource or SnsDataSource.

    • Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled.

    • Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.

    If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.

  • output_config(LabelingJobOutputConfig) / set_output_config(Option<LabelingJobOutputConfig>):

    The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

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

    The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

  • label_category_config_s3_uri(impl Into<String>) / set_label_category_config_s3_uri(Option<String>):

    The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.

    For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.

    For named entity recognition jobs, in addition to “labels”, you must provide worker instructions in the label category configuration file using the “instructions” parameter: “instructions”: {“shortInstruction”:“

    Add header

    Add Instructions

    ”, “fullInstruction”:“

    Add additional instructions.

    ”}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

    For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1, label_2,…,label_n with your label categories.

    {

    “document-version”: “2018-11-28”,

    “labels”: [{“label”: “label_1”},{“label”: “label_2”},…{“label”: “label_n”}]

    }

    Note the following about the label category configuration file:

    • For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.

    • Each label category must be unique, you cannot specify duplicate label categories.

    • If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.

  • stopping_conditions(LabelingJobStoppingConditions) / set_stopping_conditions(Option<LabelingJobStoppingConditions>):

    A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

  • labeling_job_algorithms_config(LabelingJobAlgorithmsConfig) / set_labeling_job_algorithms_config(Option<LabelingJobAlgorithmsConfig>):

    Configures the information required to perform automated data labeling.

  • human_task_config(HumanTaskConfig) / set_human_task_config(Option<HumanTaskConfig>):

    Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateLabelingJobOutput with field(s):
  • labeling_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.

• On failure, responds with SdkError<CreateLabelingJobError>

pub fn create_model(&self) -> CreateModel

Constructs a fluent builder for the CreateModel operation.

• The fluent builder is configurable:
  • model_name(impl Into<String>) / set_model_name(Option<String>):

    The name of the new model.

  • primary_container(ContainerDefinition) / set_primary_container(Option<ContainerDefinition>):

    The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

  • containers(Vec<ContainerDefinition>) / set_containers(Option<Vec<ContainerDefinition>>):

    Specifies the containers in the inference pipeline.

  • inference_execution_config(InferenceExecutionConfig) / set_inference_execution_config(Option<InferenceExecutionConfig>):

    Specifies details of how containers in a multi-container endpoint are called.

  • execution_role_arn(impl Into<String>) / set_execution_role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

    To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

  • vpc_config(VpcConfig) / set_vpc_config(Option<VpcConfig>):

    A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

  • enable_network_isolation(bool) / set_enable_network_isolation(bool):

    Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

• On success, responds with CreateModelOutput with field(s):
  • model_arn(Option<String>):

    The ARN of the model created in Amazon SageMaker.

• On failure, responds with SdkError<CreateModelError>

pub fn create_model_bias_job_definition(&self) -> CreateModelBiasJobDefinition

Constructs a fluent builder for the CreateModelBiasJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • model_bias_baseline_config(ModelBiasBaselineConfig) / set_model_bias_baseline_config(Option<ModelBiasBaselineConfig>):

    The baseline configuration for a model bias job.

  • model_bias_app_specification(ModelBiasAppSpecification) / set_model_bias_app_specification(Option<ModelBiasAppSpecification>):

    Configures the model bias job to run a specified Docker container image.

  • model_bias_job_input(ModelBiasJobInput) / set_model_bias_job_input(Option<ModelBiasJobInput>):

    Inputs for the model bias job.

  • model_bias_job_output_config(MonitoringOutputConfig) / set_model_bias_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(MonitoringResources) / set_job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(MonitoringNetworkConfig) / set_network_config(Option<MonitoringNetworkConfig>):

    Networking options for a model bias job.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(MonitoringStoppingCondition) / set_stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateModelBiasJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

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

• On failure, responds with SdkError<CreateModelBiasJobDefinitionError>

pub fn create_model_explainability_job_definition(
    &self
) -> CreateModelExplainabilityJobDefinition

Constructs a fluent builder for the CreateModelExplainabilityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • model_explainability_baseline_config(ModelExplainabilityBaselineConfig) / set_model_explainability_baseline_config(Option<ModelExplainabilityBaselineConfig>):

    The baseline configuration for a model explainability job.

  • model_explainability_app_specification(ModelExplainabilityAppSpecification) / set_model_explainability_app_specification(Option<ModelExplainabilityAppSpecification>):

    Configures the model explainability job to run a specified Docker container image.

  • model_explainability_job_input(ModelExplainabilityJobInput) / set_model_explainability_job_input(Option<ModelExplainabilityJobInput>):

    Inputs for the model explainability job.

  • model_explainability_job_output_config(MonitoringOutputConfig) / set_model_explainability_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(MonitoringResources) / set_job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(MonitoringNetworkConfig) / set_network_config(Option<MonitoringNetworkConfig>):

    Networking options for a model explainability job.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(MonitoringStoppingCondition) / set_stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateModelExplainabilityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

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

• On failure, responds with SdkError<CreateModelExplainabilityJobDefinitionError>

pub fn create_model_package(&self) -> CreateModelPackage

Constructs a fluent builder for the CreateModelPackage operation.

• The fluent builder is configurable:
  • model_package_name(impl Into<String>) / set_model_package_name(Option<String>):

    The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

    This parameter is required for unversioned models. It is not applicable to versioned models.

  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

    This parameter is required for versioned models, and does not apply to unversioned models.

  • model_package_description(impl Into<String>) / set_model_package_description(Option<String>):

    A description of the model package.

  • inference_specification(InferenceSpecification) / set_inference_specification(Option<InferenceSpecification>):

    Specifies details about inference jobs that can be run with models based on this model package, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the model package supports for inference.

  • validation_specification(ModelPackageValidationSpecification) / set_validation_specification(Option<ModelPackageValidationSpecification>):

    Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

  • source_algorithm_specification(SourceAlgorithmSpecification) / set_source_algorithm_specification(Option<SourceAlgorithmSpecification>):

    Details about the algorithm that was used to create the model package.

  • certify_for_marketplace(bool) / set_certify_for_marketplace(bool):

    Whether to certify the model package for listing on Amazon Web Services Marketplace.

    This parameter is optional for unversioned models, and does not apply to versioned models.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

  • model_approval_status(ModelApprovalStatus) / set_model_approval_status(Option<ModelApprovalStatus>):

    Whether the model is approved for deployment.

    This parameter is optional for versioned models, and does not apply to unversioned models.

    For versioned models, the value of this parameter must be set to Approved to deploy the model.

  • metadata_properties(MetadataProperties) / set_metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • model_metrics(ModelMetrics) / set_model_metrics(Option<ModelMetrics>):

    A structure that contains model metrics reports.

  • client_token(impl Into<String>) / set_client_token(Option<String>):

    A unique token that guarantees that the call to this API is idempotent.

  • customer_metadata_properties(HashMap<String, String>) / set_customer_metadata_properties(Option<HashMap<String, String>>):

    The metadata properties associated with the model package versions.

  • drift_check_baselines(DriftCheckBaselines) / set_drift_check_baselines(Option<DriftCheckBaselines>):

    Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

  • domain(impl Into<String>) / set_domain(Option<String>):

    The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

  • task(impl Into<String>) / set_task(Option<String>):

    The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.

  • sample_payload_url(impl Into<String>) / set_sample_payload_url(Option<String>):

    The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

  • additional_inference_specifications(Vec<AdditionalInferenceSpecificationDefinition>) / set_additional_inference_specifications(Option<Vec<AdditionalInferenceSpecificationDefinition>>):

    An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

• On success, responds with CreateModelPackageOutput with field(s):
  • model_package_arn(Option<String>):

    The Amazon Resource Name (ARN) of the new model package.

• On failure, responds with SdkError<CreateModelPackageError>

pub fn create_model_package_group(&self) -> CreateModelPackageGroup

Constructs a fluent builder for the CreateModelPackageGroup operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group.

  • model_package_group_description(impl Into<String>) / set_model_package_group_description(Option<String>):

    A description for the model group.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

• On success, responds with CreateModelPackageGroupOutput with field(s):
  • model_package_group_arn(Option<String>):

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

• On failure, responds with SdkError<CreateModelPackageGroupError>

pub fn create_model_quality_job_definition(
    &self
) -> CreateModelQualityJobDefinition

Constructs a fluent builder for the CreateModelQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the monitoring job definition.

  • model_quality_baseline_config(ModelQualityBaselineConfig) / set_model_quality_baseline_config(Option<ModelQualityBaselineConfig>):

    Specifies the constraints and baselines for the monitoring job.

  • model_quality_app_specification(ModelQualityAppSpecification) / set_model_quality_app_specification(Option<ModelQualityAppSpecification>):

    The container that runs the monitoring job.

  • model_quality_job_input(ModelQualityJobInput) / set_model_quality_job_input(Option<ModelQualityJobInput>):

    A list of the inputs that are monitored. Currently endpoints are supported.

  • model_quality_job_output_config(MonitoringOutputConfig) / set_model_quality_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(MonitoringResources) / set_job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(MonitoringNetworkConfig) / set_network_config(Option<MonitoringNetworkConfig>):

    Specifies the network configuration for the monitoring job.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(MonitoringStoppingCondition) / set_stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateModelQualityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

    The Amazon Resource Name (ARN) of the model quality monitoring job.

• On failure, responds with SdkError<CreateModelQualityJobDefinitionError>

pub fn create_monitoring_schedule(&self) -> CreateMonitoringSchedule

Constructs a fluent builder for the CreateMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

  • monitoring_schedule_config(MonitoringScheduleConfig) / set_monitoring_schedule_config(Option<MonitoringScheduleConfig>):

    The configuration object that specifies the monitoring schedule and defines the monitoring job.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateMonitoringScheduleOutput with field(s):
  • monitoring_schedule_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitoring schedule.

• On failure, responds with SdkError<CreateMonitoringScheduleError>

pub fn create_notebook_instance(&self) -> CreateNotebookInstance

Constructs a fluent builder for the CreateNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the new notebook instance.

  • instance_type(InstanceType) / set_instance_type(Option<InstanceType>):

    The type of ML compute instance to launch for the notebook instance.

  • subnet_id(impl Into<String>) / set_subnet_id(Option<String>):

    The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

  • security_group_ids(Vec<String>) / set_security_group_ids(Option<Vec<String>>):

    The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

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

    When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

    To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

  • kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

  • lifecycle_config_name(impl Into<String>) / set_lifecycle_config_name(Option<String>):

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

  • direct_internet_access(DirectInternetAccess) / set_direct_internet_access(Option<DirectInternetAccess>):

    Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.

    For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

  • volume_size_in_gb(i32) / set_volume_size_in_gb(Option<i32>):

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

  • accelerator_types(Vec<NotebookInstanceAcceleratorType>) / set_accelerator_types(Option<Vec<NotebookInstanceAcceleratorType>>):

    A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

  • default_code_repository(impl Into<String>) / set_default_code_repository(Option<String>):

    A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • additional_code_repositories(Vec<String>) / set_additional_code_repositories(Option<Vec<String>>):

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • root_access(RootAccess) / set_root_access(Option<RootAccess>):

    Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

    Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

  • platform_identifier(impl Into<String>) / set_platform_identifier(Option<String>):

    The platform identifier of the notebook instance runtime environment.

• On success, responds with CreateNotebookInstanceOutput with field(s):
  • notebook_instance_arn(Option<String>):

    The Amazon Resource Name (ARN) of the notebook instance.

• On failure, responds with SdkError<CreateNotebookInstanceError>

pub fn create_notebook_instance_lifecycle_config(
    &self
) -> CreateNotebookInstanceLifecycleConfig

Constructs a fluent builder for the CreateNotebookInstanceLifecycleConfig operation.

• The fluent builder is configurable:
  • notebook_instance_lifecycle_config_name(impl Into<String>) / set_notebook_instance_lifecycle_config_name(Option<String>):

    The name of the lifecycle configuration.

  • on_create(Vec<NotebookInstanceLifecycleHook>) / set_on_create(Option<Vec<NotebookInstanceLifecycleHook>>):

    A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • on_start(Vec<NotebookInstanceLifecycleHook>) / set_on_start(Option<Vec<NotebookInstanceLifecycleHook>>):

    A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

• On success, responds with CreateNotebookInstanceLifecycleConfigOutput with field(s):
  • notebook_instance_lifecycle_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lifecycle configuration.

• On failure, responds with SdkError<CreateNotebookInstanceLifecycleConfigError>

pub fn create_pipeline(&self) -> CreatePipeline

Constructs a fluent builder for the CreatePipeline operation.

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline.

  • pipeline_display_name(impl Into<String>) / set_pipeline_display_name(Option<String>):

    The display name of the pipeline.

  • pipeline_definition(impl Into<String>) / set_pipeline_definition(Option<String>):

    The JSON pipeline definition of the pipeline.

  • pipeline_definition_s3_location(PipelineDefinitionS3Location) / set_pipeline_definition_s3_location(Option<PipelineDefinitionS3Location>):

    The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

  • pipeline_description(impl Into<String>) / set_pipeline_description(Option<String>):

    A description of the pipeline.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

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

    The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to apply to the created pipeline.

  • parallelism_configuration(ParallelismConfiguration) / set_parallelism_configuration(Option<ParallelismConfiguration>):

    This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.

• On success, responds with CreatePipelineOutput with field(s):
  • pipeline_arn(Option<String>):

    The Amazon Resource Name (ARN) of the created pipeline.

• On failure, responds with SdkError<CreatePipelineError>

pub fn create_presigned_domain_url(&self) -> CreatePresignedDomainUrl

Constructs a fluent builder for the CreatePresignedDomainUrl operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The name of the UserProfile to sign-in as.

  • session_expiration_duration_in_seconds(i32) / set_session_expiration_duration_in_seconds(Option<i32>):

    The session expiration duration in seconds. This value defaults to 43200.

  • expires_in_seconds(i32) / set_expires_in_seconds(Option<i32>):

    The number of seconds until the pre-signed URL expires. This value defaults to 300.

• On success, responds with CreatePresignedDomainUrlOutput with field(s):
  • authorized_url(Option<String>):

    The presigned URL.

• On failure, responds with SdkError<CreatePresignedDomainUrlError>

pub fn create_presigned_notebook_instance_url(
    &self
) -> CreatePresignedNotebookInstanceUrl

Constructs a fluent builder for the CreatePresignedNotebookInstanceUrl operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the notebook instance.

  • session_expiration_duration_in_seconds(i32) / set_session_expiration_duration_in_seconds(Option<i32>):

    The duration of the session, in seconds. The default is 12 hours.

• On success, responds with CreatePresignedNotebookInstanceUrlOutput with field(s):
  • authorized_url(Option<String>):

    A JSON object that contains the URL string.

• On failure, responds with SdkError<CreatePresignedNotebookInstanceUrlError>

pub fn create_processing_job(&self) -> CreateProcessingJob

Constructs a fluent builder for the CreateProcessingJob operation.

• The fluent builder is configurable:
  • processing_inputs(Vec<ProcessingInput>) / set_processing_inputs(Option<Vec<ProcessingInput>>):

    An array of inputs configuring the data to download into the processing container.

  • processing_output_config(ProcessingOutputConfig) / set_processing_output_config(Option<ProcessingOutputConfig>):

    Output configuration for the processing job.

  • processing_job_name(impl Into<String>) / set_processing_job_name(Option<String>):

    The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • processing_resources(ProcessingResources) / set_processing_resources(Option<ProcessingResources>):

    Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

  • stopping_condition(ProcessingStoppingCondition) / set_stopping_condition(Option<ProcessingStoppingCondition>):

    The time limit for how long the processing job is allowed to run.

  • app_specification(AppSpecification) / set_app_specification(Option<AppSpecification>):

    Configures the processing job to run a specified Docker container image.

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

    The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.

  • network_config(NetworkConfig) / set_network_config(Option<NetworkConfig>):

    Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

  • experiment_config(ExperimentConfig) / set_experiment_config(Option<ExperimentConfig>):

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob

    • CreateTrainingJob

    • CreateTransformJob

• On success, responds with CreateProcessingJobOutput with field(s):
  • processing_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the processing job.

• On failure, responds with SdkError<CreateProcessingJobError>

pub fn create_project(&self) -> CreateProject

Constructs a fluent builder for the CreateProject operation.

• The fluent builder is configurable:
  • project_name(impl Into<String>) / set_project_name(Option<String>):

    The name of the project.

  • project_description(impl Into<String>) / set_project_description(Option<String>):

    A description for the project.

  • service_catalog_provisioning_details(ServiceCatalogProvisioningDetails) / set_service_catalog_provisioning_details(Option<ServiceCatalogProvisioningDetails>):

    The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don’t provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

• On success, responds with CreateProjectOutput with field(s):
  • project_arn(Option<String>):

    The Amazon Resource Name (ARN) of the project.

  • project_id(Option<String>):

    The ID of the new project.

• On failure, responds with SdkError<CreateProjectError>

pub fn create_studio_lifecycle_config(&self) -> CreateStudioLifecycleConfig

Constructs a fluent builder for the CreateStudioLifecycleConfig operation.

• The fluent builder is configurable:
  • studio_lifecycle_config_name(impl Into<String>) / set_studio_lifecycle_config_name(Option<String>):

    The name of the Studio Lifecycle Configuration to create.

  • studio_lifecycle_config_content(impl Into<String>) / set_studio_lifecycle_config_content(Option<String>):

    The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.

  • studio_lifecycle_config_app_type(StudioLifecycleConfigAppType) / set_studio_lifecycle_config_app_type(Option<StudioLifecycleConfigAppType>):

    The App type that the Lifecycle Configuration is attached to.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

• On success, responds with CreateStudioLifecycleConfigOutput with field(s):
  • studio_lifecycle_config_arn(Option<String>):

    The ARN of your created Lifecycle Configuration.

• On failure, responds with SdkError<CreateStudioLifecycleConfigError>

pub fn create_training_job(&self) -> CreateTrainingJob

Constructs a fluent builder for the CreateTrainingJob operation.

• The fluent builder is configurable:
  • training_job_name(impl Into<String>) / set_training_job_name(Option<String>):

    The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • hyper_parameters(HashMap<String, String>) / set_hyper_parameters(Option<HashMap<String, String>>):

    Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

    You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

  • algorithm_specification(AlgorithmSpecification) / set_algorithm_specification(Option<AlgorithmSpecification>):

    The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

    During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

    To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

  • input_data_config(Vec<Channel>) / set_input_data_config(Option<Vec<Channel>>):

    An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

    Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

    Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

  • output_data_config(OutputDataConfig) / set_output_data_config(Option<OutputDataConfig>):

    Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

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

    The resources, including the ML compute instances and ML storage volumes, to use for model training.

    ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

  • vpc_config(VpcConfig) / set_vpc_config(Option<VpcConfig>):

    A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

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

    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

  • enable_network_isolation(bool) / set_enable_network_isolation(bool):

    Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

  • enable_inter_container_traffic_encryption(bool) / set_enable_inter_container_traffic_encryption(bool):

    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

  • enable_managed_spot_training(bool) / set_enable_managed_spot_training(bool):

    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.

    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

  • checkpoint_config(CheckpointConfig) / set_checkpoint_config(Option<CheckpointConfig>):

    Contains information about the output location for managed spot training checkpoint data.

  • debug_hook_config(DebugHookConfig) / set_debug_hook_config(Option<DebugHookConfig>):

    Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

  • debug_rule_configurations(Vec<DebugRuleConfiguration>) / set_debug_rule_configurations(Option<Vec<DebugRuleConfiguration>>):

    Configuration information for Debugger rules for debugging output tensors.

  • tensor_board_output_config(TensorBoardOutputConfig) / set_tensor_board_output_config(Option<TensorBoardOutputConfig>):

    Configuration of storage locations for the Debugger TensorBoard output data.

  • experiment_config(ExperimentConfig) / set_experiment_config(Option<ExperimentConfig>):

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob

    • CreateTrainingJob

    • CreateTransformJob

  • profiler_config(ProfilerConfig) / set_profiler_config(Option<ProfilerConfig>):

    Configuration information for Debugger system monitoring, framework profiling, and storage paths.

  • profiler_rule_configurations(Vec<ProfilerRuleConfiguration>) / set_profiler_rule_configurations(Option<Vec<ProfilerRuleConfiguration>>):

    Configuration information for Debugger rules for profiling system and framework metrics.

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

    The environment variables to set in the Docker container.

  • retry_strategy(RetryStrategy) / set_retry_strategy(Option<RetryStrategy>):

    The number of times to retry the job when the job fails due to an InternalServerError.

• On success, responds with CreateTrainingJobOutput with field(s):
  • training_job_arn(Option<String>):

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

• On failure, responds with SdkError<CreateTrainingJobError>

pub fn create_transform_job(&self) -> CreateTransformJob

Constructs a fluent builder for the CreateTransformJob operation.

• The fluent builder is configurable:
  • transform_job_name(impl Into<String>) / set_transform_job_name(Option<String>):

    The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • model_name(impl Into<String>) / set_model_name(Option<String>):

    The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.

  • max_concurrent_transforms(i32) / set_max_concurrent_transforms(Option<i32>):

    The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don’t need to set a value for MaxConcurrentTransforms.

  • model_client_config(ModelClientConfig) / set_model_client_config(Option<ModelClientConfig>):

    Configures the timeout and maximum number of retries for processing a transform job invocation.

  • max_payload_in_mb(i32) / set_max_payload_in_mb(Option<i32>):

    The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.

    For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

  • batch_strategy(BatchStrategy) / set_batch_strategy(Option<BatchStrategy>):

    Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

    To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord.

    To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

    To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

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

    The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

  • transform_input(TransformInput) / set_transform_input(Option<TransformInput>):

    Describes the input source and the way the transform job consumes it.

  • transform_output(TransformOutput) / set_transform_output(Option<TransformOutput>):

    Describes the results of the transform job.

  • transform_resources(TransformResources) / set_transform_resources(Option<TransformResources>):

    Describes the resources, including ML instance types and ML instance count, to use for the transform job.

  • data_processing(DataProcessing) / set_data_processing(Option<DataProcessing>):

    The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

  • experiment_config(ExperimentConfig) / set_experiment_config(Option<ExperimentConfig>):

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob

    • CreateTrainingJob

    • CreateTransformJob

• On success, responds with CreateTransformJobOutput with field(s):
  • transform_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the transform job.

• On failure, responds with SdkError<CreateTransformJobError>

pub fn create_trial(&self) -> CreateTrial

Constructs a fluent builder for the CreateTrial operation.

• The fluent builder is configurable:
  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the trial as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialName is displayed.

  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    The name of the experiment to associate the trial with.

  • metadata_properties(MetadataProperties) / set_metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to associate with the trial. You can use Search API to search on the tags.

• On success, responds with CreateTrialOutput with field(s):
  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<CreateTrialError>

pub fn create_trial_component(&self) -> CreateTrialComponent

Constructs a fluent builder for the CreateTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the component as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialComponentName is displayed.

  • status(TrialComponentStatus) / set_status(Option<TrialComponentStatus>):

    The status of the component. States include:

    • InProgress

    • Completed

    • Failed

  • start_time(DateTime) / set_start_time(Option<DateTime>):

    When the component started.

  • end_time(DateTime) / set_end_time(Option<DateTime>):

    When the component ended.

  • parameters(HashMap<String, TrialComponentParameterValue>) / set_parameters(Option<HashMap<String, TrialComponentParameterValue>>):

    The hyperparameters for the component.

  • input_artifacts(HashMap<String, TrialComponentArtifact>) / set_input_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.

  • output_artifacts(HashMap<String, TrialComponentArtifact>) / set_output_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.

  • metadata_properties(MetadataProperties) / set_metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    A list of tags to associate with the component. You can use Search API to search on the tags.

• On success, responds with CreateTrialComponentOutput with field(s):
  • trial_component_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial component.

• On failure, responds with SdkError<CreateTrialComponentError>

pub fn create_user_profile(&self) -> CreateUserProfile

Constructs a fluent builder for the CreateUserProfile operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The ID of the associated Domain.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    A name for the UserProfile. This value is not case sensitive.

  • single_sign_on_user_identifier(impl Into<String>) / set_single_sign_on_user_identifier(Option<String>):

    A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is “UserName”. If the Domain’s AuthMode is SSO, this field is required. If the Domain’s AuthMode is not SSO, this field cannot be specified.

  • single_sign_on_user_value(impl Into<String>) / set_single_sign_on_user_value(Option<String>):

    The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain’s AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain’s AuthMode is not SSO, this field cannot be specified.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

    Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.

  • user_settings(UserSettings) / set_user_settings(Option<UserSettings>):

    A collection of settings.

• On success, responds with CreateUserProfileOutput with field(s):
  • user_profile_arn(Option<String>):

    The user profile Amazon Resource Name (ARN).

• On failure, responds with SdkError<CreateUserProfileError>

pub fn create_workforce(&self) -> CreateWorkforce

Constructs a fluent builder for the CreateWorkforce operation.

• The fluent builder is configurable:
  • cognito_config(CognitoConfig) / set_cognito_config(Option<CognitoConfig>):

    Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.

    Do not use OidcConfig if you specify values for CognitoConfig.

  • oidc_config(OidcConfig) / set_oidc_config(Option<OidcConfig>):

    Use this parameter to configure a private workforce using your own OIDC Identity Provider.

    Do not use CognitoConfig if you specify values for OidcConfig.

  • source_ip_config(SourceIpConfig) / set_source_ip_config(Option<SourceIpConfig>):

    A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an IP address within this range. By default, a workforce isn’t restricted to specific IP addresses.

  • workforce_name(impl Into<String>) / set_workforce_name(Option<String>):

    The name of the private workforce.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.

• On success, responds with CreateWorkforceOutput with field(s):
  • workforce_arn(Option<String>):

    The Amazon Resource Name (ARN) of the workforce.

• On failure, responds with SdkError<CreateWorkforceError>

pub fn create_workteam(&self) -> CreateWorkteam

Constructs a fluent builder for the CreateWorkteam operation.

• The fluent builder is configurable:
  • workteam_name(impl Into<String>) / set_workteam_name(Option<String>):

    The name of the work team. Use this name to identify the work team.

  • workforce_name(impl Into<String>) / set_workforce_name(Option<String>):

    The name of the workforce.

  • member_definitions(Vec<MemberDefinition>) / set_member_definitions(Option<Vec<MemberDefinition>>):

    A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.

    Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. Do not provide input for both of these parameters in a single request.

    For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.

    For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups.

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

    A description of the work team.

  • notification_configuration(NotificationConfiguration) / set_notification_configuration(Option<NotificationConfiguration>):

    Configures notification of workers regarding available or expiring work items.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs.

    For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

• On success, responds with CreateWorkteamOutput with field(s):
  • workteam_arn(Option<String>):

    The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.

• On failure, responds with SdkError<CreateWorkteamError>

pub fn delete_action(&self) -> DeleteAction

Constructs a fluent builder for the DeleteAction operation.

• The fluent builder is configurable:
  • action_name(impl Into<String>) / set_action_name(Option<String>):

    The name of the action to delete.

• On success, responds with DeleteActionOutput with field(s):
  • action_arn(Option<String>):

    The Amazon Resource Name (ARN) of the action.

• On failure, responds with SdkError<DeleteActionError>

pub fn delete_algorithm(&self) -> DeleteAlgorithm

Constructs a fluent builder for the DeleteAlgorithm operation.

• The fluent builder is configurable:
  • algorithm_name(impl Into<String>) / set_algorithm_name(Option<String>):

    The name of the algorithm to delete.

• On success, responds with DeleteAlgorithmOutput
• On failure, responds with SdkError<DeleteAlgorithmError>

pub fn delete_app(&self) -> DeleteApp

Constructs a fluent builder for the DeleteApp operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name.

  • app_type(AppType) / set_app_type(Option<AppType>):

    The type of app.

  • app_name(impl Into<String>) / set_app_name(Option<String>):

    The name of the app.

• On success, responds with DeleteAppOutput
• On failure, responds with SdkError<DeleteAppError>

pub fn delete_app_image_config(&self) -> DeleteAppImageConfig

Constructs a fluent builder for the DeleteAppImageConfig operation.

• The fluent builder is configurable:
  • app_image_config_name(impl Into<String>) / set_app_image_config_name(Option<String>):

    The name of the AppImageConfig to delete.

• On success, responds with DeleteAppImageConfigOutput
• On failure, responds with SdkError<DeleteAppImageConfigError>

pub fn delete_artifact(&self) -> DeleteArtifact

Constructs a fluent builder for the DeleteArtifact operation.

• The fluent builder is configurable:
  • artifact_arn(impl Into<String>) / set_artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact to delete.

  • source(ArtifactSource) / set_source(Option<ArtifactSource>):

    The URI of the source.

• On success, responds with DeleteArtifactOutput with field(s):
  • artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact.

• On failure, responds with SdkError<DeleteArtifactError>

pub fn delete_association(&self) -> DeleteAssociation

Constructs a fluent builder for the DeleteAssociation operation.

• The fluent builder is configurable:
  • source_arn(impl Into<String>) / set_source_arn(Option<String>):

    The ARN of the source.

  • destination_arn(impl Into<String>) / set_destination_arn(Option<String>):

    The Amazon Resource Name (ARN) of the destination.

• On success, responds with DeleteAssociationOutput with field(s):
  • source_arn(Option<String>):

    The ARN of the source.

  • destination_arn(Option<String>):

    The Amazon Resource Name (ARN) of the destination.

• On failure, responds with SdkError<DeleteAssociationError>

pub fn delete_code_repository(&self) -> DeleteCodeRepository

Constructs a fluent builder for the DeleteCodeRepository operation.

• The fluent builder is configurable:
  • code_repository_name(impl Into<String>) / set_code_repository_name(Option<String>):

    The name of the Git repository to delete.

• On success, responds with DeleteCodeRepositoryOutput
• On failure, responds with SdkError<DeleteCodeRepositoryError>

pub fn delete_context(&self) -> DeleteContext

Constructs a fluent builder for the DeleteContext operation.

• The fluent builder is configurable:
  • context_name(impl Into<String>) / set_context_name(Option<String>):

    The name of the context to delete.

• On success, responds with DeleteContextOutput with field(s):
  • context_arn(Option<String>):

    The Amazon Resource Name (ARN) of the context.

• On failure, responds with SdkError<DeleteContextError>

pub fn delete_data_quality_job_definition(
    &self
) -> DeleteDataQualityJobDefinition

Constructs a fluent builder for the DeleteDataQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the data quality monitoring job definition to delete.

• On success, responds with DeleteDataQualityJobDefinitionOutput
• On failure, responds with SdkError<DeleteDataQualityJobDefinitionError>

pub fn delete_device_fleet(&self) -> DeleteDeviceFleet

Constructs a fluent builder for the DeleteDeviceFleet operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet to delete.

• On success, responds with DeleteDeviceFleetOutput
• On failure, responds with SdkError<DeleteDeviceFleetError>

pub fn delete_domain(&self) -> DeleteDomain

Constructs a fluent builder for the DeleteDomain operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • retention_policy(RetentionPolicy) / set_retention_policy(Option<RetentionPolicy>):

    The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).

• On success, responds with DeleteDomainOutput
• On failure, responds with SdkError<DeleteDomainError>

pub fn delete_endpoint(&self) -> DeleteEndpoint

Constructs a fluent builder for the DeleteEndpoint operation.

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    The name of the endpoint that you want to delete.

• On success, responds with DeleteEndpointOutput
• On failure, responds with SdkError<DeleteEndpointError>

pub fn delete_endpoint_config(&self) -> DeleteEndpointConfig

Constructs a fluent builder for the DeleteEndpointConfig operation.

• The fluent builder is configurable:
  • endpoint_config_name(impl Into<String>) / set_endpoint_config_name(Option<String>):

    The name of the endpoint configuration that you want to delete.

• On success, responds with DeleteEndpointConfigOutput
• On failure, responds with SdkError<DeleteEndpointConfigError>

pub fn delete_experiment(&self) -> DeleteExperiment

Constructs a fluent builder for the DeleteExperiment operation.

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    The name of the experiment to delete.

• On success, responds with DeleteExperimentOutput with field(s):
  • experiment_arn(Option<String>):

    The Amazon Resource Name (ARN) of the experiment that is being deleted.

• On failure, responds with SdkError<DeleteExperimentError>

pub fn delete_feature_group(&self) -> DeleteFeatureGroup

Constructs a fluent builder for the DeleteFeatureGroup operation.

• The fluent builder is configurable:
  • feature_group_name(impl Into<String>) / set_feature_group_name(Option<String>):

    The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

• On success, responds with DeleteFeatureGroupOutput
• On failure, responds with SdkError<DeleteFeatureGroupError>

pub fn delete_flow_definition(&self) -> DeleteFlowDefinition

Constructs a fluent builder for the DeleteFlowDefinition operation.

• The fluent builder is configurable:
  • flow_definition_name(impl Into<String>) / set_flow_definition_name(Option<String>):

    The name of the flow definition you are deleting.

• On success, responds with DeleteFlowDefinitionOutput
• On failure, responds with SdkError<DeleteFlowDefinitionError>

pub fn delete_human_task_ui(&self) -> DeleteHumanTaskUi

Constructs a fluent builder for the DeleteHumanTaskUi operation.

• The fluent builder is configurable:
  • human_task_ui_name(impl Into<String>) / set_human_task_ui_name(Option<String>):

    The name of the human task user interface (work task template) you want to delete.

• On success, responds with DeleteHumanTaskUiOutput
• On failure, responds with SdkError<DeleteHumanTaskUiError>

pub fn delete_image(&self) -> DeleteImage

Constructs a fluent builder for the DeleteImage operation.

• The fluent builder is configurable:
  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image to delete.

• On success, responds with DeleteImageOutput
• On failure, responds with SdkError<DeleteImageError>

pub fn delete_image_version(&self) -> DeleteImageVersion

Constructs a fluent builder for the DeleteImageVersion operation.

• The fluent builder is configurable:
  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image.

  • version(i32) / set_version(Option<i32>):

    The version to delete.

• On success, responds with DeleteImageVersionOutput
• On failure, responds with SdkError<DeleteImageVersionError>

pub fn delete_model(&self) -> DeleteModel

Constructs a fluent builder for the DeleteModel operation.

• The fluent builder is configurable:
  • model_name(impl Into<String>) / set_model_name(Option<String>):

    The name of the model to delete.

• On success, responds with DeleteModelOutput
• On failure, responds with SdkError<DeleteModelError>

pub fn delete_model_bias_job_definition(&self) -> DeleteModelBiasJobDefinition

Constructs a fluent builder for the DeleteModelBiasJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model bias job definition to delete.

• On success, responds with DeleteModelBiasJobDefinitionOutput
• On failure, responds with SdkError<DeleteModelBiasJobDefinitionError>

pub fn delete_model_explainability_job_definition(
    &self
) -> DeleteModelExplainabilityJobDefinition

Constructs a fluent builder for the DeleteModelExplainabilityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model explainability job definition to delete.

• On success, responds with DeleteModelExplainabilityJobDefinitionOutput
• On failure, responds with SdkError<DeleteModelExplainabilityJobDefinitionError>

pub fn delete_model_package(&self) -> DeleteModelPackage

Constructs a fluent builder for the DeleteModelPackage operation.

• The fluent builder is configurable:
  • model_package_name(impl Into<String>) / set_model_package_name(Option<String>):

    The name or Amazon Resource Name (ARN) of the model package to delete.

    When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

• On success, responds with DeleteModelPackageOutput
• On failure, responds with SdkError<DeleteModelPackageError>

pub fn delete_model_package_group(&self) -> DeleteModelPackageGroup

Constructs a fluent builder for the DeleteModelPackageGroup operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group to delete.

• On success, responds with DeleteModelPackageGroupOutput
• On failure, responds with SdkError<DeleteModelPackageGroupError>

pub fn delete_model_package_group_policy(&self) -> DeleteModelPackageGroupPolicy

Constructs a fluent builder for the DeleteModelPackageGroupPolicy operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group for which to delete the policy.

• On success, responds with DeleteModelPackageGroupPolicyOutput
• On failure, responds with SdkError<DeleteModelPackageGroupPolicyError>

pub fn delete_model_quality_job_definition(
    &self
) -> DeleteModelQualityJobDefinition

Constructs a fluent builder for the DeleteModelQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model quality monitoring job definition to delete.

• On success, responds with DeleteModelQualityJobDefinitionOutput
• On failure, responds with SdkError<DeleteModelQualityJobDefinitionError>

pub fn delete_monitoring_schedule(&self) -> DeleteMonitoringSchedule

Constructs a fluent builder for the DeleteMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    The name of the monitoring schedule to delete.

• On success, responds with DeleteMonitoringScheduleOutput
• On failure, responds with SdkError<DeleteMonitoringScheduleError>

pub fn delete_notebook_instance(&self) -> DeleteNotebookInstance

Constructs a fluent builder for the DeleteNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the Amazon SageMaker notebook instance to delete.

• On success, responds with DeleteNotebookInstanceOutput
• On failure, responds with SdkError<DeleteNotebookInstanceError>

pub fn delete_notebook_instance_lifecycle_config(
    &self
) -> DeleteNotebookInstanceLifecycleConfig

Constructs a fluent builder for the DeleteNotebookInstanceLifecycleConfig operation.

• The fluent builder is configurable:
  • notebook_instance_lifecycle_config_name(impl Into<String>) / set_notebook_instance_lifecycle_config_name(Option<String>):

    The name of the lifecycle configuration to delete.

• On success, responds with DeleteNotebookInstanceLifecycleConfigOutput
• On failure, responds with SdkError<DeleteNotebookInstanceLifecycleConfigError>

pub fn delete_pipeline(&self) -> DeletePipeline

Constructs a fluent builder for the DeletePipeline operation.

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline to delete.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• On success, responds with DeletePipelineOutput with field(s):
  • pipeline_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline to delete.

• On failure, responds with SdkError<DeletePipelineError>

pub fn delete_project(&self) -> DeleteProject

Constructs a fluent builder for the DeleteProject operation.

• The fluent builder is configurable:
  • project_name(impl Into<String>) / set_project_name(Option<String>):

    The name of the project to delete.

• On success, responds with DeleteProjectOutput
• On failure, responds with SdkError<DeleteProjectError>

pub fn delete_studio_lifecycle_config(&self) -> DeleteStudioLifecycleConfig

Constructs a fluent builder for the DeleteStudioLifecycleConfig operation.

• The fluent builder is configurable:
  • studio_lifecycle_config_name(impl Into<String>) / set_studio_lifecycle_config_name(Option<String>):

    The name of the Studio Lifecycle Configuration to delete.

• On success, responds with DeleteStudioLifecycleConfigOutput
• On failure, responds with SdkError<DeleteStudioLifecycleConfigError>

pub fn delete_tags(&self) -> DeleteTags

Constructs a fluent builder for the DeleteTags operation.

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

    The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

  • tag_keys(Vec<String>) / set_tag_keys(Option<Vec<String>>):

    An array or one or more tag keys to delete.

• On success, responds with DeleteTagsOutput
• On failure, responds with SdkError<DeleteTagsError>

pub fn delete_trial(&self) -> DeleteTrial

Constructs a fluent builder for the DeleteTrial operation.

• The fluent builder is configurable:
  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial to delete.

• On success, responds with DeleteTrialOutput with field(s):
  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial that is being deleted.

• On failure, responds with SdkError<DeleteTrialError>

pub fn delete_trial_component(&self) -> DeleteTrialComponent

Constructs a fluent builder for the DeleteTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the component to delete.

• On success, responds with DeleteTrialComponentOutput with field(s):
  • trial_component_arn(Option<String>):

    The Amazon Resource Name (ARN) of the component is being deleted.

• On failure, responds with SdkError<DeleteTrialComponentError>

pub fn delete_user_profile(&self) -> DeleteUserProfile

Constructs a fluent builder for the DeleteUserProfile operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name.

• On success, responds with DeleteUserProfileOutput
• On failure, responds with SdkError<DeleteUserProfileError>

pub fn delete_workforce(&self) -> DeleteWorkforce

Constructs a fluent builder for the DeleteWorkforce operation.

• The fluent builder is configurable:
  • workforce_name(impl Into<String>) / set_workforce_name(Option<String>):

    The name of the workforce.

• On success, responds with DeleteWorkforceOutput
• On failure, responds with SdkError<DeleteWorkforceError>

pub fn delete_workteam(&self) -> DeleteWorkteam

Constructs a fluent builder for the DeleteWorkteam operation.

• The fluent builder is configurable:
  • workteam_name(impl Into<String>) / set_workteam_name(Option<String>):

    The name of the work team to delete.

• On success, responds with DeleteWorkteamOutput with field(s):
  • success(bool):

    Returns true if the work team was successfully deleted; otherwise, returns false.

• On failure, responds with SdkError<DeleteWorkteamError>

pub fn deregister_devices(&self) -> DeregisterDevices

Constructs a fluent builder for the DeregisterDevices operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet the devices belong to.

  • device_names(Vec<String>) / set_device_names(Option<Vec<String>>):

    The unique IDs of the devices.

• On success, responds with DeregisterDevicesOutput
• On failure, responds with SdkError<DeregisterDevicesError>

pub fn describe_action(&self) -> DescribeAction

Constructs a fluent builder for the DescribeAction operation.

• The fluent builder is configurable:
  • action_name(impl Into<String>) / set_action_name(Option<String>):

    The name of the action to describe.

• On success, responds with DescribeActionOutput with field(s):
  • action_name(Option<String>):

    The name of the action.

  • action_arn(Option<String>):

    The Amazon Resource Name (ARN) of the action.

  • source(Option<ActionSource>):

    The source of the action.

  • action_type(Option<String>):

    The type of the action.

  • description(Option<String>):

    The description of the action.

  • status(Option<ActionStatus>):

    The status of the action.

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

    A list of the action’s properties.

  • creation_time(Option<DateTime>):

    When the action was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_time(Option<DateTime>):

    When the action was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

• On failure, responds with SdkError<DescribeActionError>

pub fn describe_algorithm(&self) -> DescribeAlgorithm

Constructs a fluent builder for the DescribeAlgorithm operation.

• The fluent builder is configurable:
  • algorithm_name(impl Into<String>) / set_algorithm_name(Option<String>):

    The name of the algorithm to describe.

• On success, responds with DescribeAlgorithmOutput with field(s):
  • algorithm_name(Option<String>):

    The name of the algorithm being described.

  • algorithm_arn(Option<String>):

    The Amazon Resource Name (ARN) of the algorithm.

  • algorithm_description(Option<String>):

    A brief summary about the algorithm.

  • creation_time(Option<DateTime>):

    A timestamp specifying when the algorithm was created.

  • training_specification(Option<TrainingSpecification>):

    Details about training jobs run by this algorithm.

  • inference_specification(Option<InferenceSpecification>):

    Details about inference jobs that the algorithm runs.

  • validation_specification(Option<AlgorithmValidationSpecification>):

    Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

  • algorithm_status(Option<AlgorithmStatus>):

    The current status of the algorithm.

  • algorithm_status_details(Option<AlgorithmStatusDetails>):

    Details about the current status of the algorithm.

  • product_id(Option<String>):

    The product identifier of the algorithm.

  • certify_for_marketplace(bool):

    Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

• On failure, responds with SdkError<DescribeAlgorithmError>

pub fn describe_app(&self) -> DescribeApp

Constructs a fluent builder for the DescribeApp operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name.

  • app_type(AppType) / set_app_type(Option<AppType>):

    The type of app.

  • app_name(impl Into<String>) / set_app_name(Option<String>):

    The name of the app.

• On success, responds with DescribeAppOutput with field(s):
  • app_arn(Option<String>):

    The Amazon Resource Name (ARN) of the app.

  • app_type(Option<AppType>):

    The type of app.

  • app_name(Option<String>):

    The name of the app.

  • domain_id(Option<String>):

    The domain ID.

  • user_profile_name(Option<String>):

    The user profile name.

  • status(Option<AppStatus>):

    The status.

  • last_health_check_timestamp(Option<DateTime>):

    The timestamp of the last health check.

  • last_user_activity_timestamp(Option<DateTime>):

    The timestamp of the last user’s activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.

  • creation_time(Option<DateTime>):

    The creation time.

  • failure_reason(Option<String>):

    The failure reason.

  • resource_spec(Option<ResourceSpec>):

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

• On failure, responds with SdkError<DescribeAppError>

pub fn describe_app_image_config(&self) -> DescribeAppImageConfig

Constructs a fluent builder for the DescribeAppImageConfig operation.

• The fluent builder is configurable:
  • app_image_config_name(impl Into<String>) / set_app_image_config_name(Option<String>):

    The name of the AppImageConfig to describe.

• On success, responds with DescribeAppImageConfigOutput with field(s):
  • app_image_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the AppImageConfig.

  • app_image_config_name(Option<String>):

    The name of the AppImageConfig.

  • creation_time(Option<DateTime>):

    When the AppImageConfig was created.

  • last_modified_time(Option<DateTime>):

    When the AppImageConfig was last modified.

  • kernel_gateway_image_config(Option<KernelGatewayImageConfig>):

    The configuration of a KernelGateway app.

• On failure, responds with SdkError<DescribeAppImageConfigError>

pub fn describe_artifact(&self) -> DescribeArtifact

Constructs a fluent builder for the DescribeArtifact operation.

• The fluent builder is configurable:
  • artifact_arn(impl Into<String>) / set_artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact to describe.

• On success, responds with DescribeArtifactOutput with field(s):
  • artifact_name(Option<String>):

    The name of the artifact.

  • artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact.

  • source(Option<ArtifactSource>):

    The source of the artifact.

  • artifact_type(Option<String>):

    The type of the artifact.

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

    A list of the artifact’s properties.

  • creation_time(Option<DateTime>):

    When the artifact was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_time(Option<DateTime>):

    When the artifact was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

• On failure, responds with SdkError<DescribeArtifactError>

pub fn describe_auto_ml_job(&self) -> DescribeAutoMLJob

Constructs a fluent builder for the DescribeAutoMLJob operation.

• The fluent builder is configurable:
  • auto_ml_job_name(impl Into<String>) / set_auto_ml_job_name(Option<String>):

    Requests information about an AutoML job using its unique name.

• On success, responds with DescribeAutoMlJobOutput with field(s):
  • auto_ml_job_name(Option<String>):

    Returns the name of the AutoML job.

  • auto_ml_job_arn(Option<String>):

    Returns the ARN of the AutoML job.

  • input_data_config(Option<Vec<AutoMlChannel>>):

    Returns the input data configuration for the AutoML job..

  • output_data_config(Option<AutoMlOutputDataConfig>):

    Returns the job’s output data config.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

  • auto_ml_job_objective(Option<AutoMlJobObjective>):

    Returns the job’s objective.

  • problem_type(Option<ProblemType>):

    Returns the job’s problem type.

  • auto_ml_job_config(Option<AutoMlJobConfig>):

    Returns the configuration for the AutoML job.

  • creation_time(Option<DateTime>):

    Returns the creation time of the AutoML job.

  • end_time(Option<DateTime>):

    Returns the end time of the AutoML job.

  • last_modified_time(Option<DateTime>):

    Returns the job’s last modified time.

  • failure_reason(Option<String>):

    Returns the failure reason for an AutoML job, when applicable.

  • partial_failure_reasons(Option<Vec<AutoMlPartialFailureReason>>):

    Returns a list of reasons for partial failures within an AutoML job.

  • best_candidate(Option<AutoMlCandidate>):

    Returns the job’s best AutoMLCandidate.

  • auto_ml_job_status(Option<AutoMlJobStatus>):

    Returns the status of the AutoML job.

  • auto_ml_job_secondary_status(Option<AutoMlJobSecondaryStatus>):

    Returns the secondary status of the AutoML job.

  • generate_candidate_definitions_only(bool):

    Indicates whether the output for an AutoML job generates candidate definitions only.

  • auto_ml_job_artifacts(Option<AutoMlJobArtifacts>):

    Returns information on the job’s artifacts found in AutoMLJobArtifacts.

  • resolved_attributes(Option<ResolvedAttributes>):

    This contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are auto-inferred. If you do provide them, the values used are the ones you provide.

  • model_deploy_config(Option<ModelDeployConfig>):

    Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

  • model_deploy_result(Option<ModelDeployResult>):

    Provides information about endpoint for the model deployment.

• On failure, responds with SdkError<DescribeAutoMLJobError>

pub fn describe_code_repository(&self) -> DescribeCodeRepository

Constructs a fluent builder for the DescribeCodeRepository operation.

• The fluent builder is configurable:
  • code_repository_name(impl Into<String>) / set_code_repository_name(Option<String>):

    The name of the Git repository to describe.

• On success, responds with DescribeCodeRepositoryOutput with field(s):
  • code_repository_name(Option<String>):

    The name of the Git repository.

  • code_repository_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Git repository.

  • creation_time(Option<DateTime>):

    The date and time that the repository was created.

  • last_modified_time(Option<DateTime>):

    The date and time that the repository was last changed.

  • git_config(Option<GitConfig>):

    Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.

• On failure, responds with SdkError<DescribeCodeRepositoryError>

pub fn describe_compilation_job(&self) -> DescribeCompilationJob

Constructs a fluent builder for the DescribeCompilationJob operation.

• The fluent builder is configurable:
  • compilation_job_name(impl Into<String>) / set_compilation_job_name(Option<String>):

    The name of the model compilation job that you want information about.

• On success, responds with DescribeCompilationJobOutput with field(s):
  • compilation_job_name(Option<String>):

    The name of the model compilation job.

  • compilation_job_arn(Option<String>):

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

  • compilation_job_status(Option<CompilationJobStatus>):

    The status of the model compilation job.

  • compilation_start_time(Option<DateTime>):

    The time when the model compilation job started the CompilationJob instances.

    You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That’s because it takes time to download the compilation job, which depends on the size of the compilation job container.

  • compilation_end_time(Option<DateTime>):

    The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job’s model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.

  • stopping_condition(Option<StoppingCondition>):

    Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

  • inference_image(Option<String>):

    The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.

  • model_package_version_arn(Option<String>):

    The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.

  • creation_time(Option<DateTime>):

    The time that the model compilation job was created.

  • last_modified_time(Option<DateTime>):

    The time that the status of the model compilation job was last modified.

  • failure_reason(Option<String>):

    If a model compilation job failed, the reason it failed.

  • model_artifacts(Option<ModelArtifacts>):

    Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

  • model_digests(Option<ModelDigests>):

    Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

  • input_config(Option<InputConfig>):

    Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

  • output_config(Option<OutputConfig>):

    Information about the output location for the compiled model and the target device that the model runs on.

  • vpc_config(Option<NeoVpcConfig>):

    A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

• On failure, responds with SdkError<DescribeCompilationJobError>

pub fn describe_context(&self) -> DescribeContext

Constructs a fluent builder for the DescribeContext operation.

• The fluent builder is configurable:
  • context_name(impl Into<String>) / set_context_name(Option<String>):

    The name of the context to describe.

• On success, responds with DescribeContextOutput with field(s):
  • context_name(Option<String>):

    The name of the context.

  • context_arn(Option<String>):

    The Amazon Resource Name (ARN) of the context.

  • source(Option<ContextSource>):

    The source of the context.

  • context_type(Option<String>):

    The type of the context.

  • description(Option<String>):

    The description of the context.

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

    A list of the context’s properties.

  • creation_time(Option<DateTime>):

    When the context was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_time(Option<DateTime>):

    When the context was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

• On failure, responds with SdkError<DescribeContextError>

pub fn describe_data_quality_job_definition(
    &self
) -> DescribeDataQualityJobDefinition

Constructs a fluent builder for the DescribeDataQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the data quality monitoring job definition to describe.

• On success, responds with DescribeDataQualityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

    The Amazon Resource Name (ARN) of the data quality monitoring job definition.

  • job_definition_name(Option<String>):

    The name of the data quality monitoring job definition.

  • creation_time(Option<DateTime>):

    The time that the data quality monitoring job definition was created.

  • data_quality_baseline_config(Option<DataQualityBaselineConfig>):

    The constraints and baselines for the data quality monitoring job definition.

  • data_quality_app_specification(Option<DataQualityAppSpecification>):

    Information about the container that runs the data quality monitoring job.

  • data_quality_job_input(Option<DataQualityJobInput>):

    The list of inputs for the data quality monitoring job. Currently endpoints are supported.

  • data_quality_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(Option<MonitoringNetworkConfig>):

    The networking configuration for the data quality monitoring job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

• On failure, responds with SdkError<DescribeDataQualityJobDefinitionError>

pub fn describe_device(&self) -> DescribeDevice

Constructs a fluent builder for the DescribeDevice operation.

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

    Next token of device description.

  • device_name(impl Into<String>) / set_device_name(Option<String>):

    The unique ID of the device.

  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet the devices belong to.

• On success, responds with DescribeDeviceOutput with field(s):
  • device_arn(Option<String>):

    The Amazon Resource Name (ARN) of the device.

  • device_name(Option<String>):

    The unique identifier of the device.

  • description(Option<String>):

    A description of the device.

  • device_fleet_name(Option<String>):

    The name of the fleet the device belongs to.

  • iot_thing_name(Option<String>):

    The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.

  • registration_time(Option<DateTime>):

    The timestamp of the last registration or de-reregistration.

  • latest_heartbeat(Option<DateTime>):

    The last heartbeat received from the device.

  • models(Option<Vec<EdgeModel>>):

    Models on the device.

  • max_models(i32):

    The maximum number of models.

  • next_token(Option<String>):

    The response from the last list when returning a list large enough to need tokening.

  • agent_version(Option<String>):

    Edge Manager agent version.

• On failure, responds with SdkError<DescribeDeviceError>

pub fn describe_device_fleet(&self) -> DescribeDeviceFleet

Constructs a fluent builder for the DescribeDeviceFleet operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet.

• On success, responds with DescribeDeviceFleetOutput with field(s):
  • device_fleet_name(Option<String>):

    The name of the fleet.

  • device_fleet_arn(Option<String>):

    The The Amazon Resource Name (ARN) of the fleet.

  • output_config(Option<EdgeOutputConfig>):

    The output configuration for storing sampled data.

  • description(Option<String>):

    A description of the fleet.

  • creation_time(Option<DateTime>):

    Timestamp of when the device fleet was created.

  • last_modified_time(Option<DateTime>):

    Timestamp of when the device fleet was last updated.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

  • iot_role_alias(Option<String>):

    The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).

• On failure, responds with SdkError<DescribeDeviceFleetError>

pub fn describe_domain(&self) -> DescribeDomain

Constructs a fluent builder for the DescribeDomain operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

• On success, responds with DescribeDomainOutput with field(s):
  • domain_arn(Option<String>):

    The domain’s Amazon Resource Name (ARN).

  • domain_id(Option<String>):

    The domain ID.

  • domain_name(Option<String>):

    The domain name.

  • home_efs_file_system_id(Option<String>):

    The ID of the Amazon Elastic File System (EFS) managed by this Domain.

  • single_sign_on_managed_application_instance_id(Option<String>):

    The SSO managed application instance ID.

  • status(Option<DomainStatus>):

    The status.

  • creation_time(Option<DateTime>):

    The creation time.

  • last_modified_time(Option<DateTime>):

    The last modified time.

  • failure_reason(Option<String>):

    The failure reason.

  • auth_mode(Option<AuthMode>):

    The domain’s authentication mode.

  • default_user_settings(Option<UserSettings>):

    Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.

  • app_network_access_type(Option<AppNetworkAccessType>):

    Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.

    • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access

    • VpcOnly - All Studio traffic is through the specified VPC and subnets

  • home_efs_file_system_kms_key_id(Option<String>):

    Use KmsKeyId.

  • subnet_ids(Option<Vec<String>>):

    The VPC subnets that Studio uses for communication.

  • url(Option<String>):

    The domain’s URL.

  • vpc_id(Option<String>):

    The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.

  • kms_key_id(Option<String>):

    The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.

  • domain_settings(Option<DomainSettings>):

    A collection of Domain settings.

  • app_security_group_management(Option<AppSecurityGroupManagement>):

    The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.

  • security_group_id_for_domain_boundary(Option<String>):

    The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.

• On failure, responds with SdkError<DescribeDomainError>

pub fn describe_edge_packaging_job(&self) -> DescribeEdgePackagingJob

Constructs a fluent builder for the DescribeEdgePackagingJob operation.

• The fluent builder is configurable:
  • edge_packaging_job_name(impl Into<String>) / set_edge_packaging_job_name(Option<String>):

    The name of the edge packaging job.

• On success, responds with DescribeEdgePackagingJobOutput with field(s):
  • edge_packaging_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the edge packaging job.

  • edge_packaging_job_name(Option<String>):

    The name of the edge packaging job.

  • compilation_job_name(Option<String>):

    The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.

  • model_name(Option<String>):

    The name of the model.

  • model_version(Option<String>):

    The version of the model.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.

  • output_config(Option<EdgeOutputConfig>):

    The output configuration for the edge packaging job.

  • resource_key(Option<String>):

    The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.

  • edge_packaging_job_status(Option<EdgePackagingJobStatus>):

    The current status of the packaging job.

  • edge_packaging_job_status_message(Option<String>):

    Returns a message describing the job status and error messages.

  • creation_time(Option<DateTime>):

    The timestamp of when the packaging job was created.

  • last_modified_time(Option<DateTime>):

    The timestamp of when the job was last updated.

  • model_artifact(Option<String>):

    The Amazon Simple Storage (S3) URI where model artifacts ares stored.

  • model_signature(Option<String>):

    The signature document of files in the model artifact.

  • preset_deployment_output(Option<EdgePresetDeploymentOutput>):

    The output of a SageMaker Edge Manager deployable resource.

• On failure, responds with SdkError<DescribeEdgePackagingJobError>

pub fn describe_endpoint(&self) -> DescribeEndpoint

Constructs a fluent builder for the DescribeEndpoint operation.

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    The name of the endpoint.

• On success, responds with DescribeEndpointOutput with field(s):
  • endpoint_name(Option<String>):

    Name of the endpoint.

  • endpoint_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint.

  • endpoint_config_name(Option<String>):

    The name of the endpoint configuration associated with this endpoint.

  • production_variants(Option<Vec<ProductionVariantSummary>>):

    An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

  • data_capture_config(Option<DataCaptureConfigSummary>):

  • endpoint_status(Option<EndpointStatus>):

    The status of the endpoint.

    • OutOfService: Endpoint is not available to take incoming requests.

    • Creating: CreateEndpoint is executing.

    • Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

    • SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

    • RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

    • InService: Endpoint is available to process incoming requests.

    • Deleting: DeleteEndpoint is executing.

    • Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

  • failure_reason(Option<String>):

    If the status of the endpoint is Failed, the reason why it failed.

  • creation_time(Option<DateTime>):

    A timestamp that shows when the endpoint was created.

  • last_modified_time(Option<DateTime>):

    A timestamp that shows when the endpoint was last modified.

  • last_deployment_config(Option<DeploymentConfig>):

    The most recent deployment configuration for the endpoint.

  • async_inference_config(Option<AsyncInferenceConfig>):

    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

  • pending_deployment_summary(Option<PendingDeploymentSummary>):

    Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.

• On failure, responds with SdkError<DescribeEndpointError>

pub fn describe_endpoint_config(&self) -> DescribeEndpointConfig

Constructs a fluent builder for the DescribeEndpointConfig operation.

• The fluent builder is configurable:
  • endpoint_config_name(impl Into<String>) / set_endpoint_config_name(Option<String>):

    The name of the endpoint configuration.

• On success, responds with DescribeEndpointConfigOutput with field(s):
  • endpoint_config_name(Option<String>):

    Name of the Amazon SageMaker endpoint configuration.

  • endpoint_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint configuration.

  • production_variants(Option<Vec<ProductionVariant>>):

    An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

  • data_capture_config(Option<DataCaptureConfig>):

  • kms_key_id(Option<String>):

    Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

  • creation_time(Option<DateTime>):

    A timestamp that shows when the endpoint configuration was created.

  • async_inference_config(Option<AsyncInferenceConfig>):

    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

• On failure, responds with SdkError<DescribeEndpointConfigError>

pub fn describe_experiment(&self) -> DescribeExperiment

Constructs a fluent builder for the DescribeExperiment operation.

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    The name of the experiment to describe.

• On success, responds with DescribeExperimentOutput with field(s):
  • experiment_name(Option<String>):

    The name of the experiment.

  • experiment_arn(Option<String>):

    The Amazon Resource Name (ARN) of the experiment.

  • display_name(Option<String>):

    The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

  • source(Option<ExperimentSource>):

    The ARN of the source and, optionally, the type.

  • description(Option<String>):

    The description of the experiment.

  • creation_time(Option<DateTime>):

    When the experiment was created.

  • created_by(Option<UserContext>):

    Who created the experiment.

  • last_modified_time(Option<DateTime>):

    When the experiment was last modified.

  • last_modified_by(Option<UserContext>):

    Who last modified the experiment.

• On failure, responds with SdkError<DescribeExperimentError>

pub fn describe_feature_group(&self) -> DescribeFeatureGroup

Constructs a fluent builder for the DescribeFeatureGroup operation.

• The fluent builder is configurable:
  • feature_group_name(impl Into<String>) / set_feature_group_name(Option<String>):

    The name of the FeatureGroup you want described.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.

• On success, responds with DescribeFeatureGroupOutput with field(s):
  • feature_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the FeatureGroup.

  • feature_group_name(Option<String>):

    he name of the FeatureGroup.

  • record_identifier_feature_name(Option<String>):

    The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.

  • event_time_feature_name(Option<String>):

    The name of the feature that stores the EventTime of a Record in a FeatureGroup.

    An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup have a corresponding EventTime.

  • feature_definitions(Option<Vec<FeatureDefinition>>):

    A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.

  • creation_time(Option<DateTime>):

    A timestamp indicating when SageMaker created the FeatureGroup.

  • online_store_config(Option<OnlineStoreConfig>):

    The configuration for the OnlineStore.

  • offline_store_config(Option<OfflineStoreConfig>):

    The configuration of the OfflineStore, inducing the S3 location of the OfflineStore, Amazon Web Services Glue or Amazon Web Services Hive data catalogue configurations, and the security configuration.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.

  • feature_group_status(Option<FeatureGroupStatus>):

    The status of the feature group.

  • offline_store_status(Option<OfflineStoreStatus>):

    The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked

  • failure_reason(Option<String>):

    The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure can occur because:

    • The FeatureGroup could not be created in the OfflineStore.

    • The FeatureGroup could not be deleted from the OfflineStore.

  • description(Option<String>):

    A free form description of the feature group.

  • next_token(Option<String>):

    A token to resume pagination of the list of Features (FeatureDefinitions).

• On failure, responds with SdkError<DescribeFeatureGroupError>

pub fn describe_flow_definition(&self) -> DescribeFlowDefinition

Constructs a fluent builder for the DescribeFlowDefinition operation.

• The fluent builder is configurable:
  • flow_definition_name(impl Into<String>) / set_flow_definition_name(Option<String>):

    The name of the flow definition.

• On success, responds with DescribeFlowDefinitionOutput with field(s):
  • flow_definition_arn(Option<String>):

    The Amazon Resource Name (ARN) of the flow defintion.

  • flow_definition_name(Option<String>):

    The Amazon Resource Name (ARN) of the flow definition.

  • flow_definition_status(Option<FlowDefinitionStatus>):

    The status of the flow definition. Valid values are listed below.

  • creation_time(Option<DateTime>):

    The timestamp when the flow definition was created.

  • human_loop_request_source(Option<HumanLoopRequestSource>):

    Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

  • human_loop_activation_config(Option<HumanLoopActivationConfig>):

    An object containing information about what triggers a human review workflow.

  • human_loop_config(Option<HumanLoopConfig>):

    An object containing information about who works on the task, the workforce task price, and other task details.

  • output_config(Option<FlowDefinitionOutputConfig>):

    An object containing information about the output file.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.

  • failure_reason(Option<String>):

    The reason your flow definition failed.

• On failure, responds with SdkError<DescribeFlowDefinitionError>

pub fn describe_human_task_ui(&self) -> DescribeHumanTaskUi

Constructs a fluent builder for the DescribeHumanTaskUi operation.

• The fluent builder is configurable:
  • human_task_ui_name(impl Into<String>) / set_human_task_ui_name(Option<String>):

    The name of the human task user interface (worker task template) you want information about.

• On success, responds with DescribeHumanTaskUiOutput with field(s):
  • human_task_ui_arn(Option<String>):

    The Amazon Resource Name (ARN) of the human task user interface (worker task template).

  • human_task_ui_name(Option<String>):

    The name of the human task user interface (worker task template).

  • human_task_ui_status(Option<HumanTaskUiStatus>):

    The status of the human task user interface (worker task template). Valid values are listed below.

  • creation_time(Option<DateTime>):

    The timestamp when the human task user interface was created.

  • ui_template(Option<UiTemplateInfo>):

    Container for user interface template information.

• On failure, responds with SdkError<DescribeHumanTaskUiError>

pub fn describe_hyper_parameter_tuning_job(
    &self
) -> DescribeHyperParameterTuningJob

Constructs a fluent builder for the DescribeHyperParameterTuningJob operation.

• The fluent builder is configurable:
  • hyper_parameter_tuning_job_name(impl Into<String>) / set_hyper_parameter_tuning_job_name(Option<String>):

    The name of the tuning job.

• On success, responds with DescribeHyperParameterTuningJobOutput with field(s):
  • hyper_parameter_tuning_job_name(Option<String>):

    The name of the tuning job.

  • hyper_parameter_tuning_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the tuning job.

  • hyper_parameter_tuning_job_config(Option<HyperParameterTuningJobConfig>):

    The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

  • training_job_definition(Option<HyperParameterTrainingJobDefinition>):

    The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

  • training_job_definitions(Option<Vec<HyperParameterTrainingJobDefinition>>):

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

  • hyper_parameter_tuning_job_status(Option<HyperParameterTuningJobStatus>):

    The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

  • creation_time(Option<DateTime>):

    The date and time that the tuning job started.

  • hyper_parameter_tuning_end_time(Option<DateTime>):

    The date and time that the tuning job ended.

  • last_modified_time(Option<DateTime>):

    The date and time that the status of the tuning job was modified.

  • training_job_status_counters(Option<TrainingJobStatusCounters>):

    The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

  • objective_status_counters(Option<ObjectiveStatusCounters>):

    The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

  • best_training_job(Option<HyperParameterTrainingJobSummary>):

    A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

  • overall_best_training_job(Option<HyperParameterTrainingJobSummary>):

    If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

  • warm_start_config(Option<HyperParameterTuningJobWarmStartConfig>):

    The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

  • failure_reason(Option<String>):

    If the tuning job failed, the reason it failed.

• On failure, responds with SdkError<DescribeHyperParameterTuningJobError>

pub fn describe_image(&self) -> DescribeImage

Constructs a fluent builder for the DescribeImage operation.

• The fluent builder is configurable:
  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image to describe.

• On success, responds with DescribeImageOutput with field(s):
  • creation_time(Option<DateTime>):

    When the image was created.

  • description(Option<String>):

    The description of the image.

  • display_name(Option<String>):

    The name of the image as displayed.

  • failure_reason(Option<String>):

    When a create, update, or delete operation fails, the reason for the failure.

  • image_arn(Option<String>):

    The Amazon Resource Name (ARN) of the image.

  • image_name(Option<String>):

    The name of the image.

  • image_status(Option<ImageStatus>):

    The status of the image.

  • last_modified_time(Option<DateTime>):

    When the image was last modified.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.

• On failure, responds with SdkError<DescribeImageError>

pub fn describe_image_version(&self) -> DescribeImageVersion

Constructs a fluent builder for the DescribeImageVersion operation.

• The fluent builder is configurable:
  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image.

  • version(i32) / set_version(Option<i32>):

    The version of the image. If not specified, the latest version is described.

• On success, responds with DescribeImageVersionOutput with field(s):
  • base_image(Option<String>):

    The registry path of the container image on which this image version is based.

  • container_image(Option<String>):

    The registry path of the container image that contains this image version.

  • creation_time(Option<DateTime>):

    When the version was created.

  • failure_reason(Option<String>):

    When a create or delete operation fails, the reason for the failure.

  • image_arn(Option<String>):

    The Amazon Resource Name (ARN) of the image the version is based on.

  • image_version_arn(Option<String>):

    The ARN of the version.

  • image_version_status(Option<ImageVersionStatus>):

    The status of the version.

  • last_modified_time(Option<DateTime>):

    When the version was last modified.

  • version(Option<i32>):

    The version number.

• On failure, responds with SdkError<DescribeImageVersionError>

pub fn describe_inference_recommendations_job(
    &self
) -> DescribeInferenceRecommendationsJob

Constructs a fluent builder for the DescribeInferenceRecommendationsJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):

    The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• On success, responds with DescribeInferenceRecommendationsJobOutput with field(s):
  • job_name(Option<String>):

    The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • job_description(Option<String>):

    The job description that you provided when you initiated the job.

  • job_type(Option<RecommendationJobType>):

    The job type that you provided when you initiated the job.

  • job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.

  • status(Option<RecommendationJobStatus>):

    The status of the job.

  • creation_time(Option<DateTime>):

    A timestamp that shows when the job was created.

  • completion_time(Option<DateTime>):

    A timestamp that shows when the job completed.

  • last_modified_time(Option<DateTime>):

    A timestamp that shows when the job was last modified.

  • failure_reason(Option<String>):

    If the job fails, provides information why the job failed.

  • input_config(Option<RecommendationJobInputConfig>):

    Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.

  • stopping_conditions(Option<RecommendationJobStoppingConditions>):

    The stopping conditions that you provided when you initiated the job.

  • inference_recommendations(Option<Vec<InferenceRecommendation>>):

    The recommendations made by Inference Recommender.

• On failure, responds with SdkError<DescribeInferenceRecommendationsJobError>

pub fn describe_labeling_job(&self) -> DescribeLabelingJob

Constructs a fluent builder for the DescribeLabelingJob operation.

• The fluent builder is configurable:
  • labeling_job_name(impl Into<String>) / set_labeling_job_name(Option<String>):

    The name of the labeling job to return information for.

• On success, responds with DescribeLabelingJobOutput with field(s):
  • labeling_job_status(Option<LabelingJobStatus>):

    The processing status of the labeling job.

  • label_counters(Option<LabelCounters>):

    Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn’t be labeled, and the total number of objects labeled.

  • failure_reason(Option<String>):

    If the job failed, the reason that it failed.

  • creation_time(Option<DateTime>):

    The date and time that the labeling job was created.

  • last_modified_time(Option<DateTime>):

    The date and time that the labeling job was last updated.

  • job_reference_code(Option<String>):

    A unique identifier for work done as part of a labeling job.

  • labeling_job_name(Option<String>):

    The name assigned to the labeling job when it was created.

  • labeling_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the labeling job.

  • label_attribute_name(Option<String>):

    The attribute used as the label in the output manifest file.

  • input_config(Option<LabelingJobInputConfig>):

    Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

  • output_config(Option<LabelingJobOutputConfig>):

    The location of the job’s output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.

  • label_category_config_s3_uri(Option<String>):

    The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:

    • Semantic segmentation labeling jobs using automated labeling: 20 labels

    • Box bounding labeling jobs (all): 10 labels

    The file is a JSON structure in the following format:

    {

    “document-version”: “2018-11-28”

    “labels”: [

    {

    “label”: “label 1”

    },

    {

    “label”: “label 2”

    },

    …

    {

    “label”: “label n”

    }

    ]

    }

  • stopping_conditions(Option<LabelingJobStoppingConditions>):

    A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.

  • labeling_job_algorithms_config(Option<LabelingJobAlgorithmsConfig>):

    Configuration information for automated data labeling.

  • human_task_config(Option<HumanTaskConfig>):

    Configuration information required for human workers to complete a labeling task.

  • tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

  • labeling_job_output(Option<LabelingJobOutput>):

    The location of the output produced by the labeling job.

• On failure, responds with SdkError<DescribeLabelingJobError>

pub fn describe_lineage_group(&self) -> DescribeLineageGroup

Constructs a fluent builder for the DescribeLineageGroup operation.

• The fluent builder is configurable:
  • lineage_group_name(impl Into<String>) / set_lineage_group_name(Option<String>):

    The name of the lineage group.

• On success, responds with DescribeLineageGroupOutput with field(s):
  • lineage_group_name(Option<String>):

    The name of the lineage group.

  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

  • display_name(Option<String>):

    The display name of the lineage group.

  • description(Option<String>):

    The description of the lineage group.

  • creation_time(Option<DateTime>):

    The creation time of lineage group.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_time(Option<DateTime>):

    The last modified time of the lineage group.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

• On failure, responds with SdkError<DescribeLineageGroupError>

pub fn describe_model(&self) -> DescribeModel

Constructs a fluent builder for the DescribeModel operation.

• The fluent builder is configurable:
  • model_name(impl Into<String>) / set_model_name(Option<String>):

    The name of the model.

• On success, responds with DescribeModelOutput with field(s):
  • model_name(Option<String>):

    Name of the Amazon SageMaker model.

  • primary_container(Option<ContainerDefinition>):

    The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

  • containers(Option<Vec<ContainerDefinition>>):

    The containers in the inference pipeline.

  • inference_execution_config(Option<InferenceExecutionConfig>):

    Specifies details of how containers in a multi-container endpoint are called.

  • execution_role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

  • vpc_config(Option<VpcConfig>):

    A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud

  • creation_time(Option<DateTime>):

    A timestamp that shows when the model was created.

  • model_arn(Option<String>):

    The Amazon Resource Name (ARN) of the model.

  • enable_network_isolation(bool):

    If True, no inbound or outbound network calls can be made to or from the model container.

• On failure, responds with SdkError<DescribeModelError>

pub fn describe_model_bias_job_definition(
    &self
) -> DescribeModelBiasJobDefinition

Constructs a fluent builder for the DescribeModelBiasJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• On success, responds with DescribeModelBiasJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

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

  • job_definition_name(Option<String>):

    The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • creation_time(Option<DateTime>):

    The time at which the model bias job was created.

  • model_bias_baseline_config(Option<ModelBiasBaselineConfig>):

    The baseline configuration for a model bias job.

  • model_bias_app_specification(Option<ModelBiasAppSpecification>):

    Configures the model bias job to run a specified Docker container image.

  • model_bias_job_input(Option<ModelBiasJobInput>):

    Inputs for the model bias job.

  • model_bias_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(Option<MonitoringNetworkConfig>):

    Networking options for a model bias job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

  • stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

• On failure, responds with SdkError<DescribeModelBiasJobDefinitionError>

pub fn describe_model_explainability_job_definition(
    &self
) -> DescribeModelExplainabilityJobDefinition

Constructs a fluent builder for the DescribeModelExplainabilityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• On success, responds with DescribeModelExplainabilityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

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

  • job_definition_name(Option<String>):

    The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • creation_time(Option<DateTime>):

    The time at which the model explainability job was created.

  • model_explainability_baseline_config(Option<ModelExplainabilityBaselineConfig>):

    The baseline configuration for a model explainability job.

  • model_explainability_app_specification(Option<ModelExplainabilityAppSpecification>):

    Configures the model explainability job to run a specified Docker container image.

  • model_explainability_job_input(Option<ModelExplainabilityJobInput>):

    Inputs for the model explainability job.

  • model_explainability_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(Option<MonitoringNetworkConfig>):

    Networking options for a model explainability job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.

  • stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

• On failure, responds with SdkError<DescribeModelExplainabilityJobDefinitionError>

pub fn describe_model_package(&self) -> DescribeModelPackage

Constructs a fluent builder for the DescribeModelPackage operation.

• The fluent builder is configurable:
  • model_package_name(impl Into<String>) / set_model_package_name(Option<String>):

    The name or Amazon Resource Name (ARN) of the model package to describe.

    When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

• On success, responds with DescribeModelPackageOutput with field(s):
  • model_package_name(Option<String>):

    The name of the model package being described.

  • model_package_group_name(Option<String>):

    If the model is a versioned model, the name of the model group that the versioned model belongs to.

  • model_package_version(Option<i32>):

    The version of the model package.

  • model_package_arn(Option<String>):

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

  • model_package_description(Option<String>):

    A brief summary of the model package.

  • creation_time(Option<DateTime>):

    A timestamp specifying when the model package was created.

  • inference_specification(Option<InferenceSpecification>):

    Details about inference jobs that can be run with models based on this model package.

  • source_algorithm_specification(Option<SourceAlgorithmSpecification>):

    Details about the algorithm that was used to create the model package.

  • validation_specification(Option<ModelPackageValidationSpecification>):

    Configurations for one or more transform jobs that SageMaker runs to test the model package.

  • model_package_status(Option<ModelPackageStatus>):

    The current status of the model package.

  • model_package_status_details(Option<ModelPackageStatusDetails>):

    Details about the current status of the model package.

  • certify_for_marketplace(bool):

    Whether the model package is certified for listing on Amazon Web Services Marketplace.

  • model_approval_status(Option<ModelApprovalStatus>):

    The approval status of the model package.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • model_metrics(Option<ModelMetrics>):

    Metrics for the model.

  • last_modified_time(Option<DateTime>):

    The last time the model package was modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • approval_description(Option<String>):

    A description provided for the model approval.

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

    The metadata properties associated with the model package versions.

  • drift_check_baselines(Option<DriftCheckBaselines>):

    Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

  • domain(Option<String>):

    The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.

  • task(Option<String>):

    The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.

  • sample_payload_url(Option<String>):

    The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).

  • additional_inference_specifications(Option<Vec<AdditionalInferenceSpecificationDefinition>>):

    An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

• On failure, responds with SdkError<DescribeModelPackageError>

pub fn describe_model_package_group(&self) -> DescribeModelPackageGroup

Constructs a fluent builder for the DescribeModelPackageGroup operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group to describe.

• On success, responds with DescribeModelPackageGroupOutput with field(s):
  • model_package_group_name(Option<String>):

    The name of the model group.

  • model_package_group_arn(Option<String>):

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

  • model_package_group_description(Option<String>):

    A description of the model group.

  • creation_time(Option<DateTime>):

    The time that the model group was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • model_package_group_status(Option<ModelPackageGroupStatus>):

    The status of the model group.

• On failure, responds with SdkError<DescribeModelPackageGroupError>

pub fn describe_model_quality_job_definition(
    &self
) -> DescribeModelQualityJobDefinition

Constructs a fluent builder for the DescribeModelQualityJobDefinition operation.

• The fluent builder is configurable:
  • job_definition_name(impl Into<String>) / set_job_definition_name(Option<String>):

    The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• On success, responds with DescribeModelQualityJobDefinitionOutput with field(s):
  • job_definition_arn(Option<String>):

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

  • job_definition_name(Option<String>):

    The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • creation_time(Option<DateTime>):

    The time at which the model quality job was created.

  • model_quality_baseline_config(Option<ModelQualityBaselineConfig>):

    The baseline configuration for a model quality job.

  • model_quality_app_specification(Option<ModelQualityAppSpecification>):

    Configures the model quality job to run a specified Docker container image.

  • model_quality_job_input(Option<ModelQualityJobInput>):

    Inputs for the model quality job.

  • model_quality_job_output_config(Option<MonitoringOutputConfig>):

    The output configuration for monitoring jobs.

  • job_resources(Option<MonitoringResources>):

    Identifies the resources to deploy for a monitoring job.

  • network_config(Option<MonitoringNetworkConfig>):

    Networking options for a model quality job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • stopping_condition(Option<MonitoringStoppingCondition>):

    A time limit for how long the monitoring job is allowed to run before stopping.

• On failure, responds with SdkError<DescribeModelQualityJobDefinitionError>

pub fn describe_monitoring_schedule(&self) -> DescribeMonitoringSchedule

Constructs a fluent builder for the DescribeMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    Name of a previously created monitoring schedule.

• On success, responds with DescribeMonitoringScheduleOutput with field(s):
  • monitoring_schedule_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitoring schedule.

  • monitoring_schedule_name(Option<String>):

    Name of the monitoring schedule.

  • monitoring_schedule_status(Option<ScheduleStatus>):

    The status of an monitoring job.

  • monitoring_type(Option<MonitoringType>):

    The type of the monitoring job that this schedule runs. This is one of the following values.

    • DATA_QUALITY - The schedule is for a data quality monitoring job.

    • MODEL_QUALITY - The schedule is for a model quality monitoring job.

    • MODEL_BIAS - The schedule is for a bias monitoring job.

    • MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.

  • failure_reason(Option<String>):

    A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

  • creation_time(Option<DateTime>):

    The time at which the monitoring job was created.

  • last_modified_time(Option<DateTime>):

    The time at which the monitoring job was last modified.

  • monitoring_schedule_config(Option<MonitoringScheduleConfig>):

    The configuration object that specifies the monitoring schedule and defines the monitoring job.

  • endpoint_name(Option<String>):

    The name of the endpoint for the monitoring job.

  • last_monitoring_execution_summary(Option<MonitoringExecutionSummary>):

    Describes metadata on the last execution to run, if there was one.

• On failure, responds with SdkError<DescribeMonitoringScheduleError>

pub fn describe_notebook_instance(&self) -> DescribeNotebookInstance

Constructs a fluent builder for the DescribeNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the notebook instance that you want information about.

• On success, responds with DescribeNotebookInstanceOutput with field(s):
  • notebook_instance_arn(Option<String>):

    The Amazon Resource Name (ARN) of the notebook instance.

  • notebook_instance_name(Option<String>):

    The name of the Amazon SageMaker notebook instance.

  • notebook_instance_status(Option<NotebookInstanceStatus>):

    The status of the notebook instance.

  • failure_reason(Option<String>):

    If status is Failed, the reason it failed.

  • url(Option<String>):

    The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

  • instance_type(Option<InstanceType>):

    The type of ML compute instance running on the notebook instance.

  • subnet_id(Option<String>):

    The ID of the VPC subnet.

  • security_groups(Option<Vec<String>>):

    The IDs of the VPC security groups.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM role associated with the instance.

  • kms_key_id(Option<String>):

    The Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

  • network_interface_id(Option<String>):

    The network interface IDs that Amazon SageMaker created at the time of creating the instance.

  • last_modified_time(Option<DateTime>):

    A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

  • creation_time(Option<DateTime>):

    A timestamp. Use this parameter to return the time when the notebook instance was created

  • notebook_instance_lifecycle_config_name(Option<String>):

    Returns the name of a notebook instance lifecycle configuration.

    For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance

  • direct_internet_access(Option<DirectInternetAccess>):

    Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.

    For more information, see Notebook Instances Are Internet-Enabled by Default.

  • volume_size_in_gb(Option<i32>):

    The size, in GB, of the ML storage volume attached to the notebook instance.

  • accelerator_types(Option<Vec<NotebookInstanceAcceleratorType>>):

    A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

  • default_code_repository(Option<String>):

    The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • additional_code_repositories(Option<Vec<String>>):

    An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • root_access(Option<RootAccess>):

    Whether root access is enabled or disabled for users of the notebook instance.

    Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

  • platform_identifier(Option<String>):

    The platform identifier of the notebook instance runtime environment.

• On failure, responds with SdkError<DescribeNotebookInstanceError>

pub fn describe_notebook_instance_lifecycle_config(
    &self
) -> DescribeNotebookInstanceLifecycleConfig

Constructs a fluent builder for the DescribeNotebookInstanceLifecycleConfig operation.

• The fluent builder is configurable:
  • notebook_instance_lifecycle_config_name(impl Into<String>) / set_notebook_instance_lifecycle_config_name(Option<String>):

    The name of the lifecycle configuration to describe.

• On success, responds with DescribeNotebookInstanceLifecycleConfigOutput with field(s):
  • notebook_instance_lifecycle_config_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lifecycle configuration.

  • notebook_instance_lifecycle_config_name(Option<String>):

    The name of the lifecycle configuration.

  • on_create(Option<Vec<NotebookInstanceLifecycleHook>>):

    The shell script that runs only once, when you create a notebook instance.

  • on_start(Option<Vec<NotebookInstanceLifecycleHook>>):

    The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

  • last_modified_time(Option<DateTime>):

    A timestamp that tells when the lifecycle configuration was last modified.

  • creation_time(Option<DateTime>):

    A timestamp that tells when the lifecycle configuration was created.

• On failure, responds with SdkError<DescribeNotebookInstanceLifecycleConfigError>

pub fn describe_pipeline(&self) -> DescribePipeline

Constructs a fluent builder for the DescribePipeline operation.

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline to describe.

• On success, responds with DescribePipelineOutput with field(s):
  • pipeline_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline.

  • pipeline_name(Option<String>):

    The name of the pipeline.

  • pipeline_display_name(Option<String>):

    The display name of the pipeline.

  • pipeline_definition(Option<String>):

    The JSON pipeline definition.

  • pipeline_description(Option<String>):

    The description of the pipeline.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) that the pipeline uses to execute.

  • pipeline_status(Option<PipelineStatus>):

    The status of the pipeline execution.

  • creation_time(Option<DateTime>):

    The time when the pipeline was created.

  • last_modified_time(Option<DateTime>):

    The time when the pipeline was last modified.

  • last_run_time(Option<DateTime>):

    The time when the pipeline was last run.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • parallelism_configuration(Option<ParallelismConfiguration>):

    Lists the parallelism configuration applied to the pipeline.

• On failure, responds with SdkError<DescribePipelineError>

pub fn describe_pipeline_definition_for_execution(
    &self
) -> DescribePipelineDefinitionForExecution

Constructs a fluent builder for the DescribePipelineDefinitionForExecution operation.

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On success, responds with DescribePipelineDefinitionForExecutionOutput with field(s):
  • pipeline_definition(Option<String>):

    The JSON pipeline definition.

  • creation_time(Option<DateTime>):

    The time when the pipeline was created.

• On failure, responds with SdkError<DescribePipelineDefinitionForExecutionError>

pub fn describe_pipeline_execution(&self) -> DescribePipelineExecution

Constructs a fluent builder for the DescribePipelineExecution operation.

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On success, responds with DescribePipelineExecutionOutput with field(s):
  • pipeline_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline.

  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • pipeline_execution_display_name(Option<String>):

    The display name of the pipeline execution.

  • pipeline_execution_status(Option<PipelineExecutionStatus>):

    The status of the pipeline execution.

  • pipeline_execution_description(Option<String>):

    The description of the pipeline execution.

  • pipeline_experiment_config(Option<PipelineExperimentConfig>):

    Specifies the names of the experiment and trial created by a pipeline.

  • failure_reason(Option<String>):

    If the execution failed, a message describing why.

  • creation_time(Option<DateTime>):

    The time when the pipeline execution was created.

  • last_modified_time(Option<DateTime>):

    The time when the pipeline execution was modified last.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • parallelism_configuration(Option<ParallelismConfiguration>):

    The parallelism configuration applied to the pipeline.

• On failure, responds with SdkError<DescribePipelineExecutionError>

pub fn describe_processing_job(&self) -> DescribeProcessingJob

Constructs a fluent builder for the DescribeProcessingJob operation.

• The fluent builder is configurable:
  • processing_job_name(impl Into<String>) / set_processing_job_name(Option<String>):

    The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

• On success, responds with DescribeProcessingJobOutput with field(s):
  • processing_inputs(Option<Vec<ProcessingInput>>):

    The inputs for a processing job.

  • processing_output_config(Option<ProcessingOutputConfig>):

    Output configuration for the processing job.

  • processing_job_name(Option<String>):

    The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

  • processing_resources(Option<ProcessingResources>):

    Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

  • stopping_condition(Option<ProcessingStoppingCondition>):

    The time limit for how long the processing job is allowed to run.

  • app_specification(Option<AppSpecification>):

    Configures the processing job to run a specified container image.

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

    The environment variables set in the Docker container.

  • network_config(Option<NetworkConfig>):

    Networking options for a processing job.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • experiment_config(Option<ExperimentConfig>):

    The configuration information used to create an experiment.

  • processing_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the processing job.

  • processing_job_status(Option<ProcessingJobStatus>):

    Provides the status of a processing job.

  • exit_message(Option<String>):

    An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

  • failure_reason(Option<String>):

    A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

  • processing_end_time(Option<DateTime>):

    The time at which the processing job completed.

  • processing_start_time(Option<DateTime>):

    The time at which the processing job started.

  • last_modified_time(Option<DateTime>):

    The time at which the processing job was last modified.

  • creation_time(Option<DateTime>):

    The time at which the processing job was created.

  • monitoring_schedule_arn(Option<String>):

    The ARN of a monitoring schedule for an endpoint associated with this processing job.

  • auto_ml_job_arn(Option<String>):

    The ARN of an AutoML job associated with this processing job.

  • training_job_arn(Option<String>):

    The ARN of a training job associated with this processing job.

• On failure, responds with SdkError<DescribeProcessingJobError>

pub fn describe_project(&self) -> DescribeProject

Constructs a fluent builder for the DescribeProject operation.

• The fluent builder is configurable:
  • project_name(impl Into<String>) / set_project_name(Option<String>):

    The name of the project to describe.

• On success, responds with DescribeProjectOutput with field(s):
  • project_arn(Option<String>):

    The Amazon Resource Name (ARN) of the project.

  • project_name(Option<String>):

    The name of the project.

  • project_id(Option<String>):

    The ID of the project.

  • project_description(Option<String>):

    The description of the project.

  • service_catalog_provisioning_details(Option<ServiceCatalogProvisioningDetails>):

    Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.

  • service_catalog_provisioned_product_details(Option<ServiceCatalogProvisionedProductDetails>):

    Information about a provisioned service catalog product.

  • project_status(Option<ProjectStatus>):

    The status of the project.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

  • creation_time(Option<DateTime>):

    The time when the project was created.

  • last_modified_time(Option<DateTime>):

    The timestamp when project was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.

• On failure, responds with SdkError<DescribeProjectError>

pub fn describe_studio_lifecycle_config(&self) -> DescribeStudioLifecycleConfig

Constructs a fluent builder for the DescribeStudioLifecycleConfig operation.

• The fluent builder is configurable:
  • studio_lifecycle_config_name(impl Into<String>) / set_studio_lifecycle_config_name(Option<String>):

    The name of the Studio Lifecycle Configuration to describe.

• On success, responds with DescribeStudioLifecycleConfigOutput with field(s):
  • studio_lifecycle_config_arn(Option<String>):

    The ARN of the Lifecycle Configuration to describe.

  • studio_lifecycle_config_name(Option<String>):

    The name of the Studio Lifecycle Configuration that is described.

  • creation_time(Option<DateTime>):

    The creation time of the Studio Lifecycle Configuration.

  • last_modified_time(Option<DateTime>):

    This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.

  • studio_lifecycle_config_content(Option<String>):

    The content of your Studio Lifecycle Configuration script.

  • studio_lifecycle_config_app_type(Option<StudioLifecycleConfigAppType>):

    The App type that the Lifecycle Configuration is attached to.

• On failure, responds with SdkError<DescribeStudioLifecycleConfigError>

pub fn describe_subscribed_workteam(&self) -> DescribeSubscribedWorkteam

Constructs a fluent builder for the DescribeSubscribedWorkteam operation.

• The fluent builder is configurable:
  • workteam_arn(impl Into<String>) / set_workteam_arn(Option<String>):

    The Amazon Resource Name (ARN) of the subscribed work team to describe.

• On success, responds with DescribeSubscribedWorkteamOutput with field(s):
  • subscribed_workteam(Option<SubscribedWorkteam>):

    A Workteam instance that contains information about the work team.

• On failure, responds with SdkError<DescribeSubscribedWorkteamError>

pub fn describe_training_job(&self) -> DescribeTrainingJob

Constructs a fluent builder for the DescribeTrainingJob operation.

• The fluent builder is configurable:
  • training_job_name(impl Into<String>) / set_training_job_name(Option<String>):

    The name of the training job.

• On success, responds with DescribeTrainingJobOutput with field(s):
  • training_job_name(Option<String>):

    Name of the model training job.

  • training_job_arn(Option<String>):

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

  • tuning_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

  • labeling_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

  • auto_ml_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of an AutoML job.

  • model_artifacts(Option<ModelArtifacts>):

    Information about the Amazon S3 location that is configured for storing model artifacts.

  • training_job_status(Option<TrainingJobStatus>):

    The status of the training job.

    Amazon SageMaker provides the following training job statuses:

    • InProgress - The training is in progress.

    • Completed - The training job has completed.

    • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

    • Stopping - The training job is stopping.

    • Stopped - The training job has stopped.

    For more detailed information, see SecondaryStatus.

  • secondary_status(Option<SecondaryStatus>):

    Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

    Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

    InProgress

    • Starting - Starting the training job.

    • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

    • Training - Training is in progress.

    • Interrupted - The job stopped because the managed spot training instances were interrupted.

    • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

    Completed

    • Completed - The training job has completed.

    Failed

    • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

    Stopped

    • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

    • MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.

    • Stopped - The training job has stopped.

    Stopping

    • Stopping - Stopping the training job.

    Valid values for SecondaryStatus are subject to change.

    We no longer support the following secondary statuses:

    • LaunchingMLInstances

    • PreparingTraining

    • DownloadingTrainingImage

  • failure_reason(Option<String>):

    If the training job failed, the reason it failed.

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

    Algorithm-specific parameters.

  • algorithm_specification(Option<AlgorithmSpecification>):

    Information about the algorithm used for training, and algorithm metadata.

  • role_arn(Option<String>):

    The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

  • input_data_config(Option<Vec<Channel>>):

    An array of Channel objects that describes each data input channel.

  • output_data_config(Option<OutputDataConfig>):

    The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

  • resource_config(Option<ResourceConfig>):

    Resources, including ML compute instances and ML storage volumes, that are configured for model training.

  • vpc_config(Option<VpcConfig>):

    A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

  • stopping_condition(Option<StoppingCondition>):

    Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • creation_time(Option<DateTime>):

    A timestamp that indicates when the training job was created.

  • training_start_time(Option<DateTime>):

    Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

  • training_end_time(Option<DateTime>):

    Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

  • last_modified_time(Option<DateTime>):

    A timestamp that indicates when the status of the training job was last modified.

  • secondary_status_transitions(Option<Vec<SecondaryStatusTransition>>):

    A history of all of the secondary statuses that the training job has transitioned through.

  • final_metric_data_list(Option<Vec<MetricData>>):

    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

  • enable_network_isolation(bool):

    If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

  • enable_inter_container_traffic_encryption(bool):

    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.

  • enable_managed_spot_training(bool):

    A Boolean indicating whether managed spot training is enabled (True) or not (False).

  • checkpoint_config(Option<CheckpointConfig>):

    Contains information about the output location for managed spot training checkpoint data.

  • training_time_in_seconds(Option<i32>):

    The training time in seconds.

  • billable_time_in_seconds(Option<i32>):

    The billable time in seconds. Billable time refers to the absolute wall-clock time.

    Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

    You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

  • debug_hook_config(Option<DebugHookConfig>):

    Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

  • experiment_config(Option<ExperimentConfig>):

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob

    • CreateTrainingJob

    • CreateTransformJob

  • debug_rule_configurations(Option<Vec<DebugRuleConfiguration>>):

    Configuration information for Debugger rules for debugging output tensors.

  • tensor_board_output_config(Option<TensorBoardOutputConfig>):

    Configuration of storage locations for the Debugger TensorBoard output data.

  • debug_rule_evaluation_statuses(Option<Vec<DebugRuleEvaluationStatus>>):

    Evaluation status of Debugger rules for debugging on a training job.

  • profiler_config(Option<ProfilerConfig>):

    Configuration information for Debugger system monitoring, framework profiling, and storage paths.

  • profiler_rule_configurations(Option<Vec<ProfilerRuleConfiguration>>):

    Configuration information for Debugger rules for profiling system and framework metrics.

  • profiler_rule_evaluation_statuses(Option<Vec<ProfilerRuleEvaluationStatus>>):

    Evaluation status of Debugger rules for profiling on a training job.

  • profiling_status(Option<ProfilingStatus>):

    Profiling status of a training job.

  • retry_strategy(Option<RetryStrategy>):

    The number of times to retry the job when the job fails due to an InternalServerError.

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

    The environment variables to set in the Docker container.

• On failure, responds with SdkError<DescribeTrainingJobError>

pub fn describe_transform_job(&self) -> DescribeTransformJob

Constructs a fluent builder for the DescribeTransformJob operation.

• The fluent builder is configurable:
  • transform_job_name(impl Into<String>) / set_transform_job_name(Option<String>):

    The name of the transform job that you want to view details of.

• On success, responds with DescribeTransformJobOutput with field(s):
  • transform_job_name(Option<String>):

    The name of the transform job.

  • transform_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the transform job.

  • transform_job_status(Option<TransformJobStatus>):

    The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

  • failure_reason(Option<String>):

    If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.

  • model_name(Option<String>):

    The name of the model used in the transform job.

  • max_concurrent_transforms(Option<i32>):

    The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

  • model_client_config(Option<ModelClientConfig>):

    The timeout and maximum number of retries for processing a transform job invocation.

  • max_payload_in_mb(Option<i32>):

    The maximum payload size, in MB, used in the transform job.

  • batch_strategy(Option<BatchStrategy>):

    Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

    To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

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

    The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

  • transform_input(Option<TransformInput>):

    Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • transform_output(Option<TransformOutput>):

    Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • transform_resources(Option<TransformResources>):

    Describes the resources, including ML instance types and ML instance count, to use for the transform job.

  • creation_time(Option<DateTime>):

    A timestamp that shows when the transform Job was created.

  • transform_start_time(Option<DateTime>):

    Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

  • transform_end_time(Option<DateTime>):

    Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

  • labeling_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

  • auto_ml_job_arn(Option<String>):

    The Amazon Resource Name (ARN) of the AutoML transform job.

  • data_processing(Option<DataProcessing>):

    The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

  • experiment_config(Option<ExperimentConfig>):

    Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

    • CreateProcessingJob

    • CreateTrainingJob

    • CreateTransformJob

• On failure, responds with SdkError<DescribeTransformJobError>

pub fn describe_trial(&self) -> DescribeTrial

Constructs a fluent builder for the DescribeTrial operation.

• The fluent builder is configurable:
  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial to describe.

• On success, responds with DescribeTrialOutput with field(s):
  • trial_name(Option<String>):

    The name of the trial.

  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

  • display_name(Option<String>):

    The name of the trial as displayed. If DisplayName isn’t specified, TrialName is displayed.

  • experiment_name(Option<String>):

    The name of the experiment the trial is part of.

  • source(Option<TrialSource>):

    The Amazon Resource Name (ARN) of the source and, optionally, the job type.

  • creation_time(Option<DateTime>):

    When the trial was created.

  • created_by(Option<UserContext>):

    Who created the trial.

  • last_modified_time(Option<DateTime>):

    When the trial was last modified.

  • last_modified_by(Option<UserContext>):

    Who last modified the trial.

  • metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

• On failure, responds with SdkError<DescribeTrialError>

pub fn describe_trial_component(&self) -> DescribeTrialComponent

Constructs a fluent builder for the DescribeTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the trial component to describe.

• On success, responds with DescribeTrialComponentOutput with field(s):
  • trial_component_name(Option<String>):

    The name of the trial component.

  • trial_component_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial component.

  • display_name(Option<String>):

    The name of the component as displayed. If DisplayName isn’t specified, TrialComponentName is displayed.

  • source(Option<TrialComponentSource>):

    The Amazon Resource Name (ARN) of the source and, optionally, the job type.

  • status(Option<TrialComponentStatus>):

    The status of the component. States include:

    • InProgress

    • Completed

    • Failed

  • start_time(Option<DateTime>):

    When the component started.

  • end_time(Option<DateTime>):

    When the component ended.

  • creation_time(Option<DateTime>):

    When the component was created.

  • created_by(Option<UserContext>):

    Who created the trial component.

  • last_modified_time(Option<DateTime>):

    When the component was last modified.

  • last_modified_by(Option<UserContext>):

    Who last modified the component.

  • parameters(Option<HashMap<String, TrialComponentParameterValue>>):

    The hyperparameters of the component.

  • input_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    The input artifacts of the component.

  • output_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    The output artifacts of the component.

  • metadata_properties(Option<MetadataProperties>):

    Metadata properties of the tracking entity, trial, or trial component.

  • metrics(Option<Vec<TrialComponentMetricSummary>>):

    The metrics for the component.

  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

• On failure, responds with SdkError<DescribeTrialComponentError>

pub fn describe_user_profile(&self) -> DescribeUserProfile

Constructs a fluent builder for the DescribeUserProfile operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name. This value is not case sensitive.

• On success, responds with DescribeUserProfileOutput with field(s):
  • domain_id(Option<String>):

    The ID of the domain that contains the profile.

  • user_profile_arn(Option<String>):

    The user profile Amazon Resource Name (ARN).

  • user_profile_name(Option<String>):

    The user profile name.

  • home_efs_file_system_uid(Option<String>):

    The ID of the user’s profile in the Amazon Elastic File System (EFS) volume.

  • status(Option<UserProfileStatus>):

    The status.

  • last_modified_time(Option<DateTime>):

    The last modified time.

  • creation_time(Option<DateTime>):

    The creation time.

  • failure_reason(Option<String>):

    The failure reason.

  • single_sign_on_user_identifier(Option<String>):

    The SSO user identifier.

  • single_sign_on_user_value(Option<String>):

    The SSO user value.

  • user_settings(Option<UserSettings>):

    A collection of settings.

• On failure, responds with SdkError<DescribeUserProfileError>

pub fn describe_workforce(&self) -> DescribeWorkforce

Constructs a fluent builder for the DescribeWorkforce operation.

• The fluent builder is configurable:
  • workforce_name(impl Into<String>) / set_workforce_name(Option<String>):

    The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.

• On success, responds with DescribeWorkforceOutput with field(s):
  • workforce(Option<Workforce>):

    A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.

• On failure, responds with SdkError<DescribeWorkforceError>

pub fn describe_workteam(&self) -> DescribeWorkteam

Constructs a fluent builder for the DescribeWorkteam operation.

• The fluent builder is configurable:
  • workteam_name(impl Into<String>) / set_workteam_name(Option<String>):

    The name of the work team to return a description of.

• On success, responds with DescribeWorkteamOutput with field(s):
  • workteam(Option<Workteam>):

    A Workteam instance that contains information about the work team.

• On failure, responds with SdkError<DescribeWorkteamError>

pub fn disable_sagemaker_servicecatalog_portfolio(
    &self
) -> DisableSagemakerServicecatalogPortfolio

Constructs a fluent builder for the DisableSagemakerServicecatalogPortfolio operation.

• The fluent builder takes no input, just send it.
• On success, responds with DisableSagemakerServicecatalogPortfolioOutput
• On failure, responds with SdkError<DisableSagemakerServicecatalogPortfolioError>

pub fn disassociate_trial_component(&self) -> DisassociateTrialComponent

Constructs a fluent builder for the DisassociateTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the component to disassociate from the trial.

  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial to disassociate from.

• On success, responds with DisassociateTrialComponentOutput with field(s):
  • trial_component_arn(Option<String>):

    The ARN of the trial component.

  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<DisassociateTrialComponentError>

pub fn enable_sagemaker_servicecatalog_portfolio(
    &self
) -> EnableSagemakerServicecatalogPortfolio

Constructs a fluent builder for the EnableSagemakerServicecatalogPortfolio operation.

• The fluent builder takes no input, just send it.
• On success, responds with EnableSagemakerServicecatalogPortfolioOutput
• On failure, responds with SdkError<EnableSagemakerServicecatalogPortfolioError>

pub fn get_device_fleet_report(&self) -> GetDeviceFleetReport

Constructs a fluent builder for the GetDeviceFleetReport operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet.

• On success, responds with GetDeviceFleetReportOutput with field(s):
  • device_fleet_arn(Option<String>):

    The Amazon Resource Name (ARN) of the device.

  • device_fleet_name(Option<String>):

    The name of the fleet.

  • output_config(Option<EdgeOutputConfig>):

    The output configuration for storing sample data collected by the fleet.

  • description(Option<String>):

    Description of the fleet.

  • report_generated(Option<DateTime>):

    Timestamp of when the report was generated.

  • device_stats(Option<DeviceStats>):

    Status of devices.

  • agent_versions(Option<Vec<AgentVersion>>):

    The versions of Edge Manager agent deployed on the fleet.

  • model_stats(Option<Vec<EdgeModelStat>>):

    Status of model on device.

• On failure, responds with SdkError<GetDeviceFleetReportError>

pub fn get_lineage_group_policy(&self) -> GetLineageGroupPolicy

Constructs a fluent builder for the GetLineageGroupPolicy operation.

• The fluent builder is configurable:
  • lineage_group_name(impl Into<String>) / set_lineage_group_name(Option<String>):

    The name or Amazon Resource Name (ARN) of the lineage group.

• On success, responds with GetLineageGroupPolicyOutput with field(s):
  • lineage_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the lineage group.

  • resource_policy(Option<String>):

    The resource policy that gives access to the lineage group in another account.

• On failure, responds with SdkError<GetLineageGroupPolicyError>

pub fn get_model_package_group_policy(&self) -> GetModelPackageGroupPolicy

Constructs a fluent builder for the GetModelPackageGroupPolicy operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group for which to get the resource policy.

• On success, responds with GetModelPackageGroupPolicyOutput with field(s):
  • resource_policy(Option<String>):

    The resource policy for the model group.

• On failure, responds with SdkError<GetModelPackageGroupPolicyError>

pub fn get_sagemaker_servicecatalog_portfolio_status(
    &self
) -> GetSagemakerServicecatalogPortfolioStatus

Constructs a fluent builder for the GetSagemakerServicecatalogPortfolioStatus operation.

• The fluent builder takes no input, just send it.
• On success, responds with GetSagemakerServicecatalogPortfolioStatusOutput with field(s):
  • status(Option<SagemakerServicecatalogStatus>):

    Whether Service Catalog is enabled or disabled in SageMaker.

• On failure, responds with SdkError<GetSagemakerServicecatalogPortfolioStatusError>

pub fn get_search_suggestions(&self) -> GetSearchSuggestions

Constructs a fluent builder for the GetSearchSuggestions operation.

• The fluent builder is configurable:
  • resource(ResourceType) / set_resource(Option<ResourceType>):

    The name of the Amazon SageMaker resource to search for.

  • suggestion_query(SuggestionQuery) / set_suggestion_query(Option<SuggestionQuery>):

    Limits the property names that are included in the response.

• On success, responds with GetSearchSuggestionsOutput with field(s):
  • property_name_suggestions(Option<Vec<PropertyNameSuggestion>>):

    A list of property names for a Resource that match a SuggestionQuery.

• On failure, responds with SdkError<GetSearchSuggestionsError>

pub fn list_actions(&self) -> ListActions

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

• The fluent builder is configurable:
  • source_uri(impl Into<String>) / set_source_uri(Option<String>):

    A filter that returns only actions with the specified source URI.

  • action_type(impl Into<String>) / set_action_type(Option<String>):

    A filter that returns only actions of the specified type.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only actions created on or after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only actions created on or before the specified time.

  • sort_by(SortActionsBy) / set_sort_by(Option<SortActionsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListActions didn’t return the full set of actions, the call returns a token for getting the next set of actions.

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

    The maximum number of actions to return in the response. The default value is 10.

• On success, responds with ListActionsOutput with field(s):
  • action_summaries(Option<Vec<ActionSummary>>):

    A list of actions and their properties.

  • next_token(Option<String>):

    A token for getting the next set of actions, if there are any.

• On failure, responds with SdkError<ListActionsError>

pub fn list_algorithms(&self) -> ListAlgorithms

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only algorithms created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only algorithms created before the specified time (timestamp).

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

    The maximum number of algorithms to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.

  • sort_by(AlgorithmSortBy) / set_sort_by(Option<AlgorithmSortBy>):

    The parameter by which to sort the results. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results. The default is Ascending.

• On success, responds with ListAlgorithmsOutput with field(s):
  • algorithm_summary_list(Option<Vec<AlgorithmSummary>>):

    >An array of AlgorithmSummary objects, each of which lists an algorithm.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

• On failure, responds with SdkError<ListAlgorithmsError>

pub fn list_app_image_configs(&self) -> ListAppImageConfigs

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

• The fluent builder is configurable:
  • max_results(i32) / set_max_results(Option<i32>):

    The maximum number of AppImageConfigs to return in the response. The default value is 10.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListImages didn’t return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A filter that returns only AppImageConfigs whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only AppImageConfigs created on or before the specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only AppImageConfigs created on or after the specified time.

  • modified_time_before(DateTime) / set_modified_time_before(Option<DateTime>):

    A filter that returns only AppImageConfigs modified on or before the specified time.

  • modified_time_after(DateTime) / set_modified_time_after(Option<DateTime>):

    A filter that returns only AppImageConfigs modified on or after the specified time.

  • sort_by(AppImageConfigSortKey) / set_sort_by(Option<AppImageConfigSortKey>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

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

    A token for getting the next set of AppImageConfigs, if there are any.

  • app_image_configs(Option<Vec<AppImageConfigDetails>>):

    A list of AppImageConfigs and their properties.

• On failure, responds with SdkError<ListAppImageConfigsError>

pub fn list_apps(&self) -> ListApps

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

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

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

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

    Returns a list up to a specified limit.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results. The default is Ascending.

  • sort_by(AppSortKey) / set_sort_by(Option<AppSortKey>):

    The parameter by which to sort the results. The default is CreationTime.

  • domain_id_equals(impl Into<String>) / set_domain_id_equals(Option<String>):

    A parameter to search for the domain ID.

  • user_profile_name_equals(impl Into<String>) / set_user_profile_name_equals(Option<String>):

    A parameter to search by user profile name.

• On success, responds with ListAppsOutput with field(s):
  • apps(Option<Vec<AppDetails>>):

    The list of apps.

  • next_token(Option<String>):

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• On failure, responds with SdkError<ListAppsError>

pub fn list_artifacts(&self) -> ListArtifacts

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

• The fluent builder is configurable:
  • source_uri(impl Into<String>) / set_source_uri(Option<String>):

    A filter that returns only artifacts with the specified source URI.

  • artifact_type(impl Into<String>) / set_artifact_type(Option<String>):

    A filter that returns only artifacts of the specified type.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only artifacts created on or after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only artifacts created on or before the specified time.

  • sort_by(SortArtifactsBy) / set_sort_by(Option<SortArtifactsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListArtifacts didn’t return the full set of artifacts, the call returns a token for getting the next set of artifacts.

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

    The maximum number of artifacts to return in the response. The default value is 10.

• On success, responds with ListArtifactsOutput with field(s):
  • artifact_summaries(Option<Vec<ArtifactSummary>>):

    A list of artifacts and their properties.

  • next_token(Option<String>):

    A token for getting the next set of artifacts, if there are any.

• On failure, responds with SdkError<ListArtifactsError>

pub fn list_associations(&self) -> ListAssociations

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

• The fluent builder is configurable:
  • source_arn(impl Into<String>) / set_source_arn(Option<String>):

    A filter that returns only associations with the specified source ARN.

  • destination_arn(impl Into<String>) / set_destination_arn(Option<String>):

    A filter that returns only associations with the specified destination Amazon Resource Name (ARN).

  • source_type(impl Into<String>) / set_source_type(Option<String>):

    A filter that returns only associations with the specified source type.

  • destination_type(impl Into<String>) / set_destination_type(Option<String>):

    A filter that returns only associations with the specified destination type.

  • association_type(AssociationEdgeType) / set_association_type(Option<AssociationEdgeType>):

    A filter that returns only associations of the specified type.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only associations created on or after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only associations created on or before the specified time.

  • sort_by(SortAssociationsBy) / set_sort_by(Option<SortAssociationsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListAssociations didn’t return the full set of associations, the call returns a token for getting the next set of associations.

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

    The maximum number of associations to return in the response. The default value is 10.

• On success, responds with ListAssociationsOutput with field(s):
  • association_summaries(Option<Vec<AssociationSummary>>):

    A list of associations and their properties.

  • next_token(Option<String>):

    A token for getting the next set of associations, if there are any.

• On failure, responds with SdkError<ListAssociationsError>

pub fn list_auto_ml_jobs(&self) -> ListAutoMLJobs

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    Request a list of jobs, using a filter for time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    Request a list of jobs, using a filter for time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    Request a list of jobs, using a filter for time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    Request a list of jobs, using a filter for time.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Request a list of jobs, using a search filter for name.

  • status_equals(AutoMlJobStatus) / set_status_equals(Option<AutoMlJobStatus>):

    Request a list of jobs, using a filter for status.

  • sort_order(AutoMlSortOrder) / set_sort_order(Option<AutoMlSortOrder>):

    The sort order for the results. The default is Descending.

  • sort_by(AutoMlSortBy) / set_sort_by(Option<AutoMlSortBy>):

    The parameter by which to sort the results. The default is Name.

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

    Request a list of jobs up to a specified limit.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• On success, responds with ListAutoMlJobsOutput with field(s):
  • auto_ml_job_summaries(Option<Vec<AutoMlJobSummary>>):

    Returns a summary list of jobs.

  • next_token(Option<String>):

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• On failure, responds with SdkError<ListAutoMLJobsError>

pub fn list_candidates_for_auto_ml_job(&self) -> ListCandidatesForAutoMLJob

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

• The fluent builder is configurable:
  • auto_ml_job_name(impl Into<String>) / set_auto_ml_job_name(Option<String>):

    List the candidates created for the job by providing the job’s name.

  • status_equals(CandidateStatus) / set_status_equals(Option<CandidateStatus>):

    List the candidates for the job and filter by status.

  • candidate_name_equals(impl Into<String>) / set_candidate_name_equals(Option<String>):

    List the candidates for the job and filter by candidate name.

  • sort_order(AutoMlSortOrder) / set_sort_order(Option<AutoMlSortOrder>):

    The sort order for the results. The default is Ascending.

  • sort_by(CandidateSortBy) / set_sort_by(Option<CandidateSortBy>):

    The parameter by which to sort the results. The default is Descending.

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

    List the job’s candidates up to a specified limit.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• On success, responds with ListCandidatesForAutoMlJobOutput with field(s):
  • candidates(Option<Vec<AutoMlCandidate>>):

    Summaries about the AutoMLCandidates.

  • next_token(Option<String>):

    If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

• On failure, responds with SdkError<ListCandidatesForAutoMLJobError>

pub fn list_code_repositories(&self) -> ListCodeRepositories

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only Git repositories that were created after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only Git repositories that were created before the specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only Git repositories that were last modified after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only Git repositories that were last modified before the specified time.

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

    The maximum number of Git repositories to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

  • sort_by(CodeRepositorySortBy) / set_sort_by(Option<CodeRepositorySortBy>):

    The field to sort results by. The default is Name.

  • sort_order(CodeRepositorySortOrder) / set_sort_order(Option<CodeRepositorySortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListCodeRepositoriesOutput with field(s):
  • code_repository_summary_list(Option<Vec<CodeRepositorySummary>>):

    Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:

    • Name

    • Amazon Resource Name (ARN)

    • Creation time

    • Last modified time

    • Configuration information, including the URL location of the repository and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.

  • next_token(Option<String>):

    If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

• On failure, responds with SdkError<ListCodeRepositoriesError>

pub fn list_compilation_jobs(&self) -> ListCompilationJobs

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

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

    If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.

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

    The maximum number of model compilation jobs to return in the response.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns the model compilation jobs that were created after a specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns the model compilation jobs that were created before a specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns the model compilation jobs that were modified after a specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns the model compilation jobs that were modified before a specified time.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A filter that returns the model compilation jobs whose name contains a specified string.

  • status_equals(CompilationJobStatus) / set_status_equals(Option<CompilationJobStatus>):

    A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

  • sort_by(ListCompilationJobsSortBy) / set_sort_by(Option<ListCompilationJobsSortBy>):

    The field by which to sort results. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListCompilationJobsOutput with field(s):
  • compilation_job_summaries(Option<Vec<CompilationJobSummary>>):

    An array of CompilationJobSummary objects, each describing a model compilation job.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.

• On failure, responds with SdkError<ListCompilationJobsError>

pub fn list_contexts(&self) -> ListContexts

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

• The fluent builder is configurable:
  • source_uri(impl Into<String>) / set_source_uri(Option<String>):

    A filter that returns only contexts with the specified source URI.

  • context_type(impl Into<String>) / set_context_type(Option<String>):

    A filter that returns only contexts of the specified type.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only contexts created on or after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only contexts created on or before the specified time.

  • sort_by(SortContextsBy) / set_sort_by(Option<SortContextsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListContexts didn’t return the full set of contexts, the call returns a token for getting the next set of contexts.

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

    The maximum number of contexts to return in the response. The default value is 10.

• On success, responds with ListContextsOutput with field(s):
  • context_summaries(Option<Vec<ContextSummary>>):

    A list of contexts and their properties.

  • next_token(Option<String>):

    A token for getting the next set of contexts, if there are any.

• On failure, responds with SdkError<ListContextsError>

pub fn list_data_quality_job_definitions(&self) -> ListDataQualityJobDefinitions

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

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    A filter that lists the data quality job definitions associated with the specified endpoint.

  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.>

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

    The maximum number of data quality monitoring job definitions to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only data quality monitoring job definitions created before the specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only data quality monitoring job definitions created after the specified time.

• On success, responds with ListDataQualityJobDefinitionsOutput with field(s):
  • job_definition_summaries(Option<Vec<MonitoringJobDefinitionSummary>>):

    A list of data quality monitoring job definitions.

  • next_token(Option<String>):

    If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of data quality monitoring job definitions, use the token in the next request.

• On failure, responds with SdkError<ListDataQualityJobDefinitionsError>

pub fn list_device_fleets(&self) -> ListDeviceFleets

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

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

    The response from the last list when returning a list large enough to need tokening.

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

    The maximum number of results to select.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    Filter fleets where packaging job was created after specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    Filter fleets where the edge packaging job was created before specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    Select fleets where the job was updated after X

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    Select fleets where the job was updated before X

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Filter for fleets containing this name in their fleet device name.

  • sort_by(ListDeviceFleetsSortBy) / set_sort_by(Option<ListDeviceFleetsSortBy>):

    The column to sort by.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    What direction to sort in.

• On success, responds with ListDeviceFleetsOutput with field(s):
  • device_fleet_summaries(Option<Vec<DeviceFleetSummary>>):

    Summary of the device fleet.

  • next_token(Option<String>):

    The response from the last list when returning a list large enough to need tokening.

• On failure, responds with SdkError<ListDeviceFleetsError>

pub fn list_devices(&self) -> ListDevices

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

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

    The response from the last list when returning a list large enough to need tokening.

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

    Maximum number of results to select.

  • latest_heartbeat_after(DateTime) / set_latest_heartbeat_after(Option<DateTime>):

    Select fleets where the job was updated after X

  • model_name(impl Into<String>) / set_model_name(Option<String>):

    A filter that searches devices that contains this name in any of their models.

  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    Filter for fleets containing this name in their device fleet name.

• On success, responds with ListDevicesOutput with field(s):
  • device_summaries(Option<Vec<DeviceSummary>>):

    Summary of devices.

  • next_token(Option<String>):

    The response from the last list when returning a list large enough to need tokening.

• On failure, responds with SdkError<ListDevicesError>

pub fn list_domains(&self) -> ListDomains

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

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

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

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

    Returns a list up to a specified limit.

• On success, responds with ListDomainsOutput with field(s):
  • domains(Option<Vec<DomainDetails>>):

    The list of domains.

  • next_token(Option<String>):

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• On failure, responds with SdkError<ListDomainsError>

pub fn list_edge_packaging_jobs(&self) -> ListEdgePackagingJobs

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

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

    The response from the last list when returning a list large enough to need tokening.

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

    Maximum number of results to select.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    Select jobs where the job was created after specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    Select jobs where the job was created before specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    Select jobs where the job was updated after specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    Select jobs where the job was updated before specified time.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Filter for jobs containing this name in their packaging job name.

  • model_name_contains(impl Into<String>) / set_model_name_contains(Option<String>):

    Filter for jobs where the model name contains this string.

  • status_equals(EdgePackagingJobStatus) / set_status_equals(Option<EdgePackagingJobStatus>):

    The job status to filter for.

  • sort_by(ListEdgePackagingJobsSortBy) / set_sort_by(Option<ListEdgePackagingJobsSortBy>):

    Use to specify what column to sort by.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    What direction to sort by.

• On success, responds with ListEdgePackagingJobsOutput with field(s):
  • edge_packaging_job_summaries(Option<Vec<EdgePackagingJobSummary>>):

    Summaries of edge packaging jobs.

  • next_token(Option<String>):

    Token to use when calling the next page of results.

• On failure, responds with SdkError<ListEdgePackagingJobsError>

pub fn list_endpoint_configs(&self) -> ListEndpointConfigs

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

• The fluent builder is configurable:
  • sort_by(EndpointConfigSortKey) / set_sort_by(Option<EndpointConfigSortKey>):

    The field to sort results by. The default is CreationTime.

  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.

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

    The maximum number of training jobs to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only endpoint configurations created before the specified time (timestamp).

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

• On success, responds with ListEndpointConfigsOutput with field(s):
  • endpoint_configs(Option<Vec<EndpointConfigSummary>>):

    An array of endpoint configurations.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

• On failure, responds with SdkError<ListEndpointConfigsError>

pub fn list_endpoints(&self) -> ListEndpoints

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

• The fluent builder is configurable:
  • sort_by(EndpointSortKey) / set_sort_by(Option<EndpointSortKey>):

    Sorts the list of results. The default is CreationTime.

  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.

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

    The maximum number of endpoints to return in the response. This value defaults to 10.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only endpoints that were created before the specified time (timestamp).

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only endpoints that were modified before the specified timestamp.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only endpoints that were modified after the specified timestamp.

  • status_equals(EndpointStatus) / set_status_equals(Option<EndpointStatus>):

    A filter that returns only endpoints with the specified status.

• On success, responds with ListEndpointsOutput with field(s):
  • endpoints(Option<Vec<EndpointSummary>>):

    An array or endpoint objects.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListEndpointsError>

pub fn list_experiments(&self) -> ListExperiments

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

• The fluent builder is configurable:
  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only experiments created after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only experiments created before the specified time.

  • sort_by(SortExperimentsBy) / set_sort_by(Option<SortExperimentsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListExperiments didn’t return the full set of experiments, the call returns a token for getting the next set of experiments.

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

    The maximum number of experiments to return in the response. The default value is 10.

• On success, responds with ListExperimentsOutput with field(s):
  • experiment_summaries(Option<Vec<ExperimentSummary>>):

    A list of the summaries of your experiments.

  • next_token(Option<String>):

    A token for getting the next set of experiments, if there are any.

• On failure, responds with SdkError<ListExperimentsError>

pub fn list_feature_groups(&self) -> ListFeatureGroups

Constructs a fluent builder for the ListFeatureGroups operation.

• The fluent builder is configurable:
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name.

  • feature_group_status_equals(FeatureGroupStatus) / set_feature_group_status_equals(Option<FeatureGroupStatus>):

    A FeatureGroup status. Filters by FeatureGroup status.

  • offline_store_status_equals(OfflineStoreStatusValue) / set_offline_store_status_equals(Option<OfflineStoreStatusValue>):

    An OfflineStore status. Filters by OfflineStore status.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    Use this parameter to search for FeatureGroupss created after a specific date and time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    Use this parameter to search for FeatureGroupss created before a specific date and time.

  • sort_order(FeatureGroupSortOrder) / set_sort_order(Option<FeatureGroupSortOrder>):

    The order in which feature groups are listed.

  • sort_by(FeatureGroupSortBy) / set_sort_by(Option<FeatureGroupSortBy>):

    The value on which the feature group list is sorted.

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

    The maximum number of results returned by ListFeatureGroups.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    A token to resume pagination of ListFeatureGroups results.

• On success, responds with ListFeatureGroupsOutput with field(s):
  • feature_group_summaries(Option<Vec<FeatureGroupSummary>>):

    A summary of feature groups.

  • next_token(Option<String>):

    A token to resume pagination of ListFeatureGroups results.

• On failure, responds with SdkError<ListFeatureGroupsError>

pub fn list_flow_definitions(&self) -> ListFlowDefinitions

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only flow definitions that were created before the specified timestamp.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    An optional value that specifies whether you want the results sorted in Ascending or Descending order.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    A token to resume pagination.

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

    The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.

• On success, responds with ListFlowDefinitionsOutput with field(s):
  • flow_definition_summaries(Option<Vec<FlowDefinitionSummary>>):

    An array of objects describing the flow definitions.

  • next_token(Option<String>):

    A token to resume pagination.

• On failure, responds with SdkError<ListFlowDefinitionsError>

pub fn list_human_task_uis(&self) -> ListHumanTaskUis

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only human task user interfaces that were created before the specified timestamp.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    An optional value that specifies whether you want the results sorted in Ascending or Descending order.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    A token to resume pagination.

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

    The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.

• On success, responds with ListHumanTaskUisOutput with field(s):
  • human_task_ui_summaries(Option<Vec<HumanTaskUiSummary>>):

    An array of objects describing the human task user interfaces.

  • next_token(Option<String>):

    A token to resume pagination.

• On failure, responds with SdkError<ListHumanTaskUisError>

pub fn list_hyper_parameter_tuning_jobs(&self) -> ListHyperParameterTuningJobs

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

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

    If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

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

    The maximum number of tuning jobs to return. The default value is 10.

  • sort_by(HyperParameterTuningJobSortByOptions) / set_sort_by(Option<HyperParameterTuningJobSortByOptions>):

    The field to sort results by. The default is Name.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only tuning jobs that were created after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only tuning jobs that were created before the specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only tuning jobs that were modified after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only tuning jobs that were modified before the specified time.

  • status_equals(HyperParameterTuningJobStatus) / set_status_equals(Option<HyperParameterTuningJobStatus>):

    A filter that returns only tuning jobs with the specified status.

• On success, responds with ListHyperParameterTuningJobsOutput with field(s):
  • hyper_parameter_tuning_job_summaries(Option<Vec<HyperParameterTuningJobSummary>>):

    A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

  • next_token(Option<String>):

    If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

• On failure, responds with SdkError<ListHyperParameterTuningJobsError>

pub fn list_images(&self) -> ListImages

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only images created on or after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only images created on or before the specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only images modified on or after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only images modified on or before the specified time.

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

    The maximum number of images to return in the response. The default value is 10.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A filter that returns only images whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListImages didn’t return the full set of images, the call returns a token for getting the next set of images.

  • sort_by(ImageSortBy) / set_sort_by(Option<ImageSortBy>):

    The property used to sort results. The default value is CREATION_TIME.

  • sort_order(ImageSortOrder) / set_sort_order(Option<ImageSortOrder>):

    The sort order. The default value is DESCENDING.

• On success, responds with ListImagesOutput with field(s):
  • images(Option<Vec<Image>>):

    A list of images and their properties.

  • next_token(Option<String>):

    A token for getting the next set of images, if there are any.

• On failure, responds with SdkError<ListImagesError>

pub fn list_image_versions(&self) -> ListImageVersions

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only versions created on or after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only versions created on or before the specified time.

  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image to list the versions of.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only versions modified on or after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only versions modified on or before the specified time.

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

    The maximum number of versions to return in the response. The default value is 10.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListImageVersions didn’t return the full set of versions, the call returns a token for getting the next set of versions.

  • sort_by(ImageVersionSortBy) / set_sort_by(Option<ImageVersionSortBy>):

    The property used to sort results. The default value is CREATION_TIME.

  • sort_order(ImageVersionSortOrder) / set_sort_order(Option<ImageVersionSortOrder>):

    The sort order. The default value is DESCENDING.

• On success, responds with ListImageVersionsOutput with field(s):
  • image_versions(Option<Vec<ImageVersion>>):

    A list of versions and their properties.

  • next_token(Option<String>):

    A token for getting the next set of versions, if there are any.

• On failure, responds with SdkError<ListImageVersionsError>

pub fn list_inference_recommendations_jobs(
    &self
) -> ListInferenceRecommendationsJobs

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only jobs created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only jobs created before the specified time (timestamp).

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only jobs that were last modified after the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only jobs that were last modified before the specified time (timestamp).

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the job name. This filter returns only recommendations whose name contains the specified string.

  • status_equals(RecommendationJobStatus) / set_status_equals(Option<RecommendationJobStatus>):

    A filter that retrieves only inference recommendations jobs with a specific status.

  • sort_by(ListInferenceRecommendationsJobsSortBy) / set_sort_by(Option<ListInferenceRecommendationsJobsSortBy>):

    The parameter by which to sort the results.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the response includes a NextToken. To retrieve the next set of recommendations, use the token in the next request.

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

    The maximum number of recommendations to return in the response.

• On success, responds with ListInferenceRecommendationsJobsOutput with field(s):
  • inference_recommendations_jobs(Option<Vec<InferenceRecommendationsJob>>):

    The recommendations created from the Amazon SageMaker Inference Recommender job.

  • next_token(Option<String>):

    A token for getting the next set of recommendations, if there are any.

• On failure, responds with SdkError<ListInferenceRecommendationsJobsError>

pub fn list_labeling_jobs(&self) -> ListLabelingJobs

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only labeling jobs created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only labeling jobs created before the specified time (timestamp).

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only labeling jobs modified after the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only labeling jobs modified before the specified time (timestamp).

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

    The maximum number of labeling jobs to return in each page of the response.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

  • sort_by(SortBy) / set_sort_by(Option<SortBy>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

  • status_equals(LabelingJobStatus) / set_status_equals(Option<LabelingJobStatus>):

    A filter that retrieves only labeling jobs with a specific status.

• On success, responds with ListLabelingJobsOutput with field(s):
  • labeling_job_summary_list(Option<Vec<LabelingJobSummary>>):

    An array of LabelingJobSummary objects, each describing a labeling job.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListLabelingJobsError>

pub fn list_labeling_jobs_for_workteam(&self) -> ListLabelingJobsForWorkteam

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

• The fluent builder is configurable:
  • workteam_arn(impl Into<String>) / set_workteam_arn(Option<String>):

    The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

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

    The maximum number of labeling jobs to return in each page of the response.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only labeling jobs created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only labeling jobs created before the specified time (timestamp).

  • job_reference_code_contains(impl Into<String>) / set_job_reference_code_contains(Option<String>):

    A filter the limits jobs to only the ones whose job reference code contains the specified string.

  • sort_by(ListLabelingJobsForWorkteamSortByOptions) / set_sort_by(Option<ListLabelingJobsForWorkteamSortByOptions>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListLabelingJobsForWorkteamOutput with field(s):
  • labeling_job_summary_list(Option<Vec<LabelingJobForWorkteamSummary>>):

    An array of LabelingJobSummary objects, each describing a labeling job.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListLabelingJobsForWorkteamError>

pub fn list_lineage_groups(&self) -> ListLineageGroups

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

• The fluent builder is configurable:
  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A timestamp to filter against lineage groups created after a certain point in time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A timestamp to filter against lineage groups created before a certain point in time.

  • sort_by(SortLineageGroupsBy) / set_sort_by(Option<SortLineageGroupsBy>):

    The parameter by which to sort the results. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results. The default is Ascending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

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

    The maximum number of endpoints to return in the response. This value defaults to 10.

• On success, responds with ListLineageGroupsOutput with field(s):
  • lineage_group_summaries(Option<Vec<LineageGroupSummary>>):

    A list of lineage groups and their properties.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

• On failure, responds with SdkError<ListLineageGroupsError>

pub fn list_model_bias_job_definitions(&self) -> ListModelBiasJobDefinitions

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

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    Name of the endpoint to monitor for model bias.

  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):

    Whether to sort results by the Name or CreationTime field. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    Whether to sort the results in Ascending or Descending order. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

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

    The maximum number of model bias jobs to return in the response. The default value is 10.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Filter for model bias jobs whose name contains a specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only model bias jobs created before a specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only model bias jobs created after a specified time.

• On success, responds with ListModelBiasJobDefinitionsOutput with field(s):
  • job_definition_summaries(Option<Vec<MonitoringJobDefinitionSummary>>):

    A JSON array in which each element is a summary for a model bias jobs.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListModelBiasJobDefinitionsError>

pub fn list_model_explainability_job_definitions(
    &self
) -> ListModelExplainabilityJobDefinitions

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

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    Name of the endpoint to monitor for model explainability.

  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):

    Whether to sort results by the Name or CreationTime field. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    Whether to sort the results in Ascending or Descending order. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

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

    The maximum number of jobs to return in the response. The default value is 10.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Filter for model explainability jobs whose name contains a specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only model explainability jobs created before a specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only model explainability jobs created after a specified time.

• On success, responds with ListModelExplainabilityJobDefinitionsOutput with field(s):
  • job_definition_summaries(Option<Vec<MonitoringJobDefinitionSummary>>):

    A JSON array in which each element is a summary for a explainability bias jobs.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListModelExplainabilityJobDefinitionsError>

pub fn list_model_metadata(&self) -> ListModelMetadata

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

• The fluent builder is configurable:
  • search_expression(ModelMetadataSearchExpression) / set_search_expression(Option<ModelMetadataSearchExpression>):

    One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression’s condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to a previous ListModelMetadataResponse request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.

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

    The maximum number of models to return in the response.

• On success, responds with ListModelMetadataOutput with field(s):
  • model_metadata_summaries(Option<Vec<ModelMetadataSummary>>):

    A structure that holds model metadata.

  • next_token(Option<String>):

    A token for getting the next set of recommendations, if there are any.

• On failure, responds with SdkError<ListModelMetadataError>

pub fn list_model_package_groups(&self) -> ListModelPackageGroups

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only model groups created after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only model groups created before the specified time.

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

    The maximum number of results to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the model group name. This filter returns only model groups whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.

  • sort_by(ModelPackageGroupSortBy) / set_sort_by(Option<ModelPackageGroupSortBy>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListModelPackageGroupsOutput with field(s):
  • model_package_group_summary_list(Option<Vec<ModelPackageGroupSummary>>):

    A list of summaries of the model groups in your Amazon Web Services account.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.

• On failure, responds with SdkError<ListModelPackageGroupsError>

pub fn list_model_packages(&self) -> ListModelPackages

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only model packages created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only model packages created before the specified time (timestamp).

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

    The maximum number of model packages to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the model package name. This filter returns only model packages whose name contains the specified string.

  • model_approval_status(ModelApprovalStatus) / set_model_approval_status(Option<ModelApprovalStatus>):

    A filter that returns only the model packages with the specified approval status.

  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    A filter that returns only model versions that belong to the specified model group.

  • model_package_type(ModelPackageType) / set_model_package_type(Option<ModelPackageType>):

    A filter that returns only the model packages of the specified type. This can be one of the following values.

    • UNVERSIONED - List only unversioined models. This is the default value if no ModelPackageType is specified.

    • VERSIONED - List only versioned models.

    • BOTH - List both versioned and unversioned models.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.

  • sort_by(ModelPackageSortBy) / set_sort_by(Option<ModelPackageSortBy>):

    The parameter by which to sort the results. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results. The default is Ascending.

• On success, responds with ListModelPackagesOutput with field(s):
  • model_package_summary_list(Option<Vec<ModelPackageSummary>>):

    An array of ModelPackageSummary objects, each of which lists a model package.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.

• On failure, responds with SdkError<ListModelPackagesError>

pub fn list_model_quality_job_definitions(
    &self
) -> ListModelQualityJobDefinitions

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

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.

  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.

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

    The maximum number of results to return in a call to ListModelQualityJobDefinitions.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only model quality monitoring job definitions created before the specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only model quality monitoring job definitions created after the specified time.

• On success, responds with ListModelQualityJobDefinitionsOutput with field(s):
  • job_definition_summaries(Option<Vec<MonitoringJobDefinitionSummary>>):

    A list of summaries of model quality monitoring job definitions.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.

• On failure, responds with SdkError<ListModelQualityJobDefinitionsError>

pub fn list_models(&self) -> ListModels

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

• The fluent builder is configurable:
  • sort_by(ModelSortKey) / set_sort_by(Option<ModelSortKey>):

    Sorts the list of results. The default is CreationTime.

  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.

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

    The maximum number of models to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the model name. This filter returns only models whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only models created before the specified time (timestamp).

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

• On success, responds with ListModelsOutput with field(s):
  • models(Option<Vec<ModelSummary>>):

    An array of ModelSummary objects, each of which lists a model.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

• On failure, responds with SdkError<ListModelsError>

pub fn list_monitoring_executions(&self) -> ListMonitoringExecutions

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

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    Name of a specific schedule to fetch jobs for.

  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    Name of a specific endpoint to fetch jobs for.

  • sort_by(MonitoringExecutionSortKey) / set_sort_by(Option<MonitoringExecutionSortKey>):

    Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    Whether to sort the results in Ascending or Descending order. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

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

    The maximum number of jobs to return in the response. The default value is 10.

  • scheduled_time_before(DateTime) / set_scheduled_time_before(Option<DateTime>):

    Filter for jobs scheduled before a specified time.

  • scheduled_time_after(DateTime) / set_scheduled_time_after(Option<DateTime>):

    Filter for jobs scheduled after a specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only jobs created before a specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only jobs created after a specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only jobs modified after a specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only jobs modified before a specified time.

  • status_equals(ExecutionStatus) / set_status_equals(Option<ExecutionStatus>):

    A filter that retrieves only jobs with a specific status.

  • monitoring_job_definition_name(impl Into<String>) / set_monitoring_job_definition_name(Option<String>):

    Gets a list of the monitoring job runs of the specified monitoring job definitions.

  • monitoring_type_equals(MonitoringType) / set_monitoring_type_equals(Option<MonitoringType>):

    A filter that returns only the monitoring job runs of the specified monitoring type.

• On success, responds with ListMonitoringExecutionsOutput with field(s):
  • monitoring_execution_summaries(Option<Vec<MonitoringExecutionSummary>>):

    A JSON array in which each element is a summary for a monitoring execution.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent reques

• On failure, responds with SdkError<ListMonitoringExecutionsError>

pub fn list_monitoring_schedules(&self) -> ListMonitoringSchedules

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

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    Name of a specific endpoint to fetch schedules for.

  • sort_by(MonitoringScheduleSortKey) / set_sort_by(Option<MonitoringScheduleSortKey>):

    Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    Whether to sort the results in Ascending or Descending order. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

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

    The maximum number of jobs to return in the response. The default value is 10.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    Filter for monitoring schedules whose name contains a specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only monitoring schedules created before a specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only monitoring schedules created after a specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only monitoring schedules modified before a specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only monitoring schedules modified after a specified time.

  • status_equals(ScheduleStatus) / set_status_equals(Option<ScheduleStatus>):

    A filter that returns only monitoring schedules modified before a specified time.

  • monitoring_job_definition_name(impl Into<String>) / set_monitoring_job_definition_name(Option<String>):

    Gets a list of the monitoring schedules for the specified monitoring job definition.

  • monitoring_type_equals(MonitoringType) / set_monitoring_type_equals(Option<MonitoringType>):

    A filter that returns only the monitoring schedules for the specified monitoring type.

• On success, responds with ListMonitoringSchedulesOutput with field(s):
  • monitoring_schedule_summaries(Option<Vec<MonitoringScheduleSummary>>):

    A JSON array in which each element is a summary for a monitoring schedule.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListMonitoringSchedulesError>

pub fn list_notebook_instance_lifecycle_configs(
    &self
) -> ListNotebookInstanceLifecycleConfigs

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

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

    If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.

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

    The maximum number of lifecycle configurations to return in the response.

  • sort_by(NotebookInstanceLifecycleConfigSortKey) / set_sort_by(Option<NotebookInstanceLifecycleConfigSortKey>):

    Sorts the list of results. The default is CreationTime.

  • sort_order(NotebookInstanceLifecycleConfigSortOrder) / set_sort_order(Option<NotebookInstanceLifecycleConfigSortOrder>):

    The sort order for results.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

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

    If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.

  • notebook_instance_lifecycle_configs(Option<Vec<NotebookInstanceLifecycleConfigSummary>>):

    An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.

• On failure, responds with SdkError<ListNotebookInstanceLifecycleConfigsError>

pub fn list_notebook_instances(&self) -> ListNotebookInstances

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

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

    If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.

    You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

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

    The maximum number of notebook instances to return.

  • sort_by(NotebookInstanceSortKey) / set_sort_by(Option<NotebookInstanceSortKey>):

    The field to sort results by. The default is Name.

  • sort_order(NotebookInstanceSortOrder) / set_sort_order(Option<NotebookInstanceSortOrder>):

    The sort order for results.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the notebook instances’ name. This filter returns only notebook instances whose name contains the specified string.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only notebook instances that were created before the specified time (timestamp).

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only notebook instances that were created after the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only notebook instances that were modified before the specified time (timestamp).

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only notebook instances that were modified after the specified time (timestamp).

  • status_equals(NotebookInstanceStatus) / set_status_equals(Option<NotebookInstanceStatus>):

    A filter that returns only notebook instances with the specified status.

  • notebook_instance_lifecycle_config_name_contains(impl Into<String>) / set_notebook_instance_lifecycle_config_name_contains(Option<String>):

    A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

  • default_code_repository_contains(impl Into<String>) / set_default_code_repository_contains(Option<String>):

    A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

  • additional_code_repository_equals(impl Into<String>) / set_additional_code_repository_equals(Option<String>):

    A filter that returns only notebook instances with associated with the specified git repository.

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

    If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.

  • notebook_instances(Option<Vec<NotebookInstanceSummary>>):

    An array of NotebookInstanceSummary objects, one for each notebook instance.

• On failure, responds with SdkError<ListNotebookInstancesError>

pub fn list_pipeline_executions(&self) -> ListPipelineExecutions

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

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns the pipeline executions that were created after a specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns the pipeline executions that were created before a specified time.

  • sort_by(SortPipelineExecutionsBy) / set_sort_by(Option<SortPipelineExecutionsBy>):

    The field by which to sort results. The default is CreatedTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.

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

    The maximum number of pipeline executions to return in the response.

• On success, responds with ListPipelineExecutionsOutput with field(s):
  • pipeline_execution_summaries(Option<Vec<PipelineExecutionSummary>>):

    Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.

  • next_token(Option<String>):

    If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.

• On failure, responds with SdkError<ListPipelineExecutionsError>

pub fn list_pipeline_execution_steps(&self) -> ListPipelineExecutionSteps

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

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.

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

    The maximum number of pipeline execution steps to return in the response.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The field by which to sort results. The default is CreatedTime.

• On success, responds with ListPipelineExecutionStepsOutput with field(s):
  • pipeline_execution_steps(Option<Vec<PipelineExecutionStep>>):

    A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.

  • next_token(Option<String>):

    If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.

• On failure, responds with SdkError<ListPipelineExecutionStepsError>

pub fn list_pipeline_parameters_for_execution(
    &self
) -> ListPipelineParametersForExecution

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

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.

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

    The maximum number of parameters to return in the response.

• On success, responds with ListPipelineParametersForExecutionOutput with field(s):
  • pipeline_parameters(Option<Vec<Parameter>>):

    Contains a list of pipeline parameters. This list can be empty.

  • next_token(Option<String>):

    If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.

• On failure, responds with SdkError<ListPipelineParametersForExecutionError>

pub fn list_pipelines(&self) -> ListPipelines

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

• The fluent builder is configurable:
  • pipeline_name_prefix(impl Into<String>) / set_pipeline_name_prefix(Option<String>):

    The prefix of the pipeline name.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns the pipelines that were created after a specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns the pipelines that were created before a specified time.

  • sort_by(SortPipelinesBy) / set_sort_by(Option<SortPipelinesBy>):

    The field by which to sort results. The default is CreatedTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.

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

    The maximum number of pipelines to return in the response.

• On success, responds with ListPipelinesOutput with field(s):
  • pipeline_summaries(Option<Vec<PipelineSummary>>):

    Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.

  • next_token(Option<String>):

    If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.

• On failure, responds with SdkError<ListPipelinesError>

pub fn list_processing_jobs(&self) -> ListProcessingJobs

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only processing jobs created after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only processing jobs created after the specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only processing jobs modified after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only processing jobs modified before the specified time.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.

  • status_equals(ProcessingJobStatus) / set_status_equals(Option<ProcessingJobStatus>):

    A filter that retrieves only processing jobs with a specific status.

  • sort_by(SortBy) / set_sort_by(Option<SortBy>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListProcessingJobs request was truncated, the response includes a NextToken. To retrieve the next set of processing jobs, use the token in the next request.

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

    The maximum number of processing jobs to return in the response.

• On success, responds with ListProcessingJobsOutput with field(s):
  • processing_job_summaries(Option<Vec<ProcessingJobSummary>>):

    An array of ProcessingJobSummary objects, each listing a processing job.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListProcessingJobsError>

pub fn list_projects(&self) -> ListProjects

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns the projects that were created after a specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns the projects that were created before a specified time.

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

    The maximum number of projects to return in the response.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A filter that returns the projects whose name contains a specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListProjects request was truncated, the response includes a NextToken. To retrieve the next set of projects, use the token in the next request.

  • sort_by(ProjectSortBy) / set_sort_by(Option<ProjectSortBy>):

    The field by which to sort results. The default is CreationTime.

  • sort_order(ProjectSortOrder) / set_sort_order(Option<ProjectSortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListProjectsOutput with field(s):
  • project_summary_list(Option<Vec<ProjectSummary>>):

    A list of summaries of projects.

  • next_token(Option<String>):

    If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.

• On failure, responds with SdkError<ListProjectsError>

pub fn list_studio_lifecycle_configs(&self) -> ListStudioLifecycleConfigs

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

• The fluent builder is configurable:
  • max_results(i32) / set_max_results(Option<i32>):

    The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListStudioLifecycleConfigs didn’t return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.

  • app_type_equals(StudioLifecycleConfigAppType) / set_app_type_equals(Option<StudioLifecycleConfigAppType>):

    A parameter to search for the App Type to which the Lifecycle Configuration is attached.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only Lifecycle Configurations created on or before the specified time.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only Lifecycle Configurations created on or after the specified time.

  • modified_time_before(DateTime) / set_modified_time_before(Option<DateTime>):

    A filter that returns only Lifecycle Configurations modified before the specified time.

  • modified_time_after(DateTime) / set_modified_time_after(Option<DateTime>):

    A filter that returns only Lifecycle Configurations modified after the specified time.

  • sort_by(StudioLifecycleConfigSortKey) / set_sort_by(Option<StudioLifecycleConfigSortKey>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

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

    A token for getting the next set of actions, if there are any.

  • studio_lifecycle_configs(Option<Vec<StudioLifecycleConfigDetails>>):

    A list of Lifecycle Configurations and their properties.

• On failure, responds with SdkError<ListStudioLifecycleConfigsError>

pub fn list_subscribed_workteams(&self) -> ListSubscribedWorkteams

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

• The fluent builder is configurable:
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the work team name. This filter returns only work teams whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

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

    The maximum number of work teams to return in each page of the response.

• On success, responds with ListSubscribedWorkteamsOutput with field(s):
  • subscribed_workteams(Option<Vec<SubscribedWorkteam>>):

    An array of Workteam objects, each describing a work team.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

• On failure, responds with SdkError<ListSubscribedWorkteamsError>

pub fn list_tags(&self) -> ListTags

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

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

    The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

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

    Maximum number of tags to return.

• On success, responds with ListTagsOutput with field(s):
  • tags(Option<Vec<Tag>>):

    An array of Tag objects, each with a tag key and a value.

  • next_token(Option<String>):

    If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.

• On failure, responds with SdkError<ListTagsError>

pub fn list_training_jobs(&self) -> ListTrainingJobs

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

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

    If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

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

    The maximum number of training jobs to return in the response.

  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only training jobs created after the specified time (timestamp).

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only training jobs created before the specified time (timestamp).

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only training jobs modified after the specified time (timestamp).

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only training jobs modified before the specified time (timestamp).

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the training job name. This filter returns only training jobs whose name contains the specified string.

  • status_equals(TrainingJobStatus) / set_status_equals(Option<TrainingJobStatus>):

    A filter that retrieves only training jobs with a specific status.

  • sort_by(SortBy) / set_sort_by(Option<SortBy>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListTrainingJobsOutput with field(s):
  • training_job_summaries(Option<Vec<TrainingJobSummary>>):

    An array of TrainingJobSummary objects, each listing a training job.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListTrainingJobsError>

pub fn list_training_jobs_for_hyper_parameter_tuning_job(
    &self
) -> ListTrainingJobsForHyperParameterTuningJob

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

• The fluent builder is configurable:
  • hyper_parameter_tuning_job_name(impl Into<String>) / set_hyper_parameter_tuning_job_name(Option<String>):

    The name of the tuning job whose training jobs you want to list.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

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

    The maximum number of training jobs to return. The default value is 10.

  • status_equals(TrainingJobStatus) / set_status_equals(Option<TrainingJobStatus>):

    A filter that returns only training jobs with the specified status.

  • sort_by(TrainingJobSortByOptions) / set_sort_by(Option<TrainingJobSortByOptions>):

    The field to sort results by. The default is Name.

    If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

• On success, responds with ListTrainingJobsForHyperParameterTuningJobOutput with field(s):
  • training_job_summaries(Option<Vec<HyperParameterTrainingJobSummary>>):

    A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.

  • next_token(Option<String>):

    If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

• On failure, responds with SdkError<ListTrainingJobsForHyperParameterTuningJobError>

pub fn list_transform_jobs(&self) -> ListTransformJobs

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

• The fluent builder is configurable:
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):

    A filter that returns only transform jobs created after the specified time.

  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):

    A filter that returns only transform jobs created before the specified time.

  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):

    A filter that returns only transform jobs modified after the specified time.

  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):

    A filter that returns only transform jobs modified before the specified time.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

  • status_equals(TransformJobStatus) / set_status_equals(Option<TransformJobStatus>):

    A filter that retrieves only transform jobs with a specific status.

  • sort_by(SortBy) / set_sort_by(Option<SortBy>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.

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

    The maximum number of transform jobs to return in the response. The default value is 10.

• On success, responds with ListTransformJobsOutput with field(s):
  • transform_job_summaries(Option<Vec<TransformJobSummary>>):

    An array of TransformJobSummary objects.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.

• On failure, responds with SdkError<ListTransformJobsError>

pub fn list_trial_components(&self) -> ListTrialComponents

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

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    A filter that returns only components that are part of the specified experiment. If you specify ExperimentName, you can’t filter by SourceArn or TrialName.

  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    A filter that returns only components that are part of the specified trial. If you specify TrialName, you can’t filter by ExperimentName or SourceArn.

  • source_arn(impl Into<String>) / set_source_arn(Option<String>):

    A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify SourceArn, you can’t filter by ExperimentName or TrialName.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only components created after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only components created before the specified time.

  • sort_by(SortTrialComponentsBy) / set_sort_by(Option<SortTrialComponentsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

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

    The maximum number of components to return in the response. The default value is 10.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListTrialComponents didn’t return the full set of components, the call returns a token for getting the next set of components.

• On success, responds with ListTrialComponentsOutput with field(s):
  • trial_component_summaries(Option<Vec<TrialComponentSummary>>):

    A list of the summaries of your trial components.

  • next_token(Option<String>):

    A token for getting the next set of components, if there are any.

• On failure, responds with SdkError<ListTrialComponentsError>

pub fn list_trials(&self) -> ListTrials

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

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    A filter that returns only trials that are part of the specified experiment.

  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    A filter that returns only trials that are associated with the specified trial component.

  • created_after(DateTime) / set_created_after(Option<DateTime>):

    A filter that returns only trials created after the specified time.

  • created_before(DateTime) / set_created_before(Option<DateTime>):

    A filter that returns only trials created before the specified time.

  • sort_by(SortTrialsBy) / set_sort_by(Option<SortTrialsBy>):

    The property used to sort results. The default value is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order. The default value is Descending.

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

    The maximum number of trials to return in the response. The default value is 10.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the previous call to ListTrials didn’t return the full set of trials, the call returns a token for getting the next set of trials.

• On success, responds with ListTrialsOutput with field(s):
  • trial_summaries(Option<Vec<TrialSummary>>):

    A list of the summaries of your trials.

  • next_token(Option<String>):

    A token for getting the next set of trials, if there are any.

• On failure, responds with SdkError<ListTrialsError>

pub fn list_user_profiles(&self) -> ListUserProfiles

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

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

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

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

    Returns a list up to a specified limit.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for the results. The default is Ascending.

  • sort_by(UserProfileSortKey) / set_sort_by(Option<UserProfileSortKey>):

    The parameter by which to sort the results. The default is CreationTime.

  • domain_id_equals(impl Into<String>) / set_domain_id_equals(Option<String>):

    A parameter by which to filter the results.

  • user_profile_name_contains(impl Into<String>) / set_user_profile_name_contains(Option<String>):

    A parameter by which to filter the results.

• On success, responds with ListUserProfilesOutput with field(s):
  • user_profiles(Option<Vec<UserProfileDetails>>):

    The list of user profiles.

  • next_token(Option<String>):

    If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

• On failure, responds with SdkError<ListUserProfilesError>

pub fn list_workforces(&self) -> ListWorkforces

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

• The fluent builder is configurable:
  • sort_by(ListWorkforcesSortByOptions) / set_sort_by(Option<ListWorkforcesSortByOptions>):

    Sort workforces using the workforce name or creation date.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    Sort workforces in ascending or descending order.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A filter you can use to search for workforces using part of the workforce name.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    A token to resume pagination.

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

    The maximum number of workforces returned in the response.

• On success, responds with ListWorkforcesOutput with field(s):
  • workforces(Option<Vec<Workforce>>):

    A list containing information about your workforce.

  • next_token(Option<String>):

    A token to resume pagination.

• On failure, responds with SdkError<ListWorkforcesError>

pub fn list_workteams(&self) -> ListWorkteams

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

• The fluent builder is configurable:
  • sort_by(ListWorkteamsSortByOptions) / set_sort_by(Option<ListWorkteamsSortByOptions>):

    The field to sort results by. The default is CreationTime.

  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):

    The sort order for results. The default is Ascending.

  • name_contains(impl Into<String>) / set_name_contains(Option<String>):

    A string in the work team’s name. This filter returns only work teams whose name contains the specified string.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

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

    The maximum number of work teams to return in each page of the response.

• On success, responds with ListWorkteamsOutput with field(s):
  • workteams(Option<Vec<Workteam>>):

    An array of Workteam objects, each describing a work team.

  • next_token(Option<String>):

    If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

• On failure, responds with SdkError<ListWorkteamsError>

pub fn put_model_package_group_policy(&self) -> PutModelPackageGroupPolicy

Constructs a fluent builder for the PutModelPackageGroupPolicy operation.

• The fluent builder is configurable:
  • model_package_group_name(impl Into<String>) / set_model_package_group_name(Option<String>):

    The name of the model group to add a resource policy to.

  • resource_policy(impl Into<String>) / set_resource_policy(Option<String>):

    The resource policy for the model group.

• On success, responds with PutModelPackageGroupPolicyOutput with field(s):
  • model_package_group_arn(Option<String>):

    The Amazon Resource Name (ARN) of the model package group.

• On failure, responds with SdkError<PutModelPackageGroupPolicyError>

pub fn query_lineage(&self) -> QueryLineage

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

• The fluent builder is configurable:
  • start_arns(Vec<String>) / set_start_arns(Option<Vec<String>>):

    A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.

  • direction(Direction) / set_direction(Option<Direction>):

    Associations between lineage entities are directed. This parameter determines the direction from the StartArn(s) the query will look.

  • include_edges(bool) / set_include_edges(bool):

    Setting this value to True will retrieve not only the entities of interest but also the Associations and lineage entities on the path. Set to False to only return lineage entities that match your query.

  • filters(QueryFilters) / set_filters(Option<QueryFilters>):

    A set of filtering parameters that allow you to specify which entities should be returned.

    • Properties - Key-value pairs to match on the lineage entities’ properties.

    • LineageTypes - A set of lineage entity types to match on. For example: TrialComponent, Artifact, or Context.

    • CreatedBefore - Filter entities created before this date.

    • ModifiedBefore - Filter entities modified before this date.

    • ModifiedAfter - Filter entities modified after this date.

  • max_depth(i32) / set_max_depth(Option<i32>):

    The maximum depth in lineage relationships from the StartArns that will be traversed. Depth is a measure of the number of Associations from the StartArn entity to the matched results.

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

    Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the next page of results.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the next page of results.

• On success, responds with QueryLineageOutput with field(s):
  • vertices(Option<Vec<Vertex>>):

    A list of vertices connected to the start entity(ies) in the lineage graph.

  • edges(Option<Vec<Edge>>):

    A list of edges that connect vertices in the response.

  • next_token(Option<String>):

    Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the next page of results.

• On failure, responds with SdkError<QueryLineageError>

pub fn register_devices(&self) -> RegisterDevices

Constructs a fluent builder for the RegisterDevices operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet.

  • devices(Vec<Device>) / set_devices(Option<Vec<Device>>):

    A list of devices to register with SageMaker Edge Manager.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    The tags associated with devices.

• On success, responds with RegisterDevicesOutput
• On failure, responds with SdkError<RegisterDevicesError>

pub fn render_ui_template(&self) -> RenderUiTemplate

Constructs a fluent builder for the RenderUiTemplate operation.

• The fluent builder is configurable:
  • ui_template(UiTemplate) / set_ui_template(Option<UiTemplate>):

    A Template object containing the worker UI template to render.

  • task(RenderableTask) / set_task(Option<RenderableTask>):

    A RenderableTask object containing a representative task to render.

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

    The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

  • human_task_ui_arn(impl Into<String>) / set_human_task_ui_arn(Option<String>):

    The HumanTaskUiArn of the worker UI that you want to render. Do not provide a HumanTaskUiArn if you use the UiTemplate parameter.

    See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.

• On success, responds with RenderUiTemplateOutput with field(s):
  • rendered_content(Option<String>):

    A Liquid template that renders the HTML for the worker UI.

  • errors(Option<Vec<RenderingError>>):

    A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.

• On failure, responds with SdkError<RenderUiTemplateError>

pub fn retry_pipeline_execution(&self) -> RetryPipelineExecution

Constructs a fluent builder for the RetryPipelineExecution operation.

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

  • parallelism_configuration(ParallelismConfiguration) / set_parallelism_configuration(Option<ParallelismConfiguration>):

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline.

• On success, responds with RetryPipelineExecutionOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On failure, responds with SdkError<RetryPipelineExecutionError>

pub fn search(&self) -> Search

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

• The fluent builder is configurable:
  • resource(ResourceType) / set_resource(Option<ResourceType>):

    The name of the Amazon SageMaker resource to search for.

  • search_expression(SearchExpression) / set_search_expression(Option<SearchExpression>):

    A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.

  • sort_by(impl Into<String>) / set_sort_by(Option<String>):

    The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

  • sort_order(SearchSortOrder) / set_sort_order(Option<SearchSortOrder>):

    How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

  • next_token(impl Into<String>) / set_next_token(Option<String>):

    If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.

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

    The maximum number of results to return.

• On success, responds with SearchOutput with field(s):
  • results(Option<Vec<SearchRecord>>):

    A list of SearchRecord objects.

  • next_token(Option<String>):

    If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.

• On failure, responds with SdkError<SearchError>

pub fn send_pipeline_execution_step_failure(
    &self
) -> SendPipelineExecutionStepFailure

Constructs a fluent builder for the SendPipelineExecutionStepFailure operation.

• The fluent builder is configurable:
  • callback_token(impl Into<String>) / set_callback_token(Option<String>):

    The pipeline generated token from the Amazon SQS queue.

  • failure_reason(impl Into<String>) / set_failure_reason(Option<String>):

    A message describing why the step failed.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• On success, responds with SendPipelineExecutionStepFailureOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On failure, responds with SdkError<SendPipelineExecutionStepFailureError>

pub fn send_pipeline_execution_step_success(
    &self
) -> SendPipelineExecutionStepSuccess

Constructs a fluent builder for the SendPipelineExecutionStepSuccess operation.

• The fluent builder is configurable:
  • callback_token(impl Into<String>) / set_callback_token(Option<String>):

    The pipeline generated token from the Amazon SQS queue.

  • output_parameters(Vec<OutputParameter>) / set_output_parameters(Option<Vec<OutputParameter>>):

    A list of the output parameters of the callback step.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.

• On success, responds with SendPipelineExecutionStepSuccessOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On failure, responds with SdkError<SendPipelineExecutionStepSuccessError>

pub fn start_monitoring_schedule(&self) -> StartMonitoringSchedule

Constructs a fluent builder for the StartMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    The name of the schedule to start.

• On success, responds with StartMonitoringScheduleOutput
• On failure, responds with SdkError<StartMonitoringScheduleError>

pub fn start_notebook_instance(&self) -> StartNotebookInstance

Constructs a fluent builder for the StartNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the notebook instance to start.

• On success, responds with StartNotebookInstanceOutput
• On failure, responds with SdkError<StartNotebookInstanceError>

pub fn start_pipeline_execution(&self) -> StartPipelineExecution

Constructs a fluent builder for the StartPipelineExecution operation.

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline.

  • pipeline_execution_display_name(impl Into<String>) / set_pipeline_execution_display_name(Option<String>):

    The display name of the pipeline execution.

  • pipeline_parameters(Vec<Parameter>) / set_pipeline_parameters(Option<Vec<Parameter>>):

    Contains a list of pipeline parameters. This list can be empty.

  • pipeline_execution_description(impl Into<String>) / set_pipeline_execution_description(Option<String>):

    The description of the pipeline execution.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

  • parallelism_configuration(ParallelismConfiguration) / set_parallelism_configuration(Option<ParallelismConfiguration>):

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

• On success, responds with StartPipelineExecutionOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On failure, responds with SdkError<StartPipelineExecutionError>

pub fn stop_auto_ml_job(&self) -> StopAutoMLJob

Constructs a fluent builder for the StopAutoMLJob operation.

• The fluent builder is configurable:
  • auto_ml_job_name(impl Into<String>) / set_auto_ml_job_name(Option<String>):

    The name of the object you are requesting.

• On success, responds with StopAutoMlJobOutput
• On failure, responds with SdkError<StopAutoMLJobError>

pub fn stop_compilation_job(&self) -> StopCompilationJob

Constructs a fluent builder for the StopCompilationJob operation.

• The fluent builder is configurable:
  • compilation_job_name(impl Into<String>) / set_compilation_job_name(Option<String>):

    The name of the model compilation job to stop.

• On success, responds with StopCompilationJobOutput
• On failure, responds with SdkError<StopCompilationJobError>

pub fn stop_edge_packaging_job(&self) -> StopEdgePackagingJob

Constructs a fluent builder for the StopEdgePackagingJob operation.

• The fluent builder is configurable:
  • edge_packaging_job_name(impl Into<String>) / set_edge_packaging_job_name(Option<String>):

    The name of the edge packaging job.

• On success, responds with StopEdgePackagingJobOutput
• On failure, responds with SdkError<StopEdgePackagingJobError>

pub fn stop_hyper_parameter_tuning_job(&self) -> StopHyperParameterTuningJob

Constructs a fluent builder for the StopHyperParameterTuningJob operation.

• The fluent builder is configurable:
  • hyper_parameter_tuning_job_name(impl Into<String>) / set_hyper_parameter_tuning_job_name(Option<String>):

    The name of the tuning job to stop.

• On success, responds with StopHyperParameterTuningJobOutput
• On failure, responds with SdkError<StopHyperParameterTuningJobError>

pub fn stop_inference_recommendations_job(
    &self
) -> StopInferenceRecommendationsJob

Constructs a fluent builder for the StopInferenceRecommendationsJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):

    The name of the job you want to stop.

• On success, responds with StopInferenceRecommendationsJobOutput
• On failure, responds with SdkError<StopInferenceRecommendationsJobError>

pub fn stop_labeling_job(&self) -> StopLabelingJob

Constructs a fluent builder for the StopLabelingJob operation.

• The fluent builder is configurable:
  • labeling_job_name(impl Into<String>) / set_labeling_job_name(Option<String>):

    The name of the labeling job to stop.

• On success, responds with StopLabelingJobOutput
• On failure, responds with SdkError<StopLabelingJobError>

pub fn stop_monitoring_schedule(&self) -> StopMonitoringSchedule

Constructs a fluent builder for the StopMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    The name of the schedule to stop.

• On success, responds with StopMonitoringScheduleOutput
• On failure, responds with SdkError<StopMonitoringScheduleError>

pub fn stop_notebook_instance(&self) -> StopNotebookInstance

Constructs a fluent builder for the StopNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the notebook instance to terminate.

• On success, responds with StopNotebookInstanceOutput
• On failure, responds with SdkError<StopNotebookInstanceError>

pub fn stop_pipeline_execution(&self) -> StopPipelineExecution

Constructs a fluent builder for the StopPipelineExecution operation.

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.

• On success, responds with StopPipelineExecutionOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

• On failure, responds with SdkError<StopPipelineExecutionError>

pub fn stop_processing_job(&self) -> StopProcessingJob

Constructs a fluent builder for the StopProcessingJob operation.

• The fluent builder is configurable:
  • processing_job_name(impl Into<String>) / set_processing_job_name(Option<String>):

    The name of the processing job to stop.

• On success, responds with StopProcessingJobOutput
• On failure, responds with SdkError<StopProcessingJobError>

pub fn stop_training_job(&self) -> StopTrainingJob

Constructs a fluent builder for the StopTrainingJob operation.

• The fluent builder is configurable:
  • training_job_name(impl Into<String>) / set_training_job_name(Option<String>):

    The name of the training job to stop.

• On success, responds with StopTrainingJobOutput
• On failure, responds with SdkError<StopTrainingJobError>

pub fn stop_transform_job(&self) -> StopTransformJob

Constructs a fluent builder for the StopTransformJob operation.

• The fluent builder is configurable:
  • transform_job_name(impl Into<String>) / set_transform_job_name(Option<String>):

    The name of the batch transform job to stop.

• On success, responds with StopTransformJobOutput
• On failure, responds with SdkError<StopTransformJobError>

pub fn update_action(&self) -> UpdateAction

Constructs a fluent builder for the UpdateAction operation.

• The fluent builder is configurable:
  • action_name(impl Into<String>) / set_action_name(Option<String>):

    The name of the action to update.

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

    The new description for the action.

  • status(ActionStatus) / set_status(Option<ActionStatus>):

    The new status for the action.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    The new list of properties. Overwrites the current property list.

  • properties_to_remove(Vec<String>) / set_properties_to_remove(Option<Vec<String>>):

    A list of properties to remove.

• On success, responds with UpdateActionOutput with field(s):
  • action_arn(Option<String>):

    The Amazon Resource Name (ARN) of the action.

• On failure, responds with SdkError<UpdateActionError>

pub fn update_app_image_config(&self) -> UpdateAppImageConfig

Constructs a fluent builder for the UpdateAppImageConfig operation.

• The fluent builder is configurable:
  • app_image_config_name(impl Into<String>) / set_app_image_config_name(Option<String>):

    The name of the AppImageConfig to update.

  • kernel_gateway_image_config(KernelGatewayImageConfig) / set_kernel_gateway_image_config(Option<KernelGatewayImageConfig>):

    The new KernelGateway app to run on the image.

• On success, responds with UpdateAppImageConfigOutput with field(s):
  • app_image_config_arn(Option<String>):

    The Amazon Resource Name (ARN) for the AppImageConfig.

• On failure, responds with SdkError<UpdateAppImageConfigError>

pub fn update_artifact(&self) -> UpdateArtifact

Constructs a fluent builder for the UpdateArtifact operation.

• The fluent builder is configurable:
  • artifact_arn(impl Into<String>) / set_artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact to update.

  • artifact_name(impl Into<String>) / set_artifact_name(Option<String>):

    The new name for the artifact.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    The new list of properties. Overwrites the current property list.

  • properties_to_remove(Vec<String>) / set_properties_to_remove(Option<Vec<String>>):

    A list of properties to remove.

• On success, responds with UpdateArtifactOutput with field(s):
  • artifact_arn(Option<String>):

    The Amazon Resource Name (ARN) of the artifact.

• On failure, responds with SdkError<UpdateArtifactError>

pub fn update_code_repository(&self) -> UpdateCodeRepository

Constructs a fluent builder for the UpdateCodeRepository operation.

• The fluent builder is configurable:
  • code_repository_name(impl Into<String>) / set_code_repository_name(Option<String>):

    The name of the Git repository to update.

  • git_config(GitConfigForUpdate) / set_git_config(Option<GitConfigForUpdate>):

    The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

    {“username”: UserName, “password”: Password}

• On success, responds with UpdateCodeRepositoryOutput with field(s):
  • code_repository_arn(Option<String>):

    The ARN of the Git repository.

• On failure, responds with SdkError<UpdateCodeRepositoryError>

pub fn update_context(&self) -> UpdateContext

Constructs a fluent builder for the UpdateContext operation.

• The fluent builder is configurable:
  • context_name(impl Into<String>) / set_context_name(Option<String>):

    The name of the context to update.

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

    The new description for the context.

  • properties(HashMap<String, String>) / set_properties(Option<HashMap<String, String>>):

    The new list of properties. Overwrites the current property list.

  • properties_to_remove(Vec<String>) / set_properties_to_remove(Option<Vec<String>>):

    A list of properties to remove.

• On success, responds with UpdateContextOutput with field(s):
  • context_arn(Option<String>):

    The Amazon Resource Name (ARN) of the context.

• On failure, responds with SdkError<UpdateContextError>

pub fn update_device_fleet(&self) -> UpdateDeviceFleet

Constructs a fluent builder for the UpdateDeviceFleet operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet.

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

    The Amazon Resource Name (ARN) of the device.

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

    Description of the fleet.

  • output_config(EdgeOutputConfig) / set_output_config(Option<EdgeOutputConfig>):

    Output configuration for storing sample data collected by the fleet.

  • enable_iot_role_alias(bool) / set_enable_iot_role_alias(Option<bool>):

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: “SageMakerEdge-{DeviceFleetName}”.

    For example, if your device fleet is called “demo-fleet”, the name of the role alias will be “SageMakerEdge-demo-fleet”.

• On success, responds with UpdateDeviceFleetOutput
• On failure, responds with SdkError<UpdateDeviceFleetError>

pub fn update_devices(&self) -> UpdateDevices

Constructs a fluent builder for the UpdateDevices operation.

• The fluent builder is configurable:
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):

    The name of the fleet the devices belong to.

  • devices(Vec<Device>) / set_devices(Option<Vec<Device>>):

    List of devices to register with Edge Manager agent.

• On success, responds with UpdateDevicesOutput
• On failure, responds with SdkError<UpdateDevicesError>

pub fn update_domain(&self) -> UpdateDomain

Constructs a fluent builder for the UpdateDomain operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The ID of the domain to be updated.

  • default_user_settings(UserSettings) / set_default_user_settings(Option<UserSettings>):

    A collection of settings.

  • domain_settings_for_update(DomainSettingsForUpdate) / set_domain_settings_for_update(Option<DomainSettingsForUpdate>):

    A collection of DomainSettings configuration values to update.

• On success, responds with UpdateDomainOutput with field(s):
  • domain_arn(Option<String>):

    The Amazon Resource Name (ARN) of the domain.

• On failure, responds with SdkError<UpdateDomainError>

pub fn update_endpoint(&self) -> UpdateEndpoint

Constructs a fluent builder for the UpdateEndpoint operation.

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    The name of the endpoint whose configuration you want to update.

  • endpoint_config_name(impl Into<String>) / set_endpoint_config_name(Option<String>):

    The name of the new endpoint configuration.

  • retain_all_variant_properties(bool) / set_retain_all_variant_properties(bool):

    When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true. To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false. The default is false.

  • exclude_retained_variant_properties(Vec<VariantProperty>) / set_exclude_retained_variant_properties(Option<Vec<VariantProperty>>):

    When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don’t specify a value for ExcludeAllVariantProperties, no variant properties are overridden.

  • deployment_config(DeploymentConfig) / set_deployment_config(Option<DeploymentConfig>):

    The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

  • retain_deployment_config(bool) / set_retain_deployment_config(bool):

    Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).

• On success, responds with UpdateEndpointOutput with field(s):
  • endpoint_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint.

• On failure, responds with SdkError<UpdateEndpointError>

pub fn update_endpoint_weights_and_capacities(
    &self
) -> UpdateEndpointWeightsAndCapacities

Constructs a fluent builder for the UpdateEndpointWeightsAndCapacities operation.

• The fluent builder is configurable:
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):

    The name of an existing Amazon SageMaker endpoint.

  • desired_weights_and_capacities(Vec<DesiredWeightAndCapacity>) / set_desired_weights_and_capacities(Option<Vec<DesiredWeightAndCapacity>>):

    An object that provides new capacity and weight values for a variant.

• On success, responds with UpdateEndpointWeightsAndCapacitiesOutput with field(s):
  • endpoint_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated endpoint.

• On failure, responds with SdkError<UpdateEndpointWeightsAndCapacitiesError>

pub fn update_experiment(&self) -> UpdateExperiment

Constructs a fluent builder for the UpdateExperiment operation.

• The fluent builder is configurable:
  • experiment_name(impl Into<String>) / set_experiment_name(Option<String>):

    The name of the experiment to update.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the experiment as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, ExperimentName is displayed.

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

    The description of the experiment.

• On success, responds with UpdateExperimentOutput with field(s):
  • experiment_arn(Option<String>):

    The Amazon Resource Name (ARN) of the experiment.

• On failure, responds with SdkError<UpdateExperimentError>

pub fn update_image(&self) -> UpdateImage

Constructs a fluent builder for the UpdateImage operation.

• The fluent builder is configurable:
  • delete_properties(Vec<String>) / set_delete_properties(Option<Vec<String>>):

    A list of properties to delete. Only the Description and DisplayName properties can be deleted.

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

    The new description for the image.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The new display name for the image.

  • image_name(impl Into<String>) / set_image_name(Option<String>):

    The name of the image to update.

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

    The new Amazon Resource Name (ARN) for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.

• On success, responds with UpdateImageOutput with field(s):
  • image_arn(Option<String>):

    The Amazon Resource Name (ARN) of the image.

• On failure, responds with SdkError<UpdateImageError>

pub fn update_model_package(&self) -> UpdateModelPackage

Constructs a fluent builder for the UpdateModelPackage operation.

• The fluent builder is configurable:
  • model_package_arn(impl Into<String>) / set_model_package_arn(Option<String>):

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

  • model_approval_status(ModelApprovalStatus) / set_model_approval_status(Option<ModelApprovalStatus>):

    The approval status of the model.

  • approval_description(impl Into<String>) / set_approval_description(Option<String>):

    A description for the approval status of the model.

  • customer_metadata_properties(HashMap<String, String>) / set_customer_metadata_properties(Option<HashMap<String, String>>):

    The metadata properties associated with the model package versions.

  • customer_metadata_properties_to_remove(Vec<String>) / set_customer_metadata_properties_to_remove(Option<Vec<String>>):

    The metadata properties associated with the model package versions to remove.

  • additional_inference_specifications_to_add(Vec<AdditionalInferenceSpecificationDefinition>) / set_additional_inference_specifications_to_add(Option<Vec<AdditionalInferenceSpecificationDefinition>>):

    An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

• On success, responds with UpdateModelPackageOutput with field(s):
  • model_package_arn(Option<String>):

    The Amazon Resource Name (ARN) of the model.

• On failure, responds with SdkError<UpdateModelPackageError>

pub fn update_monitoring_schedule(&self) -> UpdateMonitoringSchedule

Constructs a fluent builder for the UpdateMonitoringSchedule operation.

• The fluent builder is configurable:
  • monitoring_schedule_name(impl Into<String>) / set_monitoring_schedule_name(Option<String>):

    The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

  • monitoring_schedule_config(MonitoringScheduleConfig) / set_monitoring_schedule_config(Option<MonitoringScheduleConfig>):

    The configuration object that specifies the monitoring schedule and defines the monitoring job.

• On success, responds with UpdateMonitoringScheduleOutput with field(s):
  • monitoring_schedule_arn(Option<String>):

    The Amazon Resource Name (ARN) of the monitoring schedule.

• On failure, responds with SdkError<UpdateMonitoringScheduleError>

pub fn update_notebook_instance(&self) -> UpdateNotebookInstance

Constructs a fluent builder for the UpdateNotebookInstance operation.

• The fluent builder is configurable:
  • notebook_instance_name(impl Into<String>) / set_notebook_instance_name(Option<String>):

    The name of the notebook instance to update.

  • instance_type(InstanceType) / set_instance_type(Option<InstanceType>):

    The Amazon ML compute instance type.

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

    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.

    To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

  • lifecycle_config_name(impl Into<String>) / set_lifecycle_config_name(Option<String>):

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

  • disassociate_lifecycle_config(bool) / set_disassociate_lifecycle_config(bool):

    Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

  • volume_size_in_gb(i32) / set_volume_size_in_gb(Option<i32>):

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can’t determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can’t decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

  • default_code_repository(impl Into<String>) / set_default_code_repository(Option<String>):

    The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • additional_code_repositories(Vec<String>) / set_additional_code_repositories(Option<Vec<String>>):

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • accelerator_types(Vec<NotebookInstanceAcceleratorType>) / set_accelerator_types(Option<Vec<NotebookInstanceAcceleratorType>>):

    A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

  • disassociate_accelerator_types(bool) / set_disassociate_accelerator_types(bool):

    A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.

  • disassociate_default_code_repository(bool) / set_disassociate_default_code_repository(bool):

    The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • disassociate_additional_code_repositories(bool) / set_disassociate_additional_code_repositories(bool):

    A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • root_access(RootAccess) / set_root_access(Option<RootAccess>):

    Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

    If you set this to Disabled, users don’t have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.

• On success, responds with UpdateNotebookInstanceOutput
• On failure, responds with SdkError<UpdateNotebookInstanceError>

pub fn update_notebook_instance_lifecycle_config(
    &self
) -> UpdateNotebookInstanceLifecycleConfig

Constructs a fluent builder for the UpdateNotebookInstanceLifecycleConfig operation.

• The fluent builder is configurable:
  • notebook_instance_lifecycle_config_name(impl Into<String>) / set_notebook_instance_lifecycle_config_name(Option<String>):

    The name of the lifecycle configuration.

  • on_create(Vec<NotebookInstanceLifecycleHook>) / set_on_create(Option<Vec<NotebookInstanceLifecycleHook>>):

    The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • on_start(Vec<NotebookInstanceLifecycleHook>) / set_on_start(Option<Vec<NotebookInstanceLifecycleHook>>):

    The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

• On success, responds with UpdateNotebookInstanceLifecycleConfigOutput
• On failure, responds with SdkError<UpdateNotebookInstanceLifecycleConfigError>

pub fn update_pipeline(&self) -> UpdatePipeline

Constructs a fluent builder for the UpdatePipeline operation.

• The fluent builder is configurable:
  • pipeline_name(impl Into<String>) / set_pipeline_name(Option<String>):

    The name of the pipeline to update.

  • pipeline_display_name(impl Into<String>) / set_pipeline_display_name(Option<String>):

    The display name of the pipeline.

  • pipeline_definition(impl Into<String>) / set_pipeline_definition(Option<String>):

    The JSON pipeline definition.

  • pipeline_definition_s3_location(PipelineDefinitionS3Location) / set_pipeline_definition_s3_location(Option<PipelineDefinitionS3Location>):

    The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.

  • pipeline_description(impl Into<String>) / set_pipeline_description(Option<String>):

    The description of the pipeline.

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

    The Amazon Resource Name (ARN) that the pipeline uses to execute.

  • parallelism_configuration(ParallelismConfiguration) / set_parallelism_configuration(Option<ParallelismConfiguration>):

    If specified, it applies to all executions of this pipeline by default.

• On success, responds with UpdatePipelineOutput with field(s):
  • pipeline_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated pipeline.

• On failure, responds with SdkError<UpdatePipelineError>

pub fn update_pipeline_execution(&self) -> UpdatePipelineExecution

Constructs a fluent builder for the UpdatePipelineExecution operation.

• The fluent builder is configurable:
  • pipeline_execution_arn(impl Into<String>) / set_pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the pipeline execution.

  • pipeline_execution_description(impl Into<String>) / set_pipeline_execution_description(Option<String>):

    The description of the pipeline execution.

  • pipeline_execution_display_name(impl Into<String>) / set_pipeline_execution_display_name(Option<String>):

    The display name of the pipeline execution.

  • parallelism_configuration(ParallelismConfiguration) / set_parallelism_configuration(Option<ParallelismConfiguration>):

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

• On success, responds with UpdatePipelineExecutionOutput with field(s):
  • pipeline_execution_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated pipeline execution.

• On failure, responds with SdkError<UpdatePipelineExecutionError>

pub fn update_project(&self) -> UpdateProject

Constructs a fluent builder for the UpdateProject operation.

• The fluent builder is configurable:
  • project_name(impl Into<String>) / set_project_name(Option<String>):

    The name of the project.

  • project_description(impl Into<String>) / set_project_description(Option<String>):

    The description for the project.

  • service_catalog_provisioning_update_details(ServiceCatalogProvisioningUpdateDetails) / set_service_catalog_provisioning_update_details(Option<ServiceCatalogProvisioningUpdateDetails>):

    The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don’t provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.

  • tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

• On success, responds with UpdateProjectOutput with field(s):
  • project_arn(Option<String>):

    The Amazon Resource Name (ARN) of the project.

• On failure, responds with SdkError<UpdateProjectError>

pub fn update_training_job(&self) -> UpdateTrainingJob

Constructs a fluent builder for the UpdateTrainingJob operation.

• The fluent builder is configurable:
  • training_job_name(impl Into<String>) / set_training_job_name(Option<String>):

    The name of a training job to update the Debugger profiling configuration.

  • profiler_config(ProfilerConfigForUpdate) / set_profiler_config(Option<ProfilerConfigForUpdate>):

    Configuration information for Debugger system monitoring, framework profiling, and storage paths.

  • profiler_rule_configurations(Vec<ProfilerRuleConfiguration>) / set_profiler_rule_configurations(Option<Vec<ProfilerRuleConfiguration>>):

    Configuration information for Debugger rules for profiling system and framework metrics.

• On success, responds with UpdateTrainingJobOutput with field(s):
  • training_job_arn(Option<String>):

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

• On failure, responds with SdkError<UpdateTrainingJobError>

pub fn update_trial(&self) -> UpdateTrial

Constructs a fluent builder for the UpdateTrial operation.

• The fluent builder is configurable:
  • trial_name(impl Into<String>) / set_trial_name(Option<String>):

    The name of the trial to update.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the trial as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialName is displayed.

• On success, responds with UpdateTrialOutput with field(s):
  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<UpdateTrialError>

pub fn update_trial_component(&self) -> UpdateTrialComponent

Constructs a fluent builder for the UpdateTrialComponent operation.

• The fluent builder is configurable:
  • trial_component_name(impl Into<String>) / set_trial_component_name(Option<String>):

    The name of the component to update.

  • display_name(impl Into<String>) / set_display_name(Option<String>):

    The name of the component as displayed. The name doesn’t need to be unique. If DisplayName isn’t specified, TrialComponentName is displayed.

  • status(TrialComponentStatus) / set_status(Option<TrialComponentStatus>):

    The new status of the component.

  • start_time(DateTime) / set_start_time(Option<DateTime>):

    When the component started.

  • end_time(DateTime) / set_end_time(Option<DateTime>):

    When the component ended.

  • parameters(HashMap<String, TrialComponentParameterValue>) / set_parameters(Option<HashMap<String, TrialComponentParameterValue>>):

    Replaces all of the component’s hyperparameters with the specified hyperparameters.

  • parameters_to_remove(Vec<String>) / set_parameters_to_remove(Option<Vec<String>>):

    The hyperparameters to remove from the component.

  • input_artifacts(HashMap<String, TrialComponentArtifact>) / set_input_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    Replaces all of the component’s input artifacts with the specified artifacts.

  • input_artifacts_to_remove(Vec<String>) / set_input_artifacts_to_remove(Option<Vec<String>>):

    The input artifacts to remove from the component.

  • output_artifacts(HashMap<String, TrialComponentArtifact>) / set_output_artifacts(Option<HashMap<String, TrialComponentArtifact>>):

    Replaces all of the component’s output artifacts with the specified artifacts.

  • output_artifacts_to_remove(Vec<String>) / set_output_artifacts_to_remove(Option<Vec<String>>):

    The output artifacts to remove from the component.

• On success, responds with UpdateTrialComponentOutput with field(s):
  • trial_component_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial component.

• On failure, responds with SdkError<UpdateTrialComponentError>

pub fn update_user_profile(&self) -> UpdateUserProfile

Constructs a fluent builder for the UpdateUserProfile operation.

• The fluent builder is configurable:
  • domain_id(impl Into<String>) / set_domain_id(Option<String>):

    The domain ID.

  • user_profile_name(impl Into<String>) / set_user_profile_name(Option<String>):

    The user profile name.

  • user_settings(UserSettings) / set_user_settings(Option<UserSettings>):

    A collection of settings.

• On success, responds with UpdateUserProfileOutput with field(s):
  • user_profile_arn(Option<String>):

    The user profile Amazon Resource Name (ARN).

• On failure, responds with SdkError<UpdateUserProfileError>

pub fn update_workforce(&self) -> UpdateWorkforce

Constructs a fluent builder for the UpdateWorkforce operation.

• The fluent builder is configurable:
  • workforce_name(impl Into<String>) / set_workforce_name(Option<String>):

    The name of the private workforce that you want to update. You can find your workforce name by using the operation.

  • source_ip_config(SourceIpConfig) / set_source_ip_config(Option<SourceIpConfig>):

    A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce.

    Maximum: Ten CIDR values

  • oidc_config(OidcConfig) / set_oidc_config(Option<OidcConfig>):

    Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.

• On success, responds with UpdateWorkforceOutput with field(s):
  • workforce(Option<Workforce>):

    A single private workforce. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.

• On failure, responds with SdkError<UpdateWorkforceError>

pub fn update_workteam(&self) -> UpdateWorkteam

Constructs a fluent builder for the UpdateWorkteam operation.

• The fluent builder is configurable:
  • workteam_name(impl Into<String>) / set_workteam_name(Option<String>):

    The name of the work team to update.

  • member_definitions(Vec<MemberDefinition>) / set_member_definitions(Option<Vec<MemberDefinition>>):

    A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.

    Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. You should not provide input for both of these parameters in a single request.

    For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.

    For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.

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

    An updated description for the work team.

  • notification_configuration(NotificationConfiguration) / set_notification_configuration(Option<NotificationConfiguration>):

    Configures SNS topic notifications for available or expiring work items

• On success, responds with UpdateWorkteamOutput with field(s):
  • workteam(Option<Workteam>):

    A Workteam object that describes the updated work team.

• On failure, responds with SdkError<UpdateWorkteamError>

impl Client

pub fn from_conf_conn<C, E>(conf: Config, conn: C) -> Self where
    C: SmithyConnector<Error = E> + Send + 'static,
    E: Into<ConnectorError>, 

Creates a client with the given service config and connector override.

pub fn new(config: &Config) -> Self

Creates a new client from a shared config.

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Self

Returns a copy 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.

impl From<Client<DynConnector, DynMiddleware<DynConnector>, Standard>> for Client

fn from(client: Client<DynConnector, DynMiddleware<DynConnector>>) -> Self

Performs the conversion.