Struct 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.

Constructing a Client

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

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

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

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

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

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

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

Using the Client

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

let result = client.add_association()
    .source_arn("example")
    .send()
    .await;

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

Waiters

This client provides wait_until methods behind the Waiters trait. To use them, simply import the trait, and then call one of the wait_until methods. This will return a waiter fluent builder that takes various parameters, which are documented on the builder type. Once parameters have been provided, the wait method can be called to initiate waiting.

For example, if there was a wait_until_thing method, it could look like:

let result = client.wait_until_thing()
    .thing_id("someId")
    .wait(Duration::from_secs(120))
    .await;

Implementations

impl Client

pub fn add_association(&self) -> AddAssociationFluentBuilder

Constructs a fluent builder for the AddAssociation operation.

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

    The ARN of the source.

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

    The Amazon Resource Name (ARN) of the destination.

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

    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>

impl Client

pub fn add_tags(&self) -> AddTagsFluentBuilder

Constructs a fluent builder for the AddTags operation.

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

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

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

    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 SageMaker resource.

• On failure, responds with SdkError<AddTagsError>

impl Client

pub fn associate_trial_component(&self) -> AssociateTrialComponentFluentBuilder

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>):
    required: true
    

    The name of the component to associated with the trial.

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

    The name of the trial to associate with.

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

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

  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<AssociateTrialComponentError>

impl Client

pub fn batch_delete_cluster_nodes(&self) -> BatchDeleteClusterNodesFluentBuilder

Constructs a fluent builder for the BatchDeleteClusterNodes operation.

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

    The name of the SageMaker HyperPod cluster from which to delete the specified nodes.

    
    
  • node_ids(impl Into<String>) / set_node_ids(Option<Vec::<String>>):
    required: true
    

    A list of node IDs to be deleted from the specified cluster.

    • For SageMaker HyperPod clusters using the Slurm workload manager, you cannot remove instances that are configured as Slurm controller nodes.

    • If you need to delete more than 99 instances, contact Support for assistance.

    
    
• On success, responds with BatchDeleteClusterNodesOutput with field(s):
  • failed(Option<Vec::<BatchDeleteClusterNodesError>>):

    A list of errors encountered when deleting the specified nodes.

  • successful(Option<Vec::<String>>):

    A list of node IDs that were successfully deleted from the specified cluster.

• On failure, responds with SdkError<BatchDeleteClusterNodesError>

impl Client

pub fn batch_describe_model_package( &self, ) -> BatchDescribeModelPackageFluentBuilder

Constructs a fluent builder for the BatchDescribeModelPackage operation.

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

    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>

impl Client

pub fn create_action(&self) -> CreateActionFluentBuilder

Constructs a fluent builder for the CreateAction operation.

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

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

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

    The source type, ID, and URI.

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

    The action type.

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

    The description of the action.

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

    The status of the action.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    A list of properties to add to the action.

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

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

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

    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>

impl Client

pub fn create_algorithm(&self) -> CreateAlgorithmFluentBuilder

Constructs a fluent builder for the CreateAlgorithm operation.

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

    The name of the algorithm.

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

    A description of the algorithm.

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

    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>):
    required: false
    

    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>):
    required: false
    

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

    
    
  • certify_for_marketplace(bool) / set_certify_for_marketplace(Option<bool>):
    required: false
    

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

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

    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>

impl Client

pub fn create_app(&self) -> CreateAppFluentBuilder

Constructs a fluent builder for the CreateApp operation.

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

    The domain ID.

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

    The user profile name. If this value is not set, then SpaceName must be set.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: false
    

    The name of the space. If this value is not set, then UserProfileName must be set.

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

    The type of app.

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

    The name of the app.

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

    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>):
    required: false
    

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

    The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

    
    
  • recovery_mode(bool) / set_recovery_mode(Option<bool>):
    required: false
    

    Indicates whether the application is launched in recovery mode.

    
    
• 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>

impl Client

pub fn create_app_image_config(&self) -> CreateAppImageConfigFluentBuilder

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>):
    required: true
    

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

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

    A list of tags to apply to the AppImageConfig.

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

    The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

    
    
  • jupyter_lab_app_image_config(JupyterLabAppImageConfig) / set_jupyter_lab_app_image_config(Option<JupyterLabAppImageConfig>):
    required: false
    

    The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

    
    
  • code_editor_app_image_config(CodeEditorAppImageConfig) / set_code_editor_app_image_config(Option<CodeEditorAppImageConfig>):
    required: false
    

    The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.

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

    The ARN of the AppImageConfig.

• On failure, responds with SdkError<CreateAppImageConfigError>

impl Client

pub fn create_artifact(&self) -> CreateArtifactFluentBuilder

Constructs a fluent builder for the CreateArtifact operation.

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

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

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

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

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

    The artifact type.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    A list of properties to add to the artifact.

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

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

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

    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>

impl Client

pub fn create_auto_ml_job(&self) -> CreateAutoMLJobFluentBuilder

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>):
    required: true
    

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

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

    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 HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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

    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>):
    required: false
    

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

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

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

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

    A collection of settings used to configure an AutoML job.

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

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

    
    
  • generate_candidate_definitions_only(bool) / set_generate_candidate_definitions_only(Option<bool>):
    required: false
    

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

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

    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 ServicesResources. Tag keys must be unique per resource.

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

    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>

impl Client

pub fn create_auto_ml_job_v2(&self) -> CreateAutoMLJobV2FluentBuilder

Constructs a fluent builder for the CreateAutoMLJobV2 operation.

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

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

    
    
  • auto_ml_job_input_data_config(AutoMlJobChannel) / set_auto_ml_job_input_data_config(Option<Vec::<AutoMlJobChannel>>):
    required: true
    

    An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

    • For tabular problem types: S3Prefix, ManifestFile.

    • For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

    • For text classification: S3Prefix.

    • For time-series forecasting: S3Prefix.

    • For text generation (LLMs fine-tuning): S3Prefix.

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

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

    
    
  • auto_ml_problem_type_config(AutoMlProblemTypeConfig) / set_auto_ml_problem_type_config(Option<AutoMlProblemTypeConfig>):
    required: true
    

    Defines the configuration settings of one of the supported problem types.

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

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

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

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    
    
  • security_config(AutoMlSecurityConfig) / set_security_config(Option<AutoMlSecurityConfig>):
    required: false
    

    The security configuration for traffic encryption or Amazon VPC settings.

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

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

    • For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

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

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

    
    
  • data_split_config(AutoMlDataSplitConfig) / set_data_split_config(Option<AutoMlDataSplitConfig>):
    required: false
    

    This structure specifies how to split the data into train and validation datasets.

    The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

    This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

    
    
  • auto_ml_compute_config(AutoMlComputeConfig) / set_auto_ml_compute_config(Option<AutoMlComputeConfig>):
    required: false
    

    Specifies the compute configuration for the AutoML job V2.

    
    
• On success, responds with CreateAutoMlJobV2Output with field(s):
  • auto_ml_job_arn(Option<String>):

    The unique ARN assigned to the AutoMLJob when it is created.

• On failure, responds with SdkError<CreateAutoMLJobV2Error>

impl Client

pub fn create_cluster(&self) -> CreateClusterFluentBuilder

Constructs a fluent builder for the CreateCluster operation.

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

    The name for the new SageMaker HyperPod cluster.

    
    
  • instance_groups(ClusterInstanceGroupSpecification) / set_instance_groups(Option<Vec::<ClusterInstanceGroupSpecification>>):
    required: true
    

    The instance groups to be created in the SageMaker HyperPod cluster.

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

    Specifies the Amazon Virtual Private Cloud (VPC) that is associated with the Amazon SageMaker HyperPod cluster. You can control access to and from your resources by configuring your VPC. For more information, see Give SageMaker access to resources in your Amazon VPC.

    When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform:

    • Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications.

    • In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment.

    Additional resources for IPv6 configuration:

    • For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC.

    • For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide.

    • To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod.

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

    Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.

    
    
  • orchestrator(ClusterOrchestrator) / set_orchestrator(Option<ClusterOrchestrator>):
    required: false
    

    The type of orchestrator to use for the SageMaker HyperPod cluster. Currently, the only supported value is “eks”, which is to use an Amazon Elastic Kubernetes Service (EKS) cluster as the orchestrator.

    
    
  • node_recovery(ClusterNodeRecovery) / set_node_recovery(Option<ClusterNodeRecovery>):
    required: false
    

    The node recovery mode for the SageMaker HyperPod cluster. When set to Automatic, SageMaker HyperPod will automatically reboot or replace faulty nodes when issues are detected. When set to None, cluster administrators will need to manually manage any faulty cluster instances.

    
    
• On success, responds with CreateClusterOutput with field(s):
  • cluster_arn(Option<String>):

    The Amazon Resource Name (ARN) of the cluster.

• On failure, responds with SdkError<CreateClusterError>

impl Client

pub fn create_cluster_scheduler_config( &self, ) -> CreateClusterSchedulerConfigFluentBuilder

Constructs a fluent builder for the CreateClusterSchedulerConfig operation.

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

    Name for the cluster policy.

    
    
  • cluster_arn(impl Into<String>) / set_cluster_arn(Option<String>):
    required: true
    

    ARN of the cluster.

    
    
  • scheduler_config(SchedulerConfig) / set_scheduler_config(Option<SchedulerConfig>):
    required: true
    

    Configuration about the monitoring schedule.

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

    Description of the cluster policy.

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

    Tags of the cluster policy.

    
    
• On success, responds with CreateClusterSchedulerConfigOutput with field(s):
  • cluster_scheduler_config_arn(Option<String>):

    ARN of the cluster policy.

  • cluster_scheduler_config_id(Option<String>):

    ID of the cluster policy.

• On failure, responds with SdkError<CreateClusterSchedulerConfigError>

impl Client

pub fn create_code_repository(&self) -> CreateCodeRepositoryFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_compilation_job(&self) -> CreateCompilationJobFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

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

    During model compilation, Amazon SageMaker AI 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 AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

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

    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>):
    required: false
    

    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>):
    required: true
    

    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>):
    required: false
    

    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>):
    required: true
    

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

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

    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 AI returns the following data in JSON format:

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

• On failure, responds with SdkError<CreateCompilationJobError>

impl Client

pub fn create_compute_quota(&self) -> CreateComputeQuotaFluentBuilder

Constructs a fluent builder for the CreateComputeQuota operation.

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

    Name to the compute allocation definition.

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

    Description of the compute allocation definition.

    
    
  • cluster_arn(impl Into<String>) / set_cluster_arn(Option<String>):
    required: true
    

    ARN of the cluster.

    
    
  • compute_quota_config(ComputeQuotaConfig) / set_compute_quota_config(Option<ComputeQuotaConfig>):
    required: true
    

    Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

    
    
  • compute_quota_target(ComputeQuotaTarget) / set_compute_quota_target(Option<ComputeQuotaTarget>):
    required: true
    

    The target entity to allocate compute resources to.

    
    
  • activation_state(ActivationState) / set_activation_state(Option<ActivationState>):
    required: false
    

    The state of the compute allocation being described. Use to enable or disable compute allocation.

    Default is Enabled.

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

    Tags of the compute allocation definition.

    
    
• On success, responds with CreateComputeQuotaOutput with field(s):
  • compute_quota_arn(Option<String>):

    ARN of the compute allocation definition.

  • compute_quota_id(Option<String>):

    ID of the compute allocation definition.

• On failure, responds with SdkError<CreateComputeQuotaError>

impl Client

pub fn create_context(&self) -> CreateContextFluentBuilder

Constructs a fluent builder for the CreateContext operation.

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

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

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

    The source type, ID, and URI.

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

    The context type.

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

    The description of the context.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    A list of properties to add to the context.

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

    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>

impl Client

pub fn create_data_quality_job_definition( &self, ) -> CreateDataQualityJobDefinitionFluentBuilder

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>):
    required: true
    

    The name for the monitoring job definition.

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

    Configures the constraints and baselines for the monitoring job.

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

    Specifies the container that runs the monitoring job.

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

    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>):
    required: true
    

    The output configuration for monitoring jobs.

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

    Identifies the resources to deploy for a monitoring job.

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

    Specifies networking configuration for the monitoring job.

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

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

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

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

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

    (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>

impl Client

pub fn create_device_fleet(&self) -> CreateDeviceFleetFluentBuilder

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>):
    required: true
    

    The name of the fleet that the device belongs to.

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

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

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

    A description of the fleet.

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

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

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

    Creates tags for the specified fleet.

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

    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>

impl Client

pub fn create_domain(&self) -> CreateDomainFluentBuilder

Constructs a fluent builder for the CreateDomain operation.

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

    A name for the domain.

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

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

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

    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.

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

    A collection of Domain settings.

    
    
  • subnet_ids(impl Into<String>) / set_subnet_ids(Option<Vec::<String>>):
    required: true
    

    The VPC subnets that the domain uses for communication.

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

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

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

    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>):
    required: false
    

    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 AI, which allows direct internet access

    • VpcOnly - All 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>):
    required: false
    

    Use KmsKeyId.

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

    SageMaker AI uses Amazon Web Services KMS to encrypt EFS and EBS volumes 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>):
    required: false
    

    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. If setting up the domain for use with RStudio, this value must be set to Service.

    
    
  • tag_propagation(TagPropagation) / set_tag_propagation(Option<TagPropagation>):
    required: false
    

    Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.

    
    
  • default_space_settings(DefaultSpaceSettings) / set_default_space_settings(Option<DefaultSpaceSettings>):
    required: false
    

    The default settings for shared spaces that users create in the domain.

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

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

  • domain_id(Option<String>):

    The ID of the created domain.

  • url(Option<String>):

    The URL to the created domain.

• On failure, responds with SdkError<CreateDomainError>

impl Client

pub fn create_edge_deployment_plan( &self, ) -> CreateEdgeDeploymentPlanFluentBuilder

Constructs a fluent builder for the CreateEdgeDeploymentPlan operation.

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

    The name of the edge deployment plan.

    
    
  • model_configs(EdgeDeploymentModelConfig) / set_model_configs(Option<Vec::<EdgeDeploymentModelConfig>>):
    required: true
    

    List of models associated with the edge deployment plan.

    
    
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):
    required: true
    

    The device fleet used for this edge deployment plan.

    
    
  • stages(DeploymentStage) / set_stages(Option<Vec::<DeploymentStage>>):
    required: false
    

    List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.

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

    List of tags with which to tag the edge deployment plan.

    
    
• On success, responds with CreateEdgeDeploymentPlanOutput with field(s):
  • edge_deployment_plan_arn(Option<String>):

    The ARN of the edge deployment plan.

• On failure, responds with SdkError<CreateEdgeDeploymentPlanError>

impl Client

pub fn create_edge_deployment_stage( &self, ) -> CreateEdgeDeploymentStageFluentBuilder

Constructs a fluent builder for the CreateEdgeDeploymentStage operation.

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

    The name of the edge deployment plan.

    
    
  • stages(DeploymentStage) / set_stages(Option<Vec::<DeploymentStage>>):
    required: true
    

    List of stages to be added to the edge deployment plan.

    
    
• On success, responds with CreateEdgeDeploymentStageOutput
• On failure, responds with SdkError<CreateEdgeDeploymentStageError>

impl Client

pub fn create_edge_packaging_job(&self) -> CreateEdgePackagingJobFluentBuilder

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>):
    required: true
    

    The name of the edge packaging job.

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

    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>):
    required: true
    

    The name of the model.

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

    The version of the model.

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

    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>):
    required: true
    

    Provides information about the output location for the packaged model.

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

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

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

    Creates tags for the packaging job.

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

impl Client

pub fn create_endpoint(&self) -> CreateEndpointFluentBuilder

Constructs a fluent builder for the CreateEndpoint operation.

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

    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 InvokeEndpoint.

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

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

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

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

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

    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>

impl Client

pub fn create_endpoint_config(&self) -> CreateEndpointConfigFluentBuilder

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>):
    required: true
    

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

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

    An array 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>):
    required: false
    

    Configuration to control how SageMaker AI captures inference data.

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

    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>):
    required: false
    

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that 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>):
    required: false
    

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

    
    
  • explainer_config(ExplainerConfig) / set_explainer_config(Option<ExplainerConfig>):
    required: false
    

    A member of CreateEndpointConfig that enables explainers.

    
    
  • shadow_production_variants(ProductionVariant) / set_shadow_production_variants(Option<Vec::<ProductionVariant>>):
    required: false
    

    An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants. If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants.

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

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform actions on your behalf. For more information, see SageMaker AI Roles.

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

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

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

    
    
  • enable_network_isolation(bool) / set_enable_network_isolation(Option<bool>):
    required: false
    

    Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

    
    
• 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>

impl Client

pub fn create_experiment(&self) -> CreateExperimentFluentBuilder

Constructs a fluent builder for the CreateExperiment operation.

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

    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>):
    required: false
    

    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>):
    required: false
    

    The description of the experiment.

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

    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>

impl Client

pub fn create_feature_group(&self) -> CreateFeatureGroupFluentBuilder

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>):
    required: true
    

    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 with an alphanumeric character.

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

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

    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 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>):
    required: true
    

    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(FeatureDefinition) / set_feature_definitions(Option<Vec::<FeatureDefinition>>):
    required: true
    

    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>):
    required: false
    

    You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig.

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

    The default value is False.

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

    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.

    • Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.

    To learn more about this parameter, see OfflineStoreConfig.

    
    
  • throughput_config(ThroughputConfig) / set_throughput_config(Option<ThroughputConfig>):
    required: false
    

    Used to set feature group throughput configuration. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.

    Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.

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

    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>):
    required: false
    

    A free-form description of a FeatureGroup.

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

    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>

impl Client

pub fn create_flow_definition(&self) -> CreateFlowDefinitionFluentBuilder

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>):
    required: true
    

    The name of your flow definition.

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

    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>):
    required: false
    

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

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

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

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

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

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

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_hub(&self) -> CreateHubFluentBuilder

Constructs a fluent builder for the CreateHub operation.

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

    The name of the hub to create.

    
    
  • hub_description(impl Into<String>) / set_hub_description(Option<String>):
    required: true
    

    A description of the hub.

    
    
  • hub_display_name(impl Into<String>) / set_hub_display_name(Option<String>):
    required: false
    

    The display name of the hub.

    
    
  • hub_search_keywords(impl Into<String>) / set_hub_search_keywords(Option<Vec::<String>>):
    required: false
    

    The searchable keywords for the hub.

    
    
  • s3_storage_config(HubS3StorageConfig) / set_s3_storage_config(Option<HubS3StorageConfig>):
    required: false
    

    The Amazon S3 storage configuration for the hub.

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

    Any tags to associate with the hub.

    
    
• On success, responds with CreateHubOutput with field(s):
  • hub_arn(Option<String>):

    The Amazon Resource Name (ARN) of the hub.

• On failure, responds with SdkError<CreateHubError>

impl Client

pub fn create_hub_content_reference( &self, ) -> CreateHubContentReferenceFluentBuilder

Constructs a fluent builder for the CreateHubContentReference operation.

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

    The name of the hub to add the hub content reference to.

    
    
  • sage_maker_public_hub_content_arn(impl Into<String>) / set_sage_maker_public_hub_content_arn(Option<String>):
    required: true
    

    The ARN of the public hub content to reference.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: false
    

    The name of the hub content to reference.

    
    
  • min_version(impl Into<String>) / set_min_version(Option<String>):
    required: false
    

    The minimum version of the hub content to reference.

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

    Any tags associated with the hub content to reference.

    
    
• On success, responds with CreateHubContentReferenceOutput with field(s):
  • hub_arn(Option<String>):

    The ARN of the hub that the hub content reference was added to.

  • hub_content_arn(Option<String>):

    The ARN of the hub content.

• On failure, responds with SdkError<CreateHubContentReferenceError>

impl Client

pub fn create_human_task_ui(&self) -> CreateHumanTaskUiFluentBuilder

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>):
    required: true
    

    The name of the user interface you are creating.

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

    The Liquid template for the worker user interface.

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

    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>

impl Client

pub fn create_hyper_parameter_tuning_job( &self, ) -> CreateHyperParameterTuningJobFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: false
    

    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(HyperParameterTrainingJobDefinition) / set_training_job_definitions(Option<Vec::<HyperParameterTrainingJobDefinition>>):
    required: false
    

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

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

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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.

    
    
  • autotune(Autotune) / set_autotune(Option<Autotune>):
    required: false
    

    Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

    • ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

    • ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

    • TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

    • RetryStrategy: The number of times to retry a training job.

    • Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

    • ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

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

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

• On failure, responds with SdkError<CreateHyperParameterTuningJobError>

impl Client

pub fn create_image(&self) -> CreateImageFluentBuilder

Constructs a fluent builder for the CreateImage operation.

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

    The description of the image.

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

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

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

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

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

    The ARN of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

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

    A list of tags to apply to the image.

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

    The ARN of the image.

• On failure, responds with SdkError<CreateImageError>

impl Client

pub fn create_image_version(&self) -> CreateImageVersionFluentBuilder

Constructs a fluent builder for the CreateImageVersion operation.

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

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

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

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

    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>):
    required: true
    

    The ImageName of the Image to create a version of.

    
    
  • aliases(impl Into<String>) / set_aliases(Option<Vec::<String>>):
    required: false
    

    A list of aliases created with the image version.

    
    
  • vendor_guidance(VendorGuidance) / set_vendor_guidance(Option<VendorGuidance>):
    required: false
    

    The stability of the image version, specified by the maintainer.

    • NOT_PROVIDED: The maintainers did not provide a status for image version stability.

    • STABLE: The image version is stable.

    • TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.

    • ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

    
    
  • job_type(JobType) / set_job_type(Option<JobType>):
    required: false
    

    Indicates SageMaker AI job type compatibility.

    • TRAINING: The image version is compatible with SageMaker AI training jobs.

    • INFERENCE: The image version is compatible with SageMaker AI inference jobs.

    • NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.

    
    
  • ml_framework(impl Into<String>) / set_ml_framework(Option<String>):
    required: false
    

    The machine learning framework vended in the image version.

    
    
  • programming_lang(impl Into<String>) / set_programming_lang(Option<String>):
    required: false
    

    The supported programming language and its version.

    
    
  • processor(Processor) / set_processor(Option<Processor>):
    required: false
    

    Indicates CPU or GPU compatibility.

    • CPU: The image version is compatible with CPU.

    • GPU: The image version is compatible with GPU.

    
    
  • horovod(bool) / set_horovod(Option<bool>):
    required: false
    

    Indicates Horovod compatibility.

    
    
  • release_notes(impl Into<String>) / set_release_notes(Option<String>):
    required: false
    

    The maintainer description of the image version.

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

    The ARN of the image version.

• On failure, responds with SdkError<CreateImageVersionError>

impl Client

pub fn create_inference_component( &self, ) -> CreateInferenceComponentFluentBuilder

Constructs a fluent builder for the CreateInferenceComponent operation.

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

    A unique name to assign to the inference component.

    
    
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):
    required: true
    

    The name of an existing endpoint where you host the inference component.

    
    
  • variant_name(impl Into<String>) / set_variant_name(Option<String>):
    required: false
    

    The name of an existing production variant where you host the inference component.

    
    
  • specification(InferenceComponentSpecification) / set_specification(Option<InferenceComponentSpecification>):
    required: true
    

    Details about the resources to deploy with this inference component, including the model, container, and compute resources.

    
    
  • runtime_config(InferenceComponentRuntimeConfig) / set_runtime_config(Option<InferenceComponentRuntimeConfig>):
    required: false
    

    Runtime settings for a model that is deployed with an inference component.

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

    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.

    
    
• On success, responds with CreateInferenceComponentOutput with field(s):
  • inference_component_arn(Option<String>):

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

• On failure, responds with SdkError<CreateInferenceComponentError>

impl Client

pub fn create_inference_experiment( &self, ) -> CreateInferenceExperimentFluentBuilder

Constructs a fluent builder for the CreateInferenceExperiment operation.

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

    The name for the inference experiment.

    
    
  • r#type(InferenceExperimentType) / set_type(Option<InferenceExperimentType>):
    required: true
    

    The type of the inference experiment that you want to run. The following types of experiments are possible:

    • ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.

    
    
  • schedule(InferenceExperimentSchedule) / set_schedule(Option<InferenceExperimentSchedule>):
    required: false
    

    The duration for which you want the inference experiment to run. If you don’t specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.

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

    A description for the inference experiment.

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

    The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

    
    
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):
    required: true
    

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

    
    
  • model_variants(ModelVariantConfig) / set_model_variants(Option<Vec::<ModelVariantConfig>>):
    required: true
    

    An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.

    
    
  • data_storage_config(InferenceExperimentDataStorageConfig) / set_data_storage_config(Option<InferenceExperimentDataStorageConfig>):
    required: false
    

    The Amazon S3 location and configuration for storing inference request and response data.

    This is an optional parameter that you can use for data capture. For more information, see Capture data.

    
    
  • shadow_mode_config(ShadowModeConfig) / set_shadow_mode_config(Option<ShadowModeConfig>):
    required: true
    

    The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

    
    
  • kms_key(impl Into<String>) / set_kms_key(Option<String>):
    required: false
    

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:

    • KMS key ID

      “1234abcd-12ab-34cd-56ef-1234567890ab”

    • Amazon Resource Name (ARN) of a KMS key

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

    • KMS key Alias

      “alias/ExampleAlias”

    • Amazon Resource Name (ARN) of a KMS key Alias

      “arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias”

    If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to “aws:kms”. For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

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

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

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

    
    
• On success, responds with CreateInferenceExperimentOutput with field(s):
  • inference_experiment_arn(Option<String>):

    The ARN for your inference experiment.

• On failure, responds with SdkError<CreateInferenceExperimentError>

impl Client

pub fn create_inference_recommendations_job( &self, ) -> CreateInferenceRecommendationsJobFluentBuilder

Constructs a fluent builder for the CreateInferenceRecommendationsJob operation.

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

    A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.

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

    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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: false
    

    Description of the recommendation job.

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

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

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

    Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.

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

    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>

impl Client

pub fn create_labeling_job(&self) -> CreateLabelingJobFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: false
    

    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”:“<h1>Add header</h1><p>Add Instructions</p>”, “fullInstruction”:“<p>Add additional instructions.</p>”}. 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>):
    required: false
    

    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>):
    required: false
    

    Configures the information required to perform automated data labeling.

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

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

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

    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>

impl Client

pub fn create_mlflow_tracking_server( &self, ) -> CreateMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the CreateMlflowTrackingServer operation.

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

    A unique string identifying the tracking server name. This string is part of the tracking server ARN.

    
    
  • artifact_store_uri(impl Into<String>) / set_artifact_store_uri(Option<String>):
    required: true
    

    The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

    
    
  • tracking_server_size(TrackingServerSize) / set_tracking_server_size(Option<TrackingServerSize>):
    required: false
    

    The size of the tracking server you want to create. You can choose between “Small”, “Medium”, and “Large”. The default MLflow Tracking Server configuration size is “Small”. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

    We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

    
    
  • mlflow_version(impl Into<String>) / set_mlflow_version(Option<String>):
    required: false
    

    The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.

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

    The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.

    
    
  • automatic_model_registration(bool) / set_automatic_model_registration(Option<bool>):
    required: false
    

    Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.

    
    
  • weekly_maintenance_window_start(impl Into<String>) / set_weekly_maintenance_window_start(Option<String>):
    required: false
    

    The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

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

    Tags consisting of key-value pairs used to manage metadata for the tracking server.

    
    
• On success, responds with CreateMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    The ARN of the tracking server.

• On failure, responds with SdkError<CreateMlflowTrackingServerError>

impl Client

pub fn create_model(&self) -> CreateModelFluentBuilder

Constructs a fluent builder for the CreateModel operation.

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

    The name of the new model.

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

    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(ContainerDefinition) / set_containers(Option<Vec::<ContainerDefinition>>):
    required: false
    

    Specifies the containers in the inference pipeline.

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

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

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

    The Amazon Resource Name (ARN) of the IAM role that 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 SageMaker Roles.

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

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

    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>):
    required: false
    

    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(Option<bool>):
    required: false
    

    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 SageMaker.

• On failure, responds with SdkError<CreateModelError>

impl Client

pub fn create_model_bias_job_definition( &self, ) -> CreateModelBiasJobDefinitionFluentBuilder

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>):
    required: true
    

    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>):
    required: false
    

    The baseline configuration for a model bias job.

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

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

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

    Inputs for the model bias job.

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

    The output configuration for monitoring jobs.

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

    Identifies the resources to deploy for a monitoring job.

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

    Networking options for a model bias job.

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

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

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

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

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

    (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>

impl Client

pub fn create_model_card(&self) -> CreateModelCardFluentBuilder

Constructs a fluent builder for the CreateModelCard operation.

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

    The unique name of the model card.

    
    
  • security_config(ModelCardSecurityConfig) / set_security_config(Option<ModelCardSecurityConfig>):
    required: false
    

    An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.

    
    
  • content(impl Into<String>) / set_content(Option<String>):
    required: true
    

    The content of the model card. Content must be in model card JSON schema and provided as a string.

    
    
  • model_card_status(ModelCardStatus) / set_model_card_status(Option<ModelCardStatus>):
    required: true
    

    The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.

    • Draft: The model card is a work in progress.

    • PendingReview: The model card is pending review.

    • Approved: The model card is approved.

    • Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

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

    Key-value pairs used to manage metadata for model cards.

    
    
• On success, responds with CreateModelCardOutput with field(s):
  • model_card_arn(Option<String>):

    The Amazon Resource Name (ARN) of the successfully created model card.

• On failure, responds with SdkError<CreateModelCardError>

impl Client

pub fn create_model_card_export_job( &self, ) -> CreateModelCardExportJobFluentBuilder

Constructs a fluent builder for the CreateModelCardExportJob operation.

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

    The name or Amazon Resource Name (ARN) of the model card to export.

    
    
  • model_card_version(i32) / set_model_card_version(Option<i32>):
    required: false
    

    The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.

    
    
  • model_card_export_job_name(impl Into<String>) / set_model_card_export_job_name(Option<String>):
    required: true
    

    The name of the model card export job.

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

    The model card output configuration that specifies the Amazon S3 path for exporting.

    
    
• On success, responds with CreateModelCardExportJobOutput with field(s):
  • model_card_export_job_arn(Option<String>):

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

• On failure, responds with SdkError<CreateModelCardExportJobError>

impl Client

pub fn create_model_explainability_job_definition( &self, ) -> CreateModelExplainabilityJobDefinitionFluentBuilder

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>):
    required: true
    

    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>):
    required: false
    

    The baseline configuration for a model explainability job.

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

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

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

    Inputs for the model explainability job.

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

    The output configuration for monitoring jobs.

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

    Identifies the resources to deploy for a monitoring job.

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

    Networking options for a model explainability job.

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

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

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

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

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

    (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>

impl Client

pub fn create_model_package(&self) -> CreateModelPackageFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A description of the model package.

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

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

    • 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>):
    required: false
    

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

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

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

    
    
  • certify_for_marketplace(bool) / set_certify_for_marketplace(Option<bool>):
    required: false
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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.

    If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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

    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>):
    required: false
    

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

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

    A structure that contains model metrics reports.

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

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

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

    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>):
    required: false
    

    The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: “IMAGE_CLASSIFICATION” | “OBJECT_DETECTION” | “TEXT_GENERATION” |“IMAGE_SEGMENTATION” | “FILL_MASK” | “CLASSIFICATION” | “REGRESSION” | “OTHER”.

    Specify “OTHER” if none of the tasks listed fit your use case.

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

    The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

    
    
  • customer_metadata_properties(impl Into<String>, impl Into<String>) / set_customer_metadata_properties(Option<HashMap::<String, String>>):
    required: false
    

    The metadata properties associated with the model package versions.

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

    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.

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

    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.

    
    
  • skip_model_validation(SkipModelValidation) / set_skip_model_validation(Option<SkipModelValidation>):
    required: false
    

    Indicates if you want to skip model validation.

    
    
  • source_uri(impl Into<String>) / set_source_uri(Option<String>):
    required: false
    

    The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

    
    
  • security_config(ModelPackageSecurityConfig) / set_security_config(Option<ModelPackageSecurityConfig>):
    required: false
    

    The KMS Key ID (KMSKeyId) used for encryption of model package information.

    
    
  • model_card(ModelPackageModelCard) / set_model_card(Option<ModelPackageModelCard>):
    required: false
    

    The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

    
    
  • model_life_cycle(ModelLifeCycle) / set_model_life_cycle(Option<ModelLifeCycle>):
    required: false
    

    A structure describing the current state of the model in its life cycle.

    
    
• 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>

impl Client

pub fn create_model_package_group(&self) -> CreateModelPackageGroupFluentBuilder

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>):
    required: true
    

    The name of the model group.

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

    A description for the model group.

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

    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>

impl Client

pub fn create_model_quality_job_definition( &self, ) -> CreateModelQualityJobDefinitionFluentBuilder

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>):
    required: true
    

    The name of the monitoring job definition.

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

    Specifies the constraints and baselines for the monitoring job.

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

    The container that runs the monitoring job.

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

    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>):
    required: true
    

    The output configuration for monitoring jobs.

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

    Identifies the resources to deploy for a monitoring job.

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

    Specifies the network configuration for the monitoring job.

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

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

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

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

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

    (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>

impl Client

pub fn create_monitoring_schedule( &self, ) -> CreateMonitoringScheduleFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

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

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

    (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>

impl Client

pub fn create_notebook_instance(&self) -> CreateNotebookInstanceFluentBuilder

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>):
    required: true
    

    The name of the new notebook instance.

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

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

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

    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(impl Into<String>) / set_security_group_ids(Option<Vec::<String>>):
    required: false
    

    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>):
    required: true
    

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

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

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

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker AI 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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    Sets whether SageMaker AI 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 SageMaker AI 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>):
    required: false
    

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

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

    This parameter is no longer supported. Elastic Inference (EI) is no longer available.

    This parameter was used to specify a list of EI instance types to associate with this notebook instance.

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

    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 SageMaker AI Notebook Instances.

    
    
  • additional_code_repositories(impl Into<String>) / set_additional_code_repositories(Option<Vec::<String>>):
    required: false
    

    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 SageMaker AI Notebook Instances.

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

    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>):
    required: false
    

    The platform identifier of the notebook instance runtime environment.

    
    
  • instance_metadata_service_configuration(InstanceMetadataServiceConfiguration) / set_instance_metadata_service_configuration(Option<InstanceMetadataServiceConfiguration>):
    required: false
    

    Information on the IMDS configuration of the notebook instance

    
    
• 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>

impl Client

pub fn create_notebook_instance_lifecycle_config( &self, ) -> CreateNotebookInstanceLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the lifecycle configuration.

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

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

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

    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.

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

    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 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>

impl Client

pub fn create_optimization_job(&self) -> CreateOptimizationJobFluentBuilder

Constructs a fluent builder for the CreateOptimizationJob operation.

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

    A custom name for the new optimization job.

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

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

    During model optimization, Amazon SageMaker AI 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 AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

    
    
  • model_source(OptimizationJobModelSource) / set_model_source(Option<OptimizationJobModelSource>):
    required: true
    

    The location of the source model to optimize with an optimization job.

    
    
  • deployment_instance_type(OptimizationJobDeploymentInstanceType) / set_deployment_instance_type(Option<OptimizationJobDeploymentInstanceType>):
    required: true
    

    The type of instance that hosts the optimized model that you create with the optimization job.

    
    
  • optimization_environment(impl Into<String>, impl Into<String>) / set_optimization_environment(Option<HashMap::<String, String>>):
    required: false
    

    The environment variables to set in the model container.

    
    
  • optimization_configs(OptimizationConfig) / set_optimization_configs(Option<Vec::<OptimizationConfig>>):
    required: true
    

    Settings for each of the optimization techniques that the job applies.

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

    Details for where to store the optimized model that you create with the optimization job.

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

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

    To stop a training job, 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.

    The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

    The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

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

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

    
    
  • vpc_config(OptimizationVpcConfig) / set_vpc_config(Option<OptimizationVpcConfig>):
    required: false
    

    A VPC in Amazon VPC that your optimized model has access to.

    
    
• On success, responds with CreateOptimizationJobOutput with field(s):
  • optimization_job_arn(Option<String>):

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

• On failure, responds with SdkError<CreateOptimizationJobError>

impl Client

pub fn create_partner_app(&self) -> CreatePartnerAppFluentBuilder

Constructs a fluent builder for the CreatePartnerApp operation.

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

    The name to give the SageMaker Partner AI App.

    
    
  • r#type(PartnerAppType) / set_type(Option<PartnerAppType>):
    required: true
    

    The type of SageMaker Partner AI App to create. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.

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

    The ARN of the IAM role that the partner application uses.

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

    SageMaker Partner AI Apps uses Amazon Web Services KMS to encrypt data at rest using an Amazon Web Services managed key by default. For more control, specify a customer managed key.

    
    
  • maintenance_config(PartnerAppMaintenanceConfig) / set_maintenance_config(Option<PartnerAppMaintenanceConfig>):
    required: false
    

    Maintenance configuration settings for the SageMaker Partner AI App.

    
    
  • tier(impl Into<String>) / set_tier(Option<String>):
    required: true
    

    Indicates the instance type and size of the cluster attached to the SageMaker Partner AI App.

    
    
  • application_config(PartnerAppConfig) / set_application_config(Option<PartnerAppConfig>):
    required: false
    

    Configuration settings for the SageMaker Partner AI App.

    
    
  • auth_type(PartnerAppAuthType) / set_auth_type(Option<PartnerAppAuthType>):
    required: true
    

    The authorization type that users use to access the SageMaker Partner AI App.

    
    
  • enable_iam_session_based_identity(bool) / set_enable_iam_session_based_identity(Option<bool>):
    required: false
    

    When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.

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

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

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

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

    
    
• On success, responds with CreatePartnerAppOutput with field(s):
  • arn(Option<String>):

    The ARN of the SageMaker Partner AI App.

• On failure, responds with SdkError<CreatePartnerAppError>

impl Client

pub fn create_partner_app_presigned_url( &self, ) -> CreatePartnerAppPresignedUrlFluentBuilder

Constructs a fluent builder for the CreatePartnerAppPresignedUrl operation.

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

    The ARN of the SageMaker Partner AI App to create the presigned URL for.

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

    The time that will pass before the presigned URL expires.

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

    Indicates how long the Amazon SageMaker Partner AI App session can be accessed for after logging in.

    
    
• On success, responds with CreatePartnerAppPresignedUrlOutput with field(s):
  • url(Option<String>):

    The presigned URL that you can use to access the SageMaker Partner AI App.

• On failure, responds with SdkError<CreatePartnerAppPresignedUrlError>

impl Client

pub fn create_pipeline(&self) -> CreatePipelineFluentBuilder

Constructs a fluent builder for the CreatePipeline operation.

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

    The name of the pipeline.

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

    The display name of the pipeline.

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

    The JSON pipeline definition of the pipeline.

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

    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>):
    required: false
    

    A description of the pipeline.

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

    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>):
    required: true
    

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

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

    A list of tags to apply to the created pipeline.

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

    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>

impl Client

pub fn create_presigned_domain_url( &self, ) -> CreatePresignedDomainUrlFluentBuilder

Constructs a fluent builder for the CreatePresignedDomainUrl operation.

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

    The domain ID.

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

    The name of the UserProfile to sign-in as.

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

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

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

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

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: false
    

    The name of the space.

    
    
  • landing_uri(impl Into<String>) / set_landing_uri(Option<String>):
    required: false
    

    The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:

    • studio::relative/path: Directs users to the relative path in Studio.

    • app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application.

    • app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application.

    • app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application.

    • app:CodeEditor:relative/path: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application.

    • app:Canvas:relative/path: Directs users to the relative path in the Canvas application.

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

    The presigned URL.

• On failure, responds with SdkError<CreatePresignedDomainUrlError>

impl Client

pub fn create_presigned_mlflow_tracking_server_url( &self, ) -> CreatePresignedMlflowTrackingServerUrlFluentBuilder

Constructs a fluent builder for the CreatePresignedMlflowTrackingServerUrl operation.

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

    The name of the tracking server to connect to your MLflow UI.

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

    The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.

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

    The duration in seconds that your MLflow UI session is valid.

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

    A presigned URL with an authorization token.

• On failure, responds with SdkError<CreatePresignedMlflowTrackingServerUrlError>

impl Client

pub fn create_presigned_notebook_instance_url( &self, ) -> CreatePresignedNotebookInstanceUrlFluentBuilder

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>):
    required: true
    

    The name of the notebook instance.

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

    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>

impl Client

pub fn create_processing_job(&self) -> CreateProcessingJobFluentBuilder

Constructs a fluent builder for the CreateProcessingJob operation.

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

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

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

    Output configuration for the processing job.

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

    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>):
    required: true
    

    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>):
    required: false
    

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

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

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

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

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

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

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

    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>):
    required: true
    

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

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

    (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.

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request tag variable or plain text fields.

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

    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>

impl Client

pub fn create_project(&self) -> CreateProjectFluentBuilder

Constructs a fluent builder for the CreateProject operation.

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

    The name of the project.

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

    A description for the project.

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

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_space(&self) -> CreateSpaceFluentBuilder

Constructs a fluent builder for the CreateSpace operation.

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

    The ID of the associated domain.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: true
    

    The name of the space.

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

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

    
    
  • space_settings(SpaceSettings) / set_space_settings(Option<SpaceSettings>):
    required: false
    

    A collection of space settings.

    
    
  • ownership_settings(OwnershipSettings) / set_ownership_settings(Option<OwnershipSettings>):
    required: false
    

    A collection of ownership settings.

    
    
  • space_sharing_settings(SpaceSharingSettings) / set_space_sharing_settings(Option<SpaceSharingSettings>):
    required: false
    

    A collection of space sharing settings.

    
    
  • space_display_name(impl Into<String>) / set_space_display_name(Option<String>):
    required: false
    

    The name of the space that appears in the SageMaker Studio UI.

    
    
• On success, responds with CreateSpaceOutput with field(s):
  • space_arn(Option<String>):

    The space’s Amazon Resource Name (ARN).

• On failure, responds with SdkError<CreateSpaceError>

impl Client

pub fn create_studio_lifecycle_config( &self, ) -> CreateStudioLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create.

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

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

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

    The App type that the Lifecycle Configuration is attached to.

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

    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>

impl Client

pub fn create_training_job(&self) -> CreateTrainingJobFluentBuilder

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>):
    required: true
    

    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(impl Into<String>, impl Into<String>) / set_hyper_parameters(Option<HashMap::<String, String>>):
    required: false
    

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

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields.

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

    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 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>):
    required: true
    

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

    During model training, 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 SageMaker Roles.

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

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

    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, 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 are available as input streams. They do not need to be downloaded.

    Your input must be in the same Amazon Web Services region as your training job.

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

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

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

    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 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>):
    required: false
    

    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>):
    required: true
    

    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, SageMaker ends the training job. Use this API to cap model training costs.

    To stop a job, 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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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.

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request tag variable or plain text fields.

    
    
  • enable_network_isolation(bool) / set_enable_network_isolation(Option<bool>):
    required: false
    

    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, 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(Option<bool>):
    required: false
    

    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(Option<bool>):
    required: false
    

    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>):
    required: false
    

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

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

    Configuration information for the Amazon SageMaker 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(DebugRuleConfiguration) / set_debug_rule_configurations(Option<Vec::<DebugRuleConfiguration>>):
    required: false
    

    Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

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

    Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

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

    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>):
    required: false
    

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

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

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

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

    The environment variables to set in the Docker container.

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

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

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

    
    
  • remote_debug_config(RemoteDebugConfig) / set_remote_debug_config(Option<RemoteDebugConfig>):
    required: false
    

    Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

    
    
  • infra_check_config(InfraCheckConfig) / set_infra_check_config(Option<InfraCheckConfig>):
    required: false
    

    Contains information about the infrastructure health check configuration for the training job.

    
    
  • session_chaining_config(SessionChainingConfig) / set_session_chaining_config(Option<SessionChainingConfig>):
    required: false
    

    Contains information about attribute-based access control (ABAC) for the training job.

    
    
• 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>

impl Client

pub fn create_training_plan(&self) -> CreateTrainingPlanFluentBuilder

Constructs a fluent builder for the CreateTrainingPlan operation.

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

    The name of the training plan to create.

    
    
  • training_plan_offering_id(impl Into<String>) / set_training_plan_offering_id(Option<String>):
    required: true
    

    The unique identifier of the training plan offering to use for creating this plan.

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

    An array of key-value pairs to apply to this training plan.

    
    
• On success, responds with CreateTrainingPlanOutput with field(s):
  • training_plan_arn(Option<String>):

    The Amazon Resource Name (ARN); of the created training plan.

• On failure, responds with SdkError<CreateTrainingPlanError>

impl Client

pub fn create_transform_job(&self) -> CreateTransformJobFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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.

    The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 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>):
    required: false
    

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

    The environment variables to set in the Docker container. Don’t include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.

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

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

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

    Describes the results of the transform job.

    
    
  • data_capture_config(BatchDataCaptureConfig) / set_data_capture_config(Option<BatchDataCaptureConfig>):
    required: false
    

    Configuration to control how SageMaker captures inference data.

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

    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>):
    required: false
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    (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>):
    required: false
    

    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>

impl Client

pub fn create_trial(&self) -> CreateTrialFluentBuilder

Constructs a fluent builder for the CreateTrial operation.

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

    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>):
    required: false
    

    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>):
    required: true
    

    The name of the experiment to associate the trial with.

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

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

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

    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>

impl Client

pub fn create_trial_component(&self) -> CreateTrialComponentFluentBuilder

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>):
    required: true
    

    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>):
    required: false
    

    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>):
    required: false
    

    The status of the component. States include:

    • InProgress

    • Completed

    • Failed

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

    When the component started.

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

    When the component ended.

    
    
  • parameters(impl Into<String>, TrialComponentParameterValue) / set_parameters(Option<HashMap::<String, TrialComponentParameterValue>>):
    required: false
    

    The hyperparameters for the component.

    
    
  • input_artifacts(impl Into<String>, TrialComponentArtifact) / set_input_artifacts(Option<HashMap::<String, TrialComponentArtifact>>):
    required: false
    

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

    
    
  • output_artifacts(impl Into<String>, TrialComponentArtifact) / set_output_artifacts(Option<HashMap::<String, TrialComponentArtifact>>):
    required: false
    

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

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

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

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

    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>

impl Client

pub fn create_user_profile(&self) -> CreateUserProfileFluentBuilder

Constructs a fluent builder for the CreateUserProfile operation.

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

    The ID of the associated Domain.

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

    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>):
    required: false
    

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

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

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

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

    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>):
    required: false
    

    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>

impl Client

pub fn create_workforce(&self) -> CreateWorkforceFluentBuilder

Constructs a fluent builder for the CreateWorkforce operation.

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

    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>):
    required: false
    

    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>):
    required: false
    

    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 log in 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>):
    required: true
    

    The name of the private workforce.

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

    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.

    
    
  • workforce_vpc_config(WorkforceVpcConfigRequest) / set_workforce_vpc_config(Option<WorkforceVpcConfigRequest>):
    required: false
    

    Use this parameter to configure a workforce using VPC.

    
    
• 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>

impl Client

pub fn create_workteam(&self) -> CreateWorkteamFluentBuilder

Constructs a fluent builder for the CreateWorkteam operation.

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

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

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

    The name of the workforce.

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

    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>):
    required: true
    

    A description of the work team.

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

    Configures notification of workers regarding available or expiring work items.

    
    
  • worker_access_configuration(WorkerAccessConfiguration) / set_worker_access_configuration(Option<WorkerAccessConfiguration>):
    required: false
    

    Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

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

    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>

impl Client

pub fn delete_action(&self) -> DeleteActionFluentBuilder

Constructs a fluent builder for the DeleteAction operation.

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

    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>

impl Client

pub fn delete_algorithm(&self) -> DeleteAlgorithmFluentBuilder

Constructs a fluent builder for the DeleteAlgorithm operation.

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

    The name of the algorithm to delete.

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

impl Client

pub fn delete_app(&self) -> DeleteAppFluentBuilder

Constructs a fluent builder for the DeleteApp operation.

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

    The domain ID.

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

    The user profile name. If this value is not set, then SpaceName must be set.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: false
    

    The name of the space. If this value is not set, then UserProfileName must be set.

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

    The type of app.

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

    The name of the app.

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

impl Client

pub fn delete_app_image_config(&self) -> DeleteAppImageConfigFluentBuilder

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>):
    required: true
    

    The name of the AppImageConfig to delete.

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

impl Client

pub fn delete_artifact(&self) -> DeleteArtifactFluentBuilder

Constructs a fluent builder for the DeleteArtifact operation.

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

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

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

    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>

impl Client

pub fn delete_association(&self) -> DeleteAssociationFluentBuilder

Constructs a fluent builder for the DeleteAssociation operation.

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

    The ARN of the source.

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

    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>

impl Client

pub fn delete_cluster(&self) -> DeleteClusterFluentBuilder

Constructs a fluent builder for the DeleteCluster operation.

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

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.

    
    
• On success, responds with DeleteClusterOutput with field(s):
  • cluster_arn(Option<String>):

    The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.

• On failure, responds with SdkError<DeleteClusterError>

impl Client

pub fn delete_cluster_scheduler_config( &self, ) -> DeleteClusterSchedulerConfigFluentBuilder

Constructs a fluent builder for the DeleteClusterSchedulerConfig operation.

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

    ID of the cluster policy.

    
    
• On success, responds with DeleteClusterSchedulerConfigOutput
• On failure, responds with SdkError<DeleteClusterSchedulerConfigError>

impl Client

pub fn delete_code_repository(&self) -> DeleteCodeRepositoryFluentBuilder

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>):
    required: true
    

    The name of the Git repository to delete.

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

impl Client

pub fn delete_compilation_job(&self) -> DeleteCompilationJobFluentBuilder

Constructs a fluent builder for the DeleteCompilationJob operation.

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

    The name of the compilation job to delete.

    
    
• On success, responds with DeleteCompilationJobOutput
• On failure, responds with SdkError<DeleteCompilationJobError>

impl Client

pub fn delete_compute_quota(&self) -> DeleteComputeQuotaFluentBuilder

Constructs a fluent builder for the DeleteComputeQuota operation.

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

    ID of the compute allocation definition.

    
    
• On success, responds with DeleteComputeQuotaOutput
• On failure, responds with SdkError<DeleteComputeQuotaError>

impl Client

pub fn delete_context(&self) -> DeleteContextFluentBuilder

Constructs a fluent builder for the DeleteContext operation.

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

    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>

impl Client

pub fn delete_data_quality_job_definition( &self, ) -> DeleteDataQualityJobDefinitionFluentBuilder

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>):
    required: true
    

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

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

impl Client

pub fn delete_device_fleet(&self) -> DeleteDeviceFleetFluentBuilder

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>):
    required: true
    

    The name of the fleet to delete.

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

impl Client

pub fn delete_domain(&self) -> DeleteDomainFluentBuilder

Constructs a fluent builder for the DeleteDomain operation.

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

    The domain ID.

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

    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>

impl Client

pub fn delete_edge_deployment_plan( &self, ) -> DeleteEdgeDeploymentPlanFluentBuilder

Constructs a fluent builder for the DeleteEdgeDeploymentPlan operation.

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

    The name of the edge deployment plan to delete.

    
    
• On success, responds with DeleteEdgeDeploymentPlanOutput
• On failure, responds with SdkError<DeleteEdgeDeploymentPlanError>

impl Client

pub fn delete_edge_deployment_stage( &self, ) -> DeleteEdgeDeploymentStageFluentBuilder

Constructs a fluent builder for the DeleteEdgeDeploymentStage operation.

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

    The name of the edge deployment plan from which the stage will be deleted.

    
    
  • stage_name(impl Into<String>) / set_stage_name(Option<String>):
    required: true
    

    The name of the stage.

    
    
• On success, responds with DeleteEdgeDeploymentStageOutput
• On failure, responds with SdkError<DeleteEdgeDeploymentStageError>

impl Client

pub fn delete_endpoint(&self) -> DeleteEndpointFluentBuilder

Constructs a fluent builder for the DeleteEndpoint operation.

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

    The name of the endpoint that you want to delete.

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

impl Client

pub fn delete_endpoint_config(&self) -> DeleteEndpointConfigFluentBuilder

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>):
    required: true
    

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

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

impl Client

pub fn delete_experiment(&self) -> DeleteExperimentFluentBuilder

Constructs a fluent builder for the DeleteExperiment operation.

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

    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>

impl Client

pub fn delete_feature_group(&self) -> DeleteFeatureGroupFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn delete_flow_definition(&self) -> DeleteFlowDefinitionFluentBuilder

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>):
    required: true
    

    The name of the flow definition you are deleting.

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

impl Client

pub fn delete_hub(&self) -> DeleteHubFluentBuilder

Constructs a fluent builder for the DeleteHub operation.

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

    The name of the hub to delete.

    
    
• On success, responds with DeleteHubOutput
• On failure, responds with SdkError<DeleteHubError>

impl Client

pub fn delete_hub_content(&self) -> DeleteHubContentFluentBuilder

Constructs a fluent builder for the DeleteHubContent operation.

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

    The name of the hub that you want to delete content in.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of content that you want to delete from a hub.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the content that you want to delete from a hub.

    
    
  • hub_content_version(impl Into<String>) / set_hub_content_version(Option<String>):
    required: true
    

    The version of the content that you want to delete from a hub.

    
    
• On success, responds with DeleteHubContentOutput
• On failure, responds with SdkError<DeleteHubContentError>

impl Client

pub fn delete_hub_content_reference( &self, ) -> DeleteHubContentReferenceFluentBuilder

Constructs a fluent builder for the DeleteHubContentReference operation.

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

    The name of the hub to delete the hub content reference from.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of hub content reference to delete. The only supported type of hub content reference to delete is ModelReference.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the hub content to delete.

    
    
• On success, responds with DeleteHubContentReferenceOutput
• On failure, responds with SdkError<DeleteHubContentReferenceError>

impl Client

pub fn delete_human_task_ui(&self) -> DeleteHumanTaskUiFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn delete_hyper_parameter_tuning_job( &self, ) -> DeleteHyperParameterTuningJobFluentBuilder

Constructs a fluent builder for the DeleteHyperParameterTuningJob operation.

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

    The name of the hyperparameter tuning job that you want to delete.

    
    
• On success, responds with DeleteHyperParameterTuningJobOutput
• On failure, responds with SdkError<DeleteHyperParameterTuningJobError>

impl Client

pub fn delete_image(&self) -> DeleteImageFluentBuilder

Constructs a fluent builder for the DeleteImage operation.

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

    The name of the image to delete.

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

impl Client

pub fn delete_image_version(&self) -> DeleteImageVersionFluentBuilder

Constructs a fluent builder for the DeleteImageVersion operation.

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

    The name of the image to delete.

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

    The version to delete.

    
    
  • alias(impl Into<String>) / set_alias(Option<String>):
    required: false
    

    The alias of the image to delete.

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

impl Client

pub fn delete_inference_component( &self, ) -> DeleteInferenceComponentFluentBuilder

Constructs a fluent builder for the DeleteInferenceComponent operation.

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

    The name of the inference component to delete.

    
    
• On success, responds with DeleteInferenceComponentOutput
• On failure, responds with SdkError<DeleteInferenceComponentError>

impl Client

pub fn delete_inference_experiment( &self, ) -> DeleteInferenceExperimentFluentBuilder

Constructs a fluent builder for the DeleteInferenceExperiment operation.

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

    The name of the inference experiment you want to delete.

    
    
• On success, responds with DeleteInferenceExperimentOutput with field(s):
  • inference_experiment_arn(Option<String>):

    The ARN of the deleted inference experiment.

• On failure, responds with SdkError<DeleteInferenceExperimentError>

impl Client

pub fn delete_mlflow_tracking_server( &self, ) -> DeleteMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the DeleteMlflowTrackingServer operation.

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

    The name of the the tracking server to delete.

    
    
• On success, responds with DeleteMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    A TrackingServerArn object, the ARN of the tracking server that is deleted if successfully found.

• On failure, responds with SdkError<DeleteMlflowTrackingServerError>

impl Client

pub fn delete_model(&self) -> DeleteModelFluentBuilder

Constructs a fluent builder for the DeleteModel operation.

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

    The name of the model to delete.

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

impl Client

pub fn delete_model_bias_job_definition( &self, ) -> DeleteModelBiasJobDefinitionFluentBuilder

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>):
    required: true
    

    The name of the model bias job definition to delete.

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

impl Client

pub fn delete_model_card(&self) -> DeleteModelCardFluentBuilder

Constructs a fluent builder for the DeleteModelCard operation.

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

    The name of the model card to delete.

    
    
• On success, responds with DeleteModelCardOutput
• On failure, responds with SdkError<DeleteModelCardError>

impl Client

pub fn delete_model_explainability_job_definition( &self, ) -> DeleteModelExplainabilityJobDefinitionFluentBuilder

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>):
    required: true
    

    The name of the model explainability job definition to delete.

    
    
• On success, responds with DeleteModelExplainabilityJobDefinitionOutput
• On failure, responds with SdkError<DeleteModelExplainabilityJobDefinitionError>

impl Client

pub fn delete_model_package(&self) -> DeleteModelPackageFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn delete_model_package_group(&self) -> DeleteModelPackageGroupFluentBuilder

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>):
    required: true
    

    The name of the model group to delete.

    
    
• On success, responds with DeleteModelPackageGroupOutput
• On failure, responds with SdkError<DeleteModelPackageGroupError>

impl Client

pub fn delete_model_package_group_policy( &self, ) -> DeleteModelPackageGroupPolicyFluentBuilder

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>):
    required: true
    

    The name of the model group for which to delete the policy.

    
    
• On success, responds with DeleteModelPackageGroupPolicyOutput
• On failure, responds with SdkError<DeleteModelPackageGroupPolicyError>

impl Client

pub fn delete_model_quality_job_definition( &self, ) -> DeleteModelQualityJobDefinitionFluentBuilder

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>):
    required: true
    

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

    
    
• On success, responds with DeleteModelQualityJobDefinitionOutput
• On failure, responds with SdkError<DeleteModelQualityJobDefinitionError>

impl Client

pub fn delete_monitoring_schedule( &self, ) -> DeleteMonitoringScheduleFluentBuilder

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>):
    required: true
    

    The name of the monitoring schedule to delete.

    
    
• On success, responds with DeleteMonitoringScheduleOutput
• On failure, responds with SdkError<DeleteMonitoringScheduleError>

impl Client

pub fn delete_notebook_instance(&self) -> DeleteNotebookInstanceFluentBuilder

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>):
    required: true
    

    The name of the SageMaker AI notebook instance to delete.

    
    
• On success, responds with DeleteNotebookInstanceOutput
• On failure, responds with SdkError<DeleteNotebookInstanceError>

impl Client

pub fn delete_notebook_instance_lifecycle_config( &self, ) -> DeleteNotebookInstanceLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the lifecycle configuration to delete.

    
    
• On success, responds with DeleteNotebookInstanceLifecycleConfigOutput
• On failure, responds with SdkError<DeleteNotebookInstanceLifecycleConfigError>

impl Client

pub fn delete_optimization_job(&self) -> DeleteOptimizationJobFluentBuilder

Constructs a fluent builder for the DeleteOptimizationJob operation.

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

    The name that you assigned to the optimization job.

    
    
• On success, responds with DeleteOptimizationJobOutput
• On failure, responds with SdkError<DeleteOptimizationJobError>

impl Client

pub fn delete_partner_app(&self) -> DeletePartnerAppFluentBuilder

Constructs a fluent builder for the DeletePartnerApp operation.

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

    The ARN of the SageMaker Partner AI App to delete.

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

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

    
    
• On success, responds with DeletePartnerAppOutput with field(s):
  • arn(Option<String>):

    The ARN of the SageMaker Partner AI App that was deleted.

• On failure, responds with SdkError<DeletePartnerAppError>

impl Client

pub fn delete_pipeline(&self) -> DeletePipelineFluentBuilder

Constructs a fluent builder for the DeletePipeline operation.

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

    The name of the pipeline to delete.

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

    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>

impl Client

pub fn delete_project(&self) -> DeleteProjectFluentBuilder

Constructs a fluent builder for the DeleteProject operation.

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

    The name of the project to delete.

    
    
• On success, responds with DeleteProjectOutput
• On failure, responds with SdkError<DeleteProjectError>

impl Client

pub fn delete_space(&self) -> DeleteSpaceFluentBuilder

Constructs a fluent builder for the DeleteSpace operation.

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

    The ID of the associated domain.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: true
    

    The name of the space.

    
    
• On success, responds with DeleteSpaceOutput
• On failure, responds with SdkError<DeleteSpaceError>

impl Client

pub fn delete_studio_lifecycle_config( &self, ) -> DeleteStudioLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the Amazon SageMaker AI Studio Lifecycle Configuration to delete.

    
    
• On success, responds with DeleteStudioLifecycleConfigOutput
• On failure, responds with SdkError<DeleteStudioLifecycleConfigError>

impl Client

pub fn delete_tags(&self) -> DeleteTagsFluentBuilder

Constructs a fluent builder for the DeleteTags operation.

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

    The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

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

    An array or one or more tag keys to delete.

    
    
• On success, responds with DeleteTagsOutput
• On failure, responds with SdkError<DeleteTagsError>

impl Client

pub fn delete_trial(&self) -> DeleteTrialFluentBuilder

Constructs a fluent builder for the DeleteTrial operation.

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

    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>

impl Client

pub fn delete_trial_component(&self) -> DeleteTrialComponentFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn delete_user_profile(&self) -> DeleteUserProfileFluentBuilder

Constructs a fluent builder for the DeleteUserProfile operation.

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

    The domain ID.

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

    The user profile name.

    
    
• On success, responds with DeleteUserProfileOutput
• On failure, responds with SdkError<DeleteUserProfileError>

impl Client

pub fn delete_workforce(&self) -> DeleteWorkforceFluentBuilder

Constructs a fluent builder for the DeleteWorkforce operation.

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

    The name of the workforce.

    
    
• On success, responds with DeleteWorkforceOutput
• On failure, responds with SdkError<DeleteWorkforceError>

impl Client

pub fn delete_workteam(&self) -> DeleteWorkteamFluentBuilder

Constructs a fluent builder for the DeleteWorkteam operation.

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

    The name of the work team to delete.

    
    
• On success, responds with DeleteWorkteamOutput with field(s):
  • success(Option<bool>):

    Returns true if the work team was successfully deleted; otherwise, returns false.

• On failure, responds with SdkError<DeleteWorkteamError>

impl Client

pub fn deregister_devices(&self) -> DeregisterDevicesFluentBuilder

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>):
    required: true
    

    The name of the fleet the devices belong to.

    
    
  • device_names(impl Into<String>) / set_device_names(Option<Vec::<String>>):
    required: true
    

    The unique IDs of the devices.

    
    
• On success, responds with DeregisterDevicesOutput
• On failure, responds with SdkError<DeregisterDevicesError>

impl Client

pub fn describe_action(&self) -> DescribeActionFluentBuilder

Constructs a fluent builder for the DescribeAction operation.

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

    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, project, or model card.

  • 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, project, or model card.

  • 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>

impl Client

pub fn describe_algorithm(&self) -> DescribeAlgorithmFluentBuilder

Constructs a fluent builder for the DescribeAlgorithm operation.

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

    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 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(Option<bool>):

    Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

• On failure, responds with SdkError<DescribeAlgorithmError>

impl Client

pub fn describe_app(&self) -> DescribeAppFluentBuilder

Constructs a fluent builder for the DescribeApp operation.

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

    The domain ID.

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

    The user profile name. If this value is not set, then SpaceName must be set.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: false
    

    The name of the space.

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

    The type of app.

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

    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.

  • space_name(Option<String>):

    The name of the space. If this value is not set, then UserProfileName must be set.

  • status(Option<AppStatus>):

    The status.

  • recovery_mode(Option<bool>):

    Indicates whether the application is launched in recovery mode.

  • 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 AI 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 of the application.

    After an application has been shut down for 24 hours, SageMaker AI deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application.

  • failure_reason(Option<String>):

    The failure reason.

  • resource_spec(Option<ResourceSpec>):

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

  • built_in_lifecycle_config_arn(Option<String>):

    The lifecycle configuration that runs before the default lifecycle configuration

• On failure, responds with SdkError<DescribeAppError>

impl Client

pub fn describe_app_image_config(&self) -> DescribeAppImageConfigFluentBuilder

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>):
    required: true
    

    The name of the AppImageConfig to describe.

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

    The 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.

  • jupyter_lab_app_image_config(Option<JupyterLabAppImageConfig>):

    The configuration of the JupyterLab app.

  • code_editor_app_image_config(Option<CodeEditorAppImageConfig>):

    The configuration of the Code Editor app.

• On failure, responds with SdkError<DescribeAppImageConfigError>

impl Client

pub fn describe_artifact(&self) -> DescribeArtifactFluentBuilder

Constructs a fluent builder for the DescribeArtifact operation.

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

    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, project, or model card.

  • 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, project, or model card.

  • 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>

impl Client

pub fn describe_auto_ml_job(&self) -> DescribeAutoMLJobFluentBuilder

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>):
    required: true
    

    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 ARN of the 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>):

    The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

  • 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(Option<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>):

    Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

  • 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>

impl Client

pub fn describe_auto_ml_job_v2(&self) -> DescribeAutoMLJobV2FluentBuilder

Constructs a fluent builder for the DescribeAutoMLJobV2 operation.

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

    Requests information about an AutoML job V2 using its unique name.

    
    
• On success, responds with DescribeAutoMlJobV2Output with field(s):
  • auto_ml_job_name(Option<String>):

    Returns the name of the AutoML job V2.

  • auto_ml_job_arn(Option<String>):

    Returns the Amazon Resource Name (ARN) of the AutoML job V2.

  • auto_ml_job_input_data_config(Option<Vec::<AutoMlJobChannel>>):

    Returns an array of channel objects describing the input data and their location.

  • output_data_config(Option<AutoMlOutputDataConfig>):

    Returns the job’s output data config.

  • role_arn(Option<String>):

    The ARN of the 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.

  • auto_ml_problem_type_config(Option<AutoMlProblemTypeConfig>):

    Returns the configuration settings of the problem type set for the AutoML job V2.

  • auto_ml_problem_type_config_name(Option<AutoMlProblemTypeConfigName>):

    Returns the name of the problem type configuration set for the AutoML job V2.

  • creation_time(Option<DateTime>):

    Returns the creation time of the AutoML job V2.

  • end_time(Option<DateTime>):

    Returns the end time of the AutoML job V2.

  • last_modified_time(Option<DateTime>):

    Returns the job’s last modified time.

  • failure_reason(Option<String>):

    Returns the reason for the failure of the AutoML job V2, when applicable.

  • partial_failure_reasons(Option<Vec::<AutoMlPartialFailureReason>>):

    Returns a list of reasons for partial failures within an AutoML job V2.

  • best_candidate(Option<AutoMlCandidate>):

    Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.

  • auto_ml_job_status(Option<AutoMlJobStatus>):

    Returns the status of the AutoML job V2.

  • auto_ml_job_secondary_status(Option<AutoMlJobSecondaryStatus>):

    Returns the secondary status of the AutoML job V2.

  • auto_ml_job_artifacts(Option<AutoMlJobArtifacts>):

    The artifacts that are generated during an AutoML job.

  • resolved_attributes(Option<AutoMlResolvedAttributes>):

    Returns the resolved attributes used by the AutoML job V2.

  • 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.

  • data_split_config(Option<AutoMlDataSplitConfig>):

    Returns the configuration settings of how the data are split into train and validation datasets.

  • security_config(Option<AutoMlSecurityConfig>):

    Returns the security configuration for traffic encryption or Amazon VPC settings.

  • auto_ml_compute_config(Option<AutoMlComputeConfig>):

    The compute configuration used for the AutoML job V2.

• On failure, responds with SdkError<DescribeAutoMLJobV2Error>

impl Client

pub fn describe_cluster(&self) -> DescribeClusterFluentBuilder

Constructs a fluent builder for the DescribeCluster operation.

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

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

    
    
• On success, responds with DescribeClusterOutput with field(s):
  • cluster_arn(Option<String>):

    The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.

  • cluster_name(Option<String>):

    The name of the SageMaker HyperPod cluster.

  • cluster_status(Option<ClusterStatus>):

    The status of the SageMaker HyperPod cluster.

  • creation_time(Option<DateTime>):

    The time when the SageMaker Cluster is created.

  • failure_message(Option<String>):

    The failure message of the SageMaker HyperPod cluster.

  • instance_groups(Option<Vec::<ClusterInstanceGroupDetails>>):

    The instance groups of the SageMaker HyperPod cluster.

  • vpc_config(Option<VpcConfig>):

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

  • orchestrator(Option<ClusterOrchestrator>):

    The type of orchestrator used for the SageMaker HyperPod cluster.

  • node_recovery(Option<ClusterNodeRecovery>):

    The node recovery mode configured for the SageMaker HyperPod cluster.

• On failure, responds with SdkError<DescribeClusterError>

impl Client

pub fn describe_cluster_node(&self) -> DescribeClusterNodeFluentBuilder

Constructs a fluent builder for the DescribeClusterNode operation.

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

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is.

    
    
  • node_id(impl Into<String>) / set_node_id(Option<String>):
    required: true
    

    The ID of the SageMaker HyperPod cluster node.

    
    
• On success, responds with DescribeClusterNodeOutput with field(s):
  • node_details(Option<ClusterNodeDetails>):

    The details of the SageMaker HyperPod cluster node.

• On failure, responds with SdkError<DescribeClusterNodeError>

impl Client

pub fn describe_cluster_scheduler_config( &self, ) -> DescribeClusterSchedulerConfigFluentBuilder

Constructs a fluent builder for the DescribeClusterSchedulerConfig operation.

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

    ID of the cluster policy.

    
    
  • cluster_scheduler_config_version(i32) / set_cluster_scheduler_config_version(Option<i32>):
    required: false
    

    Version of the cluster policy.

    
    
• On success, responds with DescribeClusterSchedulerConfigOutput with field(s):
  • cluster_scheduler_config_arn(Option<String>):

    ARN of the cluster policy.

  • cluster_scheduler_config_id(Option<String>):

    ID of the cluster policy.

  • name(Option<String>):

    Name of the cluster policy.

  • cluster_scheduler_config_version(Option<i32>):

    Version of the cluster policy.

  • status(Option<SchedulerResourceStatus>):

    Status of the cluster policy.

  • failure_reason(Option<String>):

    Failure reason of the cluster policy.

  • cluster_arn(Option<String>):

    ARN of the cluster where the cluster policy is applied.

  • scheduler_config(Option<SchedulerConfig>):

    Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.

  • description(Option<String>):

    Description of the cluster policy.

  • creation_time(Option<DateTime>):

    Creation time of the cluster policy.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • last_modified_time(Option<DateTime>):

    Last modified time of the cluster policy.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

• On failure, responds with SdkError<DescribeClusterSchedulerConfigError>

impl Client

pub fn describe_code_repository(&self) -> DescribeCodeRepositoryFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_compilation_job(&self) -> DescribeCompilationJobFluentBuilder

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>):
    required: true
    

    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 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 AI 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 AI 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 AI 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.

  • derived_information(Option<DerivedInformation>):

    Information that SageMaker Neo automatically derived about the model.

• On failure, responds with SdkError<DescribeCompilationJobError>

impl Client

pub fn describe_compute_quota(&self) -> DescribeComputeQuotaFluentBuilder

Constructs a fluent builder for the DescribeComputeQuota operation.

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

    ID of the compute allocation definition.

    
    
  • compute_quota_version(i32) / set_compute_quota_version(Option<i32>):
    required: false
    

    Version of the compute allocation definition.

    
    
• On success, responds with DescribeComputeQuotaOutput with field(s):
  • compute_quota_arn(Option<String>):

    ARN of the compute allocation definition.

  • compute_quota_id(Option<String>):

    ID of the compute allocation definition.

  • name(Option<String>):

    Name of the compute allocation definition.

  • description(Option<String>):

    Description of the compute allocation definition.

  • compute_quota_version(Option<i32>):

    Version of the compute allocation definition.

  • status(Option<SchedulerResourceStatus>):

    Status of the compute allocation definition.

  • failure_reason(Option<String>):

    Failure reason of the compute allocation definition.

  • cluster_arn(Option<String>):

    ARN of the cluster.

  • compute_quota_config(Option<ComputeQuotaConfig>):

    Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

  • compute_quota_target(Option<ComputeQuotaTarget>):

    The target entity to allocate compute resources to.

  • activation_state(Option<ActivationState>):

    The state of the compute allocation being described. Use to enable or disable compute allocation.

    Default is Enabled.

  • creation_time(Option<DateTime>):

    Creation time of the compute allocation configuration.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • last_modified_time(Option<DateTime>):

    Last modified time of the compute allocation configuration.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

• On failure, responds with SdkError<DescribeComputeQuotaError>

impl Client

pub fn describe_context(&self) -> DescribeContextFluentBuilder

Constructs a fluent builder for the DescribeContext operation.

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

    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, project, or model card.

  • 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, project, or model card.

  • lineage_group_arn(Option<String>):

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

• On failure, responds with SdkError<DescribeContextError>

impl Client

pub fn describe_data_quality_job_definition( &self, ) -> DescribeDataQualityJobDefinitionFluentBuilder

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>):
    required: true
    

    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 AI 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>

impl Client

pub fn describe_device(&self) -> DescribeDeviceFluentBuilder

Constructs a fluent builder for the DescribeDevice operation.

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

    Next token of device description.

    
    
  • device_name(impl Into<String>) / set_device_name(Option<String>):
    required: true
    

    The unique ID of the device.

    
    
  • device_fleet_name(impl Into<String>) / set_device_fleet_name(Option<String>):
    required: true
    

    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(Option<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>

impl Client

pub fn describe_device_fleet(&self) -> DescribeDeviceFleetFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_domain(&self) -> DescribeDomainFluentBuilder

Constructs a fluent builder for the DescribeDomain operation.

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

    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 managed by this Domain.

  • single_sign_on_managed_application_instance_id(Option<String>):

    The IAM Identity Center managed application instance ID.

  • single_sign_on_application_arn(Option<String>):

    The ARN of the application managed by SageMaker AI in IAM Identity Center. This value is only returned for domains created after October 1, 2023.

  • 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.

  • 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.

  • 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.

  • domain_settings(Option<DomainSettings>):

    A collection of Domain settings.

  • 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 AI, which allows direct internet access

    • VpcOnly - All 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 the domain uses for communication.

  • url(Option<String>):

    The domain’s URL.

  • vpc_id(Option<String>):

    The ID of the Amazon Virtual Private Cloud (VPC) that the domain 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.

  • 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.

  • tag_propagation(Option<TagPropagation>):

    Indicates whether custom tag propagation is supported for the domain.

  • default_space_settings(Option<DefaultSpaceSettings>):

    The default settings for shared spaces that users create in the domain.

• On failure, responds with SdkError<DescribeDomainError>

impl Client

pub fn describe_edge_deployment_plan( &self, ) -> DescribeEdgeDeploymentPlanFluentBuilder

Constructs a fluent builder for the DescribeEdgeDeploymentPlan operation.

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

    The name of the deployment plan to describe.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.

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

    The maximum number of results to select (50 by default).

    
    
• On success, responds with DescribeEdgeDeploymentPlanOutput with field(s):
  • edge_deployment_plan_arn(Option<String>):

    The ARN of edge deployment plan.

  • edge_deployment_plan_name(Option<String>):

    The name of the edge deployment plan.

  • model_configs(Option<Vec::<EdgeDeploymentModelConfig>>):

    List of models associated with the edge deployment plan.

  • device_fleet_name(Option<String>):

    The device fleet used for this edge deployment plan.

  • edge_deployment_success(Option<i32>):

    The number of edge devices with the successful deployment.

  • edge_deployment_pending(Option<i32>):

    The number of edge devices yet to pick up deployment, or in progress.

  • edge_deployment_failed(Option<i32>):

    The number of edge devices that failed the deployment.

  • stages(Option<Vec::<DeploymentStageStatusSummary>>):

    List of stages in the edge deployment plan.

  • next_token(Option<String>):

    Token to use when calling the next set of stages in the edge deployment plan.

  • creation_time(Option<DateTime>):

    The time when the edge deployment plan was created.

  • last_modified_time(Option<DateTime>):

    The time when the edge deployment plan was last updated.

• On failure, responds with SdkError<DescribeEdgeDeploymentPlanError>

impl Client

pub fn describe_edge_packaging_job( &self, ) -> DescribeEdgePackagingJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_endpoint(&self) -> DescribeEndpointFluentBuilder

Constructs a fluent builder for the DescribeEndpoint operation.

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

    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>):

    The currently active data capture configuration used by your Endpoint.

  • 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 the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

    • UpdateRollbackFailed: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint’s status to InService, see Rolling Deployments.

  • 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.

  • explainer_config(Option<ExplainerConfig>):

    The configuration parameters for an explainer.

  • shadow_production_variants(Option<Vec::<ProductionVariantSummary>>):

    An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.

• On failure, responds with SdkError<DescribeEndpointError>

impl Client

pub fn describe_endpoint_config(&self) -> DescribeEndpointConfigFluentBuilder

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>):
    required: true
    

    The name of the endpoint configuration.

    
    
• On success, responds with DescribeEndpointConfigOutput with field(s):
  • endpoint_config_name(Option<String>):

    Name of the 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>):

    Configuration to control how SageMaker AI captures inference data.

  • 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.

  • explainer_config(Option<ExplainerConfig>):

    The configuration parameters for an explainer.

  • shadow_production_variants(Option<Vec::<ProductionVariant>>):

    An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.

  • execution_role_arn(Option<String>):

    The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.

  • vpc_config(Option<VpcConfig>):

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

  • enable_network_isolation(Option<bool>):

    Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.

• On failure, responds with SdkError<DescribeEndpointConfigError>

impl Client

pub fn describe_experiment(&self) -> DescribeExperimentFluentBuilder

Constructs a fluent builder for the DescribeExperiment operation.

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

    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 Amazon Resource Name (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>

impl Client

pub fn describe_feature_group(&self) -> DescribeFeatureGroupFluentBuilder

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>):
    required: true
    

    The name or Amazon Resource Name (ARN) of the FeatureGroup you want described.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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.

  • last_modified_time(Option<DateTime>):

    A timestamp indicating when the feature group was last updated.

  • online_store_config(Option<OnlineStoreConfig>):

    The configuration for the OnlineStore.

  • offline_store_config(Option<OfflineStoreConfig>):

    The configuration of the offline store. It includes the following configurations:

    • Amazon S3 location of the offline store.

    • Configuration of the Glue data catalog.

    • Table format of the offline store.

    • Option to disable the automatic creation of a Glue table for the offline store.

    • Encryption configuration.

  • throughput_config(Option<ThroughputConfigDescription>):

    Active throughput configuration of the feature group. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.

    Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.

  • 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

  • last_update_status(Option<LastUpdateStatus>):

    A value indicating whether the update made to the feature group was successful.

  • 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).

  • online_store_total_size_bytes(Option<i64>):

    The size of the OnlineStore in bytes.

• On failure, responds with SdkError<DescribeFeatureGroupError>

impl Client

pub fn describe_feature_metadata(&self) -> DescribeFeatureMetadataFluentBuilder

Constructs a fluent builder for the DescribeFeatureMetadata operation.

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

    The name or Amazon Resource Name (ARN) of the feature group containing the feature.

    
    
  • feature_name(impl Into<String>) / set_feature_name(Option<String>):
    required: true
    

    The name of the feature.

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

    The Amazon Resource Number (ARN) of the feature group that contains the feature.

  • feature_group_name(Option<String>):

    The name of the feature group that you’ve specified.

  • feature_name(Option<String>):

    The name of the feature that you’ve specified.

  • feature_type(Option<FeatureType>):

    The data type of the feature.

  • creation_time(Option<DateTime>):

    A timestamp indicating when the feature was created.

  • last_modified_time(Option<DateTime>):

    A timestamp indicating when the metadata for the feature group was modified. For example, if you add a parameter describing the feature, the timestamp changes to reflect the last time you

  • description(Option<String>):

    The description you added to describe the feature.

  • parameters(Option<Vec::<FeatureParameter>>):

    The key-value pairs that you added to describe the feature.

• On failure, responds with SdkError<DescribeFeatureMetadataError>

impl Client

pub fn describe_flow_definition(&self) -> DescribeFlowDefinitionFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_hub(&self) -> DescribeHubFluentBuilder

Constructs a fluent builder for the DescribeHub operation.

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

    The name of the hub to describe.

    
    
• On success, responds with DescribeHubOutput with field(s):
  • hub_name(Option<String>):

    The name of the hub.

  • hub_arn(Option<String>):

    The Amazon Resource Name (ARN) of the hub.

  • hub_display_name(Option<String>):

    The display name of the hub.

  • hub_description(Option<String>):

    A description of the hub.

  • hub_search_keywords(Option<Vec::<String>>):

    The searchable keywords for the hub.

  • s3_storage_config(Option<HubS3StorageConfig>):

    The Amazon S3 storage configuration for the hub.

  • hub_status(Option<HubStatus>):

    The status of the hub.

  • failure_reason(Option<String>):

    The failure reason if importing hub content failed.

  • creation_time(Option<DateTime>):

    The date and time that the hub was created.

  • last_modified_time(Option<DateTime>):

    The date and time that the hub was last modified.

• On failure, responds with SdkError<DescribeHubError>

impl Client

pub fn describe_hub_content(&self) -> DescribeHubContentFluentBuilder

Constructs a fluent builder for the DescribeHubContent operation.

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

    The name of the hub that contains the content to describe.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of content in the hub.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the content to describe.

    
    
  • hub_content_version(impl Into<String>) / set_hub_content_version(Option<String>):
    required: false
    

    The version of the content to describe.

    
    
• On success, responds with DescribeHubContentOutput with field(s):
  • hub_content_name(Option<String>):

    The name of the hub content.

  • hub_content_arn(Option<String>):

    The Amazon Resource Name (ARN) of the hub content.

  • hub_content_version(Option<String>):

    The version of the hub content.

  • hub_content_type(Option<HubContentType>):

    The type of hub content.

  • document_schema_version(Option<String>):

    The document schema version for the hub content.

  • hub_name(Option<String>):

    The name of the hub that contains the content.

  • hub_arn(Option<String>):

    The Amazon Resource Name (ARN) of the hub that contains the content.

  • hub_content_display_name(Option<String>):

    The display name of the hub content.

  • hub_content_description(Option<String>):

    A description of the hub content.

  • hub_content_markdown(Option<String>):

    A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.

  • hub_content_document(Option<String>):

    The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.

  • sage_maker_public_hub_content_arn(Option<String>):

    The ARN of the public hub content.

  • reference_min_version(Option<String>):

    The minimum version of the hub content.

  • support_status(Option<HubContentSupportStatus>):

    The support status of the hub content.

  • hub_content_search_keywords(Option<Vec::<String>>):

    The searchable keywords for the hub content.

  • hub_content_dependencies(Option<Vec::<HubContentDependency>>):

    The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.

  • hub_content_status(Option<HubContentStatus>):

    The status of the hub content.

  • failure_reason(Option<String>):

    The failure reason if importing hub content failed.

  • creation_time(Option<DateTime>):

    The date and time that hub content was created.

  • last_modified_time(Option<DateTime>):

    The last modified time of the hub content.

• On failure, responds with SdkError<DescribeHubContentError>

impl Client

pub fn describe_human_task_ui(&self) -> DescribeHumanTaskUiFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_hyper_parameter_tuning_job( &self, ) -> DescribeHyperParameterTuningJobFluentBuilder

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>):
    required: true
    

    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 hyperparameter 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.

  • 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.

  • autotune(Option<Autotune>):

    A flag to indicate if autotune is enabled for the hyperparameter tuning job.

  • failure_reason(Option<String>):

    If the tuning job failed, the reason it failed.

  • tuning_job_completion_details(Option<HyperParameterTuningJobCompletionDetails>):

    Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.

  • consumed_resources(Option<HyperParameterTuningJobConsumedResources>):

    The total resources consumed by your hyperparameter tuning job.

• On failure, responds with SdkError<DescribeHyperParameterTuningJobError>

impl Client

pub fn describe_image(&self) -> DescribeImageFluentBuilder

Constructs a fluent builder for the DescribeImage operation.

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

    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 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 ARN of the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

• On failure, responds with SdkError<DescribeImageError>

impl Client

pub fn describe_image_version(&self) -> DescribeImageVersionFluentBuilder

Constructs a fluent builder for the DescribeImageVersion operation.

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

    The name of the image.

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

    The version of the image. If not specified, the latest version is described.

    
    
  • alias(impl Into<String>) / set_alias(Option<String>):
    required: false
    

    The alias of the image version.

    
    
• 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 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.

  • vendor_guidance(Option<VendorGuidance>):

    The stability of the image version specified by the maintainer.

    • NOT_PROVIDED: The maintainers did not provide a status for image version stability.

    • STABLE: The image version is stable.

    • TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.

    • ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

  • job_type(Option<JobType>):

    Indicates SageMaker AI job type compatibility.

    • TRAINING: The image version is compatible with SageMaker AI training jobs.

    • INFERENCE: The image version is compatible with SageMaker AI inference jobs.

    • NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.

  • ml_framework(Option<String>):

    The machine learning framework vended in the image version.

  • programming_lang(Option<String>):

    The supported programming language and its version.

  • processor(Option<Processor>):

    Indicates CPU or GPU compatibility.

    • CPU: The image version is compatible with CPU.

    • GPU: The image version is compatible with GPU.

  • horovod(Option<bool>):

    Indicates Horovod compatibility.

  • release_notes(Option<String>):

    The maintainer description of the image version.

• On failure, responds with SdkError<DescribeImageVersionError>

impl Client

pub fn describe_inference_component( &self, ) -> DescribeInferenceComponentFluentBuilder

Constructs a fluent builder for the DescribeInferenceComponent operation.

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

    The name of the inference component.

    
    
• On success, responds with DescribeInferenceComponentOutput with field(s):
  • inference_component_name(Option<String>):

    The name of the inference component.

  • inference_component_arn(Option<String>):

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

  • endpoint_name(Option<String>):

    The name of the endpoint that hosts the inference component.

  • endpoint_arn(Option<String>):

    The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.

  • variant_name(Option<String>):

    The name of the production variant that hosts the inference component.

  • failure_reason(Option<String>):

    If the inference component status is Failed, the reason for the failure.

  • specification(Option<InferenceComponentSpecificationSummary>):

    Details about the resources that are deployed with this inference component.

  • runtime_config(Option<InferenceComponentRuntimeConfigSummary>):

    Details about the runtime settings for the model that is deployed with the inference component.

  • creation_time(Option<DateTime>):

    The time when the inference component was created.

  • last_modified_time(Option<DateTime>):

    The time when the inference component was last updated.

  • inference_component_status(Option<InferenceComponentStatus>):

    The status of the inference component.

  • last_deployment_config(Option<InferenceComponentDeploymentConfig>):

    The deployment and rollback settings that you assigned to the inference component.

• On failure, responds with SdkError<DescribeInferenceComponentError>

impl Client

pub fn describe_inference_experiment( &self, ) -> DescribeInferenceExperimentFluentBuilder

Constructs a fluent builder for the DescribeInferenceExperiment operation.

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

    The name of the inference experiment to describe.

    
    
• On success, responds with DescribeInferenceExperimentOutput with field(s):
  • arn(Option<String>):

    The ARN of the inference experiment being described.

  • name(Option<String>):

    The name of the inference experiment.

  • r#type(Option<InferenceExperimentType>):

    The type of the inference experiment.

  • schedule(Option<InferenceExperimentSchedule>):

    The duration for which the inference experiment ran or will run.

  • status(Option<InferenceExperimentStatus>):

    The status of the inference experiment. The following are the possible statuses for an inference experiment:

    • Creating - Amazon SageMaker is creating your experiment.

    • Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.

    • Updating - When you make changes to your experiment, your experiment shows as updating.

    • Starting - Amazon SageMaker is beginning your experiment.

    • Running - Your experiment is in progress.

    • Stopping - Amazon SageMaker is stopping your experiment.

    • Completed - Your experiment has completed.

    • Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.

  • status_reason(Option<String>):

    The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment.

  • description(Option<String>):

    The description of the inference experiment.

  • creation_time(Option<DateTime>):

    The timestamp at which you created the inference experiment.

  • completion_time(Option<DateTime>):

    The timestamp at which the inference experiment was completed.

  • last_modified_time(Option<DateTime>):

    The timestamp at which you last modified the inference experiment.

  • role_arn(Option<String>):

    The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.

  • endpoint_metadata(Option<EndpointMetadata>):

    The metadata of the endpoint on which the inference experiment ran.

  • model_variants(Option<Vec::<ModelVariantConfigSummary>>):

    An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant.

  • data_storage_config(Option<InferenceExperimentDataStorageConfig>):

    The Amazon S3 location and configuration for storing inference request and response data.

  • shadow_mode_config(Option<ShadowModeConfig>):

    The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.

  • kms_key(Option<String>):

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment.

• On failure, responds with SdkError<DescribeInferenceExperimentError>

impl Client

pub fn describe_inference_recommendations_job( &self, ) -> DescribeInferenceRecommendationsJobFluentBuilder

Constructs a fluent builder for the DescribeInferenceRecommendationsJob operation.

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

    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.

  • endpoint_performances(Option<Vec::<EndpointPerformance>>):

    The performance results from running an Inference Recommender job on an existing endpoint.

• On failure, responds with SdkError<DescribeInferenceRecommendationsJobError>

impl Client

pub fn describe_labeling_job(&self) -> DescribeLabelingJobFluentBuilder

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>):
    required: true
    

    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 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>

impl Client

pub fn describe_lineage_group(&self) -> DescribeLineageGroupFluentBuilder

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>):
    required: true
    

    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, project, or model card.

  • 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, project, or model card.

• On failure, responds with SdkError<DescribeLineageGroupError>

impl Client

pub fn describe_mlflow_tracking_server( &self, ) -> DescribeMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the DescribeMlflowTrackingServer operation.

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

    The name of the MLflow Tracking Server to describe.

    
    
• On success, responds with DescribeMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    The ARN of the described tracking server.

  • tracking_server_name(Option<String>):

    The name of the described tracking server.

  • artifact_store_uri(Option<String>):

    The S3 URI of the general purpose bucket used as the MLflow Tracking Server artifact store.

  • tracking_server_size(Option<TrackingServerSize>):

    The size of the described tracking server.

  • mlflow_version(Option<String>):

    The MLflow version used for the described tracking server.

  • role_arn(Option<String>):

    The Amazon Resource Name (ARN) for an IAM role in your account that the described MLflow Tracking Server uses to access the artifact store in Amazon S3.

  • tracking_server_status(Option<TrackingServerStatus>):

    The current creation status of the described MLflow Tracking Server.

  • is_active(Option<IsTrackingServerActive>):

    Whether the described MLflow Tracking Server is currently active.

  • tracking_server_url(Option<String>):

    The URL to connect to the MLflow user interface for the described tracking server.

  • weekly_maintenance_window_start(Option<String>):

    The day and time of the week when weekly maintenance occurs on the described tracking server.

  • automatic_model_registration(Option<bool>):

    Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.

  • creation_time(Option<DateTime>):

    The timestamp of when the described MLflow Tracking Server was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • last_modified_time(Option<DateTime>):

    The timestamp of when the described MLflow Tracking Server was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

• On failure, responds with SdkError<DescribeMlflowTrackingServerError>

impl Client

pub fn describe_model(&self) -> DescribeModelFluentBuilder

Constructs a fluent builder for the DescribeModel operation.

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

    The name of the model.

    
    
• On success, responds with DescribeModelOutput with field(s):
  • model_name(Option<String>):

    Name of the 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(Option<bool>):

    If True, no inbound or outbound network calls can be made to or from the model container.

  • deployment_recommendation(Option<DeploymentRecommendation>):

    A set of recommended deployment configurations for the model.

• On failure, responds with SdkError<DescribeModelError>

impl Client

pub fn describe_model_bias_job_definition( &self, ) -> DescribeModelBiasJobDefinitionFluentBuilder

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>):
    required: true
    

    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 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>

impl Client

pub fn describe_model_card(&self) -> DescribeModelCardFluentBuilder

Constructs a fluent builder for the DescribeModelCard operation.

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

    The name or Amazon Resource Name (ARN) of the model card to describe.

    
    
  • model_card_version(i32) / set_model_card_version(Option<i32>):
    required: false
    

    The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.

    
    
• On success, responds with DescribeModelCardOutput with field(s):
  • model_card_arn(Option<String>):

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

  • model_card_name(Option<String>):

    The name of the model card.

  • model_card_version(Option<i32>):

    The version of the model card.

  • content(Option<String>):

    The content of the model card.

  • model_card_status(Option<ModelCardStatus>):

    The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.

    • Draft: The model card is a work in progress.

    • PendingReview: The model card is pending review.

    • Approved: The model card is approved.

    • Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

  • security_config(Option<ModelCardSecurityConfig>):

    The security configuration used to protect model card content.

  • creation_time(Option<DateTime>):

    The date and time the model card was created.

  • created_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • last_modified_time(Option<DateTime>):

    The date and time the model card was last modified.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • model_card_processing_status(Option<ModelCardProcessingStatus>):

    The processing status of model card deletion. The ModelCardProcessingStatus updates throughout the different deletion steps.

    • DeletePending: Model card deletion request received.

    • DeleteInProgress: Model card deletion is in progress.

    • ContentDeleted: Deleted model card content.

    • ExportJobsDeleted: Deleted all export jobs associated with the model card.

    • DeleteCompleted: Successfully deleted the model card.

    • DeleteFailed: The model card failed to delete.

• On failure, responds with SdkError<DescribeModelCardError>

impl Client

pub fn describe_model_card_export_job( &self, ) -> DescribeModelCardExportJobFluentBuilder

Constructs a fluent builder for the DescribeModelCardExportJob operation.

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

    The Amazon Resource Name (ARN) of the model card export job to describe.

    
    
• On success, responds with DescribeModelCardExportJobOutput with field(s):
  • model_card_export_job_name(Option<String>):

    The name of the model card export job to describe.

  • model_card_export_job_arn(Option<String>):

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

  • status(Option<ModelCardExportJobStatus>):

    The completion status of the model card export job.

    • InProgress: The model card export job is in progress.

    • Completed: The model card export job is complete.

    • Failed: The model card export job failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeModelCardExportJob call.

  • model_card_name(Option<String>):

    The name or Amazon Resource Name (ARN) of the model card that the model export job exports.

  • model_card_version(Option<i32>):

    The version of the model card that the model export job exports.

  • output_config(Option<ModelCardExportOutputConfig>):

    The export output details for the model card.

  • created_at(Option<DateTime>):

    The date and time that the model export job was created.

  • last_modified_at(Option<DateTime>):

    The date and time that the model export job was last modified.

  • failure_reason(Option<String>):

    The failure reason if the model export job fails.

  • export_artifacts(Option<ModelCardExportArtifacts>):

    The exported model card artifacts.

• On failure, responds with SdkError<DescribeModelCardExportJobError>

impl Client

pub fn describe_model_explainability_job_definition( &self, ) -> DescribeModelExplainabilityJobDefinitionFluentBuilder

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>):
    required: true
    

    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 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>

impl Client

pub fn describe_model_package(&self) -> DescribeModelPackageFluentBuilder

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>):
    required: true
    

    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 you can 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(Option<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, project, or model card.

  • 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 that 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, project, or model card.

  • approval_description(Option<String>):

    A description provided for the model approval.

  • 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).

  • 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.

  • 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.

  • skip_model_validation(Option<SkipModelValidation>):

    Indicates if you want to skip model validation.

  • source_uri(Option<String>):

    The URI of the source for the model package.

  • security_config(Option<ModelPackageSecurityConfig>):

    The KMS Key ID (KMSKeyId) used for encryption of model package information.

  • model_card(Option<ModelPackageModelCard>):

    The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

  • model_life_cycle(Option<ModelLifeCycle>):

    A structure describing the current state of the model in its life cycle.

• On failure, responds with SdkError<DescribeModelPackageError>

impl Client

pub fn describe_model_package_group( &self, ) -> DescribeModelPackageGroupFluentBuilder

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>):
    required: true
    

    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, project, or model card.

  • model_package_group_status(Option<ModelPackageGroupStatus>):

    The status of the model group.

• On failure, responds with SdkError<DescribeModelPackageGroupError>

impl Client

pub fn describe_model_quality_job_definition( &self, ) -> DescribeModelQualityJobDefinitionFluentBuilder

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>):
    required: true
    

    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 AI 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>

impl Client

pub fn describe_monitoring_schedule( &self, ) -> DescribeMonitoringScheduleFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_notebook_instance( &self, ) -> DescribeNotebookInstanceFluentBuilder

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>):
    required: true
    

    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 SageMaker AI 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 SageMaker AI 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 SageMaker AI 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 SageMaker AI 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 SageMaker AI 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>>):

    This parameter is no longer supported. Elastic Inference (EI) is no longer available.

    This parameter was used to specify a list of the EI instance types associated with this notebook instance.

  • 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 SageMaker AI 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 SageMaker AI 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.

  • instance_metadata_service_configuration(Option<InstanceMetadataServiceConfiguration>):

    Information on the IMDS configuration of the notebook instance

• On failure, responds with SdkError<DescribeNotebookInstanceError>

impl Client

pub fn describe_notebook_instance_lifecycle_config( &self, ) -> DescribeNotebookInstanceLifecycleConfigFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_optimization_job(&self) -> DescribeOptimizationJobFluentBuilder

Constructs a fluent builder for the DescribeOptimizationJob operation.

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

    The name that you assigned to the optimization job.

    
    
• On success, responds with DescribeOptimizationJobOutput with field(s):
  • optimization_job_arn(Option<String>):

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

  • optimization_job_status(Option<OptimizationJobStatus>):

    The current status of the optimization job.

  • optimization_start_time(Option<DateTime>):

    The time when the optimization job started.

  • optimization_end_time(Option<DateTime>):

    The time when the optimization job finished processing.

  • creation_time(Option<DateTime>):

    The time when you created the optimization job.

  • last_modified_time(Option<DateTime>):

    The time when the optimization job was last updated.

  • failure_reason(Option<String>):

    If the optimization job status is FAILED, the reason for the failure.

  • optimization_job_name(Option<String>):

    The name that you assigned to the optimization job.

  • model_source(Option<OptimizationJobModelSource>):

    The location of the source model to optimize with an optimization job.

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

    The environment variables to set in the model container.

  • deployment_instance_type(Option<OptimizationJobDeploymentInstanceType>):

    The type of instance that hosts the optimized model that you create with the optimization job.

  • optimization_configs(Option<Vec::<OptimizationConfig>>):

    Settings for each of the optimization techniques that the job applies.

  • output_config(Option<OptimizationJobOutputConfig>):

    Details for where to store the optimized model that you create with the optimization job.

  • optimization_output(Option<OptimizationOutput>):

    Output values produced by an optimization job.

  • role_arn(Option<String>):

    The ARN of the IAM role that you assigned to the optimization job.

  • stopping_condition(Option<StoppingCondition>):

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

    To stop a training job, 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.

    The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

    The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

  • vpc_config(Option<OptimizationVpcConfig>):

    A VPC in Amazon VPC that your optimized model has access to.

• On failure, responds with SdkError<DescribeOptimizationJobError>

impl Client

pub fn describe_partner_app(&self) -> DescribePartnerAppFluentBuilder

Constructs a fluent builder for the DescribePartnerApp operation.

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

    The ARN of the SageMaker Partner AI App to describe.

    
    
• On success, responds with DescribePartnerAppOutput with field(s):
  • arn(Option<String>):

    The ARN of the SageMaker Partner AI App that was described.

  • name(Option<String>):

    The name of the SageMaker Partner AI App.

  • r#type(Option<PartnerAppType>):

    The type of SageMaker Partner AI App. Must be one of the following: lakera-guard, comet, deepchecks-llm-evaluation, or fiddler.

  • status(Option<PartnerAppStatus>):

    The status of the SageMaker Partner AI App.

  • creation_time(Option<DateTime>):

    The time that the SageMaker Partner AI App was created.

  • last_modified_time(Option<DateTime>):

    The time that the SageMaker Partner AI App was last modified.

  • execution_role_arn(Option<String>):

    The ARN of the IAM role associated with the SageMaker Partner AI App.

  • kms_key_id(Option<String>):

    The Amazon Web Services KMS customer managed key used to encrypt the data at rest associated with SageMaker Partner AI Apps.

  • base_url(Option<String>):

    The URL of the SageMaker Partner AI App that the Application SDK uses to support in-app calls for the user.

  • maintenance_config(Option<PartnerAppMaintenanceConfig>):

    Maintenance configuration settings for the SageMaker Partner AI App.

  • tier(Option<String>):

    The instance type and size of the cluster attached to the SageMaker Partner AI App.

  • version(Option<String>):

    The version of the SageMaker Partner AI App.

  • application_config(Option<PartnerAppConfig>):

    Configuration settings for the SageMaker Partner AI App.

  • auth_type(Option<PartnerAppAuthType>):

    The authorization type that users use to access the SageMaker Partner AI App.

  • enable_iam_session_based_identity(Option<bool>):

    When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.

  • error(Option<ErrorInfo>):

    This is an error field object that contains the error code and the reason for an operation failure.

• On failure, responds with SdkError<DescribePartnerAppError>

impl Client

pub fn describe_pipeline(&self) -> DescribePipelineFluentBuilder

Constructs a fluent builder for the DescribePipeline operation.

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

    The name or Amazon Resource Name (ARN) 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, project, or model card.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • parallelism_configuration(Option<ParallelismConfiguration>):

    Lists the parallelism configuration applied to the pipeline.

• On failure, responds with SdkError<DescribePipelineError>

impl Client

pub fn describe_pipeline_definition_for_execution( &self, ) -> DescribePipelineDefinitionForExecutionFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_pipeline_execution( &self, ) -> DescribePipelineExecutionFluentBuilder

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>):
    required: true
    

    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, project, or model card.

  • last_modified_by(Option<UserContext>):

    Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

  • parallelism_configuration(Option<ParallelismConfiguration>):

    The parallelism configuration applied to the pipeline.

  • selective_execution_config(Option<SelectiveExecutionConfig>):

    The selective execution configuration applied to the pipeline run.

• On failure, responds with SdkError<DescribePipelineExecutionError>

impl Client

pub fn describe_processing_job(&self) -> DescribeProcessingJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_project(&self) -> DescribeProjectFluentBuilder

Constructs a fluent builder for the DescribeProject operation.

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

    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, project, or model card.

  • 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, project, or model card.

• On failure, responds with SdkError<DescribeProjectError>

impl Client

pub fn describe_space(&self) -> DescribeSpaceFluentBuilder

Constructs a fluent builder for the DescribeSpace operation.

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

    The ID of the associated domain.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: true
    

    The name of the space.

    
    
• On success, responds with DescribeSpaceOutput with field(s):
  • domain_id(Option<String>):

    The ID of the associated domain.

  • space_arn(Option<String>):

    The space’s Amazon Resource Name (ARN).

  • space_name(Option<String>):

    The name of the space.

  • home_efs_file_system_uid(Option<String>):

    The ID of the space’s profile in the Amazon EFS volume.

  • status(Option<SpaceStatus>):

    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.

  • space_settings(Option<SpaceSettings>):

    A collection of space settings.

  • ownership_settings(Option<OwnershipSettings>):

    The collection of ownership settings for a space.

  • space_sharing_settings(Option<SpaceSharingSettings>):

    The collection of space sharing settings for a space.

  • space_display_name(Option<String>):

    The name of the space that appears in the Amazon SageMaker Studio UI.

  • url(Option<String>):

    Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.

    The following application types are supported:

    • Studio Classic: &redirect=JupyterServer

    • JupyterLab: &redirect=JupyterLab

    • Code Editor, based on Code-OSS, Visual Studio Code - Open Source: &redirect=CodeEditor

• On failure, responds with SdkError<DescribeSpaceError>

impl Client

pub fn describe_studio_lifecycle_config( &self, ) -> DescribeStudioLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the Amazon SageMaker AI 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 Amazon SageMaker AI Studio Lifecycle Configuration that is described.

  • creation_time(Option<DateTime>):

    The creation time of the Amazon SageMaker AI Studio Lifecycle Configuration.

  • last_modified_time(Option<DateTime>):

    This value is equivalent to CreationTime because Amazon SageMaker AI Studio Lifecycle Configurations are immutable.

  • studio_lifecycle_config_content(Option<String>):

    The content of your Amazon SageMaker AI 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>

impl Client

pub fn describe_subscribed_workteam( &self, ) -> DescribeSubscribedWorkteamFluentBuilder

Constructs a fluent builder for the DescribeSubscribedWorkteam operation.

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

    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>

impl Client

pub fn describe_training_job(&self) -> DescribeTrainingJobFluentBuilder

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>):
    required: true
    

    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 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.

    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.

    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. 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.

  • warm_pool_status(Option<WarmPoolStatus>):

    The status of the warm pool associated with the training job.

  • 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, SageMaker ends the training job. Use this API to cap model training costs.

    To stop a job, 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 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(Option<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, 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(Option<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(Option<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 bills 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 Amazon SageMaker 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 Amazon SageMaker Debugger rules for debugging output tensors.

  • tensor_board_output_config(Option<TensorBoardOutputConfig>):

    Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

  • debug_rule_evaluation_statuses(Option<Vec::<DebugRuleEvaluationStatus>>):

    Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.

  • profiler_config(Option<ProfilerConfig>):

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

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

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

  • profiler_rule_evaluation_statuses(Option<Vec::<ProfilerRuleEvaluationStatus>>):

    Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.

  • profiling_status(Option<ProfilingStatus>):

    Profiling status of a training job.

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

    The environment variables to set in the Docker container.

    Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

  • retry_strategy(Option<RetryStrategy>):

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

  • remote_debug_config(Option<RemoteDebugConfig>):

    Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

  • infra_check_config(Option<InfraCheckConfig>):

    Contains information about the infrastructure health check configuration for the training job.

• On failure, responds with SdkError<DescribeTrainingJobError>

impl Client

pub fn describe_training_plan(&self) -> DescribeTrainingPlanFluentBuilder

Constructs a fluent builder for the DescribeTrainingPlan operation.

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

    The name of the training plan to describe.

    
    
• On success, responds with DescribeTrainingPlanOutput with field(s):
  • training_plan_arn(Option<String>):

    The Amazon Resource Name (ARN); of the training plan.

  • training_plan_name(Option<String>):

    The name of the training plan.

  • status(Option<TrainingPlanStatus>):

    The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the TrainingPlanSummary object.

  • status_message(Option<String>):

    A message providing additional information about the current status of the training plan.

  • duration_hours(Option<i64>):

    The number of whole hours in the total duration for this training plan.

  • duration_minutes(Option<i64>):

    The additional minutes beyond whole hours in the total duration for this training plan.

  • start_time(Option<DateTime>):

    The start time of the training plan.

  • end_time(Option<DateTime>):

    The end time of the training plan.

  • upfront_fee(Option<String>):

    The upfront fee for the training plan.

  • currency_code(Option<String>):

    The currency code for the upfront fee (e.g., USD).

  • total_instance_count(Option<i32>):

    The total number of instances reserved in this training plan.

  • available_instance_count(Option<i32>):

    The number of instances currently available for use in this training plan.

  • in_use_instance_count(Option<i32>):

    The number of instances currently in use from this training plan.

  • target_resources(Option<Vec::<SageMakerResourceName>>):

    The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod) that can use this training plan.

    Training plans are specific to their target resource.

    • A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.

    • A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster’s instance group.

  • reserved_capacity_summaries(Option<Vec::<ReservedCapacitySummary>>):

    The list of Reserved Capacity providing the underlying compute resources of the plan.

• On failure, responds with SdkError<DescribeTrainingPlanError>

impl Client

pub fn describe_transform_job(&self) -> DescribeTransformJobFluentBuilder

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>):
    required: true
    

    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.

  • data_capture_config(Option<BatchDataCaptureConfig>):

    Configuration to control how SageMaker captures inference data.

  • 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>

impl Client

pub fn describe_trial(&self) -> DescribeTrialFluentBuilder

Constructs a fluent builder for the DescribeTrial operation.

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

    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>

impl Client

pub fn describe_trial_component(&self) -> DescribeTrialComponentFluentBuilder

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>):
    required: true
    

    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.

  • sources(Option<Vec::<TrialComponentSource>>):

    A list of ARNs and, if applicable, job types for multiple sources of an experiment run.

• On failure, responds with SdkError<DescribeTrialComponentError>

impl Client

pub fn describe_user_profile(&self) -> DescribeUserProfileFluentBuilder

Constructs a fluent builder for the DescribeUserProfile operation.

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

    The domain ID.

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

    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 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 IAM Identity Center user identifier.

  • single_sign_on_user_value(Option<String>):

    The IAM Identity Center user value.

  • user_settings(Option<UserSettings>):

    A collection of settings.

• On failure, responds with SdkError<DescribeUserProfileError>

impl Client

pub fn describe_workforce(&self) -> DescribeWorkforceFluentBuilder

Constructs a fluent builder for the DescribeWorkforce operation.

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

    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>

impl Client

pub fn describe_workteam(&self) -> DescribeWorkteamFluentBuilder

Constructs a fluent builder for the DescribeWorkteam operation.

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

    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>

impl Client

pub fn disable_sagemaker_servicecatalog_portfolio( &self, ) -> DisableSagemakerServicecatalogPortfolioFluentBuilder

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>

impl Client

pub fn disassociate_trial_component( &self, ) -> DisassociateTrialComponentFluentBuilder

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>):
    required: true
    

    The name of the component to disassociate from the trial.

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

    The name of the trial to disassociate from.

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

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

  • trial_arn(Option<String>):

    The Amazon Resource Name (ARN) of the trial.

• On failure, responds with SdkError<DisassociateTrialComponentError>

impl Client

pub fn enable_sagemaker_servicecatalog_portfolio( &self, ) -> EnableSagemakerServicecatalogPortfolioFluentBuilder

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>

impl Client

pub fn get_device_fleet_report(&self) -> GetDeviceFleetReportFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn get_lineage_group_policy(&self) -> GetLineageGroupPolicyFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn get_model_package_group_policy( &self, ) -> GetModelPackageGroupPolicyFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn get_sagemaker_servicecatalog_portfolio_status( &self, ) -> GetSagemakerServicecatalogPortfolioStatusFluentBuilder

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>

impl Client

pub fn get_scaling_configuration_recommendation( &self, ) -> GetScalingConfigurationRecommendationFluentBuilder

Constructs a fluent builder for the GetScalingConfigurationRecommendation operation.

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

    The name of a previously completed Inference Recommender job.

    
    
  • recommendation_id(impl Into<String>) / set_recommendation_id(Option<String>):
    required: false
    

    The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.

    Specify either this field or the EndpointName field.

    
    
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):
    required: false
    

    The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field.

    Specify either this field or the RecommendationId field.

    
    
  • target_cpu_utilization_per_core(i32) / set_target_cpu_utilization_per_core(Option<i32>):
    required: false
    

    The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.

    
    
  • scaling_policy_objective(ScalingPolicyObjective) / set_scaling_policy_objective(Option<ScalingPolicyObjective>):
    required: false
    

    An object where you specify the anticipated traffic pattern for an endpoint.

    
    
• On success, responds with GetScalingConfigurationRecommendationOutput with field(s):
  • inference_recommendations_job_name(Option<String>):

    The name of a previously completed Inference Recommender job.

  • recommendation_id(Option<String>):

    The recommendation ID of a previously completed inference recommendation.

  • endpoint_name(Option<String>):

    The name of an endpoint benchmarked during a previously completed Inference Recommender job.

  • target_cpu_utilization_per_core(Option<i32>):

    The percentage of how much utilization you want an instance to use before autoscaling, which you specified in the request. The default value is 50%.

  • scaling_policy_objective(Option<ScalingPolicyObjective>):

    An object representing the anticipated traffic pattern for an endpoint that you specified in the request.

  • metric(Option<ScalingPolicyMetric>):

    An object with a list of metrics that were benchmarked during the previously completed Inference Recommender job.

  • dynamic_scaling_configuration(Option<DynamicScalingConfiguration>):

    An object with the recommended values for you to specify when creating an autoscaling policy.

• On failure, responds with SdkError<GetScalingConfigurationRecommendationError>

impl Client

pub fn get_search_suggestions(&self) -> GetSearchSuggestionsFluentBuilder

Constructs a fluent builder for the GetSearchSuggestions operation.

• The fluent builder is configurable:
  • resource(ResourceType) / set_resource(Option<ResourceType>):
    required: true
    

    The name of the SageMaker resource to search for.

    
    
  • suggestion_query(SuggestionQuery) / set_suggestion_query(Option<SuggestionQuery>):
    required: false
    

    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>

impl Client

pub fn import_hub_content(&self) -> ImportHubContentFluentBuilder

Constructs a fluent builder for the ImportHubContent operation.

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

    The name of the hub content to import.

    
    
  • hub_content_version(impl Into<String>) / set_hub_content_version(Option<String>):
    required: false
    

    The version of the hub content to import.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of hub content to import.

    
    
  • document_schema_version(impl Into<String>) / set_document_schema_version(Option<String>):
    required: true
    

    The version of the hub content schema to import.

    
    
  • hub_name(impl Into<String>) / set_hub_name(Option<String>):
    required: true
    

    The name of the hub to import content into.

    
    
  • hub_content_display_name(impl Into<String>) / set_hub_content_display_name(Option<String>):
    required: false
    

    The display name of the hub content to import.

    
    
  • hub_content_description(impl Into<String>) / set_hub_content_description(Option<String>):
    required: false
    

    A description of the hub content to import.

    
    
  • hub_content_markdown(impl Into<String>) / set_hub_content_markdown(Option<String>):
    required: false
    

    A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.

    
    
  • hub_content_document(impl Into<String>) / set_hub_content_document(Option<String>):
    required: true
    

    The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.

    
    
  • support_status(HubContentSupportStatus) / set_support_status(Option<HubContentSupportStatus>):
    required: false
    

    The status of the hub content resource.

    
    
  • hub_content_search_keywords(impl Into<String>) / set_hub_content_search_keywords(Option<Vec::<String>>):
    required: false
    

    The searchable keywords of the hub content.

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

    Any tags associated with the hub content.

    
    
• On success, responds with ImportHubContentOutput with field(s):
  • hub_arn(Option<String>):

    The ARN of the hub that the content was imported into.

  • hub_content_arn(Option<String>):

    The ARN of the hub content that was imported.

• On failure, responds with SdkError<ImportHubContentError>

impl Client

pub fn list_actions(&self) -> ListActionsFluentBuilder

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>):
    required: false
    

    A filter that returns only actions with the specified source URI.

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

    A filter that returns only actions of the specified type.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns only actions created on or after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only actions created on or before the specified time.

    
    
  • sort_by(SortActionsBy) / set_sort_by(Option<SortActionsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_algorithms(&self) -> ListAlgorithmsFluentBuilder

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>):
    required: false
    

    A filter that returns only algorithms created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only algorithms created before the specified time (timestamp).

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

    The maximum number of algorithms to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

• On failure, responds with SdkError<ListAlgorithmsError>

impl Client

pub fn list_aliases(&self) -> ListAliasesFluentBuilder

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

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

    The name of the image.

    
    
  • alias(impl Into<String>) / set_alias(Option<String>):
    required: false
    

    The alias of the image version.

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

    The version of the image. If image version is not specified, the aliases of all versions of the image are listed.

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

    The maximum number of aliases to return.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the previous call to ListAliases didn’t return the full set of aliases, the call returns a token for retrieving the next set of aliases.

    
    
• On success, responds with ListAliasesOutput with field(s):
  • sage_maker_image_version_aliases(Option<Vec::<String>>):

    A list of SageMaker AI image version aliases.

  • next_token(Option<String>):

    A token for getting the next set of aliases, if more aliases exist.

• On failure, responds with SdkError<ListAliasesError>

impl Client

pub fn list_app_image_configs(&self) -> ListAppImageConfigsFluentBuilder

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>):
    required: false
    

    The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only AppImageConfigs whose name contains the specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only AppImageConfigs created on or before the specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only AppImageConfigs created on or after the specified time.

    
    
  • modified_time_before(DateTime) / set_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only AppImageConfigs modified on or before the specified time.

    
    
  • modified_time_after(DateTime) / set_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only AppImageConfigs modified on or after the specified time.

    
    
  • sort_by(AppImageConfigSortKey) / set_sort_by(Option<AppImageConfigSortKey>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>

impl Client

pub fn list_apps(&self) -> ListAppsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for the results. The default is Ascending.

    
    
  • sort_by(AppSortKey) / set_sort_by(Option<AppSortKey>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • domain_id_equals(impl Into<String>) / set_domain_id_equals(Option<String>):
    required: false
    

    A parameter to search for the domain ID.

    
    
  • user_profile_name_equals(impl Into<String>) / set_user_profile_name_equals(Option<String>):
    required: false
    

    A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.

    
    
  • space_name_equals(impl Into<String>) / set_space_name_equals(Option<String>):
    required: false
    

    A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.

    
    
• 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>

impl Client

pub fn list_artifacts(&self) -> ListArtifactsFluentBuilder

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>):
    required: false
    

    A filter that returns only artifacts with the specified source URI.

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

    A filter that returns only artifacts of the specified type.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns only artifacts created on or after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only artifacts created on or before the specified time.

    
    
  • sort_by(SortArtifactsBy) / set_sort_by(Option<SortArtifactsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_associations(&self) -> ListAssociationsFluentBuilder

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>):
    required: false
    

    A filter that returns only associations with the specified source ARN.

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

    A filter that returns only associations with the specified destination Amazon Resource Name (ARN).

    
    
  • source_type(impl Into<String>) / set_source_type(Option<String>):
    required: false
    

    A filter that returns only associations with the specified source type.

    
    
  • destination_type(impl Into<String>) / set_destination_type(Option<String>):
    required: false
    

    A filter that returns only associations with the specified destination type.

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

    A filter that returns only associations of the specified type.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns only associations created on or after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only associations created on or before the specified time.

    
    
  • sort_by(SortAssociationsBy) / set_sort_by(Option<SortAssociationsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_auto_ml_jobs(&self) -> ListAutoMLJobsFluentBuilder

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>):
    required: false
    

    Request a list of jobs, using a filter for time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Request a list of jobs, using a filter for time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Request a list of jobs, using a filter for time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Request a list of jobs, using a filter for time.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Request a list of jobs, using a search filter for name.

    
    
  • status_equals(AutoMlJobStatus) / set_status_equals(Option<AutoMlJobStatus>):
    required: false
    

    Request a list of jobs, using a filter for status.

    
    
  • sort_order(AutoMlSortOrder) / set_sort_order(Option<AutoMlSortOrder>):
    required: false
    

    The sort order for the results. The default is Descending.

    
    
  • sort_by(AutoMlSortBy) / set_sort_by(Option<AutoMlSortBy>):
    required: false
    

    The parameter by which to sort the results. The default is Name.

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

    Request a list of jobs up to a specified limit.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>

impl Client

pub fn list_candidates_for_auto_ml_job( &self, ) -> ListCandidatesForAutoMLJobFluentBuilder

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>):
    required: true
    

    List the candidates created for the job by providing the job’s name.

    
    
  • status_equals(CandidateStatus) / set_status_equals(Option<CandidateStatus>):
    required: false
    

    List the candidates for the job and filter by status.

    
    
  • candidate_name_equals(impl Into<String>) / set_candidate_name_equals(Option<String>):
    required: false
    

    List the candidates for the job and filter by candidate name.

    
    
  • sort_order(AutoMlSortOrder) / set_sort_order(Option<AutoMlSortOrder>):
    required: false
    

    The sort order for the results. The default is Ascending.

    
    
  • sort_by(CandidateSortBy) / set_sort_by(Option<CandidateSortBy>):
    required: false
    

    The parameter by which to sort the results. The default is Descending.

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

    List the job’s candidates up to a specified limit.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>

impl Client

pub fn list_cluster_nodes(&self) -> ListClusterNodesFluentBuilder

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

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

    The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.

    Acceptable formats include:

    • YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z

    • YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00

    • YYYY-MM-DD, for example, 2014-10-01

    • Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

    For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

    
    
  • instance_group_name_contains(impl Into<String>) / set_instance_group_name_contains(Option<String>):
    required: false
    

    A filter that returns the instance groups whose name contain a specified string.

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

    The maximum number of nodes to return in the response.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the result of the previous ListClusterNodes request was truncated, the response includes a NextToken. To retrieve the next set of cluster nodes, use the token in the next request.

    
    
  • sort_by(ClusterSortBy) / set_sort_by(Option<ClusterSortBy>):
    required: false
    

    The field by which to sort results. The default value is CREATION_TIME.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default value is Ascending.

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

    The next token specified for listing instances in a SageMaker HyperPod cluster.

  • cluster_node_summaries(Option<Vec::<ClusterNodeSummary>>):

    The summaries of listed instances in a SageMaker HyperPod cluster

• On failure, responds with SdkError<ListClusterNodesError>

impl Client

pub fn list_cluster_scheduler_configs( &self, ) -> ListClusterSchedulerConfigsFluentBuilder

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

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

    Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filter for name containing this string.

    
    
  • cluster_arn(impl Into<String>) / set_cluster_arn(Option<String>):
    required: false
    

    Filter for ARN of the cluster.

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

    Filter for status.

    
    
  • sort_by(SortClusterSchedulerConfigBy) / set_sort_by(Option<SortClusterSchedulerConfigBy>):
    required: false
    

    Filter for sorting the list by a given value. For example, sort by name, creation time, or status.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of cluster policies to list.

    
    
• On success, responds with ListClusterSchedulerConfigsOutput with field(s):
  • cluster_scheduler_config_summaries(Option<Vec::<ClusterSchedulerConfigSummary>>):

    Summaries of the cluster policies.

  • 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<ListClusterSchedulerConfigsError>

impl Client

pub fn list_clusters(&self) -> ListClustersFluentBuilder

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

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

    Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.

    Acceptable formats include:

    • YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z

    • YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00

    • YYYY-MM-DD, for example, 2014-10-01

    • Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.

    For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.

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

    Set the maximum number of SageMaker HyperPod clusters to list.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Set the maximum number of instances to print in the list.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Set the next token to retrieve the list of SageMaker HyperPod clusters.

    
    
  • sort_by(ClusterSortBy) / set_sort_by(Option<ClusterSortBy>):
    required: false
    

    The field by which to sort results. The default value is CREATION_TIME.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default value is Ascending.

    
    
  • training_plan_arn(impl Into<String>) / set_training_plan_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN); of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

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

    If the result of the previous ListClusters request was truncated, the response includes a NextToken. To retrieve the next set of clusters, use the token in the next request.

  • cluster_summaries(Option<Vec::<ClusterSummary>>):

    The summaries of listed SageMaker HyperPod clusters.

• On failure, responds with SdkError<ListClustersError>

impl Client

pub fn list_code_repositories(&self) -> ListCodeRepositoriesFluentBuilder

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>):
    required: false
    

    A filter that returns only Git repositories that were created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only Git repositories that were last modified before the specified time.

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

    The maximum number of Git repositories to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The field to sort results by. The default is Name.

    
    
  • sort_order(CodeRepositorySortOrder) / set_sort_order(Option<CodeRepositorySortOrder>):
    required: false
    

    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>

impl Client

pub fn list_compilation_jobs(&self) -> ListCompilationJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The maximum number of model compilation jobs to return in the response.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns the model compilation jobs whose name contains a specified string.

    
    
  • status_equals(CompilationJobStatus) / set_status_equals(Option<CompilationJobStatus>):
    required: false
    

    A filter that retrieves model compilation jobs with a specific CompilationJobStatus status.

    
    
  • sort_by(ListCompilationJobsSortBy) / set_sort_by(Option<ListCompilationJobsSortBy>):
    required: false
    

    The field by which to sort results. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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 AI 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>

impl Client

pub fn list_compute_quotas(&self) -> ListComputeQuotasFluentBuilder

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

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

    Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filter for name containing this string.

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

    Filter for status.

    
    
  • cluster_arn(impl Into<String>) / set_cluster_arn(Option<String>):
    required: false
    

    Filter for ARN of the cluster.

    
    
  • sort_by(SortQuotaBy) / set_sort_by(Option<SortQuotaBy>):
    required: false
    

    Filter for sorting the list by a given value. For example, sort by name, creation time, or status.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The order of the list. By default, listed in Descending order according to by SortBy. To change the list order, you can specify SortOrder to be Ascending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of compute allocation definitions to list.

    
    
• On success, responds with ListComputeQuotasOutput with field(s):
  • compute_quota_summaries(Option<Vec::<ComputeQuotaSummary>>):

    Summaries of the compute allocation definitions.

  • 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<ListComputeQuotasError>

impl Client

pub fn list_contexts(&self) -> ListContextsFluentBuilder

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>):
    required: false
    

    A filter that returns only contexts with the specified source URI.

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

    A filter that returns only contexts of the specified type.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns only contexts created on or after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only contexts created on or before the specified time.

    
    
  • sort_by(SortContextsBy) / set_sort_by(Option<SortContextsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_data_quality_job_definitions( &self, ) -> ListDataQualityJobDefinitionsFluentBuilder

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>):
    required: false
    

    A filter that lists the data quality job definitions associated with the specified endpoint.

    
    
  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of data quality monitoring job definitions to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_device_fleets(&self) -> ListDeviceFleetsFluentBuilder

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>):
    required: false
    

    The response from the last list when returning a list large enough to need tokening.

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

    The maximum number of results to select.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Filter fleets where packaging job was created after specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Filter fleets where the edge packaging job was created before specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Select fleets where the job was updated after X

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Select fleets where the job was updated before X

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filter for fleets containing this name in their fleet device name.

    
    
  • sort_by(ListDeviceFleetsSortBy) / set_sort_by(Option<ListDeviceFleetsSortBy>):
    required: false
    

    The column to sort by.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>

impl Client

pub fn list_devices(&self) -> ListDevicesFluentBuilder

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>):
    required: false
    

    The response from the last list when returning a list large enough to need tokening.

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

    Maximum number of results to select.

    
    
  • latest_heartbeat_after(DateTime) / set_latest_heartbeat_after(Option<DateTime>):
    required: false
    

    Select fleets where the job was updated after X

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

    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>):
    required: false
    

    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>

impl Client

pub fn list_domains(&self) -> ListDomainsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

    
    
• 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>

impl Client

pub fn list_edge_deployment_plans(&self) -> ListEdgeDeploymentPlansFluentBuilder

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

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

    The response from the last list when returning a list large enough to need tokening.

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

    The maximum number of results to select (50 by default).

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Selects edge deployment plans created after this time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Selects edge deployment plans created before this time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Selects edge deployment plans that were last updated after this time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Selects edge deployment plans that were last updated before this time.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Selects edge deployment plans with names containing this name.

    
    
  • device_fleet_name_contains(impl Into<String>) / set_device_fleet_name_contains(Option<String>):
    required: false
    

    Selects edge deployment plans with a device fleet name containing this name.

    
    
  • sort_by(ListEdgeDeploymentPlansSortBy) / set_sort_by(Option<ListEdgeDeploymentPlansSortBy>):
    required: false
    

    The column by which to sort the edge deployment plans. Can be one of NAME, DEVICEFLEETNAME, CREATIONTIME, LASTMODIFIEDTIME.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The direction of the sorting (ascending or descending).

    
    
• On success, responds with ListEdgeDeploymentPlansOutput with field(s):
  • edge_deployment_plan_summaries(Option<Vec::<EdgeDeploymentPlanSummary>>):

    List of summaries of edge deployment plans.

  • next_token(Option<String>):

    The token to use when calling the next page of results.

• On failure, responds with SdkError<ListEdgeDeploymentPlansError>

impl Client

pub fn list_edge_packaging_jobs(&self) -> ListEdgePackagingJobsFluentBuilder

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>):
    required: false
    

    The response from the last list when returning a list large enough to need tokening.

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

    Maximum number of results to select.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Select jobs where the job was created after specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Select jobs where the job was created before specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Select jobs where the job was updated after specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Select jobs where the job was updated before specified time.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filter for jobs containing this name in their packaging job name.

    
    
  • model_name_contains(impl Into<String>) / set_model_name_contains(Option<String>):
    required: false
    

    Filter for jobs where the model name contains this string.

    
    
  • status_equals(EdgePackagingJobStatus) / set_status_equals(Option<EdgePackagingJobStatus>):
    required: false
    

    The job status to filter for.

    
    
  • sort_by(ListEdgePackagingJobsSortBy) / set_sort_by(Option<ListEdgePackagingJobsSortBy>):
    required: false
    

    Use to specify what column to sort by.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>

impl Client

pub fn list_endpoint_configs(&self) -> ListEndpointConfigsFluentBuilder

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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):
    required: false
    

    The sort order for results. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of training jobs to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only endpoint configurations created before the specified time (timestamp).

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

• On failure, responds with SdkError<ListEndpointConfigsError>

impl Client

pub fn list_endpoints(&self) -> ListEndpointsFluentBuilder

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>):
    required: false
    

    Sorts the list of results. The default is CreationTime.

    
    
  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):
    required: false
    

    The sort order for results. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only endpoints that were created before the specified time (timestamp).

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only endpoints that were modified after the specified timestamp.

    
    
  • status_equals(EndpointStatus) / set_status_equals(Option<EndpointStatus>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListEndpointsError>

impl Client

pub fn list_experiments(&self) -> ListExperimentsFluentBuilder

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>):
    required: false
    

    A filter that returns only experiments created after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only experiments created before the specified time.

    
    
  • sort_by(SortExperimentsBy) / set_sort_by(Option<SortExperimentsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_feature_groups(&self) -> ListFeatureGroupsFluentBuilder

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

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

    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>):
    required: false
    

    A FeatureGroup status. Filters by FeatureGroup status.

    
    
  • offline_store_status_equals(OfflineStoreStatusValue) / set_offline_store_status_equals(Option<OfflineStoreStatusValue>):
    required: false
    

    An OfflineStore status. Filters by OfflineStore status.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Use this parameter to search for FeatureGroupss created after a specific date and time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Use this parameter to search for FeatureGroupss created before a specific date and time.

    
    
  • sort_order(FeatureGroupSortOrder) / set_sort_order(Option<FeatureGroupSortOrder>):
    required: false
    

    The order in which feature groups are listed.

    
    
  • sort_by(FeatureGroupSortBy) / set_sort_by(Option<FeatureGroupSortBy>):
    required: false
    

    The value on which the feature group list is sorted.

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

    The maximum number of results returned by ListFeatureGroups.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>

impl Client

pub fn list_flow_definitions(&self) -> ListFlowDefinitionsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only flow definitions that were created before the specified timestamp.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>):
    required: false
    

    A token to resume pagination.

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

    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>

impl Client

pub fn list_hub_content_versions(&self) -> ListHubContentVersionsFluentBuilder

Constructs a fluent builder for the ListHubContentVersions operation.

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

    The name of the hub to list the content versions of.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of hub content to list versions of.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the hub content.

    
    
  • min_version(impl Into<String>) / set_min_version(Option<String>):
    required: false
    

    The lower bound of the hub content versions to list.

    
    
  • max_schema_version(impl Into<String>) / set_max_schema_version(Option<String>):
    required: false
    

    The upper bound of the hub content schema version.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list hub content versions that were created before the time specified.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Only list hub content versions that were created after the time specified.

    
    
  • sort_by(HubContentSortBy) / set_sort_by(Option<HubContentSortBy>):
    required: false
    

    Sort hub content versions by either name or creation time.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Sort hub content versions by ascending or descending order.

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

    The maximum number of hub content versions to list.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListHubContentVersions request was truncated, the response includes a NextToken. To retrieve the next set of hub content versions, use the token in the next request.

    
    
• On success, responds with ListHubContentVersionsOutput with field(s):
  • hub_content_summaries(Option<Vec::<HubContentInfo>>):

    The summaries of the listed hub content versions.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content versions, use it in the subsequent request.

• On failure, responds with SdkError<ListHubContentVersionsError>

impl Client

pub fn list_hub_contents(&self) -> ListHubContentsFluentBuilder

Constructs a fluent builder for the ListHubContents operation.

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

    The name of the hub to list the contents of.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The type of hub content to list.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Only list hub content if the name contains the specified string.

    
    
  • max_schema_version(impl Into<String>) / set_max_schema_version(Option<String>):
    required: false
    

    The upper bound of the hub content schema verion.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list hub content that was created before the time specified.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Only list hub content that was created after the time specified.

    
    
  • sort_by(HubContentSortBy) / set_sort_by(Option<HubContentSortBy>):
    required: false
    

    Sort hub content versions by either name or creation time.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Sort hubs by ascending or descending order.

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

    The maximum amount of hub content to list.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListHubContents request was truncated, the response includes a NextToken. To retrieve the next set of hub content, use the token in the next request.

    
    
• On success, responds with ListHubContentsOutput with field(s):
  • hub_content_summaries(Option<Vec::<HubContentInfo>>):

    The summaries of the listed hub content.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content, use it in the subsequent request.

• On failure, responds with SdkError<ListHubContentsError>

impl Client

pub fn list_hubs(&self) -> ListHubsFluentBuilder

Constructs a fluent builder for the ListHubs operation.

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

    Only list hubs with names that contain the specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list hubs that were created before the time specified.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Only list hubs that were created after the time specified.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Only list hubs that were last modified before the time specified.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Only list hubs that were last modified after the time specified.

    
    
  • sort_by(HubSortBy) / set_sort_by(Option<HubSortBy>):
    required: false
    

    Sort hubs by either name or creation time.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Sort hubs by ascending or descending order.

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

    The maximum number of hubs to list.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListHubs request was truncated, the response includes a NextToken. To retrieve the next set of hubs, use the token in the next request.

    
    
• On success, responds with ListHubsOutput with field(s):
  • hub_summaries(Option<Vec::<HubInfo>>):

    The summaries of the listed hubs.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of hubs, use it in the subsequent request.

• On failure, responds with SdkError<ListHubsError>

impl Client

pub fn list_human_task_uis(&self) -> ListHumanTaskUisFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only human task user interfaces that were created before the specified timestamp.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>):
    required: false
    

    A token to resume pagination.

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

    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>

impl Client

pub fn list_hyper_parameter_tuning_jobs( &self, ) -> ListHyperParameterTuningJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The maximum number of tuning jobs to return. The default value is 10.

    
    
  • sort_by(HyperParameterTuningJobSortByOptions) / set_sort_by(Option<HyperParameterTuningJobSortByOptions>):
    required: false
    

    The field to sort results by. The default is Name.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only tuning jobs that were created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only tuning jobs that were modified before the specified time.

    
    
  • status_equals(HyperParameterTuningJobStatus) / set_status_equals(Option<HyperParameterTuningJobStatus>):
    required: false
    

    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>

impl Client

pub fn list_image_versions(&self) -> ListImageVersionsFluentBuilder

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>):
    required: false
    

    A filter that returns only versions created on or after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only versions created on or before the specified time.

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

    The name of the image to list the versions of.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only versions modified on or before the specified time.

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

    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>):
    required: false
    

    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>):
    required: false
    

    The property used to sort results. The default value is CREATION_TIME.

    
    
  • sort_order(ImageVersionSortOrder) / set_sort_order(Option<ImageVersionSortOrder>):
    required: false
    

    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>

impl Client

pub fn list_images(&self) -> ListImagesFluentBuilder

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>):
    required: false
    

    A filter that returns only images created on or after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only images modified on or before the specified time.

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

    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>):
    required: false
    

    A filter that returns only images whose name contains the specified string.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The property used to sort results. The default value is CREATION_TIME.

    
    
  • sort_order(ImageSortOrder) / set_sort_order(Option<ImageSortOrder>):
    required: false
    

    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>

impl Client

pub fn list_inference_components(&self) -> ListInferenceComponentsFluentBuilder

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

• The fluent builder is configurable:
  • sort_by(InferenceComponentSortKey) / set_sort_by(Option<InferenceComponentSortKey>):
    required: false
    

    The field by which to sort the inference components in the response. The default is CreationTime.

    
    
  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):
    required: false
    

    The sort order for results. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

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

    The maximum number of inference components to return in the response. This value defaults to 10.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filters the results to only those inference components with a name that contains the specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Filters the results to only those inference components that were created before the specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Filters the results to only those inference components that were created after the specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Filters the results to only those inference components that were updated before the specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Filters the results to only those inference components that were updated after the specified time.

    
    
  • status_equals(InferenceComponentStatus) / set_status_equals(Option<InferenceComponentStatus>):
    required: false
    

    Filters the results to only those inference components with the specified status.

    
    
  • endpoint_name_equals(impl Into<String>) / set_endpoint_name_equals(Option<String>):
    required: false
    

    An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.

    
    
  • variant_name_equals(impl Into<String>) / set_variant_name_equals(Option<String>):
    required: false
    

    A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.

    
    
• On success, responds with ListInferenceComponentsOutput with field(s):
  • inference_components(Option<Vec::<InferenceComponentSummary>>):

    A list of inference components and their properties that matches any of the filters you specified in the request.

  • next_token(Option<String>):

    The token to use in a subsequent request to get the next set of results following a truncated response.

• On failure, responds with SdkError<ListInferenceComponentsError>

impl Client

pub fn list_inference_experiments( &self, ) -> ListInferenceExperimentsFluentBuilder

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

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

    Selects inference experiments whose names contain this name.

    
    
  • r#type(InferenceExperimentType) / set_type(Option<InferenceExperimentType>):
    required: false
    

    Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperiment.

    
    
  • status_equals(InferenceExperimentStatus) / set_status_equals(Option<InferenceExperimentStatus>):
    required: false
    

    Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperiment.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Selects inference experiments which were created after this timestamp.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Selects inference experiments which were created before this timestamp.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Selects inference experiments which were last modified after this timestamp.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Selects inference experiments which were last modified before this timestamp.

    
    
  • sort_by(SortInferenceExperimentsBy) / set_sort_by(Option<SortInferenceExperimentsBy>):
    required: false
    

    The column by which to sort the listed inference experiments.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The direction of sorting (ascending or descending).

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    The response from the last list when returning a list large enough to need tokening.

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

    The maximum number of results to select.

    
    
• On success, responds with ListInferenceExperimentsOutput with field(s):
  • inference_experiments(Option<Vec::<InferenceExperimentSummary>>):

    List of inference experiments.

  • next_token(Option<String>):

    The token to use when calling the next page of results.

• On failure, responds with SdkError<ListInferenceExperimentsError>

impl Client

pub fn list_inference_recommendations_job_steps( &self, ) -> ListInferenceRecommendationsJobStepsFluentBuilder

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

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

    The name for the Inference Recommender job.

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

    A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.

    
    
  • step_type(RecommendationStepType) / set_step_type(Option<RecommendationStepType>):
    required: false
    

    A filter to return details about the specified type of subtask.

    BENCHMARK: Evaluate the performance of your model on different instance types.

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

    The maximum number of results to return.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.

    
    
• On success, responds with ListInferenceRecommendationsJobStepsOutput with field(s):
  • steps(Option<Vec::<InferenceRecommendationsJobStep>>):

    A list of all subtask details in Inference Recommender.

  • next_token(Option<String>):

    A token that you can specify in your next request to return more results from the list.

• On failure, responds with SdkError<ListInferenceRecommendationsJobStepsError>

impl Client

pub fn list_inference_recommendations_jobs( &self, ) -> ListInferenceRecommendationsJobsFluentBuilder

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>):
    required: false
    

    A filter that returns only jobs created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only jobs created before the specified time (timestamp).

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that retrieves only inference recommendations jobs with a specific status.

    
    
  • sort_by(ListInferenceRecommendationsJobsSortBy) / set_sort_by(Option<ListInferenceRecommendationsJobsSortBy>):
    required: false
    

    The parameter by which to sort the results.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for the results.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of recommendations to return in the response.

    
    
  • model_name_equals(impl Into<String>) / set_model_name_equals(Option<String>):
    required: false
    

    A filter that returns only jobs that were created for this model.

    
    
  • model_package_version_arn_equals(impl Into<String>) / set_model_package_version_arn_equals(Option<String>):
    required: false
    

    A filter that returns only jobs that were created for this versioned model package.

    
    
• 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>

impl Client

pub fn list_labeling_jobs(&self) -> ListLabelingJobsFluentBuilder

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>):
    required: false
    

    A filter that returns only labeling jobs created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only labeling jobs modified before the specified time (timestamp).

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

    The maximum number of labeling jobs to return in each page of the response.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • status_equals(LabelingJobStatus) / set_status_equals(Option<LabelingJobStatus>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListLabelingJobsError>

impl Client

pub fn list_labeling_jobs_for_workteam( &self, ) -> ListLabelingJobsForWorkteamFluentBuilder

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>):
    required: true
    

    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>):
    required: false
    

    The maximum number of labeling jobs to return in each page of the response.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only labeling jobs created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListLabelingJobsForWorkteamError>

impl Client

pub fn list_lineage_groups(&self) -> ListLineageGroupsFluentBuilder

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>):
    required: false
    

    A timestamp to filter against lineage groups created after a certain point in time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A timestamp to filter against lineage groups created before a certain point in time.

    
    
  • sort_by(SortLineageGroupsBy) / set_sort_by(Option<SortLineageGroupsBy>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for the results. The default is Ascending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_mlflow_tracking_servers( &self, ) -> ListMlflowTrackingServersFluentBuilder

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

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

    Use the CreatedAfter filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as “2024-03-16T01:46:56+00:00”. The CreatedAfter parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    Use the CreatedBefore filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as “2024-03-16T01:46:56+00:00”. The CreatedBefore parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.

    
    
  • tracking_server_status(TrackingServerStatus) / set_tracking_server_status(Option<TrackingServerStatus>):
    required: false
    

    Filter for tracking servers with a specified creation status.

    
    
  • mlflow_version(impl Into<String>) / set_mlflow_version(Option<String>):
    required: false
    

    Filter for tracking servers using the specified MLflow version.

    
    
  • sort_by(SortTrackingServerBy) / set_sort_by(Option<SortTrackingServerBy>):
    required: false
    

    Filter for trackings servers sorting by name, creation time, or creation status.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Change the order of the listed tracking servers. By default, tracking servers are listed in Descending order by creation time. To change the list order, you can specify SortOrder to be Ascending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of tracking servers to list.

    
    
• On success, responds with ListMlflowTrackingServersOutput with field(s):
  • tracking_server_summaries(Option<Vec::<TrackingServerSummary>>):

    A list of tracking servers according to chosen filters.

  • 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<ListMlflowTrackingServersError>

impl Client

pub fn list_model_bias_job_definitions( &self, ) -> ListModelBiasJobDefinitionsFluentBuilder

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>):
    required: false
    

    Name of the endpoint to monitor for model bias.

    
    
  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):
    required: false
    

    Whether to sort results by the Name or CreationTime field. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    Filter for model bias jobs whose name contains a specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only model bias jobs created before a specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• On failure, responds with SdkError<ListModelBiasJobDefinitionsError>

impl Client

pub fn list_model_card_export_jobs( &self, ) -> ListModelCardExportJobsFluentBuilder

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

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

    List export jobs for the model card with the specified name.

    
    
  • model_card_version(i32) / set_model_card_version(Option<i32>):
    required: false
    

    List export jobs for the model card with the specified version.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Only list model card export jobs that were created after the time specified.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list model card export jobs that were created before the time specified.

    
    
  • model_card_export_job_name_contains(impl Into<String>) / set_model_card_export_job_name_contains(Option<String>):
    required: false
    

    Only list model card export jobs with names that contain the specified string.

    
    
  • status_equals(ModelCardExportJobStatus) / set_status_equals(Option<ModelCardExportJobStatus>):
    required: false
    

    Only list model card export jobs with the specified status.

    
    
  • sort_by(ModelCardExportJobSortBy) / set_sort_by(Option<ModelCardExportJobSortBy>):
    required: false
    

    Sort model card export jobs by either name or creation time. Sorts by creation time by default.

    
    
  • sort_order(ModelCardExportJobSortOrder) / set_sort_order(Option<ModelCardExportJobSortOrder>):
    required: false
    

    Sort model card export jobs by ascending or descending order.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListModelCardExportJobs request was truncated, the response includes a NextToken. To retrieve the next set of model card export jobs, use the token in the next request.

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

    The maximum number of model card export jobs to list.

    
    
• On success, responds with ListModelCardExportJobsOutput with field(s):
  • model_card_export_job_summaries(Option<Vec::<ModelCardExportJobSummary>>):

    The summaries of the listed model card export jobs.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of model card export jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListModelCardExportJobsError>

impl Client

pub fn list_model_card_versions(&self) -> ListModelCardVersionsFluentBuilder

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

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

    Only list model card versions that were created after the time specified.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list model card versions that were created before the time specified.

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

    The maximum number of model card versions to list.

    
    
  • model_card_name(impl Into<String>) / set_model_card_name(Option<String>):
    required: true
    

    List model card versions for the model card with the specified name or Amazon Resource Name (ARN).

    
    
  • model_card_status(ModelCardStatus) / set_model_card_status(Option<ModelCardStatus>):
    required: false
    

    Only list model card versions with the specified approval status.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListModelCardVersions request was truncated, the response includes a NextToken. To retrieve the next set of model card versions, use the token in the next request.

    
    
  • sort_by(ModelCardVersionSortBy) / set_sort_by(Option<ModelCardVersionSortBy>):
    required: false
    

    Sort listed model card versions by version. Sorts by version by default.

    
    
  • sort_order(ModelCardSortOrder) / set_sort_order(Option<ModelCardSortOrder>):
    required: false
    

    Sort model card versions by ascending or descending order.

    
    
• On success, responds with ListModelCardVersionsOutput with field(s):
  • model_card_version_summary_list(Option<Vec::<ModelCardVersionSummary>>):

    The summaries of the listed versions of the model card.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of model card versions, use it in the subsequent request.

• On failure, responds with SdkError<ListModelCardVersionsError>

impl Client

pub fn list_model_cards(&self) -> ListModelCardsFluentBuilder

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

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

    Only list model cards that were created after the time specified.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Only list model cards that were created before the time specified.

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

    The maximum number of model cards to list.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Only list model cards with names that contain the specified string.

    
    
  • model_card_status(ModelCardStatus) / set_model_card_status(Option<ModelCardStatus>):
    required: false
    

    Only list model cards with the specified approval status.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to a previous ListModelCards request was truncated, the response includes a NextToken. To retrieve the next set of model cards, use the token in the next request.

    
    
  • sort_by(ModelCardSortBy) / set_sort_by(Option<ModelCardSortBy>):
    required: false
    

    Sort model cards by either name or creation time. Sorts by creation time by default.

    
    
  • sort_order(ModelCardSortOrder) / set_sort_order(Option<ModelCardSortOrder>):
    required: false
    

    Sort model cards by ascending or descending order.

    
    
• On success, responds with ListModelCardsOutput with field(s):
  • model_card_summaries(Option<Vec::<ModelCardSummary>>):

    The summaries of the listed model cards.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of model cards, use it in the subsequent request.

• On failure, responds with SdkError<ListModelCardsError>

impl Client

pub fn list_model_explainability_job_definitions( &self, ) -> ListModelExplainabilityJobDefinitionsFluentBuilder

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>):
    required: false
    

    Name of the endpoint to monitor for model explainability.

    
    
  • sort_by(MonitoringJobDefinitionSortKey) / set_sort_by(Option<MonitoringJobDefinitionSortKey>):
    required: false
    

    Whether to sort results by the Name or CreationTime field. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    Filter for model explainability jobs whose name contains a specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only model explainability jobs created before a specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• On failure, responds with SdkError<ListModelExplainabilityJobDefinitionsError>

impl Client

pub fn list_model_metadata(&self) -> ListModelMetadataFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_model_package_groups(&self) -> ListModelPackageGroupsFluentBuilder

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>):
    required: false
    

    A filter that returns only model groups created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only model groups created before the specified time.

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

    The maximum number of results to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • cross_account_filter_option(CrossAccountFilterOption) / set_cross_account_filter_option(Option<CrossAccountFilterOption>):
    required: false
    

    A filter that returns either model groups shared with you or model groups in your own account. When the value is CrossAccount, the results show the resources made discoverable to you from other accounts. When the value is SameAccount or null, the results show resources from your account. The default is SameAccount.

    
    
• 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>

impl Client

pub fn list_model_packages(&self) -> ListModelPackagesFluentBuilder

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>):
    required: false
    

    A filter that returns only model packages created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only model packages created before the specified time (timestamp).

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

    The maximum number of model packages to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only model versions that belong to the specified model group.

    
    
  • model_package_type(ModelPackageType) / set_model_package_type(Option<ModelPackageType>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.

• On failure, responds with SdkError<ListModelPackagesError>

impl Client

pub fn list_model_quality_job_definitions( &self, ) -> ListModelQualityJobDefinitionsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of results to return in a call to ListModelQualityJobDefinitions.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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 AI 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>

impl Client

pub fn list_models(&self) -> ListModelsFluentBuilder

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>):
    required: false
    

    Sorts the list of results. The default is CreationTime.

    
    
  • sort_order(OrderKey) / set_sort_order(Option<OrderKey>):
    required: false
    

    The sort order for results. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of models to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only models created before the specified time (timestamp).

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    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, SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

• On failure, responds with SdkError<ListModelsError>

impl Client

pub fn list_monitoring_alert_history( &self, ) -> ListMonitoringAlertHistoryFluentBuilder

Constructs a fluent builder for the ListMonitoringAlertHistory 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>):
    required: false
    

    The name of a monitoring schedule.

    
    
  • monitoring_alert_name(impl Into<String>) / set_monitoring_alert_name(Option<String>):
    required: false
    

    The name of a monitoring alert.

    
    
  • sort_by(MonitoringAlertHistorySortKey) / set_sort_by(Option<MonitoringAlertHistorySortKey>):
    required: false
    

    The field used to sort results. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order, whether Ascending or Descending, of the alert history. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the result of the previous ListMonitoringAlertHistory request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.

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

    The maximum number of results to display. The default is 100.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only alerts created on or before the specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only alerts created on or after the specified time.

    
    
  • status_equals(MonitoringAlertStatus) / set_status_equals(Option<MonitoringAlertStatus>):
    required: false
    

    A filter that retrieves only alerts with a specific status.

    
    
• On success, responds with ListMonitoringAlertHistoryOutput with field(s):
  • monitoring_alert_history(Option<Vec::<MonitoringAlertHistorySummary>>):

    An alert history for a model monitoring schedule.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.

• On failure, responds with SdkError<ListMonitoringAlertHistoryError>

impl Client

pub fn list_monitoring_alerts(&self) -> ListMonitoringAlertsFluentBuilder

Constructs a fluent builder for the ListMonitoringAlerts 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>):
    required: true
    

    The name of a monitoring schedule.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the result of the previous ListMonitoringAlerts request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.

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

    The maximum number of results to display. The default is 100.

    
    
• On success, responds with ListMonitoringAlertsOutput with field(s):
  • monitoring_alert_summaries(Option<Vec::<MonitoringAlertSummary>>):

    A JSON array where each element is a summary for a monitoring alert.

  • next_token(Option<String>):

    If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.

• On failure, responds with SdkError<ListMonitoringAlertsError>

impl Client

pub fn list_monitoring_executions( &self, ) -> ListMonitoringExecutionsFluentBuilder

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>):
    required: false
    

    Name of a specific schedule to fetch jobs for.

    
    
  • endpoint_name(impl Into<String>) / set_endpoint_name(Option<String>):
    required: false
    

    Name of a specific endpoint to fetch jobs for.

    
    
  • sort_by(MonitoringExecutionSortKey) / set_sort_by(Option<MonitoringExecutionSortKey>):
    required: false
    

    Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    Filter for jobs scheduled before a specified time.

    
    
  • scheduled_time_after(DateTime) / set_scheduled_time_after(Option<DateTime>):
    required: false
    

    Filter for jobs scheduled after a specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only jobs created before a specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only jobs created after a specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only jobs modified after a specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only jobs modified before a specified time.

    
    
  • status_equals(ExecutionStatus) / set_status_equals(Option<ExecutionStatus>):
    required: false
    

    A filter that retrieves only jobs with a specific status.

    
    
  • monitoring_job_definition_name(impl Into<String>) / set_monitoring_job_definition_name(Option<String>):
    required: false
    

    Gets a list of the monitoring job runs of the specified monitoring job definitions.

    
    
  • monitoring_type_equals(MonitoringType) / set_monitoring_type_equals(Option<MonitoringType>):
    required: false
    

    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>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• On failure, responds with SdkError<ListMonitoringExecutionsError>

impl Client

pub fn list_monitoring_schedules(&self) -> ListMonitoringSchedulesFluentBuilder

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>):
    required: false
    

    Name of a specific endpoint to fetch schedules for.

    
    
  • sort_by(MonitoringScheduleSortKey) / set_sort_by(Option<MonitoringScheduleSortKey>):
    required: false
    

    Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Whether to sort the results in Ascending or Descending order. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    Filter for monitoring schedules whose name contains a specified string.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only monitoring schedules created before a specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only monitoring schedules created after a specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only monitoring schedules modified before a specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only monitoring schedules modified after a specified time.

    
    
  • status_equals(ScheduleStatus) / set_status_equals(Option<ScheduleStatus>):
    required: false
    

    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>):
    required: false
    

    Gets a list of the monitoring schedules for the specified monitoring job definition.

    
    
  • monitoring_type_equals(MonitoringType) / set_monitoring_type_equals(Option<MonitoringType>):
    required: false
    

    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>):

    The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

• On failure, responds with SdkError<ListMonitoringSchedulesError>

impl Client

pub fn list_notebook_instance_lifecycle_configs( &self, ) -> ListNotebookInstanceLifecycleConfigsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The maximum number of lifecycle configurations to return in the response.

    
    
  • sort_by(NotebookInstanceLifecycleConfigSortKey) / set_sort_by(Option<NotebookInstanceLifecycleConfigSortKey>):
    required: false
    

    Sorts the list of results. The default is CreationTime.

    
    
  • sort_order(NotebookInstanceLifecycleConfigSortOrder) / set_sort_order(Option<NotebookInstanceLifecycleConfigSortOrder>):
    required: false
    

    The sort order for results.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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, SageMaker AI 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>

impl Client

pub fn list_notebook_instances(&self) -> ListNotebookInstancesFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The maximum number of notebook instances to return.

    
    
  • sort_by(NotebookInstanceSortKey) / set_sort_by(Option<NotebookInstanceSortKey>):
    required: false
    

    The field to sort results by. The default is Name.

    
    
  • sort_order(NotebookInstanceSortOrder) / set_sort_order(Option<NotebookInstanceSortOrder>):
    required: false
    

    The sort order for results.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only notebook instances that were modified after the specified time (timestamp).

    
    
  • status_equals(NotebookInstanceStatus) / set_status_equals(Option<NotebookInstanceStatus>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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, SageMaker AI 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>

impl Client

pub fn list_optimization_jobs(&self) -> ListOptimizationJobsFluentBuilder

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

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

    A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.

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

    The maximum number of optimization jobs to return in the response. The default is 50.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Filters the results to only those optimization jobs that were created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Filters the results to only those optimization jobs that were created before the specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    Filters the results to only those optimization jobs that were updated after the specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    Filters the results to only those optimization jobs that were updated before the specified time.

    
    
  • optimization_contains(impl Into<String>) / set_optimization_contains(Option<String>):
    required: false
    

    Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either Quantization or Compilation.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    Filters the results to only those optimization jobs with a name that contains the specified string.

    
    
  • status_equals(OptimizationJobStatus) / set_status_equals(Option<OptimizationJobStatus>):
    required: false
    

    Filters the results to only those optimization jobs with the specified status.

    
    
  • sort_by(ListOptimizationJobsSortBy) / set_sort_by(Option<ListOptimizationJobsSortBy>):
    required: false
    

    The field by which to sort the optimization jobs in the response. The default is CreationTime

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending

    
    
• On success, responds with ListOptimizationJobsOutput with field(s):
  • optimization_job_summaries(Option<Vec::<OptimizationJobSummary>>):

    A list of optimization jobs and their properties that matches any of the filters you specified in the request.

  • next_token(Option<String>):

    The token to use in a subsequent request to get the next set of results following a truncated response.

• On failure, responds with SdkError<ListOptimizationJobsError>

impl Client

pub fn list_partner_apps(&self) -> ListPartnerAppsFluentBuilder

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

• The fluent builder is configurable:
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    This parameter defines the maximum number of results that can be returned in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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 success, responds with ListPartnerAppsOutput with field(s):
  • summaries(Option<Vec::<PartnerAppSummary>>):

    The information related to each of the SageMaker Partner AI Apps in an account.

  • 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<ListPartnerAppsError>

impl Client

pub fn list_pipeline_execution_steps( &self, ) -> ListPipelineExecutionStepsFluentBuilder

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>):
    required: false
    

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

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of pipeline execution steps to return in the response.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    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>

impl Client

pub fn list_pipeline_executions(&self) -> ListPipelineExecutionsFluentBuilder

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>):
    required: true
    

    The name or Amazon Resource Name (ARN) of the pipeline.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns the pipeline executions that were created after a specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns the pipeline executions that were created before a specified time.

    
    
  • sort_by(SortPipelineExecutionsBy) / set_sort_by(Option<SortPipelineExecutionsBy>):
    required: false
    

    The field by which to sort results. The default is CreatedTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_pipeline_parameters_for_execution( &self, ) -> ListPipelineParametersForExecutionFluentBuilder

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>):
    required: true
    

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

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_pipelines(&self) -> ListPipelinesFluentBuilder

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>):
    required: false
    

    The prefix of the pipeline name.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns the pipelines that were created after a specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns the pipelines that were created before a specified time.

    
    
  • sort_by(SortPipelinesBy) / set_sort_by(Option<SortPipelinesBy>):
    required: false
    

    The field by which to sort results. The default is CreatedTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_processing_jobs(&self) -> ListProcessingJobsFluentBuilder

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>):
    required: false
    

    A filter that returns only processing jobs created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only processing jobs created after the specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only processing jobs modified after the specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only processing jobs modified before the specified time.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that retrieves only processing jobs with a specific status.

    
    
  • sort_by(SortBy) / set_sort_by(Option<SortBy>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_projects(&self) -> ListProjectsFluentBuilder

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>):
    required: false
    

    A filter that returns the projects that were created after a specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns the projects that were created before a specified time.

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

    The maximum number of projects to return in the response.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    A filter that returns the projects whose name contains a specified string.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The field by which to sort results. The default is CreationTime.

    
    
  • sort_order(ProjectSortOrder) / set_sort_order(Option<ProjectSortOrder>):
    required: false
    

    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>

impl Client

pub fn list_resource_catalogs(&self) -> ListResourceCatalogsFluentBuilder

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

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

    A string that partially matches one or more ResourceCatalogs names. Filters ResourceCatalog by name.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    Use this parameter to search for ResourceCatalogs created after a specific date and time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    Use this parameter to search for ResourceCatalogs created before a specific date and time.

    
    
  • sort_order(ResourceCatalogSortOrder) / set_sort_order(Option<ResourceCatalogSortOrder>):
    required: false
    

    The order in which the resource catalogs are listed.

    
    
  • sort_by(ResourceCatalogSortBy) / set_sort_by(Option<ResourceCatalogSortBy>):
    required: false
    

    The value on which the resource catalog list is sorted.

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

    The maximum number of results returned by ListResourceCatalogs.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    A token to resume pagination of ListResourceCatalogs results.

    
    
• On success, responds with ListResourceCatalogsOutput with field(s):
  • resource_catalogs(Option<Vec::<ResourceCatalog>>):

    A list of the requested ResourceCatalogs.

  • next_token(Option<String>):

    A token to resume pagination of ListResourceCatalogs results.

• On failure, responds with SdkError<ListResourceCatalogsError>

impl Client

pub fn list_spaces(&self) -> ListSpacesFluentBuilder

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

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

    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>):
    required: false
    

    This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for the results. The default is Ascending.

    
    
  • sort_by(SpaceSortKey) / set_sort_by(Option<SpaceSortKey>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • domain_id_equals(impl Into<String>) / set_domain_id_equals(Option<String>):
    required: false
    

    A parameter to search for the domain ID.

    
    
  • space_name_contains(impl Into<String>) / set_space_name_contains(Option<String>):
    required: false
    

    A parameter by which to filter the results.

    
    
• On success, responds with ListSpacesOutput with field(s):
  • spaces(Option<Vec::<SpaceDetails>>):

    The list of spaces.

  • 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<ListSpacesError>

impl Client

pub fn list_stage_devices(&self) -> ListStageDevicesFluentBuilder

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

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

    The response from the last list when returning a list large enough to neeed tokening.

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

    The maximum number of requests to select.

    
    
  • edge_deployment_plan_name(impl Into<String>) / set_edge_deployment_plan_name(Option<String>):
    required: true
    

    The name of the edge deployment plan.

    
    
  • exclude_devices_deployed_in_other_stage(bool) / set_exclude_devices_deployed_in_other_stage(Option<bool>):
    required: false
    

    Toggle for excluding devices deployed in other stages.

    
    
  • stage_name(impl Into<String>) / set_stage_name(Option<String>):
    required: true
    

    The name of the stage in the deployment.

    
    
• On success, responds with ListStageDevicesOutput with field(s):
  • device_deployment_summaries(Option<Vec::<DeviceDeploymentSummary>>):

    List of summaries of devices allocated to the stage.

  • next_token(Option<String>):

    The token to use when calling the next page of results.

• On failure, responds with SdkError<ListStageDevicesError>

impl Client

pub fn list_studio_lifecycle_configs( &self, ) -> ListStudioLifecycleConfigsFluentBuilder

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>):
    required: false
    

    The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only Lifecycle Configurations created on or before the specified time.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only Lifecycle Configurations created on or after the specified time.

    
    
  • modified_time_before(DateTime) / set_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only Lifecycle Configurations modified before the specified time.

    
    
  • modified_time_after(DateTime) / set_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only Lifecycle Configurations modified after the specified time.

    
    
  • sort_by(StudioLifecycleConfigSortKey) / set_sort_by(Option<StudioLifecycleConfigSortKey>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

    
    
• On success, responds with ListStudioLifecycleConfigsOutput with field(s):
  • 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.

  • studio_lifecycle_configs(Option<Vec::<StudioLifecycleConfigDetails>>):

    A list of Lifecycle Configurations and their properties.

• On failure, responds with SdkError<ListStudioLifecycleConfigsError>

impl Client

pub fn list_subscribed_workteams(&self) -> ListSubscribedWorkteamsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_tags(&self) -> ListTagsFluentBuilder

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>):
    required: true
    

    The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    If the response to the previous ListTags request is truncated, 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>):
    required: false
    

    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, 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>

impl Client

pub fn list_training_jobs(&self) -> ListTrainingJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The maximum number of training jobs to return in the response.

    
    
  • creation_time_after(DateTime) / set_creation_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only training jobs created after the specified time (timestamp).

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only training jobs modified before the specified time (timestamp).

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that retrieves only training jobs with a specific status.

    
    
  • sort_by(SortBy) / set_sort_by(Option<SortBy>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • warm_pool_status_equals(WarmPoolResourceStatus) / set_warm_pool_status_equals(Option<WarmPoolResourceStatus>):
    required: false
    

    A filter that retrieves only training jobs with a specific warm pool status.

    
    
  • training_plan_arn_equals(impl Into<String>) / set_training_plan_arn_equals(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN); of the training plan to filter training jobs by. For more information about reserving GPU capacity for your SageMaker training jobs using Amazon SageMaker Training Plan, see CreateTrainingPlan .

    
    
• 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, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

• On failure, responds with SdkError<ListTrainingJobsError>

impl Client

pub fn list_training_jobs_for_hyper_parameter_tuning_job( &self, ) -> ListTrainingJobsForHyperParameterTuningJobFluentBuilder

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>):
    required: true
    

    The name of the tuning job whose training jobs you want to list.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of training jobs to return. The default value is 10.

    
    
  • status_equals(TrainingJobStatus) / set_status_equals(Option<TrainingJobStatus>):
    required: false
    

    A filter that returns only training jobs with the specified status.

    
    
  • sort_by(TrainingJobSortByOptions) / set_sort_by(Option<TrainingJobSortByOptions>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_training_plans(&self) -> ListTrainingPlansFluentBuilder

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

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

    A token to continue pagination if more results are available.

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

    The maximum number of results to return in the response.

    
    
  • start_time_after(DateTime) / set_start_time_after(Option<DateTime>):
    required: false
    

    Filter to list only training plans with an actual start time after this date.

    
    
  • start_time_before(DateTime) / set_start_time_before(Option<DateTime>):
    required: false
    

    Filter to list only training plans with an actual start time before this date.

    
    
  • sort_by(TrainingPlanSortBy) / set_sort_by(Option<TrainingPlanSortBy>):
    required: false
    

    The training plan field to sort the results by (e.g., StartTime, Status).

    
    
  • sort_order(TrainingPlanSortOrder) / set_sort_order(Option<TrainingPlanSortOrder>):
    required: false
    

    The order to sort the results (Ascending or Descending).

    
    
  • filters(TrainingPlanFilter) / set_filters(Option<Vec::<TrainingPlanFilter>>):
    required: false
    

    Additional filters to apply to the list of training plans.

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

    A token to continue pagination if more results are available.

  • training_plan_summaries(Option<Vec::<TrainingPlanSummary>>):

    A list of summary information for the training plans.

• On failure, responds with SdkError<ListTrainingPlansError>

impl Client

pub fn list_transform_jobs(&self) -> ListTransformJobsFluentBuilder

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>):
    required: false
    

    A filter that returns only transform jobs created after the specified time.

    
    
  • creation_time_before(DateTime) / set_creation_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only transform jobs created before the specified time.

    
    
  • last_modified_time_after(DateTime) / set_last_modified_time_after(Option<DateTime>):
    required: false
    

    A filter that returns only transform jobs modified after the specified time.

    
    
  • last_modified_time_before(DateTime) / set_last_modified_time_before(Option<DateTime>):
    required: false
    

    A filter that returns only transform jobs modified before the specified time.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A filter that retrieves only transform jobs with a specific status.

    
    
  • sort_by(SortBy) / set_sort_by(Option<SortBy>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_trial_components(&self) -> ListTrialComponentsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only components created after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only components created before the specified time.

    
    
  • sort_by(SortTrialComponentsBy) / set_sort_by(Option<SortTrialComponentsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

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

    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>):
    required: false
    

    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>

impl Client

pub fn list_trials(&self) -> ListTrialsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    A filter that returns only trials that are associated with the specified trial component.

    
    
  • created_after(DateTime) / set_created_after(Option<DateTime>):
    required: false
    

    A filter that returns only trials created after the specified time.

    
    
  • created_before(DateTime) / set_created_before(Option<DateTime>):
    required: false
    

    A filter that returns only trials created before the specified time.

    
    
  • sort_by(SortTrialsBy) / set_sort_by(Option<SortTrialsBy>):
    required: false
    

    The property used to sort results. The default value is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order. The default value is Descending.

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

    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>):
    required: false
    

    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>

impl Client

pub fn list_user_profiles(&self) -> ListUserProfilesFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for the results. The default is Ascending.

    
    
  • sort_by(UserProfileSortKey) / set_sort_by(Option<UserProfileSortKey>):
    required: false
    

    The parameter by which to sort the results. The default is CreationTime.

    
    
  • domain_id_equals(impl Into<String>) / set_domain_id_equals(Option<String>):
    required: false
    

    A parameter by which to filter the results.

    
    
  • user_profile_name_contains(impl Into<String>) / set_user_profile_name_contains(Option<String>):
    required: false
    

    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>

impl Client

pub fn list_workforces(&self) -> ListWorkforcesFluentBuilder

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>):
    required: false
    

    Sort workforces using the workforce name or creation date.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    Sort workforces in ascending or descending order.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    A token to resume pagination.

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

    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>

impl Client

pub fn list_workteams(&self) -> ListWorkteamsFluentBuilder

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>):
    required: false
    

    The field to sort results by. The default is CreationTime.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sort order for results. The default is Ascending.

    
    
  • name_contains(impl Into<String>) / set_name_contains(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn put_model_package_group_policy( &self, ) -> PutModelPackageGroupPolicyFluentBuilder

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>):
    required: true
    

    The name of the model group to add a resource policy to.

    
    
  • resource_policy(impl Into<String>) / set_resource_policy(Option<String>):
    required: true
    

    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>

impl Client

pub fn query_lineage(&self) -> QueryLineageFluentBuilder

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

• The fluent builder is configurable:
  • start_arns(impl Into<String>) / set_start_arns(Option<Vec::<String>>):
    required: false
    

    A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.

    
    
  • direction(Direction) / set_direction(Option<Direction>):
    required: false
    

    Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.

    
    
  • include_edges(bool) / set_include_edges(Option<bool>):
    required: false
    

    Setting this value to True retrieves 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>):
    required: false
    

    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>):
    required: false
    

    The maximum depth in lineage relationships from the StartArns that are 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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn register_devices(&self) -> RegisterDevicesFluentBuilder

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>):
    required: true
    

    The name of the fleet.

    
    
  • devices(Device) / set_devices(Option<Vec::<Device>>):
    required: true
    

    A list of devices to register with SageMaker Edge Manager.

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

    The tags associated with devices.

    
    
• On success, responds with RegisterDevicesOutput
• On failure, responds with SdkError<RegisterDevicesError>

impl Client

pub fn render_ui_template(&self) -> RenderUiTemplateFluentBuilder

Constructs a fluent builder for the RenderUiTemplate operation.

• The fluent builder is configurable:
  • ui_template(UiTemplate) / set_ui_template(Option<UiTemplate>):
    required: false
    

    A Template object containing the worker UI template to render.

    
    
  • task(RenderableTask) / set_task(Option<RenderableTask>):
    required: true
    

    A RenderableTask object containing a representative task to render.

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

    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>):
    required: false
    

    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>

impl Client

pub fn retry_pipeline_execution(&self) -> RetryPipelineExecutionFluentBuilder

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>):
    required: true
    

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

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

    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>):
    required: false
    

    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>

impl Client

pub fn search(&self) -> SearchFluentBuilder

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>):
    required: true
    

    The name of the SageMaker resource to search for.

    
    
  • search_expression(SearchExpression) / set_search_expression(Option<SearchExpression>):
    required: false
    

    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>):
    required: false
    

    The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

    
    
  • sort_order(SearchSortOrder) / set_sort_order(Option<SearchSortOrder>):
    required: false
    

    How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of results to return.

    
    
  • cross_account_filter_option(CrossAccountFilterOption) / set_cross_account_filter_option(Option<CrossAccountFilterOption>):
    required: false
    

    A cross account filter option. When the value is “CrossAccount” the search results will only include resources made discoverable to you from other accounts. When the value is “SameAccount” or null the search results will only include resources from your account. Default is null. For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of ResourceCatalogs viewable is 1000.

    
    
  • visibility_conditions(VisibilityConditions) / set_visibility_conditions(Option<Vec::<VisibilityConditions>>):
    required: false
    

    Limits the results of your search request to the resources that you can access.

    
    
• 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.

  • total_hits(Option<TotalHits>):

    The total number of matching results.

• On failure, responds with SdkError<SearchError>

impl Client

pub fn search_training_plan_offerings( &self, ) -> SearchTrainingPlanOfferingsFluentBuilder

Constructs a fluent builder for the SearchTrainingPlanOfferings operation.

• The fluent builder is configurable:
  • instance_type(ReservedCapacityInstanceType) / set_instance_type(Option<ReservedCapacityInstanceType>):
    required: false
    

    The type of instance you want to search for in the available training plan offerings. This field allows you to filter the search results based on the specific compute resources you require for your SageMaker training jobs or SageMaker HyperPod clusters. When searching for training plan offerings, specifying the instance type helps you find Reserved Instances that match your computational needs.

    
    
  • instance_count(i32) / set_instance_count(Option<i32>):
    required: false
    

    The number of instances you want to reserve in the training plan offerings. This allows you to specify the quantity of compute resources needed for your SageMaker training jobs or SageMaker HyperPod clusters, helping you find reserved capacity offerings that match your requirements.

    
    
  • start_time_after(DateTime) / set_start_time_after(Option<DateTime>):
    required: false
    

    A filter to search for training plan offerings with a start time after a specified date.

    
    
  • end_time_before(DateTime) / set_end_time_before(Option<DateTime>):
    required: false
    

    A filter to search for reserved capacity offerings with an end time before a specified date.

    
    
  • duration_hours(i64) / set_duration_hours(Option<i64>):
    required: true
    

    The desired duration in hours for the training plan offerings.

    
    
  • target_resources(SageMakerResourceName) / set_target_resources(Option<Vec::<SageMakerResourceName>>):
    required: true
    

    The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod) to search for in the offerings.

    Training plans are specific to their target resource.

    • A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.

    • A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster’s instance group.

    
    
• On success, responds with SearchTrainingPlanOfferingsOutput with field(s):
  • training_plan_offerings(Option<Vec::<TrainingPlanOffering>>):

    A list of training plan offerings that match the search criteria.

• On failure, responds with SdkError<SearchTrainingPlanOfferingsError>

impl Client

pub fn send_pipeline_execution_step_failure( &self, ) -> SendPipelineExecutionStepFailureFluentBuilder

Constructs a fluent builder for the SendPipelineExecutionStepFailure operation.

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

    The pipeline generated token from the Amazon SQS queue.

    
    
  • failure_reason(impl Into<String>) / set_failure_reason(Option<String>):
    required: false
    

    A message describing why the step failed.

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

    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>

impl Client

pub fn send_pipeline_execution_step_success( &self, ) -> SendPipelineExecutionStepSuccessFluentBuilder

Constructs a fluent builder for the SendPipelineExecutionStepSuccess operation.

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

    The pipeline generated token from the Amazon SQS queue.

    
    
  • output_parameters(OutputParameter) / set_output_parameters(Option<Vec::<OutputParameter>>):
    required: false
    

    A list of the output parameters of the callback step.

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

    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>

impl Client

pub fn start_edge_deployment_stage( &self, ) -> StartEdgeDeploymentStageFluentBuilder

Constructs a fluent builder for the StartEdgeDeploymentStage operation.

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

    The name of the edge deployment plan to start.

    
    
  • stage_name(impl Into<String>) / set_stage_name(Option<String>):
    required: true
    

    The name of the stage to start.

    
    
• On success, responds with StartEdgeDeploymentStageOutput
• On failure, responds with SdkError<StartEdgeDeploymentStageError>

impl Client

pub fn start_inference_experiment( &self, ) -> StartInferenceExperimentFluentBuilder

Constructs a fluent builder for the StartInferenceExperiment operation.

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

    The name of the inference experiment to start.

    
    
• On success, responds with StartInferenceExperimentOutput with field(s):
  • inference_experiment_arn(Option<String>):

    The ARN of the started inference experiment to start.

• On failure, responds with SdkError<StartInferenceExperimentError>

impl Client

pub fn start_mlflow_tracking_server( &self, ) -> StartMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the StartMlflowTrackingServer operation.

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

    The name of the tracking server to start.

    
    
• On success, responds with StartMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    The ARN of the started tracking server.

• On failure, responds with SdkError<StartMlflowTrackingServerError>

impl Client

pub fn start_monitoring_schedule(&self) -> StartMonitoringScheduleFluentBuilder

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>):
    required: true
    

    The name of the schedule to start.

    
    
• On success, responds with StartMonitoringScheduleOutput
• On failure, responds with SdkError<StartMonitoringScheduleError>

impl Client

pub fn start_notebook_instance(&self) -> StartNotebookInstanceFluentBuilder

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>):
    required: true
    

    The name of the notebook instance to start.

    
    
• On success, responds with StartNotebookInstanceOutput
• On failure, responds with SdkError<StartNotebookInstanceError>

impl Client

pub fn start_pipeline_execution(&self) -> StartPipelineExecutionFluentBuilder

Constructs a fluent builder for the StartPipelineExecution operation.

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

    The name or Amazon Resource Name (ARN) of the pipeline.

    
    
  • pipeline_execution_display_name(impl Into<String>) / set_pipeline_execution_display_name(Option<String>):
    required: false
    

    The display name of the pipeline execution.

    
    
  • pipeline_parameters(Parameter) / set_pipeline_parameters(Option<Vec::<Parameter>>):
    required: false
    

    Contains a list of pipeline parameters. This list can be empty.

    
    
  • pipeline_execution_description(impl Into<String>) / set_pipeline_execution_description(Option<String>):
    required: false
    

    The description of the pipeline execution.

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

    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>):
    required: false
    

    This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.

    
    
  • selective_execution_config(SelectiveExecutionConfig) / set_selective_execution_config(Option<SelectiveExecutionConfig>):
    required: false
    

    The selective execution configuration applied to the pipeline 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>

impl Client

pub fn stop_auto_ml_job(&self) -> StopAutoMLJobFluentBuilder

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>):
    required: true
    

    The name of the object you are requesting.

    
    
• On success, responds with StopAutoMlJobOutput
• On failure, responds with SdkError<StopAutoMLJobError>

impl Client

pub fn stop_compilation_job(&self) -> StopCompilationJobFluentBuilder

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>):
    required: true
    

    The name of the model compilation job to stop.

    
    
• On success, responds with StopCompilationJobOutput
• On failure, responds with SdkError<StopCompilationJobError>

impl Client

pub fn stop_edge_deployment_stage(&self) -> StopEdgeDeploymentStageFluentBuilder

Constructs a fluent builder for the StopEdgeDeploymentStage operation.

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

    The name of the edge deployment plan to stop.

    
    
  • stage_name(impl Into<String>) / set_stage_name(Option<String>):
    required: true
    

    The name of the stage to stop.

    
    
• On success, responds with StopEdgeDeploymentStageOutput
• On failure, responds with SdkError<StopEdgeDeploymentStageError>

impl Client

pub fn stop_edge_packaging_job(&self) -> StopEdgePackagingJobFluentBuilder

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>):
    required: true
    

    The name of the edge packaging job.

    
    
• On success, responds with StopEdgePackagingJobOutput
• On failure, responds with SdkError<StopEdgePackagingJobError>

impl Client

pub fn stop_hyper_parameter_tuning_job( &self, ) -> StopHyperParameterTuningJobFluentBuilder

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>):
    required: true
    

    The name of the tuning job to stop.

    
    
• On success, responds with StopHyperParameterTuningJobOutput
• On failure, responds with SdkError<StopHyperParameterTuningJobError>

impl Client

pub fn stop_inference_experiment(&self) -> StopInferenceExperimentFluentBuilder

Constructs a fluent builder for the StopInferenceExperiment operation.

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

    The name of the inference experiment to stop.

    
    
  • model_variant_actions(impl Into<String>, ModelVariantAction) / set_model_variant_actions(Option<HashMap::<String, ModelVariantAction>>):
    required: true
    

    Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:

    • Promote - Promote the shadow variant to a production variant

    • Remove - Delete the variant

    • Retain - Keep the variant as it is

    
    
  • desired_model_variants(ModelVariantConfig) / set_desired_model_variants(Option<Vec::<ModelVariantConfig>>):
    required: false
    

    An array of ModelVariantConfig objects. There is one for each variant that you want to deploy after the inference experiment stops. Each ModelVariantConfig describes the infrastructure configuration for deploying the corresponding variant.

    
    
  • desired_state(InferenceExperimentStopDesiredState) / set_desired_state(Option<InferenceExperimentStopDesiredState>):
    required: false
    

    The desired state of the experiment after stopping. The possible states are the following:

    • Completed: The experiment completed successfully

    • Cancelled: The experiment was canceled

    
    
  • reason(impl Into<String>) / set_reason(Option<String>):
    required: false
    

    The reason for stopping the experiment.

    
    
• On success, responds with StopInferenceExperimentOutput with field(s):
  • inference_experiment_arn(Option<String>):

    The ARN of the stopped inference experiment.

• On failure, responds with SdkError<StopInferenceExperimentError>

impl Client

pub fn stop_inference_recommendations_job( &self, ) -> StopInferenceRecommendationsJobFluentBuilder

Constructs a fluent builder for the StopInferenceRecommendationsJob operation.

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

    The name of the job you want to stop.

    
    
• On success, responds with StopInferenceRecommendationsJobOutput
• On failure, responds with SdkError<StopInferenceRecommendationsJobError>

impl Client

pub fn stop_labeling_job(&self) -> StopLabelingJobFluentBuilder

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>):
    required: true
    

    The name of the labeling job to stop.

    
    
• On success, responds with StopLabelingJobOutput
• On failure, responds with SdkError<StopLabelingJobError>

impl Client

pub fn stop_mlflow_tracking_server( &self, ) -> StopMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the StopMlflowTrackingServer operation.

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

    The name of the tracking server to stop.

    
    
• On success, responds with StopMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    The ARN of the stopped tracking server.

• On failure, responds with SdkError<StopMlflowTrackingServerError>

impl Client

pub fn stop_monitoring_schedule(&self) -> StopMonitoringScheduleFluentBuilder

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>):
    required: true
    

    The name of the schedule to stop.

    
    
• On success, responds with StopMonitoringScheduleOutput
• On failure, responds with SdkError<StopMonitoringScheduleError>

impl Client

pub fn stop_notebook_instance(&self) -> StopNotebookInstanceFluentBuilder

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>):
    required: true
    

    The name of the notebook instance to terminate.

    
    
• On success, responds with StopNotebookInstanceOutput
• On failure, responds with SdkError<StopNotebookInstanceError>

impl Client

pub fn stop_optimization_job(&self) -> StopOptimizationJobFluentBuilder

Constructs a fluent builder for the StopOptimizationJob operation.

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

    The name that you assigned to the optimization job.

    
    
• On success, responds with StopOptimizationJobOutput
• On failure, responds with SdkError<StopOptimizationJobError>

impl Client

pub fn stop_pipeline_execution(&self) -> StopPipelineExecutionFluentBuilder

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>):
    required: true
    

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

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

    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>

impl Client

pub fn stop_processing_job(&self) -> StopProcessingJobFluentBuilder

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>):
    required: true
    

    The name of the processing job to stop.

    
    
• On success, responds with StopProcessingJobOutput
• On failure, responds with SdkError<StopProcessingJobError>

impl Client

pub fn stop_training_job(&self) -> StopTrainingJobFluentBuilder

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>):
    required: true
    

    The name of the training job to stop.

    
    
• On success, responds with StopTrainingJobOutput
• On failure, responds with SdkError<StopTrainingJobError>

impl Client

pub fn stop_transform_job(&self) -> StopTransformJobFluentBuilder

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>):
    required: true
    

    The name of the batch transform job to stop.

    
    
• On success, responds with StopTransformJobOutput
• On failure, responds with SdkError<StopTransformJobError>

impl Client

pub fn update_action(&self) -> UpdateActionFluentBuilder

Constructs a fluent builder for the UpdateAction operation.

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

    The name of the action to update.

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

    The new description for the action.

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

    The new status for the action.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    The new list of properties. Overwrites the current property list.

    
    
  • properties_to_remove(impl Into<String>) / set_properties_to_remove(Option<Vec::<String>>):
    required: false
    

    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>

impl Client

pub fn update_app_image_config(&self) -> UpdateAppImageConfigFluentBuilder

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>):
    required: true
    

    The name of the AppImageConfig to update.

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

    The new KernelGateway app to run on the image.

    
    
  • jupyter_lab_app_image_config(JupyterLabAppImageConfig) / set_jupyter_lab_app_image_config(Option<JupyterLabAppImageConfig>):
    required: false
    

    The JupyterLab app running on the image.

    
    
  • code_editor_app_image_config(CodeEditorAppImageConfig) / set_code_editor_app_image_config(Option<CodeEditorAppImageConfig>):
    required: false
    

    The Code Editor app running on the image.

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

    The ARN for the AppImageConfig.

• On failure, responds with SdkError<UpdateAppImageConfigError>

impl Client

pub fn update_artifact(&self) -> UpdateArtifactFluentBuilder

Constructs a fluent builder for the UpdateArtifact operation.

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

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

    
    
  • artifact_name(impl Into<String>) / set_artifact_name(Option<String>):
    required: false
    

    The new name for the artifact.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    The new list of properties. Overwrites the current property list.

    
    
  • properties_to_remove(impl Into<String>) / set_properties_to_remove(Option<Vec::<String>>):
    required: false
    

    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>

impl Client

pub fn update_cluster(&self) -> UpdateClusterFluentBuilder

Constructs a fluent builder for the UpdateCluster operation.

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

    Specify the name of the SageMaker HyperPod cluster you want to update.

    
    
  • instance_groups(ClusterInstanceGroupSpecification) / set_instance_groups(Option<Vec::<ClusterInstanceGroupSpecification>>):
    required: true
    

    Specify the instance groups to update.

    
    
  • node_recovery(ClusterNodeRecovery) / set_node_recovery(Option<ClusterNodeRecovery>):
    required: false
    

    The node recovery mode to be applied to the SageMaker HyperPod cluster.

    
    
  • instance_groups_to_delete(impl Into<String>) / set_instance_groups_to_delete(Option<Vec::<String>>):
    required: false
    

    Specify the names of the instance groups to delete. Use a single , as the separator between multiple names.

    
    
• On success, responds with UpdateClusterOutput with field(s):
  • cluster_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated SageMaker HyperPod cluster.

• On failure, responds with SdkError<UpdateClusterError>

impl Client

pub fn update_cluster_scheduler_config( &self, ) -> UpdateClusterSchedulerConfigFluentBuilder

Constructs a fluent builder for the UpdateClusterSchedulerConfig operation.

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

    ID of the cluster policy.

    
    
  • target_version(i32) / set_target_version(Option<i32>):
    required: true
    

    Target version.

    
    
  • scheduler_config(SchedulerConfig) / set_scheduler_config(Option<SchedulerConfig>):
    required: false
    

    Cluster policy configuration.

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

    Description of the cluster policy.

    
    
• On success, responds with UpdateClusterSchedulerConfigOutput with field(s):
  • cluster_scheduler_config_arn(Option<String>):

    ARN of the cluster policy.

  • cluster_scheduler_config_version(Option<i32>):

    Version of the cluster policy.

• On failure, responds with SdkError<UpdateClusterSchedulerConfigError>

impl Client

pub fn update_cluster_software(&self) -> UpdateClusterSoftwareFluentBuilder

Constructs a fluent builder for the UpdateClusterSoftware operation.

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

    Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.

    
    
  • instance_groups(UpdateClusterSoftwareInstanceGroupSpecification) / set_instance_groups(Option<Vec::<UpdateClusterSoftwareInstanceGroupSpecification>>):
    required: false
    

    The array of instance groups for which to update AMI versions.

    
    
  • deployment_config(DeploymentConfiguration) / set_deployment_config(Option<DeploymentConfiguration>):
    required: false
    

    The configuration to use when updating the AMI versions.

    
    
• On success, responds with UpdateClusterSoftwareOutput with field(s):
  • cluster_arn(Option<String>):

    The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster being updated for security patching.

• On failure, responds with SdkError<UpdateClusterSoftwareError>

impl Client

pub fn update_code_repository(&self) -> UpdateCodeRepositoryFluentBuilder

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>):
    required: true
    

    The name of the Git repository to update.

    
    
  • git_config(GitConfigForUpdate) / set_git_config(Option<GitConfigForUpdate>):
    required: false
    

    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>

impl Client

pub fn update_compute_quota(&self) -> UpdateComputeQuotaFluentBuilder

Constructs a fluent builder for the UpdateComputeQuota operation.

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

    ID of the compute allocation definition.

    
    
  • target_version(i32) / set_target_version(Option<i32>):
    required: true
    

    Target version.

    
    
  • compute_quota_config(ComputeQuotaConfig) / set_compute_quota_config(Option<ComputeQuotaConfig>):
    required: false
    

    Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

    
    
  • compute_quota_target(ComputeQuotaTarget) / set_compute_quota_target(Option<ComputeQuotaTarget>):
    required: false
    

    The target entity to allocate compute resources to.

    
    
  • activation_state(ActivationState) / set_activation_state(Option<ActivationState>):
    required: false
    

    The state of the compute allocation being described. Use to enable or disable compute allocation.

    Default is Enabled.

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

    Description of the compute allocation definition.

    
    
• On success, responds with UpdateComputeQuotaOutput with field(s):
  • compute_quota_arn(Option<String>):

    ARN of the compute allocation definition.

  • compute_quota_version(Option<i32>):

    Version of the compute allocation definition.

• On failure, responds with SdkError<UpdateComputeQuotaError>

impl Client

pub fn update_context(&self) -> UpdateContextFluentBuilder

Constructs a fluent builder for the UpdateContext operation.

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

    The name of the context to update.

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

    The new description for the context.

    
    
  • properties(impl Into<String>, impl Into<String>) / set_properties(Option<HashMap::<String, String>>):
    required: false
    

    The new list of properties. Overwrites the current property list.

    
    
  • properties_to_remove(impl Into<String>) / set_properties_to_remove(Option<Vec::<String>>):
    required: false
    

    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>

impl Client

pub fn update_device_fleet(&self) -> UpdateDeviceFleetFluentBuilder

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>):
    required: true
    

    The name of the fleet.

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

    The Amazon Resource Name (ARN) of the device.

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

    Description of the fleet.

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

    Output configuration for storing sample data collected by the fleet.

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

    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>

impl Client

pub fn update_devices(&self) -> UpdateDevicesFluentBuilder

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>):
    required: true
    

    The name of the fleet the devices belong to.

    
    
  • devices(Device) / set_devices(Option<Vec::<Device>>):
    required: true
    

    List of devices to register with Edge Manager agent.

    
    
• On success, responds with UpdateDevicesOutput
• On failure, responds with SdkError<UpdateDevicesError>

impl Client

pub fn update_domain(&self) -> UpdateDomainFluentBuilder

Constructs a fluent builder for the UpdateDomain operation.

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

    The ID of the domain to be updated.

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

    A collection of settings.

    
    
  • domain_settings_for_update(DomainSettingsForUpdate) / set_domain_settings_for_update(Option<DomainSettingsForUpdate>):
    required: false
    

    A collection of DomainSettings configuration values to update.

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

    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. If setting up the domain for use with RStudio, this value must be set to Service.

    
    
  • default_space_settings(DefaultSpaceSettings) / set_default_space_settings(Option<DefaultSpaceSettings>):
    required: false
    

    The default settings for shared spaces that users create in the domain.

    
    
  • subnet_ids(impl Into<String>) / set_subnet_ids(Option<Vec::<String>>):
    required: false
    

    The VPC subnets that Studio uses for communication.

    If removing subnets, ensure there are no apps in the InService, Pending, or Deleting state.

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

    Specifies the VPC used for non-EFS traffic.

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

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

    This configuration can only be modified if there are no apps in the InService, Pending, or Deleting state. The configuration cannot be updated if DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is already set or DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided as part of the same request.

    
    
  • tag_propagation(TagPropagation) / set_tag_propagation(Option<TagPropagation>):
    required: false
    

    Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.

    
    
• 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>

impl Client

pub fn update_endpoint(&self) -> UpdateEndpointFluentBuilder

Constructs a fluent builder for the UpdateEndpoint operation.

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

    The name of the endpoint whose configuration you want to update.

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

    The name of the new endpoint configuration.

    
    
  • retain_all_variant_properties(bool) / set_retain_all_variant_properties(Option<bool>):
    required: false
    

    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(VariantProperty) / set_exclude_retained_variant_properties(Option<Vec::<VariantProperty>>):
    required: false
    

    When you are updating endpoint resources with 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 ExcludeRetainedVariantProperties, no variant properties are overridden.

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

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

    
    
  • retain_deployment_config(bool) / set_retain_deployment_config(Option<bool>):
    required: false
    

    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>

impl Client

pub fn update_endpoint_weights_and_capacities( &self, ) -> UpdateEndpointWeightsAndCapacitiesFluentBuilder

Constructs a fluent builder for the UpdateEndpointWeightsAndCapacities operation.

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

    The name of an existing SageMaker endpoint.

    
    
  • desired_weights_and_capacities(DesiredWeightAndCapacity) / set_desired_weights_and_capacities(Option<Vec::<DesiredWeightAndCapacity>>):
    required: true
    

    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>

impl Client

pub fn update_experiment(&self) -> UpdateExperimentFluentBuilder

Constructs a fluent builder for the UpdateExperiment operation.

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

    The name of the experiment to update.

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

    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>):
    required: false
    

    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>

impl Client

pub fn update_feature_group(&self) -> UpdateFeatureGroupFluentBuilder

Constructs a fluent builder for the UpdateFeatureGroup operation.

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

    The name or Amazon Resource Name (ARN) of the feature group that you’re updating.

    
    
  • feature_additions(FeatureDefinition) / set_feature_additions(Option<Vec::<FeatureDefinition>>):
    required: false
    

    Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you’ve made a valid request. It takes some time after you’ve made a valid request for Feature Store to update the feature group.

    
    
  • online_store_config(OnlineStoreConfigUpdate) / set_online_store_config(Option<OnlineStoreConfigUpdate>):
    required: false
    

    Updates the feature group online store configuration.

    
    
  • throughput_config(ThroughputConfigUpdate) / set_throughput_config(Option<ThroughputConfigUpdate>):
    required: false
    

    The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.

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

    The Amazon Resource Number (ARN) of the feature group that you’re updating.

• On failure, responds with SdkError<UpdateFeatureGroupError>

impl Client

pub fn update_feature_metadata(&self) -> UpdateFeatureMetadataFluentBuilder

Constructs a fluent builder for the UpdateFeatureMetadata operation.

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

    The name or Amazon Resource Name (ARN) of the feature group containing the feature that you’re updating.

    
    
  • feature_name(impl Into<String>) / set_feature_name(Option<String>):
    required: true
    

    The name of the feature that you’re updating.

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

    A description that you can write to better describe the feature.

    
    
  • parameter_additions(FeatureParameter) / set_parameter_additions(Option<Vec::<FeatureParameter>>):
    required: false
    

    A list of key-value pairs that you can add to better describe the feature.

    
    
  • parameter_removals(impl Into<String>) / set_parameter_removals(Option<Vec::<String>>):
    required: false
    

    A list of parameter keys that you can specify to remove parameters that describe your feature.

    
    
• On success, responds with UpdateFeatureMetadataOutput
• On failure, responds with SdkError<UpdateFeatureMetadataError>

impl Client

pub fn update_hub(&self) -> UpdateHubFluentBuilder

Constructs a fluent builder for the UpdateHub operation.

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

    The name of the hub to update.

    
    
  • hub_description(impl Into<String>) / set_hub_description(Option<String>):
    required: false
    

    A description of the updated hub.

    
    
  • hub_display_name(impl Into<String>) / set_hub_display_name(Option<String>):
    required: false
    

    The display name of the hub.

    
    
  • hub_search_keywords(impl Into<String>) / set_hub_search_keywords(Option<Vec::<String>>):
    required: false
    

    The searchable keywords for the hub.

    
    
• On success, responds with UpdateHubOutput with field(s):
  • hub_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated hub.

• On failure, responds with SdkError<UpdateHubError>

impl Client

pub fn update_hub_content(&self) -> UpdateHubContentFluentBuilder

Constructs a fluent builder for the UpdateHubContent operation.

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

    The name of the SageMaker hub that contains the hub content you want to update. You can optionally use the hub ARN instead.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the hub content resource that you want to update.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The content type of the resource that you want to update. Only specify a Model or Notebook resource for this API. To update a ModelReference, use the UpdateHubContentReference API instead.

    
    
  • hub_content_version(impl Into<String>) / set_hub_content_version(Option<String>):
    required: true
    

    The hub content version that you want to update. For example, if you have two versions of a resource in your hub, you can update the second version.

    
    
  • hub_content_display_name(impl Into<String>) / set_hub_content_display_name(Option<String>):
    required: false
    

    The display name of the hub content.

    
    
  • hub_content_description(impl Into<String>) / set_hub_content_description(Option<String>):
    required: false
    

    The description of the hub content.

    
    
  • hub_content_markdown(impl Into<String>) / set_hub_content_markdown(Option<String>):
    required: false
    

    A string that provides a description of the hub content. This string can include links, tables, and standard markdown formatting.

    
    
  • hub_content_search_keywords(impl Into<String>) / set_hub_content_search_keywords(Option<Vec::<String>>):
    required: false
    

    The searchable keywords of the hub content.

    
    
  • support_status(HubContentSupportStatus) / set_support_status(Option<HubContentSupportStatus>):
    required: false
    

    Indicates the current status of the hub content resource.

    
    
• On success, responds with UpdateHubContentOutput with field(s):
  • hub_arn(Option<String>):

    The ARN of the private model hub that contains the updated hub content.

  • hub_content_arn(Option<String>):

    The ARN of the hub content resource that was updated.

• On failure, responds with SdkError<UpdateHubContentError>

impl Client

pub fn update_hub_content_reference( &self, ) -> UpdateHubContentReferenceFluentBuilder

Constructs a fluent builder for the UpdateHubContentReference operation.

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

    The name of the SageMaker hub that contains the hub content you want to update. You can optionally use the hub ARN instead.

    
    
  • hub_content_name(impl Into<String>) / set_hub_content_name(Option<String>):
    required: true
    

    The name of the hub content resource that you want to update.

    
    
  • hub_content_type(HubContentType) / set_hub_content_type(Option<HubContentType>):
    required: true
    

    The content type of the resource that you want to update. Only specify a ModelReference resource for this API. To update a Model or Notebook resource, use the UpdateHubContent API instead.

    
    
  • min_version(impl Into<String>) / set_min_version(Option<String>):
    required: false
    

    The minimum hub content version of the referenced model that you want to use. The minimum version must be older than the latest available version of the referenced model. To support all versions of a model, set the value to 1.0.0.

    
    
• On success, responds with UpdateHubContentReferenceOutput with field(s):
  • hub_arn(Option<String>):

    The ARN of the private model hub that contains the updated hub content.

  • hub_content_arn(Option<String>):

    The ARN of the hub content resource that was updated.

• On failure, responds with SdkError<UpdateHubContentReferenceError>

impl Client

pub fn update_image(&self) -> UpdateImageFluentBuilder

Constructs a fluent builder for the UpdateImage operation.

• The fluent builder is configurable:
  • delete_properties(impl Into<String>) / set_delete_properties(Option<Vec::<String>>):
    required: false
    

    A list of properties to delete. Only the Description and DisplayName properties can be deleted.

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

    The new description for the image.

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

    The new display name for the image.

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

    The name of the image to update.

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

    The new ARN for the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

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

    The ARN of the image.

• On failure, responds with SdkError<UpdateImageError>

impl Client

pub fn update_image_version(&self) -> UpdateImageVersionFluentBuilder

Constructs a fluent builder for the UpdateImageVersion operation.

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

    The name of the image.

    
    
  • alias(impl Into<String>) / set_alias(Option<String>):
    required: false
    

    The alias of the image version.

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

    The version of the image.

    
    
  • aliases_to_add(impl Into<String>) / set_aliases_to_add(Option<Vec::<String>>):
    required: false
    

    A list of aliases to add.

    
    
  • aliases_to_delete(impl Into<String>) / set_aliases_to_delete(Option<Vec::<String>>):
    required: false
    

    A list of aliases to delete.

    
    
  • vendor_guidance(VendorGuidance) / set_vendor_guidance(Option<VendorGuidance>):
    required: false
    

    The availability of the image version specified by the maintainer.

    • NOT_PROVIDED: The maintainers did not provide a status for image version stability.

    • STABLE: The image version is stable.

    • TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.

    • ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.

    
    
  • job_type(JobType) / set_job_type(Option<JobType>):
    required: false
    

    Indicates SageMaker AI job type compatibility.

    • TRAINING: The image version is compatible with SageMaker AI training jobs.

    • INFERENCE: The image version is compatible with SageMaker AI inference jobs.

    • NOTEBOOK_KERNEL: The image version is compatible with SageMaker AI notebook kernels.

    
    
  • ml_framework(impl Into<String>) / set_ml_framework(Option<String>):
    required: false
    

    The machine learning framework vended in the image version.

    
    
  • programming_lang(impl Into<String>) / set_programming_lang(Option<String>):
    required: false
    

    The supported programming language and its version.

    
    
  • processor(Processor) / set_processor(Option<Processor>):
    required: false
    

    Indicates CPU or GPU compatibility.

    • CPU: The image version is compatible with CPU.

    • GPU: The image version is compatible with GPU.

    
    
  • horovod(bool) / set_horovod(Option<bool>):
    required: false
    

    Indicates Horovod compatibility.

    
    
  • release_notes(impl Into<String>) / set_release_notes(Option<String>):
    required: false
    

    The maintainer description of the image version.

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

    The ARN of the image version.

• On failure, responds with SdkError<UpdateImageVersionError>

impl Client

pub fn update_inference_component( &self, ) -> UpdateInferenceComponentFluentBuilder

Constructs a fluent builder for the UpdateInferenceComponent operation.

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

    The name of the inference component.

    
    
  • specification(InferenceComponentSpecification) / set_specification(Option<InferenceComponentSpecification>):
    required: false
    

    Details about the resources to deploy with this inference component, including the model, container, and compute resources.

    
    
  • runtime_config(InferenceComponentRuntimeConfig) / set_runtime_config(Option<InferenceComponentRuntimeConfig>):
    required: false
    

    Runtime settings for a model that is deployed with an inference component.

    
    
  • deployment_config(InferenceComponentDeploymentConfig) / set_deployment_config(Option<InferenceComponentDeploymentConfig>):
    required: false
    

    The deployment configuration for the inference component. The configuration contains the desired deployment strategy and rollback settings.

    
    
• On success, responds with UpdateInferenceComponentOutput with field(s):
  • inference_component_arn(Option<String>):

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

• On failure, responds with SdkError<UpdateInferenceComponentError>

impl Client

pub fn update_inference_component_runtime_config( &self, ) -> UpdateInferenceComponentRuntimeConfigFluentBuilder

Constructs a fluent builder for the UpdateInferenceComponentRuntimeConfig operation.

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

    The name of the inference component to update.

    
    
  • desired_runtime_config(InferenceComponentRuntimeConfig) / set_desired_runtime_config(Option<InferenceComponentRuntimeConfig>):
    required: true
    

    Runtime settings for a model that is deployed with an inference component.

    
    
• On success, responds with UpdateInferenceComponentRuntimeConfigOutput with field(s):
  • inference_component_arn(Option<String>):

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

• On failure, responds with SdkError<UpdateInferenceComponentRuntimeConfigError>

impl Client

pub fn update_inference_experiment( &self, ) -> UpdateInferenceExperimentFluentBuilder

Constructs a fluent builder for the UpdateInferenceExperiment operation.

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

    The name of the inference experiment to be updated.

    
    
  • schedule(InferenceExperimentSchedule) / set_schedule(Option<InferenceExperimentSchedule>):
    required: false
    

    The duration for which the inference experiment will run. If the status of the inference experiment is Created, then you can update both the start and end dates. If the status of the inference experiment is Running, then you can update only the end date.

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

    The description of the inference experiment.

    
    
  • model_variants(ModelVariantConfig) / set_model_variants(Option<Vec::<ModelVariantConfig>>):
    required: false
    

    An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure configuration you want to update.

    
    
  • data_storage_config(InferenceExperimentDataStorageConfig) / set_data_storage_config(Option<InferenceExperimentDataStorageConfig>):
    required: false
    

    The Amazon S3 location and configuration for storing inference request and response data.

    
    
  • shadow_mode_config(ShadowModeConfig) / set_shadow_mode_config(Option<ShadowModeConfig>):
    required: false
    

    The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

    
    
• On success, responds with UpdateInferenceExperimentOutput with field(s):
  • inference_experiment_arn(Option<String>):

    The ARN of the updated inference experiment.

• On failure, responds with SdkError<UpdateInferenceExperimentError>

impl Client

pub fn update_mlflow_tracking_server( &self, ) -> UpdateMlflowTrackingServerFluentBuilder

Constructs a fluent builder for the UpdateMlflowTrackingServer operation.

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

    The name of the MLflow Tracking Server to update.

    
    
  • artifact_store_uri(impl Into<String>) / set_artifact_store_uri(Option<String>):
    required: false
    

    The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.

    
    
  • tracking_server_size(TrackingServerSize) / set_tracking_server_size(Option<TrackingServerSize>):
    required: false
    

    The new size for the MLflow Tracking Server.

    
    
  • automatic_model_registration(bool) / set_automatic_model_registration(Option<bool>):
    required: false
    

    Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False

    
    
  • weekly_maintenance_window_start(impl Into<String>) / set_weekly_maintenance_window_start(Option<String>):
    required: false
    

    The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.

    
    
• On success, responds with UpdateMlflowTrackingServerOutput with field(s):
  • tracking_server_arn(Option<String>):

    The ARN of the updated MLflow Tracking Server.

• On failure, responds with SdkError<UpdateMlflowTrackingServerError>

impl Client

pub fn update_model_card(&self) -> UpdateModelCardFluentBuilder

Constructs a fluent builder for the UpdateModelCard operation.

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

    The name or Amazon Resource Name (ARN) of the model card to update.

    
    
  • content(impl Into<String>) / set_content(Option<String>):
    required: false
    

    The updated model card content. Content must be in model card JSON schema and provided as a string.

    When updating model card content, be sure to include the full content and not just updated content.

    
    
  • model_card_status(ModelCardStatus) / set_model_card_status(Option<ModelCardStatus>):
    required: false
    

    The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.

    • Draft: The model card is a work in progress.

    • PendingReview: The model card is pending review.

    • Approved: The model card is approved.

    • Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.

    
    
• On success, responds with UpdateModelCardOutput with field(s):
  • model_card_arn(Option<String>):

    The Amazon Resource Name (ARN) of the updated model card.

• On failure, responds with SdkError<UpdateModelCardError>

impl Client

pub fn update_model_package(&self) -> UpdateModelPackageFluentBuilder

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>):
    required: true
    

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

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

    The approval status of the model.

    
    
  • approval_description(impl Into<String>) / set_approval_description(Option<String>):
    required: false
    

    A description for the approval status of the model.

    
    
  • customer_metadata_properties(impl Into<String>, impl Into<String>) / set_customer_metadata_properties(Option<HashMap::<String, String>>):
    required: false
    

    The metadata properties associated with the model package versions.

    
    
  • customer_metadata_properties_to_remove(impl Into<String>) / set_customer_metadata_properties_to_remove(Option<Vec::<String>>):
    required: false
    

    The metadata properties associated with the model package versions to remove.

    
    
  • additional_inference_specifications_to_add(AdditionalInferenceSpecificationDefinition) / set_additional_inference_specifications_to_add(Option<Vec::<AdditionalInferenceSpecificationDefinition>>):
    required: false
    

    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.

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

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

    • 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.

    
    
  • source_uri(impl Into<String>) / set_source_uri(Option<String>):
    required: false
    

    The URI of the source for the model package.

    
    
  • model_card(ModelPackageModelCard) / set_model_card(Option<ModelPackageModelCard>):
    required: false
    

    The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

    
    
  • model_life_cycle(ModelLifeCycle) / set_model_life_cycle(Option<ModelLifeCycle>):
    required: false
    

    A structure describing the current state of the model in its life cycle.

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

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

    
    
• 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>

impl Client

pub fn update_monitoring_alert(&self) -> UpdateMonitoringAlertFluentBuilder

Constructs a fluent builder for the UpdateMonitoringAlert operation.

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

    The name of a monitoring schedule.

    
    
  • monitoring_alert_name(impl Into<String>) / set_monitoring_alert_name(Option<String>):
    required: true
    

    The name of a monitoring alert.

    
    
  • datapoints_to_alert(i32) / set_datapoints_to_alert(Option<i32>):
    required: true
    

    Within EvaluationPeriod, how many execution failures will raise an alert.

    
    
  • evaluation_period(i32) / set_evaluation_period(Option<i32>):
    required: true
    

    The number of most recent monitoring executions to consider when evaluating alert status.

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

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

  • monitoring_alert_name(Option<String>):

    The name of a monitoring alert.

• On failure, responds with SdkError<UpdateMonitoringAlertError>

impl Client

pub fn update_monitoring_schedule( &self, ) -> UpdateMonitoringScheduleFluentBuilder

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>):
    required: true
    

    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>):
    required: true
    

    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>

impl Client

pub fn update_notebook_instance(&self) -> UpdateNotebookInstanceFluentBuilder

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>):
    required: true
    

    The name of the notebook instance to update.

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

    The Amazon ML compute instance type.

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

    The Amazon Resource Name (ARN) of the IAM role that SageMaker AI can assume to access the notebook instance. For more information, see SageMaker AI Roles.

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

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

    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(Option<bool>):
    required: false
    

    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>):
    required: false
    

    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 SageMaker AI 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>):
    required: false
    

    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 SageMaker AI Notebook Instances.

    
    
  • additional_code_repositories(impl Into<String>) / set_additional_code_repositories(Option<Vec::<String>>):
    required: false
    

    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 SageMaker AI Notebook Instances.

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

    This parameter is no longer supported. Elastic Inference (EI) is no longer available.

    This parameter was used to specify a list of the EI instance types to associate with this notebook instance.

    
    
  • disassociate_accelerator_types(bool) / set_disassociate_accelerator_types(Option<bool>):
    required: false
    

    This parameter is no longer supported. Elastic Inference (EI) is no longer available.

    This parameter was used to specify a list of the EI instance types to remove from this notebook instance.

    
    
  • disassociate_default_code_repository(bool) / set_disassociate_default_code_repository(Option<bool>):
    required: false
    

    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(Option<bool>):
    required: false
    

    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>):
    required: false
    

    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.

    
    
  • instance_metadata_service_configuration(InstanceMetadataServiceConfiguration) / set_instance_metadata_service_configuration(Option<InstanceMetadataServiceConfiguration>):
    required: false
    

    Information on the IMDS configuration of the notebook instance

    
    
• On success, responds with UpdateNotebookInstanceOutput
• On failure, responds with SdkError<UpdateNotebookInstanceError>

impl Client

pub fn update_notebook_instance_lifecycle_config( &self, ) -> UpdateNotebookInstanceLifecycleConfigFluentBuilder

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>):
    required: true
    

    The name of the lifecycle configuration.

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

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

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

    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>

impl Client

pub fn update_partner_app(&self) -> UpdatePartnerAppFluentBuilder

Constructs a fluent builder for the UpdatePartnerApp operation.

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

    The ARN of the SageMaker Partner AI App to update.

    
    
  • maintenance_config(PartnerAppMaintenanceConfig) / set_maintenance_config(Option<PartnerAppMaintenanceConfig>):
    required: false
    

    Maintenance configuration settings for the SageMaker Partner AI App.

    
    
  • tier(impl Into<String>) / set_tier(Option<String>):
    required: false
    

    Indicates the instance type and size of the cluster attached to the SageMaker Partner AI App.

    
    
  • application_config(PartnerAppConfig) / set_application_config(Option<PartnerAppConfig>):
    required: false
    

    Configuration settings for the SageMaker Partner AI App.

    
    
  • enable_iam_session_based_identity(bool) / set_enable_iam_session_based_identity(Option<bool>):
    required: false
    

    When set to TRUE, the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user.

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

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

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

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

    
    
• On success, responds with UpdatePartnerAppOutput with field(s):
  • arn(Option<String>):

    The ARN of the SageMaker Partner AI App that was updated.

• On failure, responds with SdkError<UpdatePartnerAppError>

impl Client

pub fn update_pipeline(&self) -> UpdatePipelineFluentBuilder

Constructs a fluent builder for the UpdatePipeline operation.

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

    The name of the pipeline to update.

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

    The display name of the pipeline.

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

    The JSON pipeline definition.

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

    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>):
    required: false
    

    The description of the pipeline.

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

    The Amazon Resource Name (ARN) that the pipeline uses to execute.

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

    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>

impl Client

pub fn update_pipeline_execution(&self) -> UpdatePipelineExecutionFluentBuilder

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>):
    required: true
    

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

    
    
  • pipeline_execution_description(impl Into<String>) / set_pipeline_execution_description(Option<String>):
    required: false
    

    The description of the pipeline execution.

    
    
  • pipeline_execution_display_name(impl Into<String>) / set_pipeline_execution_display_name(Option<String>):
    required: false
    

    The display name of the pipeline execution.

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

    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>

impl Client

pub fn update_project(&self) -> UpdateProjectFluentBuilder

Constructs a fluent builder for the UpdateProject operation.

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

    The name of the project.

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

    The description for the project.

    
    
  • service_catalog_provisioning_update_details(ServiceCatalogProvisioningUpdateDetails) / set_service_catalog_provisioning_update_details(Option<ServiceCatalogProvisioningUpdateDetails>):
    required: false
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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. In addition, the project must have tag update constraints set in order to include this parameter in the request. For more information, see Amazon Web Services Service Catalog Tag Update Constraints.

    
    
• 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>

impl Client

pub fn update_space(&self) -> UpdateSpaceFluentBuilder

Constructs a fluent builder for the UpdateSpace operation.

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

    The ID of the associated domain.

    
    
  • space_name(impl Into<String>) / set_space_name(Option<String>):
    required: true
    

    The name of the space.

    
    
  • space_settings(SpaceSettings) / set_space_settings(Option<SpaceSettings>):
    required: false
    

    A collection of space settings.

    
    
  • space_display_name(impl Into<String>) / set_space_display_name(Option<String>):
    required: false
    

    The name of the space that appears in the Amazon SageMaker Studio UI.

    
    
• On success, responds with UpdateSpaceOutput with field(s):
  • space_arn(Option<String>):

    The space’s Amazon Resource Name (ARN).

• On failure, responds with SdkError<UpdateSpaceError>

impl Client

pub fn update_training_job(&self) -> UpdateTrainingJobFluentBuilder

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>):
    required: true
    

    The name of a training job to update the Debugger profiling configuration.

    
    
  • profiler_config(ProfilerConfigForUpdate) / set_profiler_config(Option<ProfilerConfigForUpdate>):
    required: false
    

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

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

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

    
    
  • resource_config(ResourceConfigForUpdate) / set_resource_config(Option<ResourceConfigForUpdate>):
    required: false
    

    The training job ResourceConfig to update warm pool retention length.

    
    
  • remote_debug_config(RemoteDebugConfigForUpdate) / set_remote_debug_config(Option<RemoteDebugConfigForUpdate>):
    required: false
    

    Configuration for remote debugging while the training job is running. You can update the remote debugging configuration when the SecondaryStatus of the job is Downloading or Training.To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

    
    
• 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>

impl Client

pub fn update_trial(&self) -> UpdateTrialFluentBuilder

Constructs a fluent builder for the UpdateTrial operation.

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

    The name of the trial to update.

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

    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>

impl Client

pub fn update_trial_component(&self) -> UpdateTrialComponentFluentBuilder

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>):
    required: true
    

    The name of the component to update.

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

    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>):
    required: false
    

    The new status of the component.

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

    When the component started.

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

    When the component ended.

    
    
  • parameters(impl Into<String>, TrialComponentParameterValue) / set_parameters(Option<HashMap::<String, TrialComponentParameterValue>>):
    required: false
    

    Replaces all of the component’s hyperparameters with the specified hyperparameters or add new hyperparameters. Existing hyperparameters are replaced if the trial component is updated with an identical hyperparameter key.

    
    
  • parameters_to_remove(impl Into<String>) / set_parameters_to_remove(Option<Vec::<String>>):
    required: false
    

    The hyperparameters to remove from the component.

    
    
  • input_artifacts(impl Into<String>, TrialComponentArtifact) / set_input_artifacts(Option<HashMap::<String, TrialComponentArtifact>>):
    required: false
    

    Replaces all of the component’s input artifacts with the specified artifacts or adds new input artifacts. Existing input artifacts are replaced if the trial component is updated with an identical input artifact key.

    
    
  • input_artifacts_to_remove(impl Into<String>) / set_input_artifacts_to_remove(Option<Vec::<String>>):
    required: false
    

    The input artifacts to remove from the component.

    
    
  • output_artifacts(impl Into<String>, TrialComponentArtifact) / set_output_artifacts(Option<HashMap::<String, TrialComponentArtifact>>):
    required: false
    

    Replaces all of the component’s output artifacts with the specified artifacts or adds new output artifacts. Existing output artifacts are replaced if the trial component is updated with an identical output artifact key.

    
    
  • output_artifacts_to_remove(impl Into<String>) / set_output_artifacts_to_remove(Option<Vec::<String>>):
    required: false
    

    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>

impl Client

pub fn update_user_profile(&self) -> UpdateUserProfileFluentBuilder

Constructs a fluent builder for the UpdateUserProfile operation.

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

    The domain ID.

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

    The user profile name.

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

    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>

impl Client

pub fn update_workforce(&self) -> UpdateWorkforceFluentBuilder

Constructs a fluent builder for the UpdateWorkforce operation.

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

    The name of the private workforce that you want to update. You can find your workforce name by using the ListWorkforces operation.

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

    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>):
    required: false
    

    Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.

    
    
  • workforce_vpc_config(WorkforceVpcConfigRequest) / set_workforce_vpc_config(Option<WorkforceVpcConfigRequest>):
    required: false
    

    Use this parameter to update your VPC configuration for a workforce.

    
    
• 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>

impl Client

pub fn update_workteam(&self) -> UpdateWorkteamFluentBuilder

Constructs a fluent builder for the UpdateWorkteam operation.

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

    The name of the work team to update.

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

    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>):
    required: false
    

    An updated description for the work team.

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

    Configures SNS topic notifications for available or expiring work items

    
    
  • worker_access_configuration(WorkerAccessConfiguration) / set_worker_access_configuration(Option<WorkerAccessConfiguration>):
    required: false
    

    Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

    
    
• 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(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

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

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

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

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

Creates a new client from an SDK Config.

Panics

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

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a 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 Waiters for Client

fn wait_until_endpoint_deleted(&self) -> EndpointDeletedFluentBuilder

Wait for endpoint_deleted

fn wait_until_endpoint_in_service(&self) -> EndpointInServiceFluentBuilder

Wait for endpoint_in_service

fn wait_until_image_created(&self) -> ImageCreatedFluentBuilder

Wait for image_created

fn wait_until_image_deleted(&self) -> ImageDeletedFluentBuilder

Wait for image_deleted

fn wait_until_image_updated(&self) -> ImageUpdatedFluentBuilder

Wait for image_updated

fn wait_until_image_version_created(&self) -> ImageVersionCreatedFluentBuilder

Wait for image_version_created

fn wait_until_image_version_deleted(&self) -> ImageVersionDeletedFluentBuilder

Wait for image_version_deleted

fn wait_until_notebook_instance_deleted( &self, ) -> NotebookInstanceDeletedFluentBuilder

Wait for notebook_instance_deleted

fn wait_until_notebook_instance_in_service( &self, ) -> NotebookInstanceInServiceFluentBuilder

Wait for notebook_instance_in_service

fn wait_until_notebook_instance_stopped( &self, ) -> NotebookInstanceStoppedFluentBuilder

Wait for notebook_instance_stopped

fn wait_until_processing_job_completed_or_stopped( &self, ) -> ProcessingJobCompletedOrStoppedFluentBuilder

Wait for processing_job_completed_or_stopped

fn wait_until_training_job_completed_or_stopped( &self, ) -> TrainingJobCompletedOrStoppedFluentBuilder

Wait for training_job_completed_or_stopped

fn wait_until_transform_job_completed_or_stopped( &self, ) -> TransformJobCompletedOrStoppedFluentBuilder

Wait for transform_job_completed_or_stopped