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$version: "2.0"
metadata suppressions = [
{
id: "HttpMethodSemantics"
namespace: "*"
}
{
id: "HttpResponseCodeSemantics"
namespace: "*"
}
{
id: "PaginatedTrait"
namespace: "*"
}
{
id: "HttpHeaderTrait"
namespace: "*"
}
{
id: "HttpUriConflict"
namespace: "*"
}
{
id: "Service"
namespace: "*"
}
]
namespace com.amazonaws.sagemaker
use aws.api#service
use aws.auth#sigv4
use aws.protocols#awsJson1_1
use smithy.waiters#waitable
/// Provides APIs for creating and managing SageMaker resources.
/// Other Resources:
///
/// -
///
/// SageMaker Developer
/// Guide
///
///
/// -
///
///
///
@service(
sdkId: "SageMaker"
arnNamespace: "sagemaker"
cloudFormationName: "SageMaker"
cloudTrailEventSource: "sagemaker.amazonaws.com"
endpointPrefix: "api.sagemaker"
)
@sigv4(
name: "sagemaker"
)
@awsJson1_1
@title("Amazon SageMaker Service")
@xmlNamespace(
uri: "http://sagemaker.amazonaws.com/doc/2017-05-13/"
)
service SageMaker {
version: "2017-07-24"
operations: [
AddAssociation
AddTags
AssociateTrialComponent
BatchDescribeModelPackage
CreateAction
CreateAlgorithm
CreateApp
CreateAppImageConfig
CreateArtifact
CreateAutoMLJob
CreateCodeRepository
CreateCompilationJob
CreateContext
CreateDataQualityJobDefinition
CreateDeviceFleet
CreateDomain
CreateEdgeDeploymentPlan
CreateEdgeDeploymentStage
CreateEdgePackagingJob
CreateEndpoint
CreateEndpointConfig
CreateExperiment
CreateFeatureGroup
CreateFlowDefinition
CreateHub
CreateHumanTaskUi
CreateHyperParameterTuningJob
CreateImage
CreateImageVersion
CreateInferenceExperiment
CreateInferenceRecommendationsJob
CreateLabelingJob
CreateModel
CreateModelBiasJobDefinition
CreateModelCard
CreateModelCardExportJob
CreateModelExplainabilityJobDefinition
CreateModelPackage
CreateModelPackageGroup
CreateModelQualityJobDefinition
CreateMonitoringSchedule
CreateNotebookInstance
CreateNotebookInstanceLifecycleConfig
CreatePipeline
CreatePresignedDomainUrl
CreatePresignedNotebookInstanceUrl
CreateProcessingJob
CreateProject
CreateSpace
CreateStudioLifecycleConfig
CreateTrainingJob
CreateTransformJob
CreateTrial
CreateTrialComponent
CreateUserProfile
CreateWorkforce
CreateWorkteam
DeleteAction
DeleteAlgorithm
DeleteApp
DeleteAppImageConfig
DeleteArtifact
DeleteAssociation
DeleteCodeRepository
DeleteContext
DeleteDataQualityJobDefinition
DeleteDeviceFleet
DeleteDomain
DeleteEdgeDeploymentPlan
DeleteEdgeDeploymentStage
DeleteEndpoint
DeleteEndpointConfig
DeleteExperiment
DeleteFeatureGroup
DeleteFlowDefinition
DeleteHub
DeleteHubContent
DeleteHumanTaskUi
DeleteImage
DeleteImageVersion
DeleteInferenceExperiment
DeleteModel
DeleteModelBiasJobDefinition
DeleteModelCard
DeleteModelExplainabilityJobDefinition
DeleteModelPackage
DeleteModelPackageGroup
DeleteModelPackageGroupPolicy
DeleteModelQualityJobDefinition
DeleteMonitoringSchedule
DeleteNotebookInstance
DeleteNotebookInstanceLifecycleConfig
DeletePipeline
DeleteProject
DeleteSpace
DeleteStudioLifecycleConfig
DeleteTags
DeleteTrial
DeleteTrialComponent
DeleteUserProfile
DeleteWorkforce
DeleteWorkteam
DeregisterDevices
DescribeAction
DescribeAlgorithm
DescribeApp
DescribeAppImageConfig
DescribeArtifact
DescribeAutoMLJob
DescribeCodeRepository
DescribeCompilationJob
DescribeContext
DescribeDataQualityJobDefinition
DescribeDevice
DescribeDeviceFleet
DescribeDomain
DescribeEdgeDeploymentPlan
DescribeEdgePackagingJob
DescribeEndpoint
DescribeEndpointConfig
DescribeExperiment
DescribeFeatureGroup
DescribeFeatureMetadata
DescribeFlowDefinition
DescribeHub
DescribeHubContent
DescribeHumanTaskUi
DescribeHyperParameterTuningJob
DescribeImage
DescribeImageVersion
DescribeInferenceExperiment
DescribeInferenceRecommendationsJob
DescribeLabelingJob
DescribeLineageGroup
DescribeModel
DescribeModelBiasJobDefinition
DescribeModelCard
DescribeModelCardExportJob
DescribeModelExplainabilityJobDefinition
DescribeModelPackage
DescribeModelPackageGroup
DescribeModelQualityJobDefinition
DescribeMonitoringSchedule
DescribeNotebookInstance
DescribeNotebookInstanceLifecycleConfig
DescribePipeline
DescribePipelineDefinitionForExecution
DescribePipelineExecution
DescribeProcessingJob
DescribeProject
DescribeSpace
DescribeStudioLifecycleConfig
DescribeSubscribedWorkteam
DescribeTrainingJob
DescribeTransformJob
DescribeTrial
DescribeTrialComponent
DescribeUserProfile
DescribeWorkforce
DescribeWorkteam
DisableSagemakerServicecatalogPortfolio
DisassociateTrialComponent
EnableSagemakerServicecatalogPortfolio
GetDeviceFleetReport
GetLineageGroupPolicy
GetModelPackageGroupPolicy
GetSagemakerServicecatalogPortfolioStatus
GetSearchSuggestions
ImportHubContent
ListActions
ListAlgorithms
ListAliases
ListAppImageConfigs
ListApps
ListArtifacts
ListAssociations
ListAutoMLJobs
ListCandidatesForAutoMLJob
ListCodeRepositories
ListCompilationJobs
ListContexts
ListDataQualityJobDefinitions
ListDeviceFleets
ListDevices
ListDomains
ListEdgeDeploymentPlans
ListEdgePackagingJobs
ListEndpointConfigs
ListEndpoints
ListExperiments
ListFeatureGroups
ListFlowDefinitions
ListHubContents
ListHubContentVersions
ListHubs
ListHumanTaskUis
ListHyperParameterTuningJobs
ListImages
ListImageVersions
ListInferenceExperiments
ListInferenceRecommendationsJobs
ListInferenceRecommendationsJobSteps
ListLabelingJobs
ListLabelingJobsForWorkteam
ListLineageGroups
ListModelBiasJobDefinitions
ListModelCardExportJobs
ListModelCards
ListModelCardVersions
ListModelExplainabilityJobDefinitions
ListModelMetadata
ListModelPackageGroups
ListModelPackages
ListModelQualityJobDefinitions
ListModels
ListMonitoringAlertHistory
ListMonitoringAlerts
ListMonitoringExecutions
ListMonitoringSchedules
ListNotebookInstanceLifecycleConfigs
ListNotebookInstances
ListPipelineExecutions
ListPipelineExecutionSteps
ListPipelineParametersForExecution
ListPipelines
ListProcessingJobs
ListProjects
ListSpaces
ListStageDevices
ListStudioLifecycleConfigs
ListSubscribedWorkteams
ListTags
ListTrainingJobs
ListTrainingJobsForHyperParameterTuningJob
ListTransformJobs
ListTrialComponents
ListTrials
ListUserProfiles
ListWorkforces
ListWorkteams
PutModelPackageGroupPolicy
QueryLineage
RegisterDevices
RenderUiTemplate
RetryPipelineExecution
Search
SendPipelineExecutionStepFailure
SendPipelineExecutionStepSuccess
StartEdgeDeploymentStage
StartInferenceExperiment
StartMonitoringSchedule
StartNotebookInstance
StartPipelineExecution
StopAutoMLJob
StopCompilationJob
StopEdgeDeploymentStage
StopEdgePackagingJob
StopHyperParameterTuningJob
StopInferenceExperiment
StopInferenceRecommendationsJob
StopLabelingJob
StopMonitoringSchedule
StopNotebookInstance
StopPipelineExecution
StopProcessingJob
StopTrainingJob
StopTransformJob
UpdateAction
UpdateAppImageConfig
UpdateArtifact
UpdateCodeRepository
UpdateContext
UpdateDeviceFleet
UpdateDevices
UpdateDomain
UpdateEndpoint
UpdateEndpointWeightsAndCapacities
UpdateExperiment
UpdateFeatureGroup
UpdateFeatureMetadata
UpdateHub
UpdateImage
UpdateImageVersion
UpdateInferenceExperiment
UpdateModelCard
UpdateModelPackage
UpdateMonitoringAlert
UpdateMonitoringSchedule
UpdateNotebookInstance
UpdateNotebookInstanceLifecycleConfig
UpdatePipeline
UpdatePipelineExecution
UpdateProject
UpdateSpace
UpdateTrainingJob
UpdateTrial
UpdateTrialComponent
UpdateUserProfile
UpdateWorkforce
UpdateWorkteam
]
}
/// Creates an association between the source and the destination. A
/// source can be associated with multiple destinations, and a destination can be associated
/// with multiple sources. An association is a lineage tracking entity. For more information, see
/// Amazon SageMaker
/// ML Lineage Tracking.
operation AddAssociation {
input: AddAssociationRequest
output: AddAssociationResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Adds or overwrites one or more tags for the specified SageMaker resource. You can add
/// tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform
/// jobs, models, labeling jobs, work teams, endpoint configurations, and
/// endpoints.
/// Each tag consists of a key and an optional value. Tag keys must be unique per
/// resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
///
/// Tags that you add to a hyperparameter tuning job by calling this API are also
/// added to any training jobs that the hyperparameter tuning job launches after you
/// call this API, but not to training jobs that the hyperparameter tuning job launched
/// before you called this API. To make sure that the tags associated with a
/// hyperparameter tuning job are also added to all training jobs that the
/// hyperparameter tuning job launches, add the tags when you first create the tuning
/// job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
///
///
///
/// Tags that you add to a SageMaker Studio Domain or User Profile by calling this API
/// are also added to any Apps that the Domain or User Profile launches after you call
/// this API, but not to Apps that the Domain or User Profile launched before you called
/// this API. To make sure that the tags associated with a Domain or User Profile are
/// also added to all Apps that the Domain or User Profile launches, add the tags when
/// you first create the Domain or User Profile by specifying them in the
/// Tags parameter of CreateDomain or CreateUserProfile.
///
operation AddTags {
input := {
/// The Amazon Resource Name (ARN) of the resource that you want to tag.
@required
ResourceArn: ResourceArn
/// 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.
@required
Tags: TagList
}
output := {
/// A list of tags associated with the SageMaker resource.
Tags: TagList
}
}
/// Associates a trial component with a trial. A trial component can be associated with
/// multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
operation AssociateTrialComponent {
input: AssociateTrialComponentRequest
output: AssociateTrialComponentResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// This action batch describes a list of versioned model packages
operation BatchDescribeModelPackage {
input := {
/// The list of Amazon Resource Name (ARN) of the model package groups.
@required
ModelPackageArnList: ModelPackageArnList
}
output := {
/// The summaries for the model package versions
ModelPackageSummaries: ModelPackageSummaries
/// A map of the resource and BatchDescribeModelPackageError objects
/// reporting the error associated with describing the model package.
BatchDescribeModelPackageErrorMap: BatchDescribeModelPackageErrorMap
}
}
/// Creates an action. An action is a lineage tracking entity that
/// represents an action or activity. For example, a model deployment or an HPO job.
/// Generally, an action involves at least one input or output artifact. For more information, see
/// Amazon SageMaker
/// ML Lineage Tracking.
operation CreateAction {
input: CreateActionRequest
output: CreateActionResponse
errors: [
ResourceLimitExceeded
]
}
/// Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
operation CreateAlgorithm {
input := {
/// The name of the algorithm.
@required
AlgorithmName: EntityName
/// A description of the algorithm.
AlgorithmDescription: EntityDescription
/// 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.
///
///
@required
TrainingSpecification: TrainingSpecification
/// 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.
///
///
InferenceSpecification: InferenceSpecification
/// 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.
ValidationSpecification: AlgorithmValidationSpecification
/// Whether to certify the algorithm so that it can be listed in Amazon Web Services
/// Marketplace.
CertifyForMarketplace: CertifyForMarketplace = 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: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the new algorithm.
@required
AlgorithmArn: AlgorithmArn
}
}
/// Creates a running app for the specified UserProfile. This operation is automatically
/// invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel
/// configurations are selected by the user. A user may have multiple Apps active simultaneously.
operation CreateApp {
input: CreateAppRequest
output: CreateAppResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a configuration for running a SageMaker image as a KernelGateway app. The
/// configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the
/// kernels in the image.
operation CreateAppImageConfig {
input: CreateAppImageConfigRequest
output: CreateAppImageConfigResponse
errors: [
ResourceInUse
]
}
/// Creates an artifact. An artifact is a lineage tracking entity that
/// represents a URI addressable object or data. Some examples are the S3 URI of a dataset and
/// the ECR registry path of an image. For more information, see
/// Amazon SageMaker
/// ML Lineage Tracking.
operation CreateArtifact {
input: CreateArtifactRequest
output: CreateArtifactResponse
errors: [
ResourceLimitExceeded
]
}
/// Creates an Autopilot job.
/// Find the best-performing model after you run an Autopilot job by calling .
/// For information about how to use Autopilot, see Automate Model
/// Development with Amazon SageMaker Autopilot.
operation CreateAutoMLJob {
input: CreateAutoMLJobRequest
output: CreateAutoMLJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a Git repository as a resource in your SageMaker account. You can associate the
/// repository with notebook instances so that you can use Git source control for the
/// notebooks you create. The Git repository is a resource in your SageMaker account, so it can
/// be associated with more than one notebook instance, and it persists independently from
/// the lifecycle of any notebook instances it is associated with.
/// The repository can be hosted either in Amazon Web Services CodeCommit
/// or in any other Git repository.
operation CreateCodeRepository {
input := {
/// 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).
@required
CodeRepositoryName: EntityName
/// Specifies details about the repository, including the URL where the repository is
/// located, the default branch, and credentials to use to access the repository.
@required
GitConfig: GitConfig
/// 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: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the new repository.
@required
CodeRepositoryArn: CodeRepositoryArn
}
}
/// Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the
/// resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
/// If
/// you choose to host your model using Amazon SageMaker hosting services, you can use the resulting
/// model artifacts as part of the model. You can also use the artifacts with
/// Amazon Web Services
/// IoT Greengrass. In that case, deploy them as an ML
/// resource.
/// In the request body, you provide the following:
///
/// -
///
A name for the compilation job
///
/// -
///
Information about the input model artifacts
///
/// -
///
The output location for the compiled model and the device (target) that the
/// model runs on
///
/// -
///
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform
/// the model compilation job.
///
///
/// You can also provide a Tag to track the model compilation job's resource
/// use and costs. The response body contains the
/// CompilationJobArn
/// for the compiled job.
/// To stop a model compilation job, use StopCompilationJob. To get
/// information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model
/// compilation jobs, use ListCompilationJobs.
operation CreateCompilationJob {
input: CreateCompilationJobRequest
output: CreateCompilationJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a context. A context is a lineage tracking entity that
/// represents a logical grouping of other tracking or experiment entities. Some examples are
/// an endpoint and a model package. For more information, see
/// Amazon SageMaker
/// ML Lineage Tracking.
operation CreateContext {
input: CreateContextRequest
output: CreateContextResponse
errors: [
ResourceLimitExceeded
]
}
/// Creates a definition for a job that monitors data quality and drift. For information
/// about model monitor, see Amazon SageMaker Model Monitor.
operation CreateDataQualityJobDefinition {
input: CreateDataQualityJobDefinitionRequest
output: CreateDataQualityJobDefinitionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a device fleet.
operation CreateDeviceFleet {
input: CreateDeviceFleetRequest
output: Unit
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated
/// Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application,
/// policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region.
/// Users within a domain can share notebook files and other artifacts with each other.
///
/// EFS storage
///
/// When a domain is created, an EFS volume is created for use by all of the users within the
/// domain. Each user receives a private home directory within the EFS volume for notebooks,
/// Git repositories, and data files.
/// SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with
/// an Amazon Web Services managed key by default. For more control, you can specify a
/// customer managed key. For more information, see
/// Protect Data at
/// Rest Using Encryption.
///
/// VPC configuration
///
/// All SageMaker Studio traffic between the domain and the EFS volume is through the specified
/// VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType
/// parameter. AppNetworkAccessType corresponds to the network access type that you
/// choose when you onboard to Studio. The following options are available:
///
/// -
///
/// PublicInternetOnly - Non-EFS traffic goes through a VPC managed by
/// Amazon SageMaker, which allows internet access. This is the default value.
///
/// -
///
/// VpcOnly - All Studio traffic is through the specified VPC and subnets.
/// Internet access is disabled by default. To allow internet access, you must specify a
/// NAT gateway.
/// When internet access is disabled, you won't be able to run a Studio notebook or to
/// train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime
/// or a NAT gateway and your security groups allow outbound connections.
///
///
///
/// NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules
/// in order to launch a SageMaker Studio app successfully.
///
/// For more information, see
/// Connect
/// SageMaker Studio Notebooks to Resources in a VPC.
operation CreateDomain {
input: CreateDomainRequest
output: CreateDomainResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
operation CreateEdgeDeploymentPlan {
input: CreateEdgeDeploymentPlanRequest
output: CreateEdgeDeploymentPlanResponse
errors: [
ResourceLimitExceeded
]
}
/// Creates a new stage in an existing edge deployment plan.
operation CreateEdgeDeploymentStage {
input: CreateEdgeDeploymentStageRequest
output: Unit
errors: [
ResourceLimitExceeded
]
}
/// Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
operation CreateEdgePackagingJob {
input: CreateEdgePackagingJobRequest
output: Unit
errors: [
ResourceLimitExceeded
]
}
/// Creates an endpoint using the endpoint configuration specified in the request. SageMaker
/// uses the endpoint to provision resources and deploy models. You create the endpoint
/// configuration with the CreateEndpointConfig API.
/// Use this API to deploy models using SageMaker hosting services.
/// For an example that calls this method when deploying a model to SageMaker hosting services,
/// see the Create Endpoint example notebook.
///
///
/// You must not delete an EndpointConfig that is in use by an endpoint
/// that is live or while the UpdateEndpoint or CreateEndpoint
/// operations are being performed on the endpoint. To update an endpoint, you must
/// create a new EndpointConfig.
///
/// The endpoint name must be unique within an Amazon Web Services Region in your
/// Amazon Web Services account.
/// When it receives the request, SageMaker creates the endpoint, launches the resources (ML
/// compute instances), and deploys the model(s) on them.
///
/// When you call CreateEndpoint, a load call is made to DynamoDB to
/// verify that your endpoint configuration exists. When you read data from a DynamoDB
/// table supporting
/// Eventually Consistent Reads
/// , the response might not
/// reflect the results of a recently completed write operation. The response might
/// include some stale data. If the dependent entities are not yet in DynamoDB, this
/// causes a validation error. If you repeat your read request after a short time, the
/// response should return the latest data. So retry logic is recommended to handle
/// these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
///
/// When SageMaker receives the request, it sets the endpoint status to
/// Creating. After it creates the endpoint, it sets the status to
/// InService. SageMaker can then process incoming requests for inferences. To
/// check the status of an endpoint, use the DescribeEndpoint
/// API.
/// If any of the models hosted at this endpoint get model data from an Amazon S3 location,
/// SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the
/// S3 path you provided. Amazon Web Services STS is activated in your IAM user account by
/// default. If you previously deactivated Amazon Web Services STS for a region, you need to
/// reactivate Amazon Web Services STS for that region. For more information, see Activating and
/// Deactivating Amazon Web Services STS in an Amazon Web Services Region in the
/// Amazon Web Services Identity and Access Management User
/// Guide.
///
/// To add the IAM role policies for using this API operation, go to the IAM console, and choose
/// Roles in the left navigation pane. Search the IAM role that you want to grant
/// access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to
/// the role.
///
/// -
///
Option 1: For a full SageMaker access, search and attach the
/// AmazonSageMakerFullAccess policy.
///
/// -
///
Option 2: For granting a limited access to an IAM role, paste the
/// following Action elements manually into the JSON file of the IAM role:
///
/// "Action": ["sagemaker:CreateEndpoint",
/// "sagemaker:CreateEndpointConfig"]
///
///
/// "Resource": [
///
///
/// "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
///
///
/// "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
///
///
/// ]
///
/// For more information, see SageMaker API
/// Permissions: Actions, Permissions, and Resources
/// Reference.
///
///
///
operation CreateEndpoint {
input := {
/// 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 .
@required
EndpointName: EndpointName
/// The name of an endpoint configuration. For more information, see CreateEndpointConfig.
@required
EndpointConfigName: EndpointConfigName
DeploymentConfig: DeploymentConfig
/// 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: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In
/// the configuration, you identify one or more models, created using the
/// CreateModel API, to deploy and the resources that you want SageMaker to
/// provision. Then you call the CreateEndpoint API.
///
/// Use this API if you want to use SageMaker hosting services to deploy models into
/// production.
///
/// In the request, you define a ProductionVariant, for each model that you
/// want to deploy. Each ProductionVariant parameter also describes the
/// resources that you want SageMaker to provision. This includes the number and type of ML
/// compute instances to deploy.
/// If you are hosting multiple models, you also assign a VariantWeight to
/// specify how much traffic you want to allocate to each model. For example, suppose that
/// you want to host two models, A and B, and you assign traffic weight 2 for model A and 1
/// for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to
/// model B.
///
/// When you call CreateEndpoint, a load call is made to DynamoDB to
/// verify that your endpoint configuration exists. When you read data from a DynamoDB
/// table supporting
/// Eventually Consistent Reads
/// , the response might not
/// reflect the results of a recently completed write operation. The response might
/// include some stale data. If the dependent entities are not yet in DynamoDB, this
/// causes a validation error. If you repeat your read request after a short time, the
/// response should return the latest data. So retry logic is recommended to handle
/// these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
///
operation CreateEndpointConfig {
input := {
/// The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
@required
EndpointConfigName: EndpointConfigName
/// An array of ProductionVariant objects, one for each model that you want
/// to host at this endpoint.
@required
ProductionVariants: ProductionVariantList
DataCaptureConfig: DataCaptureConfig
/// 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: TagList
/// 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.
///
KmsKeyId: KmsKeyId
/// Specifies configuration for how an endpoint performs asynchronous inference. This is a
/// required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
AsyncInferenceConfig: AsyncInferenceConfig
/// A member of CreateEndpointConfig that enables explainers.
ExplainerConfig: ExplainerConfig
/// 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.
ShadowProductionVariants: ProductionVariantList
}
output := {
/// The Amazon Resource Name (ARN) of the endpoint configuration.
@required
EndpointConfigArn: EndpointConfigArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates a SageMaker experiment. An experiment is a collection of
/// trials that are observed, compared and evaluated as a group. A trial is
/// a set of steps, called trial components, that produce a machine learning
/// model.
///
/// In the Studio UI, trials are referred to as run groups and trial
/// components are referred to as runs.
///
/// The goal of an experiment is to determine the components that produce the best model.
/// Multiple trials are performed, each one isolating and measuring the impact of a change to one
/// or more inputs, while keeping the remaining inputs constant.
/// When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial
/// components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you
/// must use the logging APIs provided by the SDK.
/// You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
/// To add a description to an experiment, specify the optional Description
/// parameter. To add a description later, or to change the description, call the UpdateExperiment API.
/// To get a list of all your experiments, call the ListExperiments API. To
/// view an experiment's properties, call the DescribeExperiment API. To get a
/// list of all the trials associated with an experiment, call the ListTrials
/// API. To create a trial call the CreateTrial API.
operation CreateExperiment {
input: CreateExperimentRequest
output: CreateExperimentResponse
errors: [
ResourceLimitExceeded
]
}
/// Create a new FeatureGroup. A FeatureGroup is a group of
/// Features defined in the FeatureStore to describe a
/// Record.
/// The FeatureGroup defines the schema and features contained in the
/// FeatureGroup. A FeatureGroup definition is composed of a list of
/// Features, a RecordIdentifierFeatureName, an
/// EventTimeFeatureName and configurations for its OnlineStore
/// and OfflineStore. Check Amazon Web Services service quotas to see
/// the FeatureGroups quota for your Amazon Web Services account.
///
/// You must include at least one of OnlineStoreConfig and
/// OfflineStoreConfig to create a FeatureGroup.
///
operation CreateFeatureGroup {
input: CreateFeatureGroupRequest
output: CreateFeatureGroupResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a flow definition.
operation CreateFlowDefinition {
input: CreateFlowDefinitionRequest
output: CreateFlowDefinitionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Create a hub.
operation CreateHub {
input: CreateHubRequest
output: CreateHubResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
operation CreateHumanTaskUi {
input: CreateHumanTaskUiRequest
output: CreateHumanTaskUiResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version
/// of a model by running many training jobs on your dataset using the algorithm you choose
/// and values for hyperparameters within ranges that you specify. It then chooses the
/// hyperparameter values that result in a model that performs the best, as measured by an
/// objective metric that you choose.
/// A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and
/// trial components for each training job that it runs. You can view these entities in
/// Amazon SageMaker Studio. For more information, see View
/// Experiments, Trials, and Trial Components.
///
/// Do not include any security-sensitive information including account access IDs,
/// secrets or tokens in any hyperparameter field. If the use of security-sensitive
/// credentials are detected, SageMaker will reject your training job request and return an
/// exception error.
///
operation CreateHyperParameterTuningJob {
input: CreateHyperParameterTuningJobRequest
output: CreateHyperParameterTuningJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image
/// version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see
/// Bring your own SageMaker image.
operation CreateImage {
input: CreateImageRequest
output: CreateImageResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a version of the SageMaker image specified by ImageName. The version
/// represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.
operation CreateImageVersion {
input: CreateImageVersionRequest
output: CreateImageVersionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
///
/// Creates an inference experiment using the configurations specified in the request.
///
///
/// Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For
/// more information about inference experiments, see Shadow tests.
///
///
/// Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based
/// on your specified configuration.
///
///
/// While the experiment is in progress or after it has concluded, you can view metrics that compare your model
/// variants. For more information, see View, monitor, and edit shadow tests.
///
operation CreateInferenceExperiment {
input: CreateInferenceExperimentRequest
output: CreateInferenceExperimentResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Starts a recommendation job. You can create either an instance
/// recommendation or load test job.
operation CreateInferenceRecommendationsJob {
input: CreateInferenceRecommendationsJobRequest
output: CreateInferenceRecommendationsJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a job that uses workers to label the data objects in your input dataset. You
/// can use the labeled data to train machine learning models.
/// You can select your workforce from one of three providers:
///
/// -
///
A private workforce that you create. It can include employees, contractors,
/// and outside experts. Use a private workforce when want the data to stay within
/// your organization or when a specific set of skills is required.
///
/// -
///
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide
/// expertise in specific areas.
///
/// -
///
The Amazon Mechanical Turk workforce. This is the largest workforce, but it
/// should only be used for public data or data that has been stripped of any
/// personally identifiable information.
///
///
/// You can also use automated data labeling to reduce the number of
/// data objects that need to be labeled by a human. Automated data labeling uses
/// active learning to determine if a data object can be labeled by
/// machine or if it needs to be sent to a human worker. For more information, see Using
/// Automated Data Labeling.
/// The data objects to be labeled are contained in an Amazon S3 bucket. You create a
/// manifest file that describes the location of each object. For
/// more information, see Using Input and Output Data.
/// The output can be used as the manifest file for another labeling job or as training
/// data for your machine learning models.
/// You can use this operation to create a static labeling job or a streaming labeling
/// job. A static labeling job stops if all data objects in the input manifest file
/// identified in ManifestS3Uri have been labeled. A streaming labeling job
/// runs perpetually until it is manually stopped, or remains idle for 10 days. You can send
/// new data objects to an active (InProgress) streaming labeling job in real
/// time. To learn how to create a static labeling job, see Create a Labeling Job
/// (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming
/// labeling job, see Create a Streaming Labeling
/// Job.
operation CreateLabelingJob {
input: CreateLabelingJobRequest
output: CreateLabelingJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a model in SageMaker. In the request, you name the model and describe a primary
/// container. For the primary container, you specify the Docker image that
/// contains inference code, artifacts (from prior training), and a custom environment map
/// that the inference code uses when you deploy the model for predictions.
/// Use this API to create a model if you want to use SageMaker hosting services or run a batch
/// transform job.
/// To host your model, you create an endpoint configuration with the
/// CreateEndpointConfig API, and then create an endpoint with the
/// CreateEndpoint API. SageMaker then deploys all of the containers that you
/// defined for the model in the hosting environment.
/// For an example that calls this method when deploying a model to SageMaker hosting services,
/// see Create a Model (Amazon Web Services SDK for Python (Boto 3)).
///
/// To run a batch transform using your model, you start a job with the
/// CreateTransformJob API. SageMaker uses your model and your dataset to get
/// inferences which are then saved to a specified S3 location.
/// In the request, you also provide an IAM role that SageMaker can assume to access model
/// artifacts and docker image for deployment on ML compute hosting instances or for batch
/// transform jobs. In addition, you also use the IAM role to manage permissions the
/// inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
operation CreateModel {
input := {
/// The name of the new model.
@required
ModelName: ModelName
/// 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.
PrimaryContainer: ContainerDefinition
/// Specifies the containers in the inference pipeline.
Containers: ContainerDefinitionList
/// Specifies details of how containers in a multi-container endpoint are called.
InferenceExecutionConfig: InferenceExecutionConfig
/// 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.
///
@required
ExecutionRoleArn: RoleArn
/// 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: TagList
/// 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.
VpcConfig: VpcConfig
/// Isolates the model container. No inbound or outbound network calls can be made to or
/// from the model container.
EnableNetworkIsolation: Boolean = false
}
output := {
/// The ARN of the model created in SageMaker.
@required
ModelArn: ModelArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates the definition for a model bias job.
operation CreateModelBiasJobDefinition {
input: CreateModelBiasJobDefinitionRequest
output: CreateModelBiasJobDefinitionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates an Amazon SageMaker Model Card.
/// For information about how to use model cards, see Amazon SageMaker Model Card.
operation CreateModelCard {
input: CreateModelCardRequest
output: CreateModelCardResponse
errors: [
ConflictException
ResourceLimitExceeded
]
}
/// Creates an Amazon SageMaker Model Card export job.
operation CreateModelCardExportJob {
input: CreateModelCardExportJobRequest
output: CreateModelCardExportJobResponse
errors: [
ConflictException
ResourceLimitExceeded
ResourceNotFound
]
}
/// Creates the definition for a model explainability job.
operation CreateModelExplainabilityJobDefinition {
input: CreateModelExplainabilityJobDefinitionRequest
output: CreateModelExplainabilityJobDefinitionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a model package that you can use to create SageMaker models or list on Amazon Web Services
/// Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to
/// model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
/// To create a model package by specifying a Docker container that contains your
/// inference code and the Amazon S3 location of your model artifacts, provide values for
/// InferenceSpecification. To create a model from an algorithm resource
/// that you created or subscribed to in Amazon Web Services Marketplace, provide a value for
/// SourceAlgorithmSpecification.
///
/// There are two types of model packages:
///
/// -
///
Versioned - a model that is part of a model group in the model
/// registry.
///
/// -
///
Unversioned - a model package that is not part of a model group.
///
///
///
operation CreateModelPackage {
input := {
/// 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.
ModelPackageName: EntityName
/// 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.
ModelPackageGroupName: ArnOrName
/// A description of the model package.
ModelPackageDescription: EntityDescription
/// Specifies details about inference jobs that can be run with models based on this model
/// package, including the following:
///
/// -
///
The Amazon ECR paths of containers that contain the inference code and model
/// artifacts.
///
/// -
///
The instance types that the model package supports for transform jobs and
/// real-time endpoints used for inference.
///
/// -
///
The input and output content formats that the model package supports for
/// inference.
///
///
InferenceSpecification: InferenceSpecification
/// Specifies configurations for one or more transform jobs that SageMaker runs to test the
/// model package.
ValidationSpecification: ModelPackageValidationSpecification
/// Details about the algorithm that was used to create the model package.
SourceAlgorithmSpecification: SourceAlgorithmSpecification
/// 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.
CertifyForMarketplace: CertifyForMarketplace = 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.
Tags: TagList
/// 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.
ModelApprovalStatus: ModelApprovalStatus
MetadataProperties: MetadataProperties
/// A structure that contains model metrics reports.
ModelMetrics: ModelMetrics
/// A unique token that guarantees that the call to this API is idempotent.
@idempotencyToken
ClientToken: ClientToken
/// The metadata properties associated with the model package versions.
CustomerMetadataProperties: CustomerMetadataMap
/// 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.
///
DriftCheckBaselines: DriftCheckBaselines
/// The machine learning domain of your model package and its components. Common
/// machine learning domains include computer vision and natural language processing.
Domain: String
/// 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.
Task: String
/// The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point
/// to a single gzip compressed tar archive (.tar.gz suffix).
SamplePayloadUrl: S3Uri
/// 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.
AdditionalInferenceSpecifications: AdditionalInferenceSpecifications
}
output := {
/// The Amazon Resource Name (ARN) of the new model package.
@required
ModelPackageArn: ModelPackageArn
}
errors: [
ConflictException
ResourceLimitExceeded
]
}
/// Creates a model group. A model group contains a group of model versions.
operation CreateModelPackageGroup {
input := {
/// The name of the model group.
@required
ModelPackageGroupName: EntityName
/// A description for the model group.
ModelPackageGroupDescription: EntityDescription
/// 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.
Tags: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the model group.
@required
ModelPackageGroupArn: ModelPackageGroupArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates a definition for a job that monitors model quality and drift. For information
/// about model monitor, see Amazon SageMaker Model Monitor.
operation CreateModelQualityJobDefinition {
input: CreateModelQualityJobDefinitionRequest
output: CreateModelQualityJobDefinitionResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data
/// captured for an Amazon SageMaker Endoint.
operation CreateMonitoringSchedule {
input: CreateMonitoringScheduleRequest
output: CreateMonitoringScheduleResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML)
/// compute instance running on a Jupyter notebook.
/// In a CreateNotebookInstance request, specify the type of ML compute
/// instance that you want to run. SageMaker launches the instance, installs common libraries
/// that you can use to explore datasets for model training, and attaches an ML storage
/// volume to the notebook instance.
/// SageMaker also provides a set of example notebooks. Each notebook demonstrates how to
/// use SageMaker with a specific algorithm or with a machine learning framework.
/// After receiving the request, SageMaker does the following:
///
/// -
///
Creates a network interface in the SageMaker VPC.
///
/// -
///
(Option) If you specified SubnetId, SageMaker creates a network
/// interface in your own VPC, which is inferred from the subnet ID that you provide
/// in the input. When creating this network interface, SageMaker attaches the security
/// group that you specified in the request to the network interface that it creates
/// in your VPC.
///
/// -
///
Launches an EC2 instance of the type specified in the request in the SageMaker
/// VPC. If you specified SubnetId of your VPC, SageMaker specifies both
/// network interfaces when launching this instance. This enables inbound traffic
/// from your own VPC to the notebook instance, assuming that the security groups
/// allow it.
///
///
/// After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN).
/// You can't change the name of a notebook instance after you create it.
/// After SageMaker creates the notebook instance, you can connect to the Jupyter server and
/// work in Jupyter notebooks. For example, you can write code to explore a dataset that you
/// can use for model training, train a model, host models by creating SageMaker endpoints, and
/// validate hosted models.
/// For more information, see How It Works.
operation CreateNotebookInstance {
input := {
/// The name of the new notebook instance.
@required
NotebookInstanceName: NotebookInstanceName
/// The type of ML compute instance to launch for the notebook instance.
@required
InstanceType: InstanceType
/// The ID of the subnet in a VPC to which you would like to have a connectivity from
/// your ML compute instance.
SubnetId: SubnetId
/// The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be
/// for the same VPC as specified in the subnet.
SecurityGroupIds: SecurityGroupIds
/// When you send any requests to Amazon Web Services resources from the notebook
/// instance, SageMaker assumes this role to perform tasks on your behalf. You must grant this
/// role necessary permissions so SageMaker can perform these tasks. The policy must allow the
/// SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this 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.
///
@required
RoleArn: RoleArn
/// 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 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.
KmsKeyId: KmsKeyId
/// 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: TagList
/// 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.
LifecycleConfigName: NotebookInstanceLifecycleConfigName
/// Sets whether SageMaker provides internet access to the notebook instance. If you set this
/// to Disabled this notebook instance is able to access resources only in your
/// VPC, and is not be able to connect to SageMaker training and endpoint services unless you
/// configure a NAT Gateway in your VPC.
/// For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value
/// of this parameter to Disabled only if you set a value for the
/// SubnetId parameter.
DirectInternetAccess: DirectInternetAccess
/// The size, in GB, of the ML storage volume to attach to the notebook instance. The
/// default value is 5 GB.
VolumeSizeInGB: NotebookInstanceVolumeSizeInGB
/// A list of Elastic Inference (EI) instance types to associate with this notebook
/// instance. Currently, only one instance type can be associated with a notebook instance.
/// For more information, see Using Elastic Inference in Amazon SageMaker.
AcceleratorTypes: NotebookInstanceAcceleratorTypes
/// 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 Notebook Instances.
DefaultCodeRepository: CodeRepositoryNameOrUrl
/// 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 Notebook Instances.
AdditionalCodeRepositories: AdditionalCodeRepositoryNamesOrUrls
/// 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.
///
RootAccess: RootAccess
/// The platform identifier of the notebook instance runtime environment.
PlatformIdentifier: PlatformIdentifier
/// Information on the IMDS configuration of the notebook instance
InstanceMetadataServiceConfiguration: InstanceMetadataServiceConfiguration
}
output := {
/// The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceArn: NotebookInstanceArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates a lifecycle configuration that you can associate with a notebook instance. A
/// lifecycle configuration is a collection of shell scripts that
/// run when you create or start a notebook instance.
/// Each lifecycle configuration script has a limit of 16384 characters.
/// The value of the $PATH environment variable that is available to both
/// scripts is /sbin:bin:/usr/sbin:/usr/bin.
/// View CloudWatch Logs for notebook instance lifecycle configurations in log group
/// /aws/sagemaker/NotebookInstances in log stream
/// [notebook-instance-name]/[LifecycleConfigHook].
/// Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs
/// for longer than 5 minutes, it fails and the notebook instance is not created or
/// started.
/// For information about notebook instance lifestyle configurations, see Step
/// 2.1: (Optional) Customize a Notebook Instance.
operation CreateNotebookInstanceLifecycleConfig {
input := {
/// The name of the lifecycle configuration.
@required
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// A shell script that runs only once, when you create a notebook instance. The shell
/// script must be a base64-encoded string.
OnCreate: NotebookInstanceLifecycleConfigList
/// 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.
OnStart: NotebookInstanceLifecycleConfigList
}
output := {
/// The Amazon Resource Name (ARN) of the lifecycle configuration.
NotebookInstanceLifecycleConfigArn: NotebookInstanceLifecycleConfigArn
}
errors: [
ResourceLimitExceeded
]
}
/// Creates a pipeline using a JSON pipeline definition.
operation CreatePipeline {
input: CreatePipelineRequest
output: CreatePipelineResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser,
/// the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of
/// the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume.
/// This operation can only be called when the authentication mode equals IAM.
///
/// The IAM role or user passed to this API defines the permissions to access the app. Once
/// the presigned URL is created, no additional permission is required to access this URL. IAM
/// authorization policies for this API are also enforced for every HTTP request and WebSocket
/// frame that attempts to connect to the app.
/// You can restrict access to this API and to the
/// URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more
/// information, see Connect to SageMaker Studio Through an Interface VPC Endpoint
/// .
///
/// The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you
/// are directed to the Amazon Web Services console sign-in page.
///
operation CreatePresignedDomainUrl {
input: CreatePresignedDomainUrlRequest
output: CreatePresignedDomainUrlResponse
errors: [
ResourceNotFound
]
}
/// Returns a URL that you can use to connect to the Jupyter server from a notebook
/// instance. In the SageMaker console, when you choose Open next to a notebook
/// instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook
/// instance. The console uses this API to get the URL and show the page.
/// The IAM role or user used to call this API defines the permissions to access the
/// notebook instance. Once the presigned URL is created, no additional permission is
/// required to access this URL. IAM authorization policies for this API are also enforced
/// for every HTTP request and WebSocket frame that attempts to connect to the notebook
/// instance.
/// You can restrict access to this API and to the URL that it returns to a list of IP
/// addresses that you specify. Use the NotIpAddress condition operator and the
/// aws:SourceIP condition context key to specify the list of IP addresses
/// that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
///
/// The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If
/// you try to use the URL after the 5-minute limit expires, you are directed to the
/// Amazon Web Services console sign-in page.
///
operation CreatePresignedNotebookInstanceUrl {
input := {
/// The name of the notebook instance.
@required
NotebookInstanceName: NotebookInstanceName
/// The duration of the session, in seconds. The default is 12 hours.
SessionExpirationDurationInSeconds: SessionExpirationDurationInSeconds
}
output := {
/// A JSON object that contains the URL string.
AuthorizedUrl: NotebookInstanceUrl
}
}
/// Creates a processing job.
operation CreateProcessingJob {
input: CreateProcessingJobRequest
output: CreateProcessingJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Creates a machine learning (ML) project that can contain one or more templates that set
/// up an ML pipeline from training to deploying an approved model.
operation CreateProject {
input := {
/// The name of the project.
@required
ProjectName: ProjectEntityName
/// A description for the project.
ProjectDescription: EntityDescription
/// 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.
@required
ServiceCatalogProvisioningDetails: ServiceCatalogProvisioningDetails
/// 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.
Tags: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the project.
@required
ProjectArn: ProjectArn
/// The ID of the new project.
@required
ProjectId: ProjectId
}
errors: [
ResourceLimitExceeded
]
}
/// Creates a space used for real time collaboration in a Domain.
operation CreateSpace {
input: CreateSpaceRequest
output: CreateSpaceResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Creates a new Studio Lifecycle Configuration.
operation CreateStudioLifecycleConfig {
input: CreateStudioLifecycleConfigRequest
output: CreateStudioLifecycleConfigResponse
errors: [
ResourceInUse
]
}
/// Starts a model training job. After training completes, SageMaker saves the resulting
/// model artifacts to an Amazon S3 location that you specify.
/// If you choose to host your model using SageMaker hosting services, you can use the
/// resulting model artifacts as part of the model. You can also use the artifacts in a
/// machine learning service other than SageMaker, provided that you know how to use them for
/// inference.
///
/// In the request body, you provide the following:
///
/// -
///
/// AlgorithmSpecification - Identifies the training algorithm to
/// use.
///
///
/// -
///
/// HyperParameters - Specify these algorithm-specific parameters to
/// enable the estimation of model parameters during training. Hyperparameters can
/// be tuned to optimize this learning process. For a list of hyperparameters for
/// each training algorithm provided by SageMaker, see Algorithms.
///
/// Do not include any security-sensitive information including account access
/// IDs, secrets or tokens in any hyperparameter field. If the use of
/// security-sensitive credentials are detected, SageMaker will reject your training
/// job request and return an exception error.
///
///
/// -
///
/// InputDataConfig - Describes the input required by the training
/// job and the Amazon S3, EFS, or FSx location where it is stored.
///
/// -
///
/// OutputDataConfig - Identifies the Amazon S3 bucket where you want
/// SageMaker to save the results of model training.
///
/// -
///
/// ResourceConfig - Identifies the resources, ML compute
/// instances, and ML storage volumes to deploy for model training. In distributed
/// training, you specify more than one instance.
///
/// -
///
/// EnableManagedSpotTraining - Optimize the cost of training machine
/// learning models by up to 80% by using Amazon EC2 Spot instances. For more
/// information, see Managed Spot
/// Training.
///
/// -
///
/// RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on
/// your behalf during model training.
///
/// You must grant this role the necessary permissions so that SageMaker can successfully
/// complete model training.
///
/// -
///
/// StoppingCondition - To help cap training costs, use
/// MaxRuntimeInSeconds to set a time limit for training. Use
/// MaxWaitTimeInSeconds to specify how long a managed spot
/// training job has to complete.
///
/// -
///
/// Environment - The environment variables to set in the Docker
/// container.
///
/// -
///
/// RetryStrategy - The number of times to retry the job when the job
/// fails due to an InternalServerError.
///
///
/// For more information about SageMaker, see How It Works.
operation CreateTrainingJob {
input: CreateTrainingJobRequest
output: CreateTrainingJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Starts a transform job. A transform job uses a trained model to get inferences on a
/// dataset and saves these results to an Amazon S3 location that you specify.
/// To perform batch transformations, you create a transform job and use the data that you
/// have readily available.
/// In the request body, you provide the following:
///
/// -
///
/// TransformJobName - Identifies the transform job. The name must be
/// unique within an Amazon Web Services Region in an Amazon Web Services account.
///
/// -
///
/// ModelName - Identifies the model to use. ModelName
/// must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services
/// account. For information on creating a model, see CreateModel.
///
/// -
///
/// TransformInput - Describes the dataset to be transformed and the
/// Amazon S3 location where it is stored.
///
/// -
///
/// TransformOutput - Identifies the Amazon S3 location where you want
/// Amazon SageMaker to save the results from the transform job.
///
/// -
///
/// TransformResources - Identifies the ML compute instances for the
/// transform job.
///
///
/// For more information about how batch transformation works, see Batch
/// Transform.
operation CreateTransformJob {
input: CreateTransformJobRequest
output: CreateTransformJobResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Creates an SageMaker trial. A trial is a set of steps called
/// trial components that produce a machine learning model. A trial is part
/// of a single SageMaker experiment.
/// When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial
/// components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you
/// must use the logging APIs provided by the SDK.
/// You can add tags to a trial and then use the Search API to search for
/// the tags.
/// To get a list of all your trials, call the ListTrials API. To view a
/// trial's properties, call the DescribeTrial API. To create a trial component,
/// call the CreateTrialComponent API.
operation CreateTrial {
input: CreateTrialRequest
output: CreateTrialResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Creates a trial component, which is a stage of a machine learning
/// trial. A trial is composed of one or more trial components. A trial
/// component can be used in multiple trials.
/// Trial components include pre-processing jobs, training jobs, and batch transform
/// jobs.
/// When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial
/// components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you
/// must use the logging APIs provided by the SDK.
/// You can add tags to a trial component and then use the Search API to
/// search for the tags.
operation CreateTrialComponent {
input: CreateTrialComponentRequest
output: CreateTrialComponentResponse
errors: [
ResourceLimitExceeded
]
}
/// Creates a user profile. A user profile represents a single user within a domain, and is
/// the main way to reference a "person" for the purposes of sharing, reporting, and other
/// user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an
/// administrator invites a person by email or imports them from IAM Identity Center, a user profile is
/// automatically created. A user profile is the primary holder of settings for an individual
/// user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
///
operation CreateUserProfile {
input: CreateUserProfileRequest
output: CreateUserProfileResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Use this operation to create a workforce. This operation will return an error
/// if a workforce already exists in the Amazon Web Services Region that you specify. You can only
/// create one workforce in each Amazon Web Services Region per Amazon Web Services account.
/// If you want to create a new workforce in an Amazon Web Services Region where
/// a workforce already exists, use the API
/// operation to delete the existing workforce and then use CreateWorkforce
/// to create a new workforce.
/// To create a private workforce using Amazon Cognito, you must specify a Cognito user pool
/// in CognitoConfig.
/// You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
/// For more information, see
///
/// Create a Private Workforce (Amazon Cognito).
/// To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP
/// configuration in OidcConfig. Your OIDC IdP must support groups
/// because groups are used by Ground Truth and Amazon A2I to create work teams.
/// For more information, see
/// Create a Private Workforce (OIDC IdP).
operation CreateWorkforce {
input: CreateWorkforceRequest
output: CreateWorkforceResponse
}
/// Creates a new work team for labeling your data. A work team is defined by one or more
/// Amazon Cognito user pools. You must first create the user pools before you can create a work
/// team.
/// You cannot create more than 25 work teams in an account and region.
operation CreateWorkteam {
input: CreateWorkteamRequest
output: CreateWorkteamResponse
errors: [
ResourceInUse
ResourceLimitExceeded
]
}
/// Deletes an action.
operation DeleteAction {
input: DeleteActionRequest
output: DeleteActionResponse
errors: [
ResourceNotFound
]
}
/// Removes the specified algorithm from your account.
operation DeleteAlgorithm {
input := {
/// The name of the algorithm to delete.
@required
AlgorithmName: EntityName
}
output: Unit
}
/// Used to stop and delete an app.
operation DeleteApp {
input: DeleteAppRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes an AppImageConfig.
operation DeleteAppImageConfig {
input: DeleteAppImageConfigRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes an artifact. Either ArtifactArn or Source must be
/// specified.
operation DeleteArtifact {
input: DeleteArtifactRequest
output: DeleteArtifactResponse
errors: [
ResourceNotFound
]
}
/// Deletes an association.
operation DeleteAssociation {
input: DeleteAssociationRequest
output: DeleteAssociationResponse
errors: [
ResourceNotFound
]
}
/// Deletes the specified Git repository from your account.
operation DeleteCodeRepository {
input := {
/// The name of the Git repository to delete.
@required
CodeRepositoryName: EntityName
}
output: Unit
}
/// Deletes an context.
operation DeleteContext {
input: DeleteContextRequest
output: DeleteContextResponse
errors: [
ResourceNotFound
]
}
/// Deletes a data quality monitoring job definition.
operation DeleteDataQualityJobDefinition {
input: DeleteDataQualityJobDefinitionRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes a fleet.
operation DeleteDeviceFleet {
input: DeleteDeviceFleetRequest
output: Unit
errors: [
ResourceInUse
]
}
/// Used to delete a domain.
/// If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center.
/// Use with caution. All of the members of the domain will lose access to their EFS volume,
/// including data, notebooks, and other artifacts.
///
operation DeleteDomain {
input: DeleteDomainRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
operation DeleteEdgeDeploymentPlan {
input: DeleteEdgeDeploymentPlanRequest
output: Unit
errors: [
ResourceInUse
]
}
/// Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
operation DeleteEdgeDeploymentStage {
input: DeleteEdgeDeploymentStageRequest
output: Unit
errors: [
ResourceInUse
]
}
/// Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the
/// endpoint was created.
/// SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't
/// need to use the RevokeGrant API call.
/// When you delete your endpoint, SageMaker asynchronously deletes associated endpoint
/// resources such as KMS key grants. You might still see these resources in your account
/// for a few minutes after deleting your endpoint. Do not delete or revoke the permissions
/// for your
/// ExecutionRoleArn
/// , otherwise SageMaker cannot delete these
/// resources.
operation DeleteEndpoint {
input := {
/// The name of the endpoint that you want to delete.
@required
EndpointName: EndpointName
}
output: Unit
}
/// Deletes an endpoint configuration. The DeleteEndpointConfig API
/// deletes only the specified configuration. It does not delete endpoints created using the
/// configuration.
/// You must not delete an EndpointConfig in use by an endpoint that is
/// live or while the UpdateEndpoint or CreateEndpoint operations
/// are being performed on the endpoint. If you delete the EndpointConfig of an
/// endpoint that is active or being created or updated you may lose visibility into the
/// instance type the endpoint is using. The endpoint must be deleted in order to stop
/// incurring charges.
operation DeleteEndpointConfig {
input := {
/// The name of the endpoint configuration that you want to delete.
@required
EndpointConfigName: EndpointConfigName
}
output: Unit
}
/// Deletes an SageMaker experiment. All trials associated with the experiment must be deleted
/// first. Use the ListTrials API to get a list of the trials associated with
/// the experiment.
operation DeleteExperiment {
input: DeleteExperimentRequest
output: DeleteExperimentResponse
errors: [
ResourceNotFound
]
}
/// Delete the FeatureGroup and any data that was written to the
/// OnlineStore of the FeatureGroup. Data cannot be accessed from
/// the OnlineStore immediately after DeleteFeatureGroup is called.
/// Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue
/// database and tables that are automatically created for your OfflineStore are
/// not deleted.
operation DeleteFeatureGroup {
input: DeleteFeatureGroupRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes the specified flow definition.
operation DeleteFlowDefinition {
input: DeleteFlowDefinitionRequest
output: DeleteFlowDefinitionResponse
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Delete a hub.
operation DeleteHub {
input: DeleteHubRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Delete the contents of a hub.
operation DeleteHubContent {
input: DeleteHubContentRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Use this operation to delete a human task user interface (worker task template).
///
/// To see a list of human task user interfaces
/// (work task templates) in your account, use .
/// When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
operation DeleteHumanTaskUi {
input: DeleteHumanTaskUiRequest
output: DeleteHumanTaskUiResponse
errors: [
ResourceNotFound
]
}
/// Deletes a SageMaker image and all versions of the image. The container images aren't
/// deleted.
operation DeleteImage {
input: DeleteImageRequest
output: DeleteImageResponse
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes a version of a SageMaker image. The container image the version represents isn't
/// deleted.
operation DeleteImageVersion {
input: DeleteImageVersionRequest
output: DeleteImageVersionResponse
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes an inference experiment.
///
///
/// This operation does not delete your endpoint, variants, or any underlying resources. This operation only
/// deletes the metadata of your experiment.
///
///
operation DeleteInferenceExperiment {
input: DeleteInferenceExperimentRequest
output: DeleteInferenceExperimentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Deletes a model. The DeleteModel API deletes only the model entry that
/// was created in SageMaker when you called the CreateModel API. It does not delete
/// model artifacts, inference code, or the IAM role that you specified when creating the
/// model.
operation DeleteModel {
input := {
/// The name of the model to delete.
@required
ModelName: ModelName
}
output: Unit
}
/// Deletes an Amazon SageMaker model bias job definition.
operation DeleteModelBiasJobDefinition {
input: DeleteModelBiasJobDefinitionRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes an Amazon SageMaker Model Card.
operation DeleteModelCard {
input: DeleteModelCardRequest
output: Unit
errors: [
ConflictException
ResourceNotFound
]
}
/// Deletes an Amazon SageMaker model explainability job definition.
operation DeleteModelExplainabilityJobDefinition {
input: DeleteModelExplainabilityJobDefinitionRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes a model package.
/// A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can
/// subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
operation DeleteModelPackage {
input := {
/// 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).
@required
ModelPackageName: VersionedArnOrName
}
output: Unit
errors: [
ConflictException
]
}
/// Deletes the specified model group.
operation DeleteModelPackageGroup {
input := {
/// The name of the model group to delete.
@required
ModelPackageGroupName: ArnOrName
}
output: Unit
errors: [
ConflictException
]
}
/// Deletes a model group resource policy.
operation DeleteModelPackageGroupPolicy {
input := {
/// The name of the model group for which to delete the policy.
@required
ModelPackageGroupName: EntityName
}
output: Unit
}
/// Deletes the secified model quality monitoring job definition.
operation DeleteModelQualityJobDefinition {
input: DeleteModelQualityJobDefinitionRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes a monitoring schedule. Also stops the schedule had not already been stopped.
/// This does not delete the job execution history of the monitoring schedule.
operation DeleteMonitoringSchedule {
input: DeleteMonitoringScheduleRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you
/// must call the StopNotebookInstance API.
///
/// When you delete a notebook instance, you lose all of your data. SageMaker removes
/// the ML compute instance, and deletes the ML storage volume and the network interface
/// associated with the notebook instance.
///
operation DeleteNotebookInstance {
input := {
/// The name of the SageMaker notebook instance to delete.
@required
NotebookInstanceName: NotebookInstanceName
}
output: Unit
}
/// Deletes a notebook instance lifecycle configuration.
operation DeleteNotebookInstanceLifecycleConfig {
input := {
/// The name of the lifecycle configuration to delete.
@required
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
}
output: Unit
}
/// Deletes a pipeline if there are no running instances of the pipeline. To delete a
/// pipeline, you must stop all running instances of the pipeline using the
/// StopPipelineExecution API. When you delete a pipeline, all instances of the
/// pipeline are deleted.
operation DeletePipeline {
input: DeletePipelineRequest
output: DeletePipelineResponse
errors: [
ResourceNotFound
]
}
/// Delete the specified project.
operation DeleteProject {
input := {
/// The name of the project to delete.
@required
ProjectName: ProjectEntityName
}
output: Unit
errors: [
ConflictException
]
}
/// Used to delete a space.
operation DeleteSpace {
input: DeleteSpaceRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
operation DeleteStudioLifecycleConfig {
input: DeleteStudioLifecycleConfigRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Deletes the specified tags from an SageMaker resource.
/// To list a resource's tags, use the ListTags API.
///
/// When you call this API to delete tags from a hyperparameter tuning job, the
/// deleted tags are not removed from training jobs that the hyperparameter tuning job
/// launched before you called this API.
///
///
/// When you call this API to delete tags from a SageMaker Studio Domain or User
/// Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain
/// or User Profile launched before you called this API.
///
operation DeleteTags {
input := {
/// The Amazon Resource Name (ARN) of the resource whose tags you want to
/// delete.
@required
ResourceArn: ResourceArn
/// An array or one or more tag keys to delete.
@required
TagKeys: TagKeyList
}
output := {}
}
/// Deletes the specified trial. All trial components that make up the trial must be deleted
/// first. Use the DescribeTrialComponent API to get the list of trial
/// components.
operation DeleteTrial {
input: DeleteTrialRequest
output: DeleteTrialResponse
errors: [
ResourceNotFound
]
}
/// Deletes the specified trial component. A trial component must be disassociated from all
/// trials before the trial component can be deleted. To disassociate a trial component from a
/// trial, call the DisassociateTrialComponent API.
operation DeleteTrialComponent {
input: DeleteTrialComponentRequest
output: DeleteTrialComponentResponse
errors: [
ResourceNotFound
]
}
/// Deletes a user profile. When a user profile is deleted, the user loses access to their EFS
/// volume, including data, notebooks, and other artifacts.
operation DeleteUserProfile {
input: DeleteUserProfileRequest
output: Unit
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Use this operation to delete a workforce.
/// If you want to create a new workforce in an Amazon Web Services Region where
/// a workforce already exists, use this operation to delete the
/// existing workforce and then use
/// to create a new workforce.
///
/// If a private workforce contains one or more work teams, you must use
/// the
/// operation to delete all work teams before you delete the workforce.
/// If you try to delete a workforce that contains one or more work teams,
/// you will recieve a ResourceInUse error.
///
operation DeleteWorkforce {
input: DeleteWorkforceRequest
output: DeleteWorkforceResponse
}
/// Deletes an existing work team. This operation can't be undone.
operation DeleteWorkteam {
input: DeleteWorkteamRequest
output: DeleteWorkteamResponse
errors: [
ResourceLimitExceeded
]
}
/// Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
operation DeregisterDevices {
input: DeregisterDevicesRequest
output: Unit
}
/// Describes an action.
operation DescribeAction {
input: DescribeActionRequest
output: DescribeActionResponse
errors: [
ResourceNotFound
]
}
/// Returns a description of the specified algorithm that is in your account.
operation DescribeAlgorithm {
input := {
/// The name of the algorithm to describe.
@required
AlgorithmName: ArnOrName
}
output := {
/// The name of the algorithm being described.
@required
AlgorithmName: EntityName
/// The Amazon Resource Name (ARN) of the algorithm.
@required
AlgorithmArn: AlgorithmArn
/// A brief summary about the algorithm.
AlgorithmDescription: EntityDescription
/// A timestamp specifying when the algorithm was created.
@required
CreationTime: CreationTime
/// Details about training jobs run by this algorithm.
@required
TrainingSpecification: TrainingSpecification
/// Details about inference jobs that the algorithm runs.
InferenceSpecification: InferenceSpecification
/// Details about configurations for one or more training jobs that SageMaker runs to test the
/// algorithm.
ValidationSpecification: AlgorithmValidationSpecification
/// The current status of the algorithm.
@required
AlgorithmStatus: AlgorithmStatus
/// Details about the current status of the algorithm.
@required
AlgorithmStatusDetails: AlgorithmStatusDetails
/// The product identifier of the algorithm.
ProductId: ProductId
/// Whether the algorithm is certified to be listed in Amazon Web Services
/// Marketplace.
CertifyForMarketplace: CertifyForMarketplace = false
}
}
/// Describes the app.
operation DescribeApp {
input: DescribeAppRequest
output: DescribeAppResponse
errors: [
ResourceNotFound
]
}
/// Describes an AppImageConfig.
operation DescribeAppImageConfig {
input: DescribeAppImageConfigRequest
output: DescribeAppImageConfigResponse
errors: [
ResourceNotFound
]
}
/// Describes an artifact.
operation DescribeArtifact {
input: DescribeArtifactRequest
output: DescribeArtifactResponse
errors: [
ResourceNotFound
]
}
/// Returns information about an Amazon SageMaker AutoML job.
operation DescribeAutoMLJob {
input: DescribeAutoMLJobRequest
output: DescribeAutoMLJobResponse
errors: [
ResourceNotFound
]
}
/// Gets details about the specified Git repository.
operation DescribeCodeRepository {
input := {
/// The name of the Git repository to describe.
@required
CodeRepositoryName: EntityName
}
output := {
/// The name of the Git repository.
@required
CodeRepositoryName: EntityName
/// The Amazon Resource Name (ARN) of the Git repository.
@required
CodeRepositoryArn: CodeRepositoryArn
/// The date and time that the repository was created.
@required
CreationTime: CreationTime
/// The date and time that the repository was last changed.
@required
LastModifiedTime: LastModifiedTime
/// 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.
GitConfig: GitConfig
}
}
/// Returns information about a model compilation job.
/// To create a model compilation job, use CreateCompilationJob. To get
/// information about multiple model compilation jobs, use ListCompilationJobs.
operation DescribeCompilationJob {
input: DescribeCompilationJobRequest
output: DescribeCompilationJobResponse
errors: [
ResourceNotFound
]
}
/// Describes a context.
operation DescribeContext {
input: DescribeContextRequest
output: DescribeContextResponse
errors: [
ResourceNotFound
]
}
/// Gets the details of a data quality monitoring job definition.
operation DescribeDataQualityJobDefinition {
input: DescribeDataQualityJobDefinitionRequest
output: DescribeDataQualityJobDefinitionResponse
errors: [
ResourceNotFound
]
}
/// Describes the device.
operation DescribeDevice {
input: DescribeDeviceRequest
output: DescribeDeviceResponse
errors: [
ResourceNotFound
]
}
/// A description of the fleet the device belongs to.
operation DescribeDeviceFleet {
input: DescribeDeviceFleetRequest
output: DescribeDeviceFleetResponse
errors: [
ResourceNotFound
]
}
/// The description of the domain.
operation DescribeDomain {
input: DescribeDomainRequest
output: DescribeDomainResponse
errors: [
ResourceNotFound
]
}
/// Describes an edge deployment plan with deployment status per stage.
operation DescribeEdgeDeploymentPlan {
input: DescribeEdgeDeploymentPlanRequest
output: DescribeEdgeDeploymentPlanResponse
errors: [
ResourceNotFound
]
}
/// A description of edge packaging jobs.
operation DescribeEdgePackagingJob {
input: DescribeEdgePackagingJobRequest
output: DescribeEdgePackagingJobResponse
errors: [
ResourceNotFound
]
}
/// Returns the description of an endpoint.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
EndpointDeleted: {
acceptors: [
{
state: "success"
matcher: {
errorType: "ValidationException"
}
}
{
state: "failure"
matcher: {
output: {
path: "EndpointStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
]
minDelay: 30
}
EndpointInService: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "EndpointStatus"
expected: "InService"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "EndpointStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 30
}
)
operation DescribeEndpoint {
input := {
/// The name of the endpoint.
@required
EndpointName: EndpointName
}
output := {
/// Name of the endpoint.
@required
EndpointName: EndpointName
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
/// The name of the endpoint configuration associated with this endpoint.
@required
EndpointConfigName: EndpointConfigName
/// An array of ProductionVariantSummary objects, one for each model
/// hosted behind this endpoint.
ProductionVariants: ProductionVariantSummaryList
DataCaptureConfig: DataCaptureConfigSummary
/// The status of the endpoint.
///
/// -
///
/// OutOfService: Endpoint is not available to take incoming
/// requests.
///
/// -
///
/// Creating: CreateEndpoint is executing.
///
/// -
///
/// Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
///
/// -
///
/// SystemUpdating: Endpoint is undergoing maintenance and cannot be
/// updated or deleted or re-scaled until it has completed. This maintenance
/// operation does not change any customer-specified values such as VPC config, KMS
/// encryption, model, instance type, or instance count.
///
/// -
///
/// RollingBack: Endpoint fails to scale up or down or change its
/// variant weight and is in the process of rolling back to its previous
/// configuration. Once the rollback completes, endpoint returns to an
/// InService status. This transitional status only applies to an
/// endpoint that has autoscaling enabled and is undergoing variant weight or
/// capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called
/// explicitly.
///
/// -
///
/// InService: Endpoint is available to process incoming
/// requests.
///
/// -
///
/// Deleting: DeleteEndpoint is executing.
///
/// -
///
/// Failed: Endpoint could not be created, updated, or re-scaled. Use
/// DescribeEndpointOutput$FailureReason for information about
/// the failure. DeleteEndpoint is the only operation that can be
/// performed on a failed endpoint.
///
///
@required
EndpointStatus: EndpointStatus
/// If the status of the endpoint is Failed, the reason why it failed.
///
FailureReason: FailureReason
/// A timestamp that shows when the endpoint was created.
@required
CreationTime: Timestamp
/// A timestamp that shows when the endpoint was last modified.
@required
LastModifiedTime: Timestamp
/// The most recent deployment configuration for the endpoint.
LastDeploymentConfig: DeploymentConfig
/// Returns the description of an endpoint configuration created using the
/// CreateEndpointConfig
/// API.
AsyncInferenceConfig: AsyncInferenceConfig
/// 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.
PendingDeploymentSummary: PendingDeploymentSummary
/// The configuration parameters for an explainer.
ExplainerConfig: ExplainerConfig
/// 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.
ShadowProductionVariants: ProductionVariantSummaryList
}
}
/// Returns the description of an endpoint configuration created using the
/// CreateEndpointConfig API.
operation DescribeEndpointConfig {
input := {
/// The name of the endpoint configuration.
@required
EndpointConfigName: EndpointConfigName
}
output := {
/// Name of the SageMaker endpoint configuration.
@required
EndpointConfigName: EndpointConfigName
/// The Amazon Resource Name (ARN) of the endpoint configuration.
@required
EndpointConfigArn: EndpointConfigArn
/// An array of ProductionVariant objects, one for each model that you
/// want to host at this endpoint.
@required
ProductionVariants: ProductionVariantList
DataCaptureConfig: DataCaptureConfig
/// Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on
/// the ML storage volume attached to the instance.
KmsKeyId: KmsKeyId
/// A timestamp that shows when the endpoint configuration was created.
@required
CreationTime: Timestamp
/// Returns the description of an endpoint configuration created using the
/// CreateEndpointConfig
/// API.
AsyncInferenceConfig: AsyncInferenceConfig
/// The configuration parameters for an explainer.
ExplainerConfig: ExplainerConfig
/// 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.
ShadowProductionVariants: ProductionVariantList
}
}
/// Provides a list of an experiment's properties.
operation DescribeExperiment {
input: DescribeExperimentRequest
output: DescribeExperimentResponse
errors: [
ResourceNotFound
]
}
/// Use this operation to describe a FeatureGroup. The response includes
/// information on the creation time, FeatureGroup name, the unique identifier for
/// each FeatureGroup, and more.
operation DescribeFeatureGroup {
input: DescribeFeatureGroupRequest
output: DescribeFeatureGroupResponse
errors: [
ResourceNotFound
]
}
/// Shows the metadata for a feature within a feature group.
operation DescribeFeatureMetadata {
input: DescribeFeatureMetadataRequest
output: DescribeFeatureMetadataResponse
errors: [
ResourceNotFound
]
}
/// Returns information about the specified flow definition.
operation DescribeFlowDefinition {
input: DescribeFlowDefinitionRequest
output: DescribeFlowDefinitionResponse
errors: [
ResourceNotFound
]
}
/// Describe a hub.
operation DescribeHub {
input: DescribeHubRequest
output: DescribeHubResponse
errors: [
ResourceNotFound
]
}
/// Describe the content of a hub.
operation DescribeHubContent {
input: DescribeHubContentRequest
output: DescribeHubContentResponse
errors: [
ResourceNotFound
]
}
/// Returns information about the requested human task user interface (worker task template).
operation DescribeHumanTaskUi {
input: DescribeHumanTaskUiRequest
output: DescribeHumanTaskUiResponse
errors: [
ResourceNotFound
]
}
/// Gets
/// a description of a hyperparameter tuning job.
operation DescribeHyperParameterTuningJob {
input: DescribeHyperParameterTuningJobRequest
output: DescribeHyperParameterTuningJobResponse
errors: [
ResourceNotFound
]
}
/// Describes a SageMaker image.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
ImageCreated: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "ImageStatus"
expected: "CREATED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "ImageStatus"
expected: "CREATE_FAILED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
ImageDeleted: {
acceptors: [
{
state: "success"
matcher: {
errorType: "ResourceNotFoundException"
}
}
{
state: "failure"
matcher: {
output: {
path: "ImageStatus"
expected: "DELETE_FAILED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
ImageUpdated: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "ImageStatus"
expected: "CREATED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "ImageStatus"
expected: "UPDATE_FAILED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
)
operation DescribeImage {
input: DescribeImageRequest
output: DescribeImageResponse
errors: [
ResourceNotFound
]
}
/// Describes a version of a SageMaker image.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
ImageVersionCreated: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "ImageVersionStatus"
expected: "CREATED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "ImageVersionStatus"
expected: "CREATE_FAILED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
ImageVersionDeleted: {
acceptors: [
{
state: "success"
matcher: {
errorType: "ResourceNotFoundException"
}
}
{
state: "failure"
matcher: {
output: {
path: "ImageVersionStatus"
expected: "DELETE_FAILED"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
)
operation DescribeImageVersion {
input: DescribeImageVersionRequest
output: DescribeImageVersionResponse
errors: [
ResourceNotFound
]
}
/// Returns details about an inference experiment.
operation DescribeInferenceExperiment {
input: DescribeInferenceExperimentRequest
output: DescribeInferenceExperimentResponse
errors: [
ResourceNotFound
]
}
/// Provides the results of the Inference Recommender job.
/// One or more recommendation jobs are returned.
operation DescribeInferenceRecommendationsJob {
input: DescribeInferenceRecommendationsJobRequest
output: DescribeInferenceRecommendationsJobResponse
errors: [
ResourceNotFound
]
}
/// Gets information about a labeling job.
operation DescribeLabelingJob {
input: DescribeLabelingJobRequest
output: DescribeLabelingJobResponse
errors: [
ResourceNotFound
]
}
/// Provides a list of properties for the requested lineage group.
/// For more information, see
/// Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
operation DescribeLineageGroup {
input: DescribeLineageGroupRequest
output: DescribeLineageGroupResponse
errors: [
ResourceNotFound
]
}
/// Describes a model that you created using the CreateModel
/// API.
operation DescribeModel {
input := {
/// The name of the model.
@required
ModelName: ModelName
}
output := {
/// Name of the SageMaker model.
@required
ModelName: ModelName
/// The location of the primary inference code, associated artifacts, and custom
/// environment map that the inference code uses when it is deployed in production.
///
PrimaryContainer: ContainerDefinition
/// The containers in the inference pipeline.
Containers: ContainerDefinitionList
/// Specifies details of how containers in a multi-container endpoint are called.
InferenceExecutionConfig: InferenceExecutionConfig
/// The Amazon Resource Name (ARN) of the IAM role that you specified for the
/// model.
@required
ExecutionRoleArn: RoleArn
/// 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
///
VpcConfig: VpcConfig
/// A timestamp that shows when the model was created.
@required
CreationTime: Timestamp
/// The Amazon Resource Name (ARN) of the model.
@required
ModelArn: ModelArn
/// If True, no inbound or outbound network calls can be made to or from the
/// model container.
EnableNetworkIsolation: Boolean = false
}
}
/// Returns a description of a model bias job definition.
operation DescribeModelBiasJobDefinition {
input: DescribeModelBiasJobDefinitionRequest
output: DescribeModelBiasJobDefinitionResponse
errors: [
ResourceNotFound
]
}
/// Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
operation DescribeModelCard {
input: DescribeModelCardRequest
output: DescribeModelCardResponse
errors: [
ResourceNotFound
]
}
/// Describes an Amazon SageMaker Model Card export job.
operation DescribeModelCardExportJob {
input: DescribeModelCardExportJobRequest
output: DescribeModelCardExportJobResponse
errors: [
ResourceNotFound
]
}
/// Returns a description of a model explainability job definition.
operation DescribeModelExplainabilityJobDefinition {
input: DescribeModelExplainabilityJobDefinitionRequest
output: DescribeModelExplainabilityJobDefinitionResponse
errors: [
ResourceNotFound
]
}
/// Returns a description of the specified model package, which is used to create SageMaker
/// models or list them on Amazon Web Services Marketplace.
/// To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services
/// Marketplace.
operation DescribeModelPackage {
input := {
/// 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).
@required
ModelPackageName: VersionedArnOrName
}
output := {
/// The name of the model package being described.
@required
ModelPackageName: EntityName
/// If the model is a versioned model, the name of the model group that the versioned
/// model belongs to.
ModelPackageGroupName: EntityName
/// The version of the model package.
ModelPackageVersion: ModelPackageVersion
/// The Amazon Resource Name (ARN) of the model package.
@required
ModelPackageArn: ModelPackageArn
/// A brief summary of the model package.
ModelPackageDescription: EntityDescription
/// A timestamp specifying when the model package was created.
@required
CreationTime: CreationTime
/// Details about inference jobs that can be run with models based on this model
/// package.
InferenceSpecification: InferenceSpecification
/// Details about the algorithm that was used to create the model package.
SourceAlgorithmSpecification: SourceAlgorithmSpecification
/// Configurations for one or more transform jobs that SageMaker runs to test the model
/// package.
ValidationSpecification: ModelPackageValidationSpecification
/// The current status of the model package.
@required
ModelPackageStatus: ModelPackageStatus
/// Details about the current status of the model package.
@required
ModelPackageStatusDetails: ModelPackageStatusDetails
/// Whether the model package is certified for listing on Amazon Web Services Marketplace.
CertifyForMarketplace: CertifyForMarketplace = false
/// The approval status of the model package.
ModelApprovalStatus: ModelApprovalStatus
CreatedBy: UserContext
MetadataProperties: MetadataProperties
/// Metrics for the model.
ModelMetrics: ModelMetrics
/// The last time that the model package was modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// A description provided for the model approval.
ApprovalDescription: ApprovalDescription
/// The metadata properties associated with the model package versions.
CustomerMetadataProperties: CustomerMetadataMap
/// 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.
///
DriftCheckBaselines: DriftCheckBaselines
/// The machine learning domain of the model package you specified. Common machine
/// learning domains include computer vision and natural language processing.
Domain: String
/// The machine learning task you specified that your model package accomplishes.
/// Common machine learning tasks include object detection and image classification.
Task: 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).
SamplePayloadUrl: String
/// 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.
AdditionalInferenceSpecifications: AdditionalInferenceSpecifications
}
}
/// Gets a description for the specified model group.
operation DescribeModelPackageGroup {
input := {
/// The name of gthe model group to describe.
@required
ModelPackageGroupName: ArnOrName
}
output := {
/// The name of the model group.
@required
ModelPackageGroupName: EntityName
/// The Amazon Resource Name (ARN) of the model group.
@required
ModelPackageGroupArn: ModelPackageGroupArn
/// A description of the model group.
ModelPackageGroupDescription: EntityDescription
/// The time that the model group was created.
@required
CreationTime: CreationTime
@required
CreatedBy: UserContext
/// The status of the model group.
@required
ModelPackageGroupStatus: ModelPackageGroupStatus
}
}
/// Returns a description of a model quality job definition.
operation DescribeModelQualityJobDefinition {
input: DescribeModelQualityJobDefinitionRequest
output: DescribeModelQualityJobDefinitionResponse
errors: [
ResourceNotFound
]
}
/// Describes the schedule for a monitoring job.
operation DescribeMonitoringSchedule {
input: DescribeMonitoringScheduleRequest
output: DescribeMonitoringScheduleResponse
errors: [
ResourceNotFound
]
}
/// Returns information about a notebook instance.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
NotebookInstanceDeleted: {
acceptors: [
{
state: "success"
matcher: {
errorType: "ValidationException"
}
}
{
state: "failure"
matcher: {
output: {
path: "NotebookInstanceStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
]
minDelay: 30
}
NotebookInstanceInService: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "NotebookInstanceStatus"
expected: "InService"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "NotebookInstanceStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
]
minDelay: 30
}
NotebookInstanceStopped: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "NotebookInstanceStatus"
expected: "Stopped"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "NotebookInstanceStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
]
minDelay: 30
}
)
operation DescribeNotebookInstance {
input := {
/// The name of the notebook instance that you want information about.
@required
NotebookInstanceName: NotebookInstanceName
}
output := {
/// The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceArn: NotebookInstanceArn
/// The name of the SageMaker notebook instance.
NotebookInstanceName: NotebookInstanceName
/// The status of the notebook instance.
NotebookInstanceStatus: NotebookInstanceStatus
/// If status is Failed, the reason it failed.
FailureReason: FailureReason
/// The URL that you use to connect to the Jupyter notebook that is running in your
/// notebook instance.
Url: NotebookInstanceUrl
/// The type of ML compute instance running on the notebook instance.
InstanceType: InstanceType
/// The ID of the VPC subnet.
SubnetId: SubnetId
/// The IDs of the VPC security groups.
SecurityGroups: SecurityGroupIds
/// The Amazon Resource Name (ARN) of the IAM role associated with the instance.
///
RoleArn: RoleArn
/// The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the
/// ML storage volume attached to the instance.
KmsKeyId: KmsKeyId
/// The network interface IDs that SageMaker created at the time of creating the instance.
///
NetworkInterfaceId: NetworkInterfaceId
/// A timestamp. Use this parameter to retrieve the time when the notebook instance was
/// last modified.
LastModifiedTime: LastModifiedTime
/// A timestamp. Use this parameter to return the time when the notebook instance was
/// created
CreationTime: CreationTime
/// 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
///
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// Describes whether SageMaker provides internet access to the notebook instance. If this
/// value is set to Disabled, the notebook instance does not have
/// internet access, and cannot connect to SageMaker training and endpoint services.
/// For more information, see Notebook Instances Are Internet-Enabled by Default.
DirectInternetAccess: DirectInternetAccess
/// The size, in GB, of the ML storage volume attached to the notebook instance.
VolumeSizeInGB: NotebookInstanceVolumeSizeInGB
/// A list of the Elastic Inference (EI) instance types associated with this notebook
/// instance. Currently only one EI instance type can be associated with a notebook
/// instance. For more information, see Using Elastic Inference in Amazon
/// SageMaker.
AcceleratorTypes: NotebookInstanceAcceleratorTypes
/// 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 Notebook Instances.
DefaultCodeRepository: CodeRepositoryNameOrUrl
/// 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 Notebook Instances.
AdditionalCodeRepositories: AdditionalCodeRepositoryNamesOrUrls
/// 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.
///
RootAccess: RootAccess
/// The platform identifier of the notebook instance runtime environment.
PlatformIdentifier: PlatformIdentifier
/// Information on the IMDS configuration of the notebook instance
InstanceMetadataServiceConfiguration: InstanceMetadataServiceConfiguration
}
}
/// Returns a description of a notebook instance lifecycle configuration.
/// For information about notebook instance lifestyle configurations, see Step
/// 2.1: (Optional) Customize a Notebook Instance.
operation DescribeNotebookInstanceLifecycleConfig {
input := {
/// The name of the lifecycle configuration to describe.
@required
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
}
output := {
/// The Amazon Resource Name (ARN) of the lifecycle configuration.
NotebookInstanceLifecycleConfigArn: NotebookInstanceLifecycleConfigArn
/// The name of the lifecycle configuration.
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// The shell script that runs only once, when you create a notebook instance.
OnCreate: NotebookInstanceLifecycleConfigList
/// The shell script that runs every time you start a notebook instance, including when
/// you create the notebook instance.
OnStart: NotebookInstanceLifecycleConfigList
/// A timestamp that tells when the lifecycle configuration was last modified.
LastModifiedTime: LastModifiedTime
/// A timestamp that tells when the lifecycle configuration was created.
CreationTime: CreationTime
}
}
/// Describes the details of a pipeline.
operation DescribePipeline {
input: DescribePipelineRequest
output: DescribePipelineResponse
errors: [
ResourceNotFound
]
}
/// Describes the details of an execution's pipeline definition.
operation DescribePipelineDefinitionForExecution {
input: DescribePipelineDefinitionForExecutionRequest
output: DescribePipelineDefinitionForExecutionResponse
errors: [
ResourceNotFound
]
}
/// Describes the details of a pipeline execution.
operation DescribePipelineExecution {
input: DescribePipelineExecutionRequest
output: DescribePipelineExecutionResponse
errors: [
ResourceNotFound
]
}
/// Returns a description of a processing job.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
ProcessingJobCompletedOrStopped: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "ProcessingJobStatus"
expected: "Completed"
comparator: "stringEquals"
}
}
}
{
state: "success"
matcher: {
output: {
path: "ProcessingJobStatus"
expected: "Stopped"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "ProcessingJobStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
)
operation DescribeProcessingJob {
input: DescribeProcessingJobRequest
output: DescribeProcessingJobResponse
errors: [
ResourceNotFound
]
}
/// Describes the details of a project.
operation DescribeProject {
input := {
/// The name of the project to describe.
@required
ProjectName: ProjectEntityName
}
output := {
/// The Amazon Resource Name (ARN) of the project.
@required
ProjectArn: ProjectArn
/// The name of the project.
@required
ProjectName: ProjectEntityName
/// The ID of the project.
@required
ProjectId: ProjectId
/// The description of the project.
ProjectDescription: EntityDescription
/// Information used to provision a service catalog product. For information, see What is Amazon Web Services Service
/// Catalog.
@required
ServiceCatalogProvisioningDetails: ServiceCatalogProvisioningDetails
/// Information about a provisioned service catalog product.
ServiceCatalogProvisionedProductDetails: ServiceCatalogProvisionedProductDetails
/// The status of the project.
@required
ProjectStatus: ProjectStatus
CreatedBy: UserContext
/// The time when the project was created.
@required
CreationTime: Timestamp
/// The timestamp when project was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
}
}
/// Describes the space.
operation DescribeSpace {
input: DescribeSpaceRequest
output: DescribeSpaceResponse
errors: [
ResourceNotFound
]
}
/// Describes the Studio Lifecycle Configuration.
operation DescribeStudioLifecycleConfig {
input: DescribeStudioLifecycleConfigRequest
output: DescribeStudioLifecycleConfigResponse
errors: [
ResourceNotFound
]
}
/// Gets information about a work team provided by a vendor. It returns details about the
/// subscription with a vendor in the Amazon Web Services Marketplace.
operation DescribeSubscribedWorkteam {
input: DescribeSubscribedWorkteamRequest
output: DescribeSubscribedWorkteamResponse
}
/// Returns information about a training job.
/// Some of the attributes below only appear if the training job successfully starts.
/// If the training job fails, TrainingJobStatus is Failed and,
/// depending on the FailureReason, attributes like
/// TrainingStartTime, TrainingTimeInSeconds,
/// TrainingEndTime, and BillableTimeInSeconds may not be
/// present in the response.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
TrainingJobCompletedOrStopped: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "TrainingJobStatus"
expected: "Completed"
comparator: "stringEquals"
}
}
}
{
state: "success"
matcher: {
output: {
path: "TrainingJobStatus"
expected: "Stopped"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "TrainingJobStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 120
}
)
operation DescribeTrainingJob {
input: DescribeTrainingJobRequest
output: DescribeTrainingJobResponse
errors: [
ResourceNotFound
]
}
/// Returns information about a transform job.
@suppress([
"WaitableTraitInvalidErrorType"
])
@waitable(
TransformJobCompletedOrStopped: {
acceptors: [
{
state: "success"
matcher: {
output: {
path: "TransformJobStatus"
expected: "Completed"
comparator: "stringEquals"
}
}
}
{
state: "success"
matcher: {
output: {
path: "TransformJobStatus"
expected: "Stopped"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
output: {
path: "TransformJobStatus"
expected: "Failed"
comparator: "stringEquals"
}
}
}
{
state: "failure"
matcher: {
errorType: "ValidationException"
}
}
]
minDelay: 60
}
)
operation DescribeTransformJob {
input: DescribeTransformJobRequest
output: DescribeTransformJobResponse
errors: [
ResourceNotFound
]
}
/// Provides a list of a trial's properties.
operation DescribeTrial {
input: DescribeTrialRequest
output: DescribeTrialResponse
errors: [
ResourceNotFound
]
}
/// Provides a list of a trials component's properties.
operation DescribeTrialComponent {
input: DescribeTrialComponentRequest
output: DescribeTrialComponentResponse
errors: [
ResourceNotFound
]
}
/// Describes a user profile. For more information, see CreateUserProfile.
operation DescribeUserProfile {
input: DescribeUserProfileRequest
output: DescribeUserProfileResponse
errors: [
ResourceNotFound
]
}
/// Lists private workforce information, including workforce name, Amazon Resource Name
/// (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address
/// ranges are the IP addresses that workers can use to access tasks.
///
/// This operation applies only to private workforces.
///
operation DescribeWorkforce {
input: DescribeWorkforceRequest
output: DescribeWorkforceResponse
}
/// Gets information about a specific work team. You can see information such as the
/// create date, the last updated date, membership information, and the work team's Amazon
/// Resource Name (ARN).
operation DescribeWorkteam {
input: DescribeWorkteamRequest
output: DescribeWorkteamResponse
}
/// Disables using Service Catalog in SageMaker. Service Catalog is used to create
/// SageMaker projects.
operation DisableSagemakerServicecatalogPortfolio {
input := {}
output := {}
}
/// Disassociates a trial component from a trial. This doesn't effect other trials the
/// component is associated with. Before you can delete a component, you must disassociate the
/// component from all trials it is associated with. To associate a trial component with a trial,
/// call the AssociateTrialComponent API.
/// To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter.
/// The list appears in the response under Results.TrialComponent.Parents.
operation DisassociateTrialComponent {
input: DisassociateTrialComponentRequest
output: DisassociateTrialComponentResponse
errors: [
ResourceNotFound
]
}
/// Enables using Service Catalog in SageMaker. Service Catalog is used to create
/// SageMaker projects.
operation EnableSagemakerServicecatalogPortfolio {
input := {}
output := {}
}
/// Describes a fleet.
operation GetDeviceFleetReport {
input: GetDeviceFleetReportRequest
output: GetDeviceFleetReportResponse
}
/// The resource policy for the lineage group.
operation GetLineageGroupPolicy {
input: GetLineageGroupPolicyRequest
output: GetLineageGroupPolicyResponse
errors: [
ResourceNotFound
]
}
/// Gets a resource policy that manages access for a model group. For information about
/// resource policies, see Identity-based
/// policies and resource-based policies in the Amazon Web Services Identity and
/// Access Management User Guide..
operation GetModelPackageGroupPolicy {
input := {
/// The name of the model group for which to get the resource policy.
@required
ModelPackageGroupName: EntityName
}
output := {
/// The resource policy for the model group.
@required
ResourcePolicy: PolicyString
}
}
/// Gets the status of Service Catalog in SageMaker. Service Catalog is used to create
/// SageMaker projects.
operation GetSagemakerServicecatalogPortfolioStatus {
input := {}
output := {
/// Whether Service Catalog is enabled or disabled in SageMaker.
Status: SagemakerServicecatalogStatus
}
}
/// An auto-complete API for the search functionality in the Amazon SageMaker console. It returns
/// suggestions of possible matches for the property name to use in Search
/// queries. Provides suggestions for HyperParameters, Tags, and
/// Metrics.
operation GetSearchSuggestions {
input: GetSearchSuggestionsRequest
output: GetSearchSuggestionsResponse
}
/// Import hub content.
operation ImportHubContent {
input: ImportHubContentRequest
output: ImportHubContentResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Lists the actions in your account and their properties.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ActionSummaries"
pageSize: "MaxResults"
)
operation ListActions {
input: ListActionsRequest
output: ListActionsResponse
errors: [
ResourceNotFound
]
}
/// Lists the machine learning algorithms that have been created.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "AlgorithmSummaryList"
pageSize: "MaxResults"
)
operation ListAlgorithms {
input := {
/// A filter that returns only algorithms created after the specified time
/// (timestamp).
CreationTimeAfter: CreationTime
/// A filter that returns only algorithms created before the specified time
/// (timestamp).
CreationTimeBefore: CreationTime
/// The maximum number of algorithms to return in the response.
MaxResults: MaxResults
/// A string in the algorithm name. This filter returns only algorithms whose name
/// contains the specified string.
NameContains: NameContains
/// 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.
NextToken: NextToken
/// The parameter by which to sort the results. The default is
/// CreationTime.
SortBy: AlgorithmSortBy
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
}
output := {
/// >An array of AlgorithmSummary objects, each of which lists an
/// algorithm.
@required
AlgorithmSummaryList: AlgorithmSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// algorithms, use it in the subsequent request.
NextToken: NextToken
}
}
/// Lists the aliases of a specified image or image version.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "SageMakerImageVersionAliases"
pageSize: "MaxResults"
)
operation ListAliases {
input: ListAliasesRequest
output: ListAliasesResponse
errors: [
ResourceNotFound
]
}
/// Lists the AppImageConfigs in your account and their properties. The list can be
/// filtered by creation time or modified time, and whether the AppImageConfig name contains
/// a specified string.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "AppImageConfigs"
pageSize: "MaxResults"
)
operation ListAppImageConfigs {
input: ListAppImageConfigsRequest
output: ListAppImageConfigsResponse
}
/// Lists apps.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Apps"
pageSize: "MaxResults"
)
operation ListApps {
input: ListAppsRequest
output: ListAppsResponse
}
/// Lists the artifacts in your account and their properties.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ArtifactSummaries"
pageSize: "MaxResults"
)
operation ListArtifacts {
input: ListArtifactsRequest
output: ListArtifactsResponse
errors: [
ResourceNotFound
]
}
/// Lists the associations in your account and their properties.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "AssociationSummaries"
pageSize: "MaxResults"
)
operation ListAssociations {
input: ListAssociationsRequest
output: ListAssociationsResponse
errors: [
ResourceNotFound
]
}
/// Request a list of jobs.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "AutoMLJobSummaries"
pageSize: "MaxResults"
)
operation ListAutoMLJobs {
input: ListAutoMLJobsRequest
output: ListAutoMLJobsResponse
}
/// List the candidates created for the job.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Candidates"
pageSize: "MaxResults"
)
operation ListCandidatesForAutoMLJob {
input: ListCandidatesForAutoMLJobRequest
output: ListCandidatesForAutoMLJobResponse
errors: [
ResourceNotFound
]
}
/// Gets a list of the Git repositories in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "CodeRepositorySummaryList"
pageSize: "MaxResults"
)
operation ListCodeRepositories {
input := {
/// A filter that returns only Git repositories that were created after the specified
/// time.
CreationTimeAfter: CreationTime
/// A filter that returns only Git repositories that were created before the specified
/// time.
CreationTimeBefore: CreationTime
/// A filter that returns only Git repositories that were last modified after the
/// specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only Git repositories that were last modified before the
/// specified time.
LastModifiedTimeBefore: Timestamp
/// The maximum number of Git repositories to return in the response.
MaxResults: MaxResults
/// A string in the Git repositories name. This filter returns only repositories whose
/// name contains the specified string.
NameContains: CodeRepositoryNameContains
/// 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.
NextToken: NextToken
/// The field to sort results by. The default is Name.
SortBy: CodeRepositorySortBy
/// The sort order for results. The default is Ascending.
SortOrder: CodeRepositorySortOrder
}
output := {
/// 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.
///
///
@required
CodeRepositorySummaryList: CodeRepositorySummaryList
/// 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.
NextToken: NextToken
}
}
/// Lists model compilation jobs that satisfy various filters.
/// To create a model compilation job, use CreateCompilationJob. To get
/// information about a particular model compilation job you have created, use DescribeCompilationJob.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "CompilationJobSummaries"
pageSize: "MaxResults"
)
operation ListCompilationJobs {
input: ListCompilationJobsRequest
output: ListCompilationJobsResponse
}
/// Lists the contexts in your account and their properties.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ContextSummaries"
pageSize: "MaxResults"
)
operation ListContexts {
input: ListContextsRequest
output: ListContextsResponse
errors: [
ResourceNotFound
]
}
/// Lists the data quality job definitions in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "JobDefinitionSummaries"
pageSize: "MaxResults"
)
operation ListDataQualityJobDefinitions {
input: ListDataQualityJobDefinitionsRequest
output: ListDataQualityJobDefinitionsResponse
}
/// Returns a list of devices in the fleet.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "DeviceFleetSummaries"
pageSize: "MaxResults"
)
operation ListDeviceFleets {
input: ListDeviceFleetsRequest
output: ListDeviceFleetsResponse
}
/// A list of devices.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "DeviceSummaries"
pageSize: "MaxResults"
)
operation ListDevices {
input: ListDevicesRequest
output: ListDevicesResponse
}
/// Lists the domains.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Domains"
pageSize: "MaxResults"
)
operation ListDomains {
input: ListDomainsRequest
output: ListDomainsResponse
}
/// Lists all edge deployment plans.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "EdgeDeploymentPlanSummaries"
pageSize: "MaxResults"
)
operation ListEdgeDeploymentPlans {
input: ListEdgeDeploymentPlansRequest
output: ListEdgeDeploymentPlansResponse
}
/// Returns a list of edge packaging jobs.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "EdgePackagingJobSummaries"
pageSize: "MaxResults"
)
operation ListEdgePackagingJobs {
input: ListEdgePackagingJobsRequest
output: ListEdgePackagingJobsResponse
}
/// Lists endpoint configurations.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "EndpointConfigs"
pageSize: "MaxResults"
)
operation ListEndpointConfigs {
input := {
/// The field to sort results by. The default is CreationTime.
SortBy: EndpointConfigSortKey
/// The sort order for results. The default is Descending.
SortOrder: OrderKey
/// 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.
NextToken: PaginationToken
/// The maximum number of training jobs to return in the response.
MaxResults: MaxResults
/// A string in the endpoint configuration name. This filter returns only endpoint
/// configurations whose name contains the specified string.
NameContains: EndpointConfigNameContains
/// A filter that returns only endpoint configurations created before the specified
/// time (timestamp).
CreationTimeBefore: Timestamp
/// A filter that returns only endpoint configurations with a creation time greater
/// than or equal to the specified time (timestamp).
CreationTimeAfter: Timestamp
}
output := {
/// An array of endpoint configurations.
@required
EndpointConfigs: EndpointConfigSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// endpoint configurations, use it in the subsequent request
NextToken: PaginationToken
}
}
/// Lists endpoints.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Endpoints"
pageSize: "MaxResults"
)
operation ListEndpoints {
input := {
/// Sorts the list of results. The default is CreationTime.
SortBy: EndpointSortKey
/// The sort order for results. The default is Descending.
SortOrder: OrderKey
/// 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.
NextToken: PaginationToken
/// The maximum number of endpoints to return in the response. This value defaults to
/// 10.
MaxResults: MaxResults
/// A string in endpoint names. This filter returns only endpoints whose name contains
/// the specified string.
NameContains: EndpointNameContains
/// A filter that returns only endpoints that were created before the specified time
/// (timestamp).
CreationTimeBefore: Timestamp
/// A filter that returns only endpoints with a creation time greater than or equal to
/// the specified time (timestamp).
CreationTimeAfter: Timestamp
/// A filter that returns only endpoints that were modified before the specified
/// timestamp.
LastModifiedTimeBefore: Timestamp
/// A filter that returns only endpoints that were modified after the specified
/// timestamp.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only endpoints with the specified status.
StatusEquals: EndpointStatus
}
output := {
/// An array or endpoint objects.
@required
Endpoints: EndpointSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// training jobs, use it in the subsequent request.
NextToken: PaginationToken
}
}
/// Lists all the experiments in your account. The list can be filtered to show only
/// experiments that were created in a specific time range. The list can be sorted by experiment
/// name or creation time.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ExperimentSummaries"
pageSize: "MaxResults"
)
operation ListExperiments {
input: ListExperimentsRequest
output: ListExperimentsResponse
}
/// List FeatureGroups based on given filter and order.
operation ListFeatureGroups {
input: ListFeatureGroupsRequest
output: ListFeatureGroupsResponse
}
/// Returns information about the flow definitions in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "FlowDefinitionSummaries"
pageSize: "MaxResults"
)
operation ListFlowDefinitions {
input: ListFlowDefinitionsRequest
output: ListFlowDefinitionsResponse
}
/// List the contents of a hub.
operation ListHubContents {
input: ListHubContentsRequest
output: ListHubContentsResponse
errors: [
ResourceNotFound
]
}
/// List hub content versions.
operation ListHubContentVersions {
input: ListHubContentVersionsRequest
output: ListHubContentVersionsResponse
errors: [
ResourceNotFound
]
}
/// List all existing hubs.
operation ListHubs {
input: ListHubsRequest
output: ListHubsResponse
}
/// Returns information about the human task user interfaces in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "HumanTaskUiSummaries"
pageSize: "MaxResults"
)
operation ListHumanTaskUis {
input: ListHumanTaskUisRequest
output: ListHumanTaskUisResponse
}
/// Gets a list of HyperParameterTuningJobSummary objects that
/// describe
/// the hyperparameter tuning jobs launched in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "HyperParameterTuningJobSummaries"
pageSize: "MaxResults"
)
operation ListHyperParameterTuningJobs {
input: ListHyperParameterTuningJobsRequest
output: ListHyperParameterTuningJobsResponse
}
/// Lists the images in your account and their properties. The list can be filtered by
/// creation time or modified time, and whether the image name contains a specified string.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Images"
pageSize: "MaxResults"
)
operation ListImages {
input: ListImagesRequest
output: ListImagesResponse
}
/// Lists the versions of a specified image and their properties. The list can be filtered
/// by creation time or modified time.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ImageVersions"
pageSize: "MaxResults"
)
operation ListImageVersions {
input: ListImageVersionsRequest
output: ListImageVersionsResponse
errors: [
ResourceNotFound
]
}
/// Returns the list of all inference experiments.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "InferenceExperiments"
pageSize: "MaxResults"
)
operation ListInferenceExperiments {
input: ListInferenceExperimentsRequest
output: ListInferenceExperimentsResponse
}
/// Lists recommendation jobs that satisfy various filters.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "InferenceRecommendationsJobs"
pageSize: "MaxResults"
)
operation ListInferenceRecommendationsJobs {
input: ListInferenceRecommendationsJobsRequest
output: ListInferenceRecommendationsJobsResponse
}
/// Returns a list of the subtasks for an Inference Recommender job.
/// The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Steps"
pageSize: "MaxResults"
)
operation ListInferenceRecommendationsJobSteps {
input: ListInferenceRecommendationsJobStepsRequest
output: ListInferenceRecommendationsJobStepsResponse
errors: [
ResourceNotFound
]
}
/// Gets a list of labeling jobs.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "LabelingJobSummaryList"
pageSize: "MaxResults"
)
operation ListLabelingJobs {
input: ListLabelingJobsRequest
output: ListLabelingJobsResponse
}
/// Gets a list of labeling jobs assigned to a specified work team.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "LabelingJobSummaryList"
pageSize: "MaxResults"
)
operation ListLabelingJobsForWorkteam {
input: ListLabelingJobsForWorkteamRequest
output: ListLabelingJobsForWorkteamResponse
errors: [
ResourceNotFound
]
}
/// A list of lineage groups shared with your Amazon Web Services account.
/// For more information, see
/// Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "LineageGroupSummaries"
pageSize: "MaxResults"
)
operation ListLineageGroups {
input: ListLineageGroupsRequest
output: ListLineageGroupsResponse
}
/// Lists model bias jobs definitions that satisfy various filters.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "JobDefinitionSummaries"
pageSize: "MaxResults"
)
operation ListModelBiasJobDefinitions {
input: ListModelBiasJobDefinitionsRequest
output: ListModelBiasJobDefinitionsResponse
}
/// List the export jobs for the Amazon SageMaker Model Card.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelCardExportJobSummaries"
pageSize: "MaxResults"
)
operation ListModelCardExportJobs {
input: ListModelCardExportJobsRequest
output: ListModelCardExportJobsResponse
}
/// List existing model cards.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelCardSummaries"
pageSize: "MaxResults"
)
operation ListModelCards {
input: ListModelCardsRequest
output: ListModelCardsResponse
}
/// List existing versions of an Amazon SageMaker Model Card.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelCardVersionSummaryList"
pageSize: "MaxResults"
)
operation ListModelCardVersions {
input: ListModelCardVersionsRequest
output: ListModelCardVersionsResponse
errors: [
ResourceNotFound
]
}
/// Lists model explainability job definitions that satisfy various filters.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "JobDefinitionSummaries"
pageSize: "MaxResults"
)
operation ListModelExplainabilityJobDefinitions {
input: ListModelExplainabilityJobDefinitionsRequest
output: ListModelExplainabilityJobDefinitionsResponse
}
/// Lists the domain, framework, task, and model name of standard
/// machine learning models found in common model zoos.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelMetadataSummaries"
pageSize: "MaxResults"
)
operation ListModelMetadata {
input: ListModelMetadataRequest
output: ListModelMetadataResponse
}
/// Gets a list of the model groups in your Amazon Web Services account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelPackageGroupSummaryList"
pageSize: "MaxResults"
)
operation ListModelPackageGroups {
input := {
/// A filter that returns only model groups created after the specified time.
CreationTimeAfter: CreationTime
/// A filter that returns only model groups created before the specified time.
CreationTimeBefore: CreationTime
/// The maximum number of results to return in the response.
MaxResults: MaxResults
/// A string in the model group name. This filter returns only model groups whose name
/// contains the specified string.
NameContains: NameContains
/// 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.
NextToken: NextToken
/// The field to sort results by. The default is CreationTime.
SortBy: ModelPackageGroupSortBy
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
}
output := {
/// A list of summaries of the model groups in your Amazon Web Services account.
@required
ModelPackageGroupSummaryList: ModelPackageGroupSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set
/// of model groups, use it in the subsequent request.
NextToken: NextToken
}
}
/// Lists the model packages that have been created.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ModelPackageSummaryList"
pageSize: "MaxResults"
)
operation ListModelPackages {
input := {
/// A filter that returns only model packages created after the specified time
/// (timestamp).
CreationTimeAfter: CreationTime
/// A filter that returns only model packages created before the specified time
/// (timestamp).
CreationTimeBefore: CreationTime
/// The maximum number of model packages to return in the response.
MaxResults: MaxResults
/// A string in the model package name. This filter returns only model packages whose name
/// contains the specified string.
NameContains: NameContains
/// A filter that returns only the model packages with the specified approval
/// status.
ModelApprovalStatus: ModelApprovalStatus
/// A filter that returns only model versions that belong to the specified model group.
ModelPackageGroupName: ArnOrName
/// 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.
///
///
ModelPackageType: ModelPackageType
/// 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.
NextToken: NextToken
/// The parameter by which to sort the results. The default is
/// CreationTime.
SortBy: ModelPackageSortBy
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
}
output := {
/// An array of ModelPackageSummary objects, each of which lists a model
/// package.
@required
ModelPackageSummaryList: ModelPackageSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// model packages, use it in the subsequent request.
NextToken: NextToken
}
}
/// Gets a list of model quality monitoring job definitions in your account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "JobDefinitionSummaries"
pageSize: "MaxResults"
)
operation ListModelQualityJobDefinitions {
input: ListModelQualityJobDefinitionsRequest
output: ListModelQualityJobDefinitionsResponse
}
/// Lists models created with the CreateModel API.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Models"
pageSize: "MaxResults"
)
operation ListModels {
input := {
/// Sorts the list of results. The default is CreationTime.
SortBy: ModelSortKey
/// The sort order for results. The default is Descending.
SortOrder: OrderKey
/// 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.
NextToken: PaginationToken
/// The maximum number of models to return in the response.
MaxResults: MaxResults
/// A string in the model name. This filter returns only models whose name contains the
/// specified string.
NameContains: ModelNameContains
/// A filter that returns only models created before the specified time
/// (timestamp).
CreationTimeBefore: Timestamp
/// A filter that returns only models with a creation time greater than or equal to the
/// specified time (timestamp).
CreationTimeAfter: Timestamp
}
output := {
/// An array of ModelSummary objects, each of which lists a
/// model.
@required
Models: ModelSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// models, use it in the subsequent request.
NextToken: PaginationToken
}
}
/// Gets a list of past alerts in a model monitoring schedule.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "MonitoringAlertHistory"
pageSize: "MaxResults"
)
operation ListMonitoringAlertHistory {
input: ListMonitoringAlertHistoryRequest
output: ListMonitoringAlertHistoryResponse
errors: [
ResourceNotFound
]
}
/// Gets the alerts for a single monitoring schedule.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "MonitoringAlertSummaries"
pageSize: "MaxResults"
)
operation ListMonitoringAlerts {
input: ListMonitoringAlertsRequest
output: ListMonitoringAlertsResponse
errors: [
ResourceNotFound
]
}
/// Returns list of all monitoring job executions.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "MonitoringExecutionSummaries"
pageSize: "MaxResults"
)
operation ListMonitoringExecutions {
input: ListMonitoringExecutionsRequest
output: ListMonitoringExecutionsResponse
}
/// Returns list of all monitoring schedules.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "MonitoringScheduleSummaries"
pageSize: "MaxResults"
)
operation ListMonitoringSchedules {
input: ListMonitoringSchedulesRequest
output: ListMonitoringSchedulesResponse
}
/// Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "NotebookInstanceLifecycleConfigs"
pageSize: "MaxResults"
)
operation ListNotebookInstanceLifecycleConfigs {
input := {
/// 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.
NextToken: NextToken
/// The maximum number of lifecycle configurations to return in the response.
MaxResults: MaxResults
/// Sorts the list of results. The default is CreationTime.
SortBy: NotebookInstanceLifecycleConfigSortKey
/// The sort order for results.
SortOrder: NotebookInstanceLifecycleConfigSortOrder
/// A string in the lifecycle configuration name. This filter returns only lifecycle
/// configurations whose name contains the specified string.
NameContains: NotebookInstanceLifecycleConfigNameContains
/// A filter that returns only lifecycle configurations that were created before the
/// specified time (timestamp).
CreationTimeBefore: CreationTime
/// A filter that returns only lifecycle configurations that were created after the
/// specified time (timestamp).
CreationTimeAfter: CreationTime
/// A filter that returns only lifecycle configurations that were modified before the
/// specified time (timestamp).
LastModifiedTimeBefore: LastModifiedTime
/// A filter that returns only lifecycle configurations that were modified after the
/// specified time (timestamp).
LastModifiedTimeAfter: LastModifiedTime
}
output := {
/// If the response is truncated, SageMaker returns this token. To get the next set of
/// lifecycle configurations, use it in the next request.
NextToken: NextToken
/// An array of NotebookInstanceLifecycleConfiguration objects, each listing
/// a lifecycle configuration.
NotebookInstanceLifecycleConfigs: NotebookInstanceLifecycleConfigSummaryList
}
}
/// Returns a list of the SageMaker notebook instances in the requester's account in an
/// Amazon Web Services Region.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "NotebookInstances"
pageSize: "MaxResults"
)
operation ListNotebookInstances {
input := {
/// 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.
///
NextToken: NextToken
/// The maximum number of notebook instances to return.
MaxResults: MaxResults
/// The field to sort results by. The default is Name.
SortBy: NotebookInstanceSortKey
/// The sort order for results.
SortOrder: NotebookInstanceSortOrder
/// A string in the notebook instances' name. This filter returns only notebook
/// instances whose name contains the specified string.
NameContains: NotebookInstanceNameContains
/// A filter that returns only notebook instances that were created before the
/// specified time (timestamp).
CreationTimeBefore: CreationTime
/// A filter that returns only notebook instances that were created after the specified
/// time (timestamp).
CreationTimeAfter: CreationTime
/// A filter that returns only notebook instances that were modified before the
/// specified time (timestamp).
LastModifiedTimeBefore: LastModifiedTime
/// A filter that returns only notebook instances that were modified after the
/// specified time (timestamp).
LastModifiedTimeAfter: LastModifiedTime
/// A filter that returns only notebook instances with the specified status.
StatusEquals: NotebookInstanceStatus
/// 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.
NotebookInstanceLifecycleConfigNameContains: NotebookInstanceLifecycleConfigName
/// 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.
DefaultCodeRepositoryContains: CodeRepositoryContains
/// A filter that returns only notebook instances with associated with the specified git
/// repository.
AdditionalCodeRepositoryEquals: CodeRepositoryNameOrUrl
}
output := {
/// If the response to the previous ListNotebookInstances request was
/// truncated, SageMaker returns this token. To retrieve the next set of notebook instances, use
/// the token in the next request.
NextToken: NextToken
/// An array of NotebookInstanceSummary objects, one for each notebook
/// instance.
NotebookInstances: NotebookInstanceSummaryList
}
}
/// Gets a list of the pipeline executions.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "PipelineExecutionSummaries"
pageSize: "MaxResults"
)
operation ListPipelineExecutions {
input: ListPipelineExecutionsRequest
output: ListPipelineExecutionsResponse
errors: [
ResourceNotFound
]
}
/// Gets a list of PipeLineExecutionStep objects.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "PipelineExecutionSteps"
pageSize: "MaxResults"
)
operation ListPipelineExecutionSteps {
input: ListPipelineExecutionStepsRequest
output: ListPipelineExecutionStepsResponse
errors: [
ResourceNotFound
]
}
/// Gets a list of parameters for a pipeline execution.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "PipelineParameters"
pageSize: "MaxResults"
)
operation ListPipelineParametersForExecution {
input: ListPipelineParametersForExecutionRequest
output: ListPipelineParametersForExecutionResponse
errors: [
ResourceNotFound
]
}
/// Gets a list of pipelines.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "PipelineSummaries"
pageSize: "MaxResults"
)
operation ListPipelines {
input: ListPipelinesRequest
output: ListPipelinesResponse
}
/// Lists processing jobs that satisfy various filters.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "ProcessingJobSummaries"
pageSize: "MaxResults"
)
operation ListProcessingJobs {
input: ListProcessingJobsRequest
output: ListProcessingJobsResponse
}
/// Gets a list of the projects in an Amazon Web Services account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
pageSize: "MaxResults"
)
operation ListProjects {
input := {
/// A filter that returns the projects that were created after a specified
/// time.
CreationTimeAfter: Timestamp
/// A filter that returns the projects that were created before a specified
/// time.
CreationTimeBefore: Timestamp
/// The maximum number of projects to return in the response.
MaxResults: MaxResults
/// A filter that returns the projects whose name contains a specified
/// string.
NameContains: ProjectEntityName
/// 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.
NextToken: NextToken
/// The field by which to sort results. The default is CreationTime.
SortBy: ProjectSortBy
/// The sort order for results. The default is Ascending.
SortOrder: ProjectSortOrder
}
output := {
/// A list of summaries of projects.
@required
ProjectSummaryList: ProjectSummaryList
/// 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.
NextToken: NextToken
}
}
/// Lists spaces.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Spaces"
pageSize: "MaxResults"
)
operation ListSpaces {
input: ListSpacesRequest
output: ListSpacesResponse
}
/// Lists devices allocated to the stage, containing detailed device information and deployment status.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "DeviceDeploymentSummaries"
pageSize: "MaxResults"
)
operation ListStageDevices {
input: ListStageDevicesRequest
output: ListStageDevicesResponse
}
/// Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "StudioLifecycleConfigs"
pageSize: "MaxResults"
)
operation ListStudioLifecycleConfigs {
input: ListStudioLifecycleConfigsRequest
output: ListStudioLifecycleConfigsResponse
errors: [
ResourceInUse
]
}
/// Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The
/// list may be empty if no work team satisfies the filter specified in the
/// NameContains parameter.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "SubscribedWorkteams"
pageSize: "MaxResults"
)
operation ListSubscribedWorkteams {
input: ListSubscribedWorkteamsRequest
output: ListSubscribedWorkteamsResponse
}
/// Returns the tags for the specified SageMaker resource.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Tags"
pageSize: "MaxResults"
)
operation ListTags {
input := {
/// The Amazon Resource Name (ARN) of the resource whose tags you want to
/// retrieve.
@required
ResourceArn: ResourceArn
/// 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.
///
NextToken: NextToken
/// Maximum number of tags to return.
MaxResults: ListTagsMaxResults
}
output := {
/// An array of Tag objects, each with a tag key and a value.
Tags: TagList
/// 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.
NextToken: NextToken
}
}
/// Lists training jobs.
///
/// When StatusEquals and MaxResults are set at the same
/// time, the MaxResults number of training jobs are first retrieved
/// ignoring the StatusEquals parameter and then they are filtered by the
/// StatusEquals parameter, which is returned as a response.
/// For example, if ListTrainingJobs is invoked with the following
/// parameters:
///
/// { ... MaxResults: 100, StatusEquals: InProgress ... }
///
/// First, 100 trainings jobs with any status, including those other than
/// InProgress, are selected (sorted according to the creation time,
/// from the most current to the oldest). Next, those with a status of
/// InProgress are returned.
/// You can quickly test the API using the following Amazon Web Services CLI
/// code.
///
/// aws sagemaker list-training-jobs --max-results 100 --status-equals
/// InProgress
///
///
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "TrainingJobSummaries"
pageSize: "MaxResults"
)
operation ListTrainingJobs {
input: ListTrainingJobsRequest
output: ListTrainingJobsResponse
}
/// Gets a list of TrainingJobSummary objects that describe the training
/// jobs that a hyperparameter tuning job launched.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "TrainingJobSummaries"
pageSize: "MaxResults"
)
operation ListTrainingJobsForHyperParameterTuningJob {
input: ListTrainingJobsForHyperParameterTuningJobRequest
output: ListTrainingJobsForHyperParameterTuningJobResponse
errors: [
ResourceNotFound
]
}
/// Lists transform jobs.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "TransformJobSummaries"
pageSize: "MaxResults"
)
operation ListTransformJobs {
input: ListTransformJobsRequest
output: ListTransformJobsResponse
}
/// Lists the trial components in your account. You can sort the list by trial component name
/// or creation time. You can filter the list to show only components that were created in a
/// specific time range. You can also filter on one of the following:
///
/// -
///
/// ExperimentName
///
///
/// -
///
/// SourceArn
///
///
/// -
///
/// TrialName
///
///
///
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "TrialComponentSummaries"
pageSize: "MaxResults"
)
operation ListTrialComponents {
input: ListTrialComponentsRequest
output: ListTrialComponentsResponse
errors: [
ResourceNotFound
]
}
/// Lists the trials in your account. Specify an experiment name to limit the list to the
/// trials that are part of that experiment. Specify a trial component name to limit the list to
/// the trials that associated with that trial component. The list can be filtered to show only
/// trials that were created in a specific time range. The list can be sorted by trial name or
/// creation time.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "TrialSummaries"
pageSize: "MaxResults"
)
operation ListTrials {
input: ListTrialsRequest
output: ListTrialsResponse
errors: [
ResourceNotFound
]
}
/// Lists user profiles.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "UserProfiles"
pageSize: "MaxResults"
)
operation ListUserProfiles {
input: ListUserProfilesRequest
output: ListUserProfilesResponse
}
/// Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only
/// have one private workforce per Amazon Web Services Region.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Workforces"
pageSize: "MaxResults"
)
operation ListWorkforces {
input: ListWorkforcesRequest
output: ListWorkforcesResponse
}
/// Gets a list of private work teams that you have defined in a region. The list may be empty if
/// no work team satisfies the filter specified in the NameContains
/// parameter.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Workteams"
pageSize: "MaxResults"
)
operation ListWorkteams {
input: ListWorkteamsRequest
output: ListWorkteamsResponse
}
/// Adds a resouce policy to control access to a model group. For information about
/// resoure policies, see Identity-based
/// policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
operation PutModelPackageGroupPolicy {
input := {
/// The name of the model group to add a resource policy to.
@required
ModelPackageGroupName: EntityName
/// The resource policy for the model group.
@required
ResourcePolicy: PolicyString
}
output := {
/// The Amazon Resource Name (ARN) of the model package group.
@required
ModelPackageGroupArn: ModelPackageGroupArn
}
}
/// Use this action to inspect your lineage and discover relationships between entities.
/// For more information, see
/// Querying Lineage Entities in the Amazon SageMaker Developer Guide.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
pageSize: "MaxResults"
)
operation QueryLineage {
input: QueryLineageRequest
output: QueryLineageResponse
errors: [
ResourceNotFound
]
}
/// Register devices.
operation RegisterDevices {
input: RegisterDevicesRequest
output: Unit
errors: [
ResourceLimitExceeded
]
}
/// Renders the UI template so that you can preview the worker's experience.
operation RenderUiTemplate {
input: RenderUiTemplateRequest
output: RenderUiTemplateResponse
errors: [
ResourceNotFound
]
}
/// Retry the execution of the pipeline.
operation RetryPipelineExecution {
input: RetryPipelineExecutionRequest
output: RetryPipelineExecutionResponse
errors: [
ConflictException
ResourceLimitExceeded
ResourceNotFound
]
}
/// Finds Amazon SageMaker resources that match a search query. Matching resources are returned
/// as a list of SearchRecord objects in the response. You can sort the search
/// results by any resource property in a ascending or descending order.
/// You can query against the following value types: numeric, text, Boolean, and
/// timestamp.
@paginated(
inputToken: "NextToken"
outputToken: "NextToken"
items: "Results"
pageSize: "MaxResults"
)
operation Search {
input: SearchRequest
output: SearchResponse
}
/// Notifies the pipeline that the execution of a callback step failed, along with a
/// message describing why. When a callback step is run, the pipeline generates a callback
/// token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
operation SendPipelineExecutionStepFailure {
input: SendPipelineExecutionStepFailureRequest
output: SendPipelineExecutionStepFailureResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Notifies the pipeline that the execution of a callback step succeeded and provides a
/// list of the step's output parameters. When a callback step is run, the pipeline generates
/// a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
operation SendPipelineExecutionStepSuccess {
input: SendPipelineExecutionStepSuccessRequest
output: SendPipelineExecutionStepSuccessResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Starts a stage in an edge deployment plan.
operation StartEdgeDeploymentStage {
input: StartEdgeDeploymentStageRequest
output: Unit
}
/// Starts an inference experiment.
operation StartInferenceExperiment {
input: StartInferenceExperimentRequest
output: StartInferenceExperimentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Starts a previously stopped monitoring schedule.
///
/// By default, when you successfully create a new schedule, the status of a monitoring
/// schedule is scheduled.
///
operation StartMonitoringSchedule {
input: StartMonitoringScheduleRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Launches an ML compute instance with the latest version of the libraries and
/// attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the
/// notebook instance status to InService. A notebook instance's status must be
/// InService before you can connect to your Jupyter notebook.
operation StartNotebookInstance {
input := {
/// The name of the notebook instance to start.
@required
NotebookInstanceName: NotebookInstanceName
}
output: Unit
errors: [
ResourceLimitExceeded
]
}
/// Starts a pipeline execution.
operation StartPipelineExecution {
input: StartPipelineExecutionRequest
output: StartPipelineExecutionResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// A method for forcing the termination of a running job.
operation StopAutoMLJob {
input: StopAutoMLJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a model compilation job.
/// To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the
/// job down. If the job hasn't stopped, it sends the SIGKILL signal.
/// When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to
/// Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
///
operation StopCompilationJob {
input: StopCompilationJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a stage in an edge deployment plan.
operation StopEdgeDeploymentStage {
input: StopEdgeDeploymentStageRequest
output: Unit
}
/// Request to stop an edge packaging job.
operation StopEdgePackagingJob {
input: StopEdgePackagingJobRequest
output: Unit
}
/// Stops a running hyperparameter tuning job and all running training jobs that the
/// tuning job launched.
/// All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All
/// data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the
/// tuning job moves to the Stopped state, it releases all
/// reserved
/// resources for the tuning job.
operation StopHyperParameterTuningJob {
input: StopHyperParameterTuningJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops an inference experiment.
operation StopInferenceExperiment {
input: StopInferenceExperimentRequest
output: StopInferenceExperimentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Stops an Inference Recommender job.
operation StopInferenceRecommendationsJob {
input: StopInferenceRecommendationsJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a running labeling job. A job that is stopped cannot be restarted. Any results
/// obtained before the job is stopped are placed in the Amazon S3 output bucket.
operation StopLabelingJob {
input: StopLabelingJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a previously started monitoring schedule.
operation StopMonitoringSchedule {
input: StopMonitoringScheduleRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Terminates the ML compute instance. Before terminating the instance, SageMaker
/// disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker
/// stops charging you for the ML compute instance when you call
/// StopNotebookInstance.
/// To access data on the ML storage volume for a notebook instance that has been
/// terminated, call the StartNotebookInstance API.
/// StartNotebookInstance launches another ML compute instance, configures
/// it, and attaches the preserved ML storage volume so you can continue your work.
///
operation StopNotebookInstance {
input := {
/// The name of the notebook instance to terminate.
@required
NotebookInstanceName: NotebookInstanceName
}
output: Unit
}
/// Stops a pipeline execution.
///
/// Callback Step
///
/// A pipeline execution won't stop while a callback step is running.
/// When you call StopPipelineExecution
/// on a pipeline execution with a running callback step, SageMaker Pipelines sends an
/// additional Amazon SQS message to the specified SQS queue. The body of the SQS message
/// contains a "Status" field which is set to "Stopping".
/// You should add logic to your Amazon SQS message consumer to take any needed action (for
/// example, resource cleanup) upon receipt of the message followed by a call to
/// SendPipelineExecutionStepSuccess or
/// SendPipelineExecutionStepFailure.
/// Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
///
/// Lambda Step
///
/// A pipeline execution can't be stopped while a lambda step is running because the Lambda
/// function invoked by the lambda step can't be stopped. If you attempt to stop the execution
/// while the Lambda function is running, the pipeline waits for the Lambda function to finish
/// or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function
/// finishes, the pipeline execution status is Stopped. If the timeout is hit
/// the pipeline execution status is Failed.
operation StopPipelineExecution {
input: StopPipelineExecutionRequest
output: StopPipelineExecutionResponse
errors: [
ResourceNotFound
]
}
/// Stops a processing job.
operation StopProcessingJob {
input: StopProcessingJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a training job. To stop a job, SageMaker sends the algorithm the
/// SIGTERM signal, which delays job termination for 120 seconds.
/// Algorithms might use this 120-second window to save the model artifacts, so the results
/// of the training is not lost.
/// When it receives a StopTrainingJob request, SageMaker changes the status of
/// the job to Stopping. After SageMaker stops the job, it sets the status to
/// Stopped.
operation StopTrainingJob {
input: StopTrainingJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Stops a batch transform job.
/// When Amazon SageMaker receives a StopTransformJob request, the status of the job
/// changes to Stopping. After Amazon SageMaker
/// stops
/// the job, the status is set to Stopped. When you stop a batch transform job before
/// it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
operation StopTransformJob {
input: StopTransformJobRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Updates an action.
operation UpdateAction {
input: UpdateActionRequest
output: UpdateActionResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates the properties of an AppImageConfig.
operation UpdateAppImageConfig {
input: UpdateAppImageConfigRequest
output: UpdateAppImageConfigResponse
errors: [
ResourceNotFound
]
}
/// Updates an artifact.
operation UpdateArtifact {
input: UpdateArtifactRequest
output: UpdateArtifactResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates the specified Git repository with the specified values.
operation UpdateCodeRepository {
input := {
/// The name of the Git repository to update.
@required
CodeRepositoryName: EntityName
/// 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}
///
GitConfig: GitConfigForUpdate
}
output := {
/// The ARN of the Git repository.
@required
CodeRepositoryArn: CodeRepositoryArn
}
}
/// Updates a context.
operation UpdateContext {
input: UpdateContextRequest
output: UpdateContextResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates a fleet of devices.
operation UpdateDeviceFleet {
input: UpdateDeviceFleetRequest
output: Unit
errors: [
ResourceInUse
]
}
/// Updates one or more devices in a fleet.
operation UpdateDevices {
input: UpdateDevicesRequest
output: Unit
}
/// Updates the default settings for new user profiles in the domain.
operation UpdateDomain {
input: UpdateDomainRequest
output: UpdateDomainResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Deploys the new EndpointConfig specified in the request, switches to
/// using newly created endpoint, and then deletes resources provisioned for the endpoint
/// using the previous EndpointConfig (there is no availability loss).
/// When SageMaker receives the request, it sets the endpoint status to
/// Updating. After updating the endpoint, it sets the status to
/// InService. To check the status of an endpoint, use the DescribeEndpoint API.
///
///
///
/// You must not delete an EndpointConfig in use by an endpoint that is
/// live or while the UpdateEndpoint or CreateEndpoint
/// operations are being performed on the endpoint. To update an endpoint, you must
/// create a new EndpointConfig.
/// If you delete the EndpointConfig of an endpoint that is active or
/// being created or updated you may lose visibility into the instance type the endpoint
/// is using. The endpoint must be deleted in order to stop incurring charges.
///
operation UpdateEndpoint {
input := {
/// The name of the endpoint whose configuration you want to update.
@required
EndpointName: EndpointName
/// The name of the new endpoint configuration.
@required
EndpointConfigName: EndpointConfigName
/// 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.
RetainAllVariantProperties: Boolean = false
/// When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties, whose value is set to
/// true, ExcludeRetainedVariantProperties specifies the list
/// of type VariantProperty to override with the values provided by
/// EndpointConfig. If you don't specify a value for
/// ExcludeRetainedVariantProperties, no variant properties are overridden.
///
ExcludeRetainedVariantProperties: VariantPropertyList
/// The deployment configuration for an endpoint, which contains the desired deployment
/// strategy and rollback configurations.
DeploymentConfig: DeploymentConfig
/// Specifies whether to reuse the last deployment configuration. The default value is
/// false (the configuration is not reused).
RetainDeploymentConfig: Boolean = false
}
output := {
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
}
errors: [
ResourceLimitExceeded
]
}
/// Updates variant weight of one or more variants associated with an existing
/// endpoint, or capacity of one variant associated with an existing endpoint. When it
/// receives the request, SageMaker sets the endpoint status to Updating. After
/// updating the endpoint, it sets the status to InService. To check the status
/// of an endpoint, use the DescribeEndpoint API.
operation UpdateEndpointWeightsAndCapacities {
input := {
/// The name of an existing SageMaker endpoint.
@required
EndpointName: EndpointName
/// An object that provides new capacity and weight values for a variant.
@required
DesiredWeightsAndCapacities: DesiredWeightAndCapacityList
}
output := {
/// The Amazon Resource Name (ARN) of the updated endpoint.
@required
EndpointArn: EndpointArn
}
errors: [
ResourceLimitExceeded
]
}
/// Adds, updates, or removes the description of an experiment. Updates the display name of an
/// experiment.
operation UpdateExperiment {
input: UpdateExperimentRequest
output: UpdateExperimentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates the feature group.
operation UpdateFeatureGroup {
input: UpdateFeatureGroupRequest
output: UpdateFeatureGroupResponse
errors: [
ResourceNotFound
]
}
/// Updates the description and parameters of the feature group.
operation UpdateFeatureMetadata {
input: UpdateFeatureMetadataRequest
output: Unit
errors: [
ResourceNotFound
]
}
/// Update a hub.
operation UpdateHub {
input: UpdateHubRequest
output: UpdateHubResponse
errors: [
ResourceNotFound
]
}
/// Updates the properties of a SageMaker image. To change the image's tags, use the
/// AddTags and DeleteTags APIs.
operation UpdateImage {
input: UpdateImageRequest
output: UpdateImageResponse
errors: [
ResourceInUse
ResourceNotFound
]
}
/// Updates the properties of a SageMaker image version.
operation UpdateImageVersion {
input: UpdateImageVersionRequest
output: UpdateImageVersionResponse
errors: [
ResourceInUse
ResourceNotFound
]
}
///
/// Updates an inference experiment that you created. The status of the inference experiment has to be either
/// Created, Running. For more information on the status of an inference experiment,
/// see DescribeInferenceExperimentResponse$Status.
///
operation UpdateInferenceExperiment {
input: UpdateInferenceExperimentRequest
output: UpdateInferenceExperimentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Update an Amazon SageMaker Model Card.
///
/// You cannot update both model card content and model card status in a single call.
///
operation UpdateModelCard {
input: UpdateModelCardRequest
output: UpdateModelCardResponse
errors: [
ConflictException
ResourceLimitExceeded
ResourceNotFound
]
}
/// Updates a versioned model.
operation UpdateModelPackage {
input := {
/// The Amazon Resource Name (ARN) of the model package.
@required
ModelPackageArn: ModelPackageArn
/// The approval status of the model.
ModelApprovalStatus: ModelApprovalStatus
/// A description for the approval status of the model.
ApprovalDescription: ApprovalDescription
/// The metadata properties associated with the model package versions.
CustomerMetadataProperties: CustomerMetadataMap
/// The metadata properties associated with the model package versions to remove.
CustomerMetadataPropertiesToRemove: CustomerMetadataKeyList
/// 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.
AdditionalInferenceSpecificationsToAdd: AdditionalInferenceSpecifications
}
output := {
/// The Amazon Resource Name (ARN) of the model.
@required
ModelPackageArn: ModelPackageArn
}
}
/// Update the parameters of a model monitor alert.
operation UpdateMonitoringAlert {
input: UpdateMonitoringAlertRequest
output: UpdateMonitoringAlertResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Updates a previously created schedule.
operation UpdateMonitoringSchedule {
input: UpdateMonitoringScheduleRequest
output: UpdateMonitoringScheduleResponse
errors: [
ResourceLimitExceeded
ResourceNotFound
]
}
/// Updates a notebook instance. NotebookInstance updates include upgrading or
/// downgrading the ML compute instance used for your notebook instance to accommodate
/// changes in your workload requirements.
operation UpdateNotebookInstance {
input := {
/// The name of the notebook instance to update.
@required
NotebookInstanceName: NotebookInstanceName
/// The Amazon ML compute instance type.
InstanceType: InstanceType
/// The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access the
/// notebook instance. 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.
///
RoleArn: RoleArn
/// 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.
LifecycleConfigName: NotebookInstanceLifecycleConfigName
/// 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.
DisassociateLifecycleConfig: DisassociateNotebookInstanceLifecycleConfig = 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 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.
VolumeSizeInGB: NotebookInstanceVolumeSizeInGB
/// 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 Notebook Instances.
DefaultCodeRepository: CodeRepositoryNameOrUrl
/// 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 Notebook Instances.
AdditionalCodeRepositories: AdditionalCodeRepositoryNamesOrUrls
/// A list of the Elastic Inference (EI) instance types to associate with this notebook
/// instance. Currently only one EI instance type can be associated with a notebook
/// instance. For more information, see Using Elastic Inference in Amazon
/// SageMaker.
AcceleratorTypes: NotebookInstanceAcceleratorTypes
/// A list of the Elastic Inference (EI) instance types to remove from this notebook
/// instance. This operation is idempotent. If you specify an accelerator type that is not
/// associated with the notebook instance when you call this method, it does not throw an
/// error.
DisassociateAcceleratorTypes: DisassociateNotebookInstanceAcceleratorTypes = 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.
DisassociateDefaultCodeRepository: DisassociateDefaultCodeRepository = 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.
DisassociateAdditionalCodeRepositories: DisassociateAdditionalCodeRepositories = 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.
///
RootAccess: RootAccess
/// Information on the IMDS configuration of the notebook instance
InstanceMetadataServiceConfiguration: InstanceMetadataServiceConfiguration
}
output := {}
errors: [
ResourceLimitExceeded
]
}
/// Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
operation UpdateNotebookInstanceLifecycleConfig {
input := {
/// The name of the lifecycle configuration.
@required
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// The shell script that runs only once, when you create a notebook instance. The shell
/// script must be a base64-encoded string.
OnCreate: NotebookInstanceLifecycleConfigList
/// 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.
OnStart: NotebookInstanceLifecycleConfigList
}
output := {}
errors: [
ResourceLimitExceeded
]
}
/// Updates a pipeline.
operation UpdatePipeline {
input: UpdatePipelineRequest
output: UpdatePipelineResponse
errors: [
ResourceNotFound
]
}
/// Updates a pipeline execution.
operation UpdatePipelineExecution {
input: UpdatePipelineExecutionRequest
output: UpdatePipelineExecutionResponse
errors: [
ResourceNotFound
]
}
/// Updates a machine learning (ML) project that is created from a template that
/// sets up an ML pipeline from training to deploying an approved model.
///
/// You must not update a project that is in use. If you update the
/// ServiceCatalogProvisioningUpdateDetails of a project that is active
/// or being created, or updated, you may lose resources already created by the
/// project.
///
operation UpdateProject {
input := {
/// The name of the project.
@required
ProjectName: ProjectEntityName
/// The description for the project.
ProjectDescription: EntityDescription
/// 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.
///
ServiceCatalogProvisioningUpdateDetails: ServiceCatalogProvisioningUpdateDetails
/// 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.
Tags: TagList
}
output := {
/// The Amazon Resource Name (ARN) of the project.
@required
ProjectArn: ProjectArn
}
}
/// Updates the settings of a space.
operation UpdateSpace {
input: UpdateSpaceRequest
output: UpdateSpaceResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Update a model training job to request a new Debugger profiling configuration or to
/// change warm pool retention length.
operation UpdateTrainingJob {
input: UpdateTrainingJobRequest
output: UpdateTrainingJobResponse
errors: [
ResourceNotFound
]
}
/// Updates the display name of a trial.
operation UpdateTrial {
input: UpdateTrialRequest
output: UpdateTrialResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates one or more properties of a trial component.
operation UpdateTrialComponent {
input: UpdateTrialComponentRequest
output: UpdateTrialComponentResponse
errors: [
ConflictException
ResourceNotFound
]
}
/// Updates a user profile.
operation UpdateUserProfile {
input: UpdateUserProfileRequest
output: UpdateUserProfileResponse
errors: [
ResourceInUse
ResourceLimitExceeded
ResourceNotFound
]
}
/// Use this operation to update your workforce. You can use this operation to
/// require that workers use specific IP addresses to work on tasks
/// and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
/// The worker portal is now supported in VPC and public internet.
/// Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses.
/// You specify allowed IP addresses by creating a list of up to ten CIDRs.
/// By default, a workforce isn't restricted to specific IP addresses. If you specify a
/// range of IP addresses, workers who attempt to access tasks using any IP address outside
/// the specified range are denied and get a Not Found error message on
/// the worker portal.
/// To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "0.0.0.0/0".
///
/// Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.
///
/// Use OidcConfig to update the configuration of a workforce created using
/// your own OIDC IdP.
///
/// You can only update your OIDC IdP configuration when there are no work teams
/// associated with your workforce. You can delete work teams using the operation.
///
/// After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you
/// can view details about your update workforce using the
/// operation.
///
/// This operation only applies to private workforces.
///
operation UpdateWorkforce {
input: UpdateWorkforceRequest
output: UpdateWorkforceResponse
errors: [
ConflictException
]
}
/// Updates an existing work team with new member definitions or description.
operation UpdateWorkteam {
input: UpdateWorkteamRequest
output: UpdateWorkteamResponse
errors: [
ResourceLimitExceeded
]
}
/// A structure describing the source of an action.
structure ActionSource {
/// The URI of the source.
@required
SourceUri: String2048
/// The type of the source.
SourceType: String256
/// The ID of the source.
SourceId: String256
}
/// Lists the properties of an action. An action represents an action
/// or activity. Some examples are a workflow step and a model deployment. Generally, an
/// action involves at least one input artifact or output artifact.
structure ActionSummary {
/// The Amazon Resource Name (ARN) of the action.
ActionArn: ActionArn
/// The name of the action.
ActionName: ExperimentEntityName
/// The source of the action.
Source: ActionSource
/// The type of the action.
ActionType: String64
/// The status of the action.
Status: ActionStatus
/// When the action was created.
CreationTime: Timestamp
/// When the action was last modified.
LastModifiedTime: Timestamp
}
@input
structure AddAssociationRequest {
/// The ARN of the source.
@required
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination.
@required
DestinationArn: AssociationEntityArn
/// 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.
///
///
AssociationType: AssociationEdgeType
}
@output
structure AddAssociationResponse {
/// The ARN of the source.
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination.
DestinationArn: AssociationEntityArn
}
/// A structure of additional Inference Specification. Additional Inference Specification
/// specifies details about inference jobs that can be run with models based on
/// this model package
structure AdditionalInferenceSpecificationDefinition {
/// A unique name to identify the additional inference specification. The name must
/// be unique within the list of your additional inference specifications for a
/// particular model package.
@required
Name: EntityName
/// A description of the additional Inference specification
Description: EntityDescription
/// The Amazon ECR registry path of the Docker image that contains the inference code.
@required
Containers: ModelPackageContainerDefinitionList
/// A list of the instance types on which a transformation job can be run
/// or on which an endpoint can be deployed.
SupportedTransformInstanceTypes: TransformInstanceTypes
/// A list of the instance types that are used to generate inferences in real-time.
SupportedRealtimeInferenceInstanceTypes: RealtimeInferenceInstanceTypes
/// The supported MIME types for the input data.
SupportedContentTypes: ContentTypes
/// The supported MIME types for the output data.
SupportedResponseMIMETypes: ResponseMIMETypes
}
/// Edge Manager agent version.
structure AgentVersion {
/// Version of the agent.
@required
Version: EdgeVersion
/// The number of Edge Manager agents.
@required
AgentCount: Long = 0
}
/// An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.
structure Alarm {
/// The name of a CloudWatch alarm in your account.
AlarmName: AlarmName
}
/// Specifies the training algorithm to use in a CreateTrainingJob
/// request.
/// For more information about algorithms provided by SageMaker, see Algorithms. For
/// information about using your own algorithms, see Using Your Own Algorithms with Amazon
/// SageMaker.
structure AlgorithmSpecification {
/// The registry path of the Docker image
/// that contains the training algorithm.
/// For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry
/// Paths and Example Code in the Amazon SageMaker developer guide.
/// SageMaker supports both registry/repository[:tag] and
/// registry/repository[@digest] image path formats. For more information
/// about using your custom training container, see Using Your Own Algorithms with
/// Amazon SageMaker.
///
/// You must specify either the algorithm name to the AlgorithmName
/// parameter or the image URI of the algorithm container to the
/// TrainingImage parameter.
/// For more information, see the note in the AlgorithmName parameter
/// description.
///
TrainingImage: AlgorithmImage
/// The name of the algorithm resource to use for the training job. This must be an
/// algorithm resource that you created or subscribe to on Amazon Web Services
/// Marketplace.
///
/// You must specify either the algorithm name to the AlgorithmName
/// parameter or the image URI of the algorithm container to the
/// TrainingImage parameter.
/// Note that the AlgorithmName parameter is mutually exclusive with the
/// TrainingImage parameter. If you specify a value for the
/// AlgorithmName parameter, you can't specify a value for
/// TrainingImage, and vice versa.
/// If you specify values for both parameters, the training job might break; if you
/// don't specify any value for both parameters, the training job might raise a
/// null error.
///
AlgorithmName: ArnOrName
@required
TrainingInputMode: TrainingInputMode
/// A list of metric definition objects. Each object specifies the metric name and regular
/// expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
MetricDefinitions: MetricDefinitionList
/// To generate and save time-series metrics during training, set to true.
/// The default is false and time-series metrics aren't generated except in the
/// following cases:
///
/// -
///
You use one of the SageMaker built-in algorithms
///
/// -
///
You use one of the following Prebuilt SageMaker Docker Images:
///
/// -
///
Tensorflow (version >= 1.15)
///
/// -
///
MXNet (version >= 1.6)
///
/// -
///
PyTorch (version >= 1.3)
///
///
///
/// -
///
You specify at least one MetricDefinition
///
///
///
EnableSageMakerMetricsTimeSeries: Boolean = false
/// The entrypoint script
/// for a Docker container used to run a training job. This script takes
/// precedence over the default train processing instructions. See How Amazon SageMaker
/// Runs Your Training Image for more information.
ContainerEntrypoint: TrainingContainerEntrypoint
/// The arguments for a container used to run a training job. See How Amazon SageMaker
/// Runs Your Training Image for additional information.
ContainerArguments: TrainingContainerArguments
/// The configuration to use an image from a private Docker registry for a training job.
TrainingImageConfig: TrainingImageConfig
}
/// Specifies the validation and image scan statuses of the algorithm.
structure AlgorithmStatusDetails {
/// The status of algorithm validation.
ValidationStatuses: AlgorithmStatusItemList
/// The status of the scan of the algorithm's Docker image container.
ImageScanStatuses: AlgorithmStatusItemList
}
/// Represents the overall status of an algorithm.
structure AlgorithmStatusItem {
/// The name of the algorithm for which the overall status is being reported.
@required
Name: EntityName
/// The current status.
@required
Status: DetailedAlgorithmStatus
/// if the overall status is Failed, the reason for the failure.
FailureReason: String
}
/// Provides summary information about an algorithm.
structure AlgorithmSummary {
/// The name of the algorithm that is described by the summary.
@required
AlgorithmName: EntityName
/// The Amazon Resource Name (ARN) of the algorithm.
@required
AlgorithmArn: AlgorithmArn
/// A brief description of the algorithm.
AlgorithmDescription: EntityDescription
/// A timestamp that shows when the algorithm was created.
@required
CreationTime: CreationTime
/// The overall status of the algorithm.
@required
AlgorithmStatus: AlgorithmStatus
}
/// Defines a training job and a batch transform job that SageMaker runs to validate your
/// algorithm.
/// The data provided in the validation profile is made available to your buyers on
/// Amazon Web Services Marketplace.
structure AlgorithmValidationProfile {
/// The name of the profile for the algorithm. The name must have 1 to 63 characters.
/// Valid characters are a-z, A-Z, 0-9, and - (hyphen).
@required
ProfileName: EntityName
/// The TrainingJobDefinition object that describes the training job that
/// SageMaker runs to validate your algorithm.
@required
TrainingJobDefinition: TrainingJobDefinition
/// The TransformJobDefinition object that describes the transform job that
/// SageMaker runs to validate your algorithm.
TransformJobDefinition: TransformJobDefinition
}
/// Specifies configurations for one or more training jobs that SageMaker runs to test the
/// algorithm.
structure AlgorithmValidationSpecification {
/// The IAM roles that SageMaker uses to run the training jobs.
@required
ValidationRole: RoleArn
/// An array of AlgorithmValidationProfile objects, each of which specifies a
/// training job and batch transform job that SageMaker runs to validate your algorithm.
@required
ValidationProfiles: AlgorithmValidationProfiles
}
/// Configures how labels are consolidated across human workers and processes output data.
///
structure AnnotationConsolidationConfig {
/// The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
/// This parameter is required for all labeling jobs. For built-in task types, use one
/// of the following Amazon SageMaker Ground Truth Lambda function ARNs for
/// AnnotationConsolidationLambdaArn. For custom labeling workflows, see
/// Post-annotation Lambda.
///
/// Bounding box - Finds the most similar boxes from
/// different workers based on the Jaccard index of the boxes.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
///
///
///
///
/// Image classification - Uses a variant of the
/// Expectation Maximization approach to estimate the true class of an image based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
///
///
///
///
/// Multi-label image classification - Uses a variant of
/// the Expectation Maximization approach to estimate the true classes of an image based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
///
///
///
///
/// Semantic segmentation - Treats each pixel in an image
/// as a multi-class classification and treats pixel annotations from workers as "votes" for
/// the correct label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
///
///
///
///
/// Text classification - Uses a variant of the
/// Expectation Maximization approach to estimate the true class of text based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
///
///
///
///
/// Multi-label text classification - Uses a variant of
/// the Expectation Maximization approach to estimate the true classes of text based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
///
///
///
///
/// Named entity recognition - Groups similar selections
/// and calculates aggregate boundaries, resolving to most-assigned label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
///
///
///
///
/// Video Classification - Use this task type when you need workers to classify videos using
/// predefined labels that you specify. Workers are shown videos and are asked to choose one
/// label for each video.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
///
///
///
///
/// Video Frame Object Detection - Use this task type to
/// have workers identify and locate objects in a sequence of video frames (images extracted
/// from a video) using bounding boxes. For example, you can use this task to ask workers to
/// identify and localize various objects in a series of video frames, such as cars, bikes,
/// and pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
///
///
///
///
/// Video Frame Object Tracking - Use this task type to
/// have workers track the movement of objects in a sequence of video frames (images
/// extracted from a video) using bounding boxes. For example, you can use this task to ask
/// workers to track the movement of objects, such as cars, bikes, and pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
///
///
///
///
/// 3D Point Cloud Object Detection - Use this task type
/// when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids
/// around objects. For example, you can use this task type to ask workers to identify
/// different types of objects in a point cloud, such as cars, bikes, and
/// pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
///
///
///
///
/// 3D Point Cloud Object Tracking - Use this task type
/// when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D
/// point cloud frames. For example, you can use this task type to ask workers to track the
/// movement of vehicles across multiple point cloud frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
///
///
///
///
/// 3D Point Cloud Semantic Segmentation - Use this task
/// type when you want workers to create a point-level semantic segmentation masks by
/// painting objects in a 3D point cloud using different colors where each color is assigned
/// to one of the classes you specify.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
///
///
///
///
/// Use the following ARNs for Label Verification and Adjustment Jobs
///
/// Use label verification and adjustment jobs to review and adjust labels. To learn more,
/// see Verify and Adjust Labels .
///
/// Semantic Segmentation Adjustment - Treats each pixel
/// in an image as a multi-class classification and treats pixel adjusted annotations from
/// workers as "votes" for the correct label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
///
///
///
///
/// Semantic Segmentation Verification - Uses a variant
/// of the Expectation Maximization approach to estimate the true class of verification
/// judgment for semantic segmentation labels based on annotations from individual
/// workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
///
///
///
///
/// Bounding Box Adjustment - Finds the most similar
/// boxes from different workers based on the Jaccard index of the adjusted
/// annotations.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
///
///
///
///
/// Bounding Box Verification - Uses a variant of the
/// Expectation Maximization approach to estimate the true class of verification judgement
/// for bounding box labels based on annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
///
///
///
///
/// Video Frame Object Detection Adjustment -
/// Use this task type when you want workers to adjust bounding boxes that workers have added
/// to video frames to classify and localize objects in a sequence of video frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
///
///
///
///
/// Video Frame Object Tracking Adjustment -
/// Use this task type when you want workers to adjust bounding boxes that workers have added
/// to video frames to track object movement across a sequence of video frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
///
///
///
///
/// 3D Point Cloud Object Detection Adjustment - Use this
/// task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
///
///
///
///
/// 3D Point Cloud Object Tracking Adjustment - Use this
/// task type when you want workers to adjust 3D cuboids around objects that appear in a
/// sequence of 3D point cloud frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
///
///
///
///
/// 3D Point Cloud Semantic Segmentation Adjustment - Use this task
/// type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
///
///
///
@required
AnnotationConsolidationLambdaArn: LambdaFunctionArn
}
/// Details about an Amazon SageMaker app.
structure AppDetails {
/// The domain ID.
DomainId: DomainId
/// The user profile name.
UserProfileName: UserProfileName
/// The type of app.
AppType: AppType
/// The name of the app.
AppName: AppName
/// The status.
Status: AppStatus
/// The creation time.
CreationTime: CreationTime
/// The name of the space.
SpaceName: SpaceName
}
/// The configuration for running a SageMaker image as a KernelGateway app.
structure AppImageConfigDetails {
/// The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigArn: AppImageConfigArn
/// The name of the AppImageConfig. Must be unique to your account.
AppImageConfigName: AppImageConfigName
/// When the AppImageConfig was created.
CreationTime: Timestamp
/// When the AppImageConfig was last modified.
LastModifiedTime: Timestamp
/// The configuration for the file system and kernels in the SageMaker image.
KernelGatewayImageConfig: KernelGatewayImageConfig
}
/// Configuration to run a processing job in a specified container image.
structure AppSpecification {
/// The container image to be run by the processing job.
@required
ImageUri: ImageUri
/// The entrypoint for a container used to run a processing job.
ContainerEntrypoint: ContainerEntrypoint
/// The arguments for a container used to run a processing job.
ContainerArguments: ContainerArguments
}
/// A structure describing the source of an artifact.
structure ArtifactSource {
/// The URI of the source.
@required
SourceUri: String2048
/// A list of source types.
SourceTypes: ArtifactSourceTypes
}
/// The ID and ID type of an artifact source.
structure ArtifactSourceType {
/// The type of ID.
@required
SourceIdType: ArtifactSourceIdType
/// The ID.
@required
Value: String256
}
/// Lists a summary of the properties of an artifact. An artifact represents a URI
/// addressable object or data. Some examples are a dataset and a model.
structure ArtifactSummary {
/// The Amazon Resource Name (ARN) of the artifact.
ArtifactArn: ArtifactArn
/// The name of the artifact.
ArtifactName: ExperimentEntityName
/// The source of the artifact.
Source: ArtifactSource
/// The type of the artifact.
ArtifactType: String256
/// When the artifact was created.
CreationTime: Timestamp
/// When the artifact was last modified.
LastModifiedTime: Timestamp
}
@input
structure AssociateTrialComponentRequest {
/// The name of the component to associated with the trial.
@required
TrialComponentName: ExperimentEntityName
/// The name of the trial to associate with.
@required
TrialName: ExperimentEntityName
}
@output
structure AssociateTrialComponentResponse {
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
}
/// Lists a summary of the properties of an association. An association is an entity that
/// links other lineage or experiment entities. An example would be an association between a
/// training job and a model.
structure AssociationSummary {
/// The ARN of the source.
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination.
DestinationArn: AssociationEntityArn
/// The source type.
SourceType: String256
/// The destination type.
DestinationType: String256
/// The type of the association.
AssociationType: AssociationEdgeType
/// The name of the source.
SourceName: ExperimentEntityName
/// The name of the destination.
DestinationName: ExperimentEntityName
/// When the association was created.
CreationTime: Timestamp
CreatedBy: UserContext
}
/// Configures the behavior of the client used by SageMaker to interact with the model
/// container during asynchronous inference.
structure AsyncInferenceClientConfig {
/// The maximum number of concurrent requests sent by the SageMaker client to the model
/// container. If no value is provided, SageMaker chooses an optimal value.
MaxConcurrentInvocationsPerInstance: MaxConcurrentInvocationsPerInstance
}
/// Specifies configuration for how an endpoint performs asynchronous inference.
structure AsyncInferenceConfig {
/// Configures the behavior of the client used by SageMaker to interact with the model
/// container during asynchronous inference.
ClientConfig: AsyncInferenceClientConfig
/// Specifies the configuration for asynchronous inference invocation outputs.
@required
OutputConfig: AsyncInferenceOutputConfig
}
/// Specifies the configuration for notifications of inference results for asynchronous
/// inference.
structure AsyncInferenceNotificationConfig {
/// Amazon SNS topic to post a notification to when inference completes successfully. If no
/// topic is provided, no notification is sent on success.
SuccessTopic: SnsTopicArn
/// Amazon SNS topic to post a notification to when inference fails. If no topic is provided,
/// no notification is sent on failure.
ErrorTopic: SnsTopicArn
}
/// Specifies the configuration for asynchronous inference invocation outputs.
structure AsyncInferenceOutputConfig {
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker
/// uses to encrypt the asynchronous inference output in Amazon S3.
///
KmsKeyId: KmsKeyId
/// The Amazon S3 location to upload inference responses to.
@required
S3OutputPath: DestinationS3Uri
/// Specifies the configuration for notifications of inference results for asynchronous
/// inference.
NotificationConfig: AsyncInferenceNotificationConfig
}
/// Configuration for Athena Dataset Definition input.
structure AthenaDatasetDefinition {
@required
Catalog: AthenaCatalog
@required
Database: AthenaDatabase
@required
QueryString: AthenaQueryString
WorkGroup: AthenaWorkGroup
/// The location in Amazon S3 where Athena query results are stored.
@required
OutputS3Uri: S3Uri
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from
/// an Athena query execution.
KmsKeyId: KmsKeyId
@required
OutputFormat: AthenaResultFormat
OutputCompression: AthenaResultCompressionType
}
/// Information about a candidate produced by an AutoML training job, including its status,
/// steps, and other properties.
structure AutoMLCandidate {
/// The name of the candidate.
@required
CandidateName: CandidateName
FinalAutoMLJobObjectiveMetric: FinalAutoMLJobObjectiveMetric
/// The objective's status.
@required
ObjectiveStatus: ObjectiveStatus
/// Information about the candidate's steps.
@required
CandidateSteps: CandidateSteps
/// The candidate's status.
@required
CandidateStatus: CandidateStatus
/// Information about the inference container definitions.
InferenceContainers: AutoMLContainerDefinitions
/// The creation time.
@required
CreationTime: Timestamp
/// The end time.
EndTime: Timestamp
/// The last modified time.
@required
LastModifiedTime: Timestamp
/// The failure reason.
FailureReason: AutoMLFailureReason
/// The properties of an AutoML candidate job.
CandidateProperties: CandidateProperties
}
/// Stores the config information for how a candidate is generated (optional).
structure AutoMLCandidateGenerationConfig {
/// A URL to the Amazon S3 data source containing selected features from the input data source to
/// run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON
/// format as shown below:
///
/// { "FeatureAttributeNames":["col1", "col2", ...] }.
/// You can also specify the data type of the feature (optional) in the format shown
/// below:
///
/// { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
///
///
/// These column keys may not include the target column.
///
/// In ensembling mode, Autopilot will only support the following data types:
/// numeric, categorical, text and
/// datetime. In HPO mode, Autopilot can support numeric,
/// categorical, text, datetime and
/// sequence.
/// If only FeatureDataTypes is provided, the column keys (col1,
/// col2,..) should be a subset of the column names in the input data.
/// If both FeatureDataTypes and FeatureAttributeNames are
/// provided, then the column keys should be a subset of the column names provided in
/// FeatureAttributeNames.
/// The key name FeatureAttributeNames is fixed. The values listed in
/// ["col1", "col2", ...] is case sensitive and should be a list of strings
/// containing unique values that are a subset of the column names in the input data. The list
/// of columns provided must not include the target column.
FeatureSpecificationS3Uri: S3Uri
}
/// Information about the steps for a candidate and what step it is working on.
structure AutoMLCandidateStep {
/// Whether the candidate is at the transform, training, or processing step.
@required
CandidateStepType: CandidateStepType
/// The ARN for the candidate's step.
@required
CandidateStepArn: CandidateStepArn
/// The name for the candidate's step.
@required
CandidateStepName: CandidateStepName
}
/// A channel is a named input source that training algorithms can consume. The validation
/// dataset size is limited to less than 2 GB. The training dataset size must be less than 100
/// GB. For more information, see .
///
/// A validation dataset must contain the same headers as the training dataset.
///
///
structure AutoMLChannel {
/// The data source for an AutoML channel.
@required
DataSource: AutoMLDataSource
/// You can use Gzip or None. The default value is
/// None.
CompressionType: CompressionType
/// The name of the target variable in supervised learning, usually represented by
/// 'y'.
@required
TargetAttributeName: TargetAttributeName
/// The content type of the data from the input source. You can use
/// text/csv;header=present or x-application/vnd.amazon+parquet.
/// The default value is text/csv;header=present.
ContentType: ContentType
/// The channel type (optional) is an enum string. The default value is
/// training. Channels for training and validation must share the same
/// ContentType and TargetAttributeName. For information on
/// specifying training and validation channel types, see
/// How to specify training and validation datasets
/// .
ChannelType: AutoMLChannelType
}
/// A list of container definitions that describe the different containers that make up an
/// AutoML candidate. For more information, see .
structure AutoMLContainerDefinition {
/// The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more
/// information, see .
@required
Image: ContainerImage
/// The location of the model artifacts. For more information, see .
@required
ModelDataUrl: Url
/// The environment variables to set in the container. For more information, see .
Environment: EnvironmentMap
}
/// The data source for the Autopilot job.
structure AutoMLDataSource {
/// The Amazon S3 location of the input data.
@required
S3DataSource: AutoMLS3DataSource
}
/// This structure specifies how to split the data into train and validation datasets. The
/// validation and training datasets must contain the same headers. The validation dataset must
/// be less than 2 GB in size.
structure AutoMLDataSplitConfig {
/// The validation fraction (optional) is a float that specifies the portion of the training
/// dataset to be used for validation. The default value is 0.2, and values must be greater
/// than 0 and less than 1. We recommend setting this value to be less than 0.5.
ValidationFraction: ValidationFraction
}
/// The artifacts that are generated during an AutoML job.
structure AutoMLJobArtifacts {
/// The URL of the notebook location.
CandidateDefinitionNotebookLocation: CandidateDefinitionNotebookLocation
/// The URL of the notebook location.
DataExplorationNotebookLocation: DataExplorationNotebookLocation
}
/// How long a job is allowed to run, or how many candidates a job is allowed to
/// generate.
structure AutoMLJobCompletionCriteria {
/// The maximum number of times a training job is allowed to run.
MaxCandidates: MaxCandidates
/// The maximum time, in seconds, that each training job executed inside hyperparameter
/// tuning is allowed to run as part of a hyperparameter tuning job. For more information, see
/// the used by the action.
MaxRuntimePerTrainingJobInSeconds: MaxRuntimePerTrainingJobInSeconds
/// The maximum runtime, in seconds, an AutoML job has to complete.
/// If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its
/// processing is ended gracefully. The AutoML job identifies the best model whose training was
/// completed and marks it as the best-performing model. Any unfinished steps of the job, such
/// as automatic one-click Autopilot model deployment, will not be completed.
MaxAutoMLJobRuntimeInSeconds: MaxAutoMLJobRuntimeInSeconds
}
/// A collection of settings used for an AutoML job.
structure AutoMLJobConfig {
/// How long an AutoML job is allowed to run, or how many candidates a job is allowed to
/// generate.
CompletionCriteria: AutoMLJobCompletionCriteria
/// The security configuration for traffic encryption or Amazon VPC settings.
SecurityConfig: AutoMLSecurityConfig
/// The configuration for splitting the input training dataset.
/// Type: AutoMLDataSplitConfig
DataSplitConfig: AutoMLDataSplitConfig
/// The configuration for generating a candidate for an AutoML job (optional).
CandidateGenerationConfig: AutoMLCandidateGenerationConfig
/// The method that Autopilot uses to train the data. You can either specify the mode manually
/// or let Autopilot choose for you based on the dataset size by selecting AUTO. In
/// AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than
/// 100 MB, and HYPERPARAMETER_TUNING for larger ones.
/// The ENSEMBLING mode uses a multi-stack ensemble model to predict
/// classification and regression tasks directly from your dataset. This machine learning mode
/// combines several base models to produce an optimal predictive model. It then uses a
/// stacking ensemble method to combine predictions from contributing members. A multi-stack
/// ensemble model can provide better performance over a single model by combining the
/// predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by
/// ENSEMBLING mode.
/// The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train
/// the best version of a model. HPO will automatically select an algorithm for the type of
/// problem you want to solve. Then HPO finds the best hyperparameters according to your
/// objective metric. See Autopilot algorithm support for a list of algorithms supported by
/// HYPERPARAMETER_TUNING mode.
Mode: AutoMLMode
}
/// Specifies a metric to minimize or maximize as the objective of a job.
structure AutoMLJobObjective {
/// The name of the objective metric used to measure the predictive quality of a machine
/// learning system. This metric is optimized during training to provide the best estimate for
/// model parameter values from data.
/// Here are the options:
///
/// - Accuracy
/// -
///
The ratio of the number of correctly classified items to the total number of
/// (correctly and incorrectly) classified items. It is used for both binary and
/// multiclass classification. Accuracy measures how close the predicted class values
/// are to the actual values. Values for accuracy metrics vary between zero (0) and
/// one (1). A value of 1 indicates perfect accuracy, and 0 indicates perfect
/// inaccuracy.
///
/// - AUC
/// -
///
The area under the curve (AUC) metric is used to compare and evaluate binary
/// classification by algorithms that return probabilities, such as logistic
/// regression. To map the probabilities into classifications, these are compared
/// against a threshold value.
/// The relevant curve is the receiver operating characteristic curve (ROC curve).
/// The ROC curve plots the true positive rate (TPR) of predictions (or recall)
/// against the false positive rate (FPR) as a function of the threshold value, above
/// which a prediction is considered positive. Increasing the threshold results in
/// fewer false positives, but more false negatives.
/// AUC is the area under this ROC curve. Therefore, AUC provides an aggregated
/// measure of the model performance across all possible classification thresholds.
/// AUC scores vary between 0 and 1. A score of 1 indicates perfect accuracy, and a
/// score of one half (0.5) indicates that the prediction is not better than a random
/// classifier.
///
/// - BalancedAccuracy
/// -
///
/// BalancedAccuracy is a metric that measures the ratio of accurate
/// predictions to all predictions. This ratio is calculated after normalizing true
/// positives (TP) and true negatives (TN) by the total number of positive (P) and
/// negative (N) values. It is used in both binary and multiclass classification and
/// is defined as follows: 0.5*((TP/P)+(TN/N)), with values ranging from 0 to 1.
/// BalancedAccuracy gives a better measure of accuracy when the
/// number of positives or negatives differ greatly from each other in an imbalanced
/// dataset. For example, when only 1% of email is spam.
///
/// - F1
/// -
///
The F1 score is the harmonic mean of the precision and recall,
/// defined as follows: F1 = 2 * (precision * recall) / (precision + recall). It is
/// used for binary classification into classes traditionally referred to as positive
/// and negative. Predictions are said to be true when they match their actual
/// (correct) class, and false when they do not.
/// Precision is the ratio of the true positive predictions to all positive
/// predictions, and it includes the false positives in a dataset. Precision measures
/// the quality of the prediction when it predicts the positive class.
/// Recall (or sensitivity) is the ratio of the true positive predictions to all
/// actual positive instances. Recall measures how completely a model predicts the
/// actual class members in a dataset.
/// F1 scores vary between 0 and 1. A score of 1 indicates the best possible
/// performance, and 0 indicates the worst.
///
/// - F1macro
/// -
///
The F1macro score applies F1 scoring to multiclass classification
/// problems. It does this by calculating the precision and recall, and then taking
/// their harmonic mean to calculate the F1 score for each class. Lastly, the F1macro
/// averages the individual scores to obtain the F1macro score.
/// F1macro scores vary between 0 and 1. A score of 1 indicates the
/// best possible performance, and 0 indicates the worst.
///
/// - MAE
/// -
///
The mean absolute error (MAE) is a measure of how different the predicted and
/// actual values are, when they're averaged over all values. MAE is commonly used in
/// regression analysis to understand model prediction error. If there is linear
/// regression, MAE represents the average distance from a predicted line to the
/// actual value. MAE is defined as the sum of absolute errors divided by the number
/// of observations. Values range from 0 to infinity, with smaller numbers indicating
/// a better model fit to the data.
///
/// - MSE
/// -
///
The mean squared error (MSE) is the average of the squared differences between
/// the predicted and actual values. It is used for regression. MSE values are always
/// positive. The better a model is at predicting the actual values, the smaller the
/// MSE value is
///
/// - Precision
/// -
///
Precision measures how well an algorithm predicts the true positives (TP) out
/// of all of the positives that it identifies. It is defined as follows: Precision =
/// TP/(TP+FP), with values ranging from zero (0) to one (1), and is used in binary
/// classification. Precision is an important metric when the cost of a false positive
/// is high. For example, the cost of a false positive is very high if an airplane
/// safety system is falsely deemed safe to fly. A false positive (FP) reflects a
/// positive prediction that is actually negative in the data.
///
/// - PrecisionMacro
/// -
///
The precision macro computes precision for multiclass classification problems.
/// It does this by calculating precision for each class and averaging scores to
/// obtain precision for several classes. PrecisionMacro scores range
/// from zero (0) to one (1). Higher scores reflect the model's ability to predict
/// true positives (TP) out of all of the positives that it identifies, averaged
/// across multiple classes.
///
/// - R2
/// -
///
R2, also known as the coefficient of determination, is used in regression to
/// quantify how much a model can explain the variance of a dependent variable. Values
/// range from one (1) to negative one (-1). Higher numbers indicate a higher fraction
/// of explained variability. R2 values close to zero (0) indicate that
/// very little of the dependent variable can be explained by the model. Negative
/// values indicate a poor fit and that the model is outperformed by a constant
/// function. For linear regression, this is a horizontal line.
///
/// - Recall
/// -
///
Recall measures how well an algorithm correctly predicts all of the true
/// positives (TP) in a dataset. A true positive is a positive prediction that is also
/// an actual positive value in the data. Recall is defined as follows: Recall =
/// TP/(TP+FN), with values ranging from 0 to 1. Higher scores reflect a better
/// ability of the model to predict true positives (TP) in the data, and is used in
/// binary classification.
/// Recall is important when testing for cancer because it's used to find all of
/// the true positives. A false positive (FP) reflects a positive prediction that is
/// actually negative in the data. It is often insufficient to measure only recall,
/// because predicting every output as a true positive will yield a perfect recall
/// score.
///
/// - RecallMacro
/// -
///
The RecallMacro computes recall for multiclass classification problems by
/// calculating recall for each class and averaging scores to obtain recall for
/// several classes. RecallMacro scores range from 0 to 1. Higher scores reflect the
/// model's ability to predict true positives (TP) in a dataset. Whereas, a true
/// positive reflects a positive prediction that is also an actual positive value in
/// the data. It is often insufficient to measure only recall, because predicting
/// every output as a true positive will yield a perfect recall score.
///
/// - RMSE
/// -
///
Root mean squared error (RMSE) measures the square root of the squared
/// difference between predicted and actual values, and it's averaged over all values.
/// It is used in regression analysis to understand model prediction error. It's an
/// important metric to indicate the presence of large model errors and outliers.
/// Values range from zero (0) to infinity, with smaller numbers indicating a better
/// model fit to the data. RMSE is dependent on scale, and should not be used to
/// compare datasets of different sizes.
///
///
/// If you do not specify a metric explicitly, the default behavior is to automatically
/// use:
///
/// -
///
/// MSE: for regression.
///
/// -
///
/// F1: for binary classification
///
/// -
///
/// Accuracy: for multiclass classification.
///
///
@required
MetricName: AutoMLMetricEnum
}
/// Metadata for an AutoML job step.
structure AutoMLJobStepMetadata {
/// The Amazon Resource Name (ARN) of the AutoML job.
Arn: AutoMLJobArn
}
/// Provides a summary about an AutoML job.
structure AutoMLJobSummary {
/// The name of the AutoML job you are requesting.
@required
AutoMLJobName: AutoMLJobName
/// The ARN of the AutoML job.
@required
AutoMLJobArn: AutoMLJobArn
/// The status of the AutoML job.
@required
AutoMLJobStatus: AutoMLJobStatus
/// The secondary status of the AutoML job.
@required
AutoMLJobSecondaryStatus: AutoMLJobSecondaryStatus
/// When the AutoML job was created.
@required
CreationTime: Timestamp
/// The end time of an AutoML job.
EndTime: Timestamp
/// When the AutoML job was last modified.
@required
LastModifiedTime: Timestamp
/// The failure reason of an AutoML job.
FailureReason: AutoMLFailureReason
/// The list of reasons for partial failures within an AutoML job.
PartialFailureReasons: AutoMLPartialFailureReasons
}
/// The output data configuration.
structure AutoMLOutputDataConfig {
/// The Key Management Service (KMS) encryption key ID.
KmsKeyId: KmsKeyId
/// The Amazon S3 output path. Must be 128 characters or less.
@required
S3OutputPath: S3Uri
}
/// The reason for a partial failure of an AutoML job.
structure AutoMLPartialFailureReason {
/// The message containing the reason for a partial failure of an AutoML job.
PartialFailureMessage: AutoMLFailureReason
}
/// The Amazon S3 data source.
structure AutoMLS3DataSource {
/// The data type.
/// A ManifestFile should have the format shown below:
///
/// [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
///
///
///
/// "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
///
///
/// "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
///
///
/// ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
///
/// An S3Prefix should have the following format:
///
/// s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
///
@required
S3DataType: AutoMLS3DataType
/// The URL to the Amazon S3 data source.
@required
S3Uri: S3Uri
}
/// Security options.
structure AutoMLSecurityConfig {
/// The key used to encrypt stored data.
VolumeKmsKeyId: KmsKeyId
/// Whether to use traffic encryption between the container layers.
EnableInterContainerTrafficEncryption: Boolean = false
/// The VPC configuration.
VpcConfig: VpcConfig
}
/// Automatic rollback configuration for handling endpoint deployment failures and
/// recovery.
structure AutoRollbackConfig {
/// List of CloudWatch alarms in your account that are configured to monitor metrics on an
/// endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the
/// deployment.
Alarms: AlarmList
}
/// Configuration to control how SageMaker captures inference data for batch transform jobs.
structure BatchDataCaptureConfig {
/// The Amazon S3 location being used to capture the data.
@required
DestinationS3Uri: S3Uri
/// 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 batch transform job.
/// 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
///
///
///
KmsKeyId: KmsKeyId
/// Flag that indicates whether to append inference id to the output.
GenerateInferenceId: Boolean = false
}
/// The error code and error description associated with the resource.
structure BatchDescribeModelPackageError {
///
@required
ErrorCode: String
///
@required
ErrorResponse: String
}
/// Provides summary information about the model package.
structure BatchDescribeModelPackageSummary {
/// The group name for the model package
@required
ModelPackageGroupName: EntityName
/// The version number of a versioned model.
ModelPackageVersion: ModelPackageVersion
/// The Amazon Resource Name (ARN) of the model package.
@required
ModelPackageArn: ModelPackageArn
/// The description of the model package.
ModelPackageDescription: EntityDescription
/// The creation time of the mortgage package summary.
@required
CreationTime: CreationTime
@required
InferenceSpecification: InferenceSpecification
/// The status of the mortgage package.
@required
ModelPackageStatus: ModelPackageStatus
/// The approval status of the model.
ModelApprovalStatus: ModelApprovalStatus
}
/// Input object for the batch transform job.
structure BatchTransformInput {
/// The Amazon S3 location being used to capture the data.
@required
DataCapturedDestinationS3Uri: DestinationS3Uri
/// The dataset format for your batch transform job.
@required
DatasetFormat: MonitoringDatasetFormat
/// Path to the filesystem where the batch transform data is available to the container.
@required
LocalPath: ProcessingLocalPath
/// Whether the Pipe or File is used as the input mode for
/// transferring data for the monitoring job. Pipe mode is recommended for large
/// datasets. File mode is useful for small files that fit in memory. Defaults to
/// File.
S3InputMode: ProcessingS3InputMode
/// Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key.
/// Defaults to FullyReplicated
///
S3DataDistributionType: ProcessingS3DataDistributionType
/// The attributes of the input data that are the input features.
FeaturesAttribute: String
/// The attribute of the input data that represents the ground truth label.
InferenceAttribute: String
/// In a classification problem, the attribute that represents the class probability.
ProbabilityAttribute: String
/// The threshold for the class probability to be evaluated as a positive result.
ProbabilityThresholdAttribute: ProbabilityThresholdAttribute
/// If specified, monitoring jobs substract this time from the start time. For information
/// about using offsets for scheduling monitoring jobs, see Schedule Model
/// Quality Monitoring Jobs.
StartTimeOffset: MonitoringTimeOffsetString
/// If specified, monitoring jobs substract this time from the end time. For information
/// about using offsets for scheduling monitoring jobs, see Schedule Model
/// Quality Monitoring Jobs.
EndTimeOffset: MonitoringTimeOffsetString
}
/// A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.
structure BestObjectiveNotImproving {
/// The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving: MaxNumberOfTrainingJobsNotImproving
}
/// Contains bias metrics for a model.
structure Bias {
/// The bias report for a model
Report: MetricsSource
/// The pre-training bias report for a model.
PreTrainingReport: MetricsSource
/// The post-training bias report for a model.
PostTrainingReport: MetricsSource
}
/// Update policy for a blue/green deployment. If this update policy is specified, SageMaker
/// creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips
/// traffic to the new fleet according to the specified traffic routing configuration. Only
/// one update policy should be used in the deployment configuration. If no update policy is
/// specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting
/// by default.
structure BlueGreenUpdatePolicy {
/// Defines the traffic routing strategy to shift traffic from the old fleet to the new
/// fleet during an endpoint deployment.
@required
TrafficRoutingConfiguration: TrafficRoutingConfig
/// Additional waiting time in seconds after the completion of an endpoint deployment
/// before terminating the old endpoint fleet. Default is 0.
TerminationWaitInSeconds: TerminationWaitInSeconds
/// Maximum execution timeout for the deployment. Note that the timeout value should be
/// larger than the total waiting time specified in TerminationWaitInSeconds
/// and WaitIntervalInSeconds.
MaximumExecutionTimeoutInSeconds: MaximumExecutionTimeoutInSeconds
}
/// Details on the cache hit of a pipeline execution step.
structure CacheHitResult {
/// The Amazon Resource Name (ARN) of the pipeline execution.
SourcePipelineExecutionArn: PipelineExecutionArn
}
/// Metadata about a callback step.
structure CallbackStepMetadata {
/// The pipeline generated token from the Amazon SQS queue.
CallbackToken: CallbackToken
/// The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
SqsQueueUrl: String256
/// A list of the output parameters of the callback step.
OutputParameters: OutputParameterList
}
/// The location of artifacts for an AutoML candidate job.
structure CandidateArtifactLocations {
/// The Amazon S3 prefix to the explainability artifacts generated for the AutoML
/// candidate.
@required
Explainability: ExplainabilityLocation
/// The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
ModelInsights: ModelInsightsLocation
}
/// The properties of an AutoML candidate job.
structure CandidateProperties {
/// The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
CandidateArtifactLocations: CandidateArtifactLocations
/// Information about the candidate metrics for an AutoML job.
CandidateMetrics: MetricDataList
}
/// The SageMaker Canvas app settings.
structure CanvasAppSettings {
/// Time series forecast settings for the Canvas app.
TimeSeriesForecastingSettings: TimeSeriesForecastingSettings
}
/// Specifies the endpoint capacity to activate for production.
structure CapacitySize {
/// Specifies the endpoint capacity type.
///
/// -
///
/// INSTANCE_COUNT: The endpoint activates based on the number of
/// instances.
///
/// -
///
/// CAPACITY_PERCENT: The endpoint activates based on the specified
/// percentage of capacity.
///
///
@required
Type: CapacitySizeType
/// Defines the capacity size, either as a number of instances or a capacity
/// percentage.
@required
Value: CapacitySizeValue
}
/// Configuration specifying how to treat different headers. If no headers are specified SageMaker
/// will by default base64 encode when capturing the data.
structure CaptureContentTypeHeader {
/// The list of all content type headers that SageMaker will treat as CSV and capture accordingly.
CsvContentTypes: CsvContentTypes
/// The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
JsonContentTypes: JsonContentTypes
}
/// Specifies data Model Monitor will capture.
structure CaptureOption {
/// Specify the boundary of data to capture.
@required
CaptureMode: CaptureMode
}
/// Environment parameters you want to benchmark your load test against.
structure CategoricalParameter {
/// The Name of the environment variable.
@required
Name: String64
/// The list of values you can pass.
@required
Value: CategoricalParameterRangeValues
}
/// A list of categorical hyperparameters to tune.
structure CategoricalParameterRange {
/// The name of the categorical hyperparameter to tune.
@required
Name: ParameterKey
/// A list of the categories
/// for
/// the hyperparameter.
@required
Values: ParameterValues
}
/// Defines the possible values for a categorical hyperparameter.
structure CategoricalParameterRangeSpecification {
/// The allowed categories for the hyperparameter.
@required
Values: ParameterValues
}
/// A channel is a named input source that training algorithms can consume.
structure Channel {
/// The name of the channel.
@required
ChannelName: ChannelName
/// The location of the channel data.
@required
DataSource: DataSource
/// The MIME type of the data.
ContentType: ContentType
/// If training data is compressed, the compression type. The default value is
/// None. CompressionType is used only in Pipe input mode. In
/// File mode, leave this field unset or set it to None.
CompressionType: CompressionType
///
/// Specify RecordIO as the value when input data is in raw format but the training
/// algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3
/// object in a RecordIO record. If the input data is already in RecordIO format, you don't
/// need to set this attribute. For more information, see Create
/// a Dataset Using RecordIO.
/// In File mode, leave this field unset or set it to None.
RecordWrapperType: RecordWrapper
/// (Optional) The input mode to use for the data channel in a training job. If you don't
/// set a value for InputMode, SageMaker uses the value set for
/// TrainingInputMode. Use this parameter to override the
/// TrainingInputMode setting in a AlgorithmSpecification
/// request when you have a channel that needs a different input mode from the training
/// job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
/// storage volume, and mount the directory to a Docker volume, use File input
/// mode. To stream data directly from Amazon S3 to the container, choose Pipe input
/// mode.
/// To use a model for incremental training, choose File input model.
InputMode: TrainingInputMode
/// A configuration for a shuffle option for input data in a channel. If you use
/// S3Prefix for S3DataType, this shuffles the results of the
/// S3 key prefix matches. If you use ManifestFile, the order of the S3 object
/// references in the ManifestFile is shuffled. If you use
/// AugmentedManifestFile, the order of the JSON lines in the
/// AugmentedManifestFile is shuffled. The shuffling order is determined
/// using the Seed value.
/// For Pipe input mode, shuffling is done at the start of every epoch. With large
/// datasets this ensures that the order of the training data is different for each epoch,
/// it helps reduce bias and possible overfitting. In a multi-node training job when
/// ShuffleConfig is combined with S3DataDistributionType of
/// ShardedByS3Key, the data is shuffled across nodes so that the content
/// sent to a particular node on the first epoch might be sent to a different node on the
/// second epoch.
ShuffleConfig: ShuffleConfig
}
/// Defines a named input source, called a channel, to be used by an algorithm.
structure ChannelSpecification {
/// The name of the channel.
@required
Name: ChannelName
/// A brief description of the channel.
Description: EntityDescription
/// Indicates whether the channel is required by the algorithm.
IsRequired: Boolean = false
/// The supported MIME types for the data.
@required
SupportedContentTypes: ContentTypes
/// The allowed compression types, if data compression is used.
SupportedCompressionTypes: CompressionTypes
/// The allowed input mode, either FILE or PIPE.
/// In FILE mode, Amazon SageMaker copies the data from the input source onto the local
/// Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm.
/// This is the most commonly used input mode.
/// In PIPE mode, Amazon SageMaker streams input data from the source directly to your
/// algorithm without using the EBS volume.
@required
SupportedInputModes: InputModes
}
/// Contains information about the output location for managed spot training checkpoint
/// data.
structure CheckpointConfig {
/// Identifies the S3 path where you want SageMaker to store checkpoints. For example,
/// s3://bucket-name/key-name-prefix.
@required
S3Uri: S3Uri
/// (Optional) The local directory where checkpoints are written. The default directory is
/// /opt/ml/checkpoints/.
LocalPath: DirectoryPath
}
/// The container for the metadata for the ClarifyCheck step. For more information,
/// see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.
///
structure ClarifyCheckStepMetadata {
/// The type of the Clarify Check step
CheckType: String256
/// The Amazon S3 URI of baseline constraints file to be used for the drift check.
BaselineUsedForDriftCheckConstraints: String1024
/// The Amazon S3 URI of the newly calculated baseline constraints file.
CalculatedBaselineConstraints: String1024
/// The model package group name.
ModelPackageGroupName: String256
/// The Amazon S3 URI of the violation report if violations are detected.
ViolationReport: String1024
/// The Amazon Resource Name (ARN) of the check processing job that was run by this step's execution.
CheckJobArn: String256
/// This flag indicates if the drift check against the previous baseline will be skipped or not.
/// If it is set to False, the previous baseline of the configured check type must be available.
SkipCheck: Boolean = false
/// This flag indicates if a newly calculated baseline can be accessed through step properties
/// BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics.
/// If it is set to False, the previous baseline of the configured check type must also be available.
/// These can be accessed through the BaselineUsedForDriftCheckConstraints property.
RegisterNewBaseline: Boolean = false
}
/// The configuration parameters for the SageMaker Clarify explainer.
structure ClarifyExplainerConfig {
/// A JMESPath boolean expression used to filter which records to explain. Explanations
/// are activated by default. See
/// EnableExplanations
/// for additional information.
EnableExplanations: ClarifyEnableExplanations
/// The inference configuration parameter for the model container.
InferenceConfig: ClarifyInferenceConfig
/// The configuration for SHAP analysis.
@required
ShapConfig: ClarifyShapConfig
}
/// The inference configuration parameter for the model container.
structure ClarifyInferenceConfig {
/// Provides the JMESPath expression to extract the features from a model container input
/// in JSON Lines format. For example, if FeaturesAttribute is the JMESPath
/// expression 'myfeatures', it extracts a list of features
/// [1,2,3] from request data '{"myfeatures":[1,2,3]}'.
FeaturesAttribute: ClarifyFeaturesAttribute
/// A template string used to format a JSON record into an acceptable model container
/// input. For example, a ContentTemplate string
/// '{"myfeatures":$features}' will format a list of features
/// [1,2,3] into the record string '{"myfeatures":[1,2,3]}'.
/// Required only when the model container input is in JSON Lines format.
ContentTemplate: ClarifyContentTemplate
/// The maximum number of records in a request that the model container can process when
/// querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be
/// made on, for example, a single line in CSV data. If MaxRecordCount is
/// 1, the model container expects one record per request. A value of 2 or
/// greater means that the model expects batch requests, which can reduce overhead and speed
/// up the inferencing process. If this parameter is not provided, the explainer will tune
/// the record count per request according to the model container's capacity at
/// runtime.
MaxRecordCount: ClarifyMaxRecordCount
/// The maximum payload size (MB) allowed of a request from the explainer to the model
/// container. Defaults to 6 MB.
MaxPayloadInMB: ClarifyMaxPayloadInMB
/// A zero-based index used to extract a probability value (score) or list from model
/// container output in CSV format. If this value is not provided, the entire model
/// container output will be treated as a probability value (score) or list.
///
/// Example for a single class model: If the model
/// container output consists of a string-formatted prediction label followed by its
/// probability: '1,0.6', set ProbabilityIndex to 1
/// to select the probability value 0.6.
///
/// Example for a multiclass model: If the model
/// container output consists of a string-formatted prediction label followed by its
/// probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set
/// ProbabilityIndex to 1 to select the probability values
/// [0.1,0.6,0.3].
ProbabilityIndex: ClarifyProbabilityIndex
/// A zero-based index used to extract a label header or list of label headers from model
/// container output in CSV format.
///
/// Example for a multiclass model: If the model
/// container output consists of label headers followed by probabilities:
/// '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set
/// LabelIndex to 0 to select the label headers
/// ['cat','dog','fish'].
LabelIndex: ClarifyLabelIndex
/// A JMESPath expression used to extract the probability (or score) from the model
/// container output if the model container is in JSON Lines format.
///
/// Example: If the model container output of a single
/// request is '{"predicted_label":1,"probability":0.6}', then set
/// ProbabilityAttribute to 'probability'.
ProbabilityAttribute: ClarifyProbabilityAttribute
/// A JMESPath expression used to locate the list of label headers in the model container
/// output.
///
/// Example: If the model container output of a batch
/// request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}',
/// then set LabelAttribute to 'labels' to extract the list of
/// label headers ["cat","dog","fish"]
///
LabelAttribute: ClarifyLabelAttribute
/// For multiclass classification problems, the label headers are the names of the
/// classes. Otherwise, the label header is the name of the predicted label. These are used
/// to help readability for the output of the InvokeEndpoint API. See the
/// response section under Invoke the endpoint
/// in the Developer Guide for more information. If there are no label headers in the model
/// container output, provide them manually using this parameter.
LabelHeaders: ClarifyLabelHeaders
/// The names of the features. If provided, these are included in the endpoint response
/// payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint
/// in the Developer Guide for more information.
FeatureHeaders: ClarifyFeatureHeaders
/// A list of data types of the features (optional). Applicable only to NLP
/// explainability. If provided, FeatureTypes must have at least one
/// 'text' string (for example, ['text']). If
/// FeatureTypes is not provided, the explainer infers the feature types
/// based on the baseline data. The feature types are included in the endpoint response
/// payload. For additional information see the response section under Invoke the endpoint
/// in the Developer Guide for more information.
FeatureTypes: ClarifyFeatureTypes
}
/// The configuration for the SHAP
/// baseline (also called the background or reference dataset) of the Kernal
/// SHAP algorithm.
///
///
/// -
///
The number of records in the baseline data determines the size of the
/// synthetic dataset, which has an impact on latency of explainability
/// requests. For more information, see the Synthetic
/// data of Configure and create an endpoint.
///
/// -
///
/// ShapBaseline and ShapBaselineUri are mutually
/// exclusive parameters. One or the either is required to configure a SHAP
/// baseline.
///
///
///
structure ClarifyShapBaselineConfig {
/// The MIME type of the baseline data. Choose from 'text/csv' or
/// 'application/jsonlines'. Defaults to 'text/csv'.
MimeType: ClarifyMimeType
/// The inline SHAP baseline data in string format. ShapBaseline can have one
/// or multiple records to be used as the baseline dataset. The format of the SHAP baseline
/// file should be the same format as the training dataset. For example, if the training
/// dataset is in CSV format and each record contains four features, and all features are
/// numerical, then the format of the baseline data should also share these characteristics.
/// For natural language processing (NLP) of text columns, the baseline value should be the
/// value used to replace the unit of text specified by the Granularity of the
/// TextConfig parameter. The size limit for ShapBasline is 4
/// KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB
/// of baseline data.
ShapBaseline: ClarifyShapBaseline
/// The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is
/// stored. The format of the SHAP baseline file should be the same format as the format of
/// the training dataset. For example, if the training dataset is in CSV format, and each
/// record in the training dataset has four features, and all features are numerical, then
/// the baseline file should also have this same format. Each record should contain only the
/// features. If you are using a virtual private cloud (VPC), the
/// ShapBaselineUri should be accessible to the VPC. For more information
/// about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to
/// Resources in your Amazon Virtual Private Cloud.
ShapBaselineUri: Url
}
/// The configuration for SHAP analysis using SageMaker Clarify Explainer.
structure ClarifyShapConfig {
/// The configuration for the SHAP baseline of the Kernal SHAP algorithm.
@required
ShapBaselineConfig: ClarifyShapBaselineConfig
/// The number of samples to be used for analysis by the Kernal SHAP algorithm.
///
/// The number of samples determines the size of the synthetic dataset, which has an
/// impact on latency of explainability requests. For more information, see the
/// Synthetic data of Configure and create an endpoint.
///
NumberOfSamples: ClarifyShapNumberOfSamples
/// A Boolean toggle to indicate if you want to use the logit function (true) or log-odds
/// units (false) for model predictions. Defaults to false.
UseLogit: ClarifyShapUseLogit
/// The starting value used to initialize the random number generator in the explainer.
/// Provide a value for this parameter to obtain a deterministic SHAP result.
Seed: ClarifyShapSeed
/// A parameter that indicates if text features are treated as text and explanations are
/// provided for individual units of text. Required for natural language processing (NLP)
/// explainability only.
TextConfig: ClarifyTextConfig
}
/// A parameter used to configure the SageMaker Clarify explainer to treat text features as text so
/// that explanations are provided for individual units of text. Required only for natural
/// language processing (NLP) explainability.
structure ClarifyTextConfig {
/// Specifies the language of the text features in ISO 639-1 or
/// ISO 639-3 code of a
/// supported language.
///
/// For a mix of multiple languages, use code 'xx'.
///
@required
Language: ClarifyTextLanguage
/// The unit of granularity for the analysis of text features. For example, if the unit is
/// 'token', then each token (like a word in English) of the text is
/// treated as a feature. SHAP values are computed for each unit/feature.
@required
Granularity: ClarifyTextGranularity
}
/// A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
structure CodeRepository {
/// The URL of the Git repository.
@required
RepositoryUrl: RepositoryUrl
}
/// Specifies summary information about a Git repository.
structure CodeRepositorySummary {
/// The name of the Git repository.
@required
CodeRepositoryName: EntityName
/// The Amazon Resource Name (ARN) of the Git repository.
@required
CodeRepositoryArn: CodeRepositoryArn
/// The date and time that the Git repository was created.
@required
CreationTime: CreationTime
/// The date and time that the Git repository was last modified.
@required
LastModifiedTime: LastModifiedTime
/// Configuration details for the Git repository, including the URL where it is located
/// and the ARN of the Amazon Web Services Secrets Manager secret that contains the
/// credentials used to access the repository.
GitConfig: GitConfig
}
/// Use this parameter to configure your Amazon Cognito workforce.
/// A single Cognito workforce is created using and corresponds to a single
///
/// Amazon Cognito user pool.
structure CognitoConfig {
/// A
/// user pool is a user directory in Amazon Cognito.
/// With a user pool, your users can sign in to your web or mobile app through Amazon Cognito.
/// Your users can also sign in through social identity providers like
/// Google, Facebook, Amazon, or Apple, and through SAML identity providers.
@required
UserPool: CognitoUserPool
/// The client ID for your Amazon Cognito user pool.
@required
ClientId: ClientId
}
/// Identifies a Amazon Cognito user group. A user group can be used in on or more work
/// teams.
structure CognitoMemberDefinition {
/// An identifier for a user pool. The user pool must be in the same region as the service
/// that you are calling.
@required
UserPool: CognitoUserPool
/// An identifier for a user group.
@required
UserGroup: CognitoUserGroup
/// An identifier for an application client. You must create the app client ID using
/// Amazon Cognito.
@required
ClientId: ClientId
}
/// Configuration information for the Amazon SageMaker Debugger output tensor collections.
structure CollectionConfiguration {
/// The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionName: CollectionName
/// Parameter values for the tensor collection. The allowed parameters are
/// "name", "include_regex", "reduction_config",
/// "save_config", "tensor_names", and
/// "save_histogram".
CollectionParameters: CollectionParameters
}
/// A summary of a model compilation job.
structure CompilationJobSummary {
/// The name of the model compilation job that you want a summary for.
@required
CompilationJobName: EntityName
/// The Amazon Resource Name (ARN) of the model compilation job.
@required
CompilationJobArn: CompilationJobArn
/// The time when the model compilation job was created.
@required
CreationTime: CreationTime
/// The time when the model compilation job started.
CompilationStartTime: Timestamp
/// The time when the model compilation job completed.
CompilationEndTime: Timestamp
/// The type of device that the model will run on after the compilation job has
/// completed.
CompilationTargetDevice: TargetDevice
/// The type of OS that the model will run on after the compilation job has
/// completed.
CompilationTargetPlatformOs: TargetPlatformOs
/// The type of architecture that the model will run on after the compilation job has
/// completed.
CompilationTargetPlatformArch: TargetPlatformArch
/// The type of accelerator that the model will run on after the compilation job has
/// completed.
CompilationTargetPlatformAccelerator: TargetPlatformAccelerator
/// The time when the model compilation job was last modified.
LastModifiedTime: LastModifiedTime
/// The status of the model compilation job.
@required
CompilationJobStatus: CompilationJobStatus
}
/// Metadata for a Condition step.
structure ConditionStepMetadata {
/// The outcome of the Condition step evaluation.
Outcome: ConditionOutcome
}
/// There was a conflict when you attempted to modify a SageMaker entity such as an
/// Experiment or Artifact.
@error("client")
structure ConflictException {
Message: FailureReason
}
/// Describes the container, as part of model definition.
structure ContainerDefinition {
/// This parameter is ignored for models that contain only a
/// PrimaryContainer.
/// When a ContainerDefinition is part of an inference pipeline, the value of
/// the parameter uniquely identifies the container for the purposes of logging and metrics.
/// For information, see Use Logs and Metrics
/// to Monitor an Inference Pipeline. If you don't specify a value for this
/// parameter for a ContainerDefinition that is part of an inference pipeline,
/// a unique name is automatically assigned based on the position of the
/// ContainerDefinition in the pipeline. If you specify a value for the
/// ContainerHostName for any ContainerDefinition that is part
/// of an inference pipeline, you must specify a value for the
/// ContainerHostName parameter of every ContainerDefinition
/// in that pipeline.
ContainerHostname: ContainerHostname
/// The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a
/// Docker registry that is accessible from the same VPC that you configure for your
/// endpoint. If you are using your own custom algorithm instead of an algorithm provided by
/// SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
/// registry/repository[:tag] and registry/repository[@digest]
/// image path formats. For more information, see Using Your Own Algorithms with Amazon
/// SageMaker
///
Image: ContainerImage
/// Specifies whether the model container is in Amazon ECR or a private Docker registry
/// accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a
/// private Docker registry, see Use a
/// Private Docker Registry for Real-Time Inference Containers
///
ImageConfig: ImageConfig
/// Whether the container hosts a single model or multiple models.
Mode: ContainerMode
/// The S3 path where the model artifacts, which result from model training, are stored.
/// This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3
/// path is required for SageMaker built-in algorithms, but not if you use your own algorithms.
/// For more information on built-in algorithms, see Common
/// Parameters.
///
/// The model artifacts must be in an S3 bucket that is in the same region as the
/// model or endpoint you are creating.
///
/// If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token
/// Service to download model artifacts from the S3 path you provide. Amazon Web Services STS
/// is activated in your IAM user account by default. If you previously deactivated
/// Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS
/// for that region. For more information, see Activating and
/// Deactivating Amazon Web Services STS in an Amazon Web Services Region in the
/// Amazon Web Services Identity and Access Management User
/// Guide.
///
/// If you use a built-in algorithm to create a model, SageMaker requires that you provide
/// a S3 path to the model artifacts in ModelDataUrl.
///
ModelDataUrl: Url
/// The environment variables to set in the Docker container. Each key and value in the
/// Environment string to string map can have length of up to 1024. We
/// support up to 16 entries in the map.
Environment: EnvironmentMap
/// The name or Amazon Resource Name (ARN) of the model package to use to create the
/// model.
ModelPackageName: VersionedArnOrName
/// The inference specification name in the model package version.
InferenceSpecificationName: InferenceSpecificationName
/// Specifies additional configuration for multi-model endpoints.
MultiModelConfig: MultiModelConfig
}
/// A structure describing the source of a context.
structure ContextSource {
/// The URI of the source.
@required
SourceUri: String2048
/// The type of the source.
SourceType: String256
/// The ID of the source.
SourceId: String256
}
/// Lists a summary of the properties of a context. A context provides a logical grouping
/// of other entities.
structure ContextSummary {
/// The Amazon Resource Name (ARN) of the context.
ContextArn: ContextArn
/// The name of the context.
ContextName: ExperimentEntityName
/// The source of the context.
Source: ContextSource
/// The type of the context.
ContextType: String256
/// When the context was created.
CreationTime: Timestamp
/// When the context was last modified.
LastModifiedTime: Timestamp
}
/// A list of continuous hyperparameters to tune.
structure ContinuousParameterRange {
/// The name of the continuous hyperparameter to tune.
@required
Name: ParameterKey
/// The minimum value for the hyperparameter.
/// The
/// tuning job uses floating-point values between this value and MaxValuefor
/// tuning.
@required
MinValue: ParameterValue
/// The maximum value for the hyperparameter. The tuning job uses floating-point values
/// between MinValue value and this value for tuning.
@required
MaxValue: ParameterValue
/// The scale that hyperparameter tuning uses to search the hyperparameter range. For
/// information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
///
/// - Auto
/// -
///
SageMaker hyperparameter tuning chooses the best scale for the
/// hyperparameter.
///
/// - Linear
/// -
///
Hyperparameter tuning searches the values in the hyperparameter range by
/// using a linear scale.
///
/// - Logarithmic
/// -
///
Hyperparameter tuning searches the values in the hyperparameter range by
/// using a logarithmic scale.
/// Logarithmic scaling works only for ranges that have only values greater
/// than 0.
///
/// - ReverseLogarithmic
/// -
///
Hyperparameter tuning searches the values in the hyperparameter range by
/// using a reverse logarithmic scale.
/// Reverse logarithmic scaling works only for ranges that are entirely within
/// the range 0<=x<1.0.
///
///
ScalingType: HyperParameterScalingType
}
/// Defines the possible values for a continuous hyperparameter.
structure ContinuousParameterRangeSpecification {
/// The minimum floating-point value allowed.
@required
MinValue: ParameterValue
/// The maximum floating-point value allowed.
@required
MaxValue: ParameterValue
}
/// A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.
structure ConvergenceDetected {
/// A flag to stop a tuning job once AMT has detected that the job has converged.
CompleteOnConvergence: CompleteOnConvergence
}
@input
structure CreateActionRequest {
/// The name of the action. Must be unique to your account in an Amazon Web Services Region.
@required
ActionName: ExperimentEntityName
/// The source type, ID, and URI.
@required
Source: ActionSource
/// The action type.
@required
ActionType: String256
/// The description of the action.
Description: ExperimentDescription
/// The status of the action.
Status: ActionStatus
/// A list of properties to add to the action.
Properties: LineageEntityParameters
MetadataProperties: MetadataProperties
/// A list of tags to apply to the action.
Tags: TagList
}
@output
structure CreateActionResponse {
/// The Amazon Resource Name (ARN) of the action.
ActionArn: ActionArn
}
@input
structure CreateAppImageConfigRequest {
/// The name of the AppImageConfig. Must be unique to your account.
@required
AppImageConfigName: AppImageConfigName
/// A list of tags to apply to the AppImageConfig.
Tags: TagList
/// 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.
KernelGatewayImageConfig: KernelGatewayImageConfig
}
@output
structure CreateAppImageConfigResponse {
/// The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigArn: AppImageConfigArn
}
@input
structure CreateAppRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name. If this value is not set, then SpaceName must be set.
UserProfileName: UserProfileName
/// The type of app.
@required
AppType: AppType
/// The name of the app.
@required
AppName: AppName
/// Each tag consists of a key and an optional value.
/// Tag keys must be unique per resource.
Tags: TagList
/// The instance type and the Amazon Resource Name (ARN) of the SageMaker 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.
///
ResourceSpec: ResourceSpec
/// The name of the space. If this value is not set, then UserProfileName must be set.
SpaceName: SpaceName
}
@output
structure CreateAppResponse {
/// The Amazon Resource Name (ARN) of the app.
AppArn: AppArn
}
@input
structure CreateArtifactRequest {
/// The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
ArtifactName: ExperimentEntityName
/// The ID, ID type, and URI of the source.
@required
Source: ArtifactSource
/// The artifact type.
@required
ArtifactType: String256
/// A list of properties to add to the artifact.
Properties: LineageEntityParameters
MetadataProperties: MetadataProperties
/// A list of tags to apply to the artifact.
Tags: TagList
}
@output
structure CreateArtifactResponse {
/// The Amazon Resource Name (ARN) of the artifact.
ArtifactArn: ArtifactArn
}
@input
structure CreateAutoMLJobRequest {
/// Identifies an Autopilot job. The name must be unique to your account and is
/// case-insensitive.
@required
AutoMLJobName: AutoMLJobName
/// An array of channel objects that describes the input data and its location. Each channel
/// is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, 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.
@required
InputDataConfig: AutoMLInputDataConfig
/// Provides information about encryption and the Amazon S3 output path needed to store artifacts
/// from an AutoML job. Format(s) supported: CSV.
@required
OutputDataConfig: AutoMLOutputDataConfig
/// Defines the type of supervised learning available for the candidates. For more
/// information, see
/// Amazon SageMaker Autopilot problem types and algorithm support.
ProblemType: ProblemType
/// Defines the objective metric used to measure the predictive quality of an AutoML job. You
/// provide an AutoMLJobObjective$MetricName and Autopilot infers whether to
/// minimize or maximize it.
AutoMLJobObjective: AutoMLJobObjective
/// A collection of settings used to configure an AutoML job.
AutoMLJobConfig: AutoMLJobConfig
/// The ARN of the role that is used to access the data.
@required
RoleArn: RoleArn
/// Generates possible candidates without training the models. A candidate is a combination
/// of data preprocessors, algorithms, and algorithm parameter settings.
GenerateCandidateDefinitionsOnly: GenerateCandidateDefinitionsOnly = false
/// Each tag consists of a key and an optional value. Tag keys must be unique per
/// resource.
Tags: TagList
/// Specifies how to generate the endpoint name for an automatic one-click Autopilot model
/// deployment.
ModelDeployConfig: ModelDeployConfig
}
@output
structure CreateAutoMLJobResponse {
/// The unique ARN assigned to the AutoML job when it is created.
@required
AutoMLJobArn: AutoMLJobArn
}
@input
structure CreateCompilationJobRequest {
/// 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.
@required
CompilationJobName: EntityName
/// The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on
/// your behalf.
/// During model compilation, Amazon SageMaker needs your permission to:
///
/// -
///
Read input data from an S3 bucket
///
/// -
///
Write model artifacts to an S3 bucket
///
/// -
///
Write logs to Amazon CloudWatch Logs
///
/// -
///
Publish metrics to Amazon CloudWatch
///
///
/// You grant permissions for all of these tasks to an IAM role. To pass this role to
/// Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For
/// more information, see Amazon SageMaker
/// Roles.
///
@required
RoleArn: RoleArn
/// 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.
ModelPackageVersionArn: ModelPackageArn
/// 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.
InputConfig: InputConfig
/// Provides information about the output location for the compiled model and the target
/// device the model runs on.
@required
OutputConfig: OutputConfig
/// 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.
VpcConfig: NeoVpcConfig
/// Specifies a limit to how long a model compilation job can run. When the job reaches
/// the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training
/// costs.
@required
StoppingCondition: StoppingCondition
/// 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: TagList
}
@output
structure CreateCompilationJobResponse {
/// If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns
/// the following data in JSON format:
///
/// -
///
/// CompilationJobArn: The Amazon Resource Name (ARN) of the compiled
/// job.
///
///
@required
CompilationJobArn: CompilationJobArn
}
@input
structure CreateContextRequest {
/// The name of the context. Must be unique to your account in an Amazon Web Services Region.
@required
ContextName: ExperimentEntityName
/// The source type, ID, and URI.
@required
Source: ContextSource
/// The context type.
@required
ContextType: String256
/// The description of the context.
Description: ExperimentDescription
/// A list of properties to add to the context.
Properties: LineageEntityParameters
/// A list of tags to apply to the context.
Tags: TagList
}
@output
structure CreateContextResponse {
/// The Amazon Resource Name (ARN) of the context.
ContextArn: ContextArn
}
@input
structure CreateDataQualityJobDefinitionRequest {
/// The name for the monitoring job definition.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// Configures the constraints and baselines for the monitoring job.
DataQualityBaselineConfig: DataQualityBaselineConfig
/// Specifies the container that runs the monitoring job.
@required
DataQualityAppSpecification: DataQualityAppSpecification
/// A list of inputs for the monitoring job. Currently endpoints are supported as monitoring
/// inputs.
@required
DataQualityJobInput: DataQualityJobInput
@required
DataQualityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Specifies networking configuration for the monitoring job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
/// (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.
Tags: TagList
}
@output
structure CreateDataQualityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the job definition.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
}
@input
structure CreateDeviceFleetRequest {
/// The name of the fleet that the device belongs to.
@required
DeviceFleetName: EntityName
/// The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
RoleArn: RoleArn
/// A description of the fleet.
Description: DeviceFleetDescription
/// The output configuration for storing sample data collected by the fleet.
@required
OutputConfig: EdgeOutputConfig
/// Creates tags for the specified fleet.
Tags: TagList
/// 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".
EnableIotRoleAlias: EnableIotRoleAlias
}
@input
structure CreateDomainRequest {
/// A name for the domain.
@required
DomainName: DomainName
/// The mode of authentication that members use to access the domain.
@required
AuthMode: AuthMode
/// 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.
@required
DefaultUserSettings: UserSettings
/// The VPC subnets that Studio uses for communication.
@required
SubnetIds: Subnets
/// The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
@required
VpcId: VpcId
/// 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.
Tags: TagList
/// Specifies the VPC used for non-EFS traffic. The default value is
/// PublicInternetOnly.
///
/// -
///
/// PublicInternetOnly - Non-EFS traffic is through a VPC managed by
/// Amazon SageMaker, which allows direct internet access
///
/// -
///
/// VpcOnly - All Studio traffic is through the specified VPC and subnets
///
///
AppNetworkAccessType: AppNetworkAccessType
/// Use KmsKeyId.
@deprecated(
message: "This property is deprecated, use KmsKeyId instead."
)
HomeEfsFileSystemKmsKeyId: KmsKeyId
/// SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed
/// key by default. For more control, specify a customer managed key.
KmsKeyId: KmsKeyId
/// 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.
AppSecurityGroupManagement: AppSecurityGroupManagement
/// A collection of Domain settings.
DomainSettings: DomainSettings
/// The default settings used to create a space.
DefaultSpaceSettings: DefaultSpaceSettings
}
@output
structure CreateDomainResponse {
/// The Amazon Resource Name (ARN) of the created domain.
DomainArn: DomainArn
/// The URL to the created domain.
Url: String1024
}
@input
structure CreateEdgeDeploymentPlanRequest {
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// List of models associated with the edge deployment plan.
@required
ModelConfigs: EdgeDeploymentModelConfigs
/// The device fleet used for this edge deployment plan.
@required
DeviceFleetName: EntityName
/// List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.
Stages: DeploymentStages
/// List of tags with which to tag the edge deployment plan.
Tags: TagList
}
@output
structure CreateEdgeDeploymentPlanResponse {
/// The ARN of the edge deployment plan.
@required
EdgeDeploymentPlanArn: EdgeDeploymentPlanArn
}
@input
structure CreateEdgeDeploymentStageRequest {
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// List of stages to be added to the edge deployment plan.
@required
Stages: DeploymentStages
}
@input
structure CreateEdgePackagingJobRequest {
/// The name of the edge packaging job.
@required
EdgePackagingJobName: EntityName
/// The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
@required
CompilationJobName: EntityName
/// The name of the model.
@required
ModelName: EntityName
/// The version of the model.
@required
ModelVersion: EdgeVersion
/// The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
@required
RoleArn: RoleArn
/// Provides information about the output location for the packaged model.
@required
OutputConfig: EdgeOutputConfig
/// The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
ResourceKey: KmsKeyId
/// Creates tags for the packaging job.
Tags: TagList
}
@input
structure CreateExperimentRequest {
/// The name of the experiment. The name must be unique in your Amazon Web Services account and is not
/// case-sensitive.
@required
ExperimentName: ExperimentEntityName
/// 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.
DisplayName: ExperimentEntityName
/// The description of the experiment.
Description: ExperimentDescription
/// A list of tags to associate with the experiment. You can use Search API
/// to search on the tags.
Tags: TagList
}
@output
structure CreateExperimentResponse {
/// The Amazon Resource Name (ARN) of the experiment.
ExperimentArn: ExperimentArn
}
@input
structure CreateFeatureGroupRequest {
/// The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region
/// in an Amazon Web Services account. The name:
///
/// -
///
Must start and end with an alphanumeric character.
///
/// -
///
Can only contain alphanumeric character and hyphens. Spaces are not allowed.
///
///
///
@required
FeatureGroupName: FeatureGroupName
/// The name of the Feature whose value uniquely identifies a
/// Record defined in the FeatureStore. Only the latest record per
/// identifier value will be stored in the OnlineStore.
/// RecordIdentifierFeatureName must be one of feature definitions'
/// names.
/// You use the RecordIdentifierFeatureName to access data in a
/// FeatureStore.
/// This name:
///
/// -
///
Must start and end with an alphanumeric character.
///
/// -
///
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not
/// allowed.
///
///
@required
RecordIdentifierFeatureName: FeatureName
/// 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.
///
///
@required
EventTimeFeatureName: FeatureName
/// 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.
@required
FeatureDefinitions: FeatureDefinitions
/// You can turn the OnlineStore on or off by specifying True for
/// the EnableOnlineStore flag in OnlineStoreConfig; the default
/// value is False.
/// You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of
/// the OnlineStore.
OnlineStoreConfig: OnlineStoreConfig
/// 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.
OfflineStoreConfig: OfflineStoreConfig
/// The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the
/// OfflineStore if an OfflineStoreConfig is provided.
RoleArn: RoleArn
/// A free-form description of a FeatureGroup.
Description: Description
/// Tags used to identify Features in each FeatureGroup.
Tags: TagList
}
@output
structure CreateFeatureGroupResponse {
/// The Amazon Resource Name (ARN) of the FeatureGroup. This is a unique
/// identifier for the feature group.
@required
FeatureGroupArn: FeatureGroupArn
}
@input
structure CreateFlowDefinitionRequest {
/// The name of your flow definition.
@required
FlowDefinitionName: FlowDefinitionName
/// Container for configuring the source of human task requests. Use to specify if
/// Amazon Rekognition or Amazon Textract is used as an integration source.
HumanLoopRequestSource: HumanLoopRequestSource
/// An object containing information about the events that trigger a human workflow.
HumanLoopActivationConfig: HumanLoopActivationConfig
/// An object containing information about the tasks the human reviewers will perform.
@required
HumanLoopConfig: HumanLoopConfig
/// An object containing information about where the human review results will be uploaded.
@required
OutputConfig: FlowDefinitionOutputConfig
/// 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.
@required
RoleArn: RoleArn
/// 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.
Tags: TagList
}
@output
structure CreateFlowDefinitionResponse {
/// The Amazon Resource Name (ARN) of the flow definition you create.
@required
FlowDefinitionArn: FlowDefinitionArn
}
@input
structure CreateHubRequest {
/// The name of the hub to create.
@required
HubName: HubName
/// A description of the hub.
@required
HubDescription: HubDescription
/// The display name of the hub.
HubDisplayName: HubDisplayName
/// The searchable keywords for the hub.
HubSearchKeywords: HubSearchKeywordList
/// The Amazon S3 storage configuration for the hub.
S3StorageConfig: HubS3StorageConfig
/// Any tags to associate with the hub.
Tags: TagList
}
@output
structure CreateHubResponse {
/// The Amazon Resource Name (ARN) of the hub.
@required
HubArn: HubArn
}
@input
structure CreateHumanTaskUiRequest {
/// The name of the user interface you are creating.
@required
HumanTaskUiName: HumanTaskUiName
@required
UiTemplate: UiTemplate
/// 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.
Tags: TagList
}
@output
structure CreateHumanTaskUiResponse {
/// The Amazon Resource Name (ARN) of the human review workflow user interface you create.
@required
HumanTaskUiArn: HumanTaskUiArn
}
@input
structure CreateHyperParameterTuningJobRequest {
/// 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.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
/// 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.
@required
HyperParameterTuningJobConfig: HyperParameterTuningJobConfig
/// 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.
TrainingJobDefinition: HyperParameterTrainingJobDefinition
/// A list of the HyperParameterTrainingJobDefinition objects launched
/// for this tuning job.
TrainingJobDefinitions: HyperParameterTrainingJobDefinitions
/// 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.
///
WarmStartConfig: HyperParameterTuningJobWarmStartConfig
/// 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.
Tags: TagList
}
@output
structure CreateHyperParameterTuningJobResponse {
/// The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a
/// hyperparameter tuning job when you create it.
@required
HyperParameterTuningJobArn: HyperParameterTuningJobArn
}
@input
structure CreateImageRequest {
/// The description of the image.
Description: ImageDescription
/// The display name of the image. If not provided, ImageName is displayed.
DisplayName: ImageDisplayName
/// The name of the image. Must be unique to your account.
@required
ImageName: ImageName
/// The ARN of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
@required
RoleArn: RoleArn
/// A list of tags to apply to the image.
Tags: TagList
}
@output
structure CreateImageResponse {
/// The ARN of the image.
ImageArn: ImageArn
}
@input
structure CreateImageVersionRequest {
/// The registry path of the container image to use as the starting point for this
/// version. The path is an Amazon Elastic Container Registry (ECR) URI in the following format:
///
/// .dkr.ecr..amazonaws.com/
@required
BaseImage: ImageBaseImage
/// 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.
@idempotencyToken
@required
ClientToken: ClientToken
/// The ImageName of the Image to create a version of.
@required
ImageName: ImageName
/// A list of aliases created with the image version.
Aliases: SageMakerImageVersionAliases
/// 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.
///
///
VendorGuidance: VendorGuidance
/// Indicates SageMaker job type compatibility.
///
/// -
///
/// TRAINING: The image version is compatible with SageMaker training jobs.
///
/// -
///
/// INFERENCE: The image version is compatible with SageMaker inference jobs.
///
/// -
///
/// NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.
///
///
JobType: JobType
/// The machine learning framework vended in the image version.
MLFramework: MLFramework
/// The supported programming language and its version.
ProgrammingLang: ProgrammingLang
/// Indicates CPU or GPU compatibility.
///
/// -
///
/// CPU: The image version is compatible with CPU.
///
/// -
///
/// GPU: The image version is compatible with GPU.
///
///
Processor: Processor
/// Indicates Horovod compatibility.
Horovod: Horovod = false
/// The maintainer description of the image version.
ReleaseNotes: ReleaseNotes
}
@output
structure CreateImageVersionResponse {
/// The ARN of the image version.
ImageVersionArn: ImageVersionArn
}
@input
structure CreateInferenceExperimentRequest {
/// The name for the inference experiment.
@required
Name: InferenceExperimentName
///
/// 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.
///
///
///
@required
Type: InferenceExperimentType
///
/// 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.
///
Schedule: InferenceExperimentSchedule
/// A description for the inference experiment.
Description: InferenceExperimentDescription
///
/// 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.
///
@required
RoleArn: RoleArn
///
/// The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
///
@required
EndpointName: EndpointName
///
/// 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.
///
@required
ModelVariants: ModelVariantConfigList
///
/// 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.
///
DataStorageConfig: InferenceExperimentDataStorageConfig
///
/// 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.
///
@required
ShadowModeConfig: ShadowModeConfig
///
/// 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.
///
KmsKey: KmsKeyId
///
/// 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.
///
Tags: TagList
}
@output
structure CreateInferenceExperimentResponse {
/// The ARN for your inference experiment.
@required
InferenceExperimentArn: InferenceExperimentArn
}
@input
structure CreateInferenceRecommendationsJobRequest {
/// A name for the recommendation job. The name must be unique within
/// the Amazon Web Services Region and within your Amazon Web Services account.
@required
JobName: RecommendationJobName
/// 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.
@required
JobType: RecommendationJobType
/// The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker
/// to perform tasks on your behalf.
@required
RoleArn: RoleArn
/// Provides information about the versioned model package Amazon Resource Name (ARN),
/// the traffic pattern, and endpoint configurations.
@required
InputConfig: RecommendationJobInputConfig
/// Description of the recommendation job.
JobDescription: RecommendationJobDescription
/// A set of conditions for stopping a recommendation job. If any of
/// the conditions are met, the job is automatically stopped.
StoppingConditions: RecommendationJobStoppingConditions
/// Provides information about the output artifacts and the KMS key
/// to use for Amazon S3 server-side encryption.
OutputConfig: RecommendationJobOutputConfig
/// 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.
Tags: TagList
}
@output
structure CreateInferenceRecommendationsJobResponse {
/// The Amazon Resource Name (ARN) of the recommendation job.
@required
JobArn: RecommendationJobArn
}
@input
structure CreateLabelingJobRequest {
/// 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.
@required
LabelingJobName: LabelingJobName
/// 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.
///
@required
LabelAttributeName: LabelAttributeName
/// 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.
@required
InputConfig: LabelingJobInputConfig
/// 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.
@required
OutputConfig: LabelingJobOutputConfig
/// 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.
@required
RoleArn: RoleArn
/// The S3 URI of the file, referred to as a label category configuration
/// file, that defines the categories used to label the data objects.
/// For 3D point cloud and video frame task types, you can add label category attributes
/// and frame attributes to your label category configuration file. To learn how, see Create a
/// Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
/// For named entity recognition jobs, in addition to "labels", you must
/// provide worker instructions in the label category configuration file using the
/// "instructions" parameter: "instructions":
/// {"shortInstruction":"Add header
Add Instructions
",
/// "fullInstruction":"Add additional instructions.
"}. For details
/// and an example, see Create a
/// Named Entity Recognition Labeling Job (API) .
/// For all other built-in task types and custom
/// tasks, your label category configuration file must be a JSON file in the
/// following format. Identify the labels you want to use by replacing label_1,
/// label_2,...,label_n with your label
/// categories.
///
/// {
///
///
/// "document-version": "2018-11-28",
///
///
/// "labels": [{"label": "label_1"},{"label": "label_2"},...{"label":
/// "label_n"}]
///
///
/// }
///
/// Note the following about the label category configuration file:
///
/// -
///
For image classification and text classification (single and multi-label) you
/// must specify at least two label categories. For all other task types, the
/// minimum number of label categories required is one.
///
/// -
///
Each label category must be unique, you cannot specify duplicate label
/// categories.
///
/// -
///
If you create a 3D point cloud or video frame adjustment or verification
/// labeling job, you must include auditLabelAttributeName in the label
/// category configuration. Use this parameter to enter the
/// LabelAttributeName
/// of the labeling job you want to
/// adjust or verify annotations of.
///
///
LabelCategoryConfigS3Uri: S3Uri
/// 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.
StoppingConditions: LabelingJobStoppingConditions
/// Configures the information required to perform automated data labeling.
LabelingJobAlgorithmsConfig: LabelingJobAlgorithmsConfig
/// Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
@required
HumanTaskConfig: HumanTaskConfig
/// 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.
Tags: TagList
}
@output
structure CreateLabelingJobResponse {
/// The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the
/// labeling job.
@required
LabelingJobArn: LabelingJobArn
}
@input
structure CreateModelBiasJobDefinitionRequest {
/// The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The baseline configuration for a model bias job.
ModelBiasBaselineConfig: ModelBiasBaselineConfig
/// Configures the model bias job to run a specified Docker container image.
@required
ModelBiasAppSpecification: ModelBiasAppSpecification
/// Inputs for the model bias job.
@required
ModelBiasJobInput: ModelBiasJobInput
@required
ModelBiasJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Networking options for a model bias job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
/// (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.
Tags: TagList
}
@output
structure CreateModelBiasJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model bias job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
}
@input
structure CreateModelCardExportJobRequest {
/// The name of the model card to export.
@required
ModelCardName: EntityName
/// The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.
ModelCardVersion: Integer = 0
/// The name of the model card export job.
@required
ModelCardExportJobName: EntityName
/// The model card output configuration that specifies the Amazon S3 path for exporting.
@required
OutputConfig: ModelCardExportOutputConfig
}
@output
structure CreateModelCardExportJobResponse {
/// The Amazon Resource Name (ARN) of the model card export job.
@required
ModelCardExportJobArn: ModelCardExportJobArn
}
@input
structure CreateModelCardRequest {
/// The unique name of the model card.
@required
ModelCardName: EntityName
/// An optional Key Management Service
/// key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with
/// highly sensitive data.
SecurityConfig: ModelCardSecurityConfig
/// The content of the model card. Content must be in model card JSON schema and provided as a string.
@required
Content: ModelCardContent
/// 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.
///
///
@required
ModelCardStatus: ModelCardStatus
/// Key-value pairs used to manage metadata for model cards.
Tags: TagList
}
@output
structure CreateModelCardResponse {
/// The Amazon Resource Name (ARN) of the successfully created model card.
@required
ModelCardArn: ModelCardArn
}
@input
structure CreateModelExplainabilityJobDefinitionRequest {
/// 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.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The baseline configuration for a model explainability job.
ModelExplainabilityBaselineConfig: ModelExplainabilityBaselineConfig
/// Configures the model explainability job to run a specified Docker container
/// image.
@required
ModelExplainabilityAppSpecification: ModelExplainabilityAppSpecification
/// Inputs for the model explainability job.
@required
ModelExplainabilityJobInput: ModelExplainabilityJobInput
@required
ModelExplainabilityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Networking options for a model explainability job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
/// (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.
Tags: TagList
}
@output
structure CreateModelExplainabilityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model explainability job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
}
@input
structure CreateModelQualityJobDefinitionRequest {
/// The name of the monitoring job definition.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// Specifies the constraints and baselines for the monitoring job.
ModelQualityBaselineConfig: ModelQualityBaselineConfig
/// The container that runs the monitoring job.
@required
ModelQualityAppSpecification: ModelQualityAppSpecification
/// A list of the inputs that are monitored. Currently endpoints are supported.
@required
ModelQualityJobInput: ModelQualityJobInput
@required
ModelQualityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Specifies the network configuration for the monitoring job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
/// (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.
Tags: TagList
}
@output
structure CreateModelQualityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model quality monitoring job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
}
@input
structure CreateMonitoringScheduleRequest {
/// The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within
/// an Amazon Web Services account.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The configuration object that specifies the monitoring schedule and defines the
/// monitoring job.
@required
MonitoringScheduleConfig: MonitoringScheduleConfig
/// (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.
Tags: TagList
}
@output
structure CreateMonitoringScheduleResponse {
/// The Amazon Resource Name (ARN) of the monitoring schedule.
@required
MonitoringScheduleArn: MonitoringScheduleArn
}
@input
structure CreatePipelineRequest {
/// The name of the pipeline.
@required
PipelineName: PipelineName
/// The display name of the pipeline.
PipelineDisplayName: PipelineName
/// The JSON pipeline definition of the pipeline.
PipelineDefinition: PipelineDefinition
/// The location of the pipeline definition stored in Amazon S3. If specified,
/// SageMaker will retrieve the pipeline definition from this location.
PipelineDefinitionS3Location: PipelineDefinitionS3Location
/// A description of the pipeline.
PipelineDescription: PipelineDescription
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than one time.
@idempotencyToken
@required
ClientRequestToken: IdempotencyToken
/// The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
@required
RoleArn: RoleArn
/// A list of tags to apply to the created pipeline.
Tags: TagList
/// This is the configuration that controls the parallelism of the pipeline.
/// If specified, it applies to all runs of this pipeline by default.
ParallelismConfiguration: ParallelismConfiguration
}
@output
structure CreatePipelineResponse {
/// The Amazon Resource Name (ARN) of the created pipeline.
PipelineArn: PipelineArn
}
@input
structure CreatePresignedDomainUrlRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The name of the UserProfile to sign-in as.
@required
UserProfileName: UserProfileName
/// The session expiration duration in seconds. This value defaults to 43200.
SessionExpirationDurationInSeconds: SessionExpirationDurationInSeconds
/// The number of seconds until the pre-signed URL expires. This value defaults to
/// 300.
ExpiresInSeconds: ExpiresInSeconds
/// The name of the space.
SpaceName: SpaceName
}
@output
structure CreatePresignedDomainUrlResponse {
/// The presigned URL.
AuthorizedUrl: PresignedDomainUrl
}
@input
structure CreateProcessingJobRequest {
/// An array of inputs configuring the data to download into the
/// processing container.
ProcessingInputs: ProcessingInputs
/// Output configuration for the processing job.
ProcessingOutputConfig: ProcessingOutputConfig
/// The name of the processing job. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
ProcessingJobName: ProcessingJobName
/// 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.
@required
ProcessingResources: ProcessingResources
/// The time limit for how long the processing job is allowed to run.
StoppingCondition: ProcessingStoppingCondition
/// Configures the processing job to run a specified Docker container image.
@required
AppSpecification: AppSpecification
/// The environment variables to set in the Docker container. Up to
/// 100 key and values entries in the map are supported.
Environment: ProcessingEnvironmentMap
/// 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.
NetworkConfig: NetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on
/// your behalf.
@required
RoleArn: RoleArn
/// (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.
Tags: TagList
ExperimentConfig: ExperimentConfig
}
@output
structure CreateProcessingJobResponse {
/// The Amazon Resource Name (ARN) of the processing job.
@required
ProcessingJobArn: ProcessingJobArn
}
@input
structure CreateSpaceRequest {
/// The ID of the associated Domain.
@required
DomainId: DomainId
/// The name of the space.
@required
SpaceName: SpaceName
/// 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.
Tags: TagList
/// A collection of space settings.
SpaceSettings: SpaceSettings
}
@output
structure CreateSpaceResponse {
/// The space's Amazon Resource Name (ARN).
SpaceArn: SpaceArn
}
@input
structure CreateStudioLifecycleConfigRequest {
/// The name of the Studio Lifecycle Configuration to create.
@required
StudioLifecycleConfigName: StudioLifecycleConfigName
/// The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
@required
StudioLifecycleConfigContent: StudioLifecycleConfigContent
/// The App type that the Lifecycle Configuration is attached to.
@required
StudioLifecycleConfigAppType: StudioLifecycleConfigAppType
/// 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.
Tags: TagList
}
@output
structure CreateStudioLifecycleConfigResponse {
/// The ARN of your created Lifecycle Configuration.
StudioLifecycleConfigArn: StudioLifecycleConfigArn
}
@input
structure CreateTrainingJobRequest {
/// The name of the training job. The name must be unique within an Amazon Web Services
/// Region in an Amazon Web Services account.
@required
TrainingJobName: TrainingJobName
/// 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 field. If the use of security-sensitive
/// credentials are detected, SageMaker will reject your training job request and return an
/// exception error.
///
HyperParameters: HyperParameters
/// 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.
@required
AlgorithmSpecification: AlgorithmSpecification
/// 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.
///
@required
RoleArn: RoleArn
/// 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.
InputDataConfig: InputDataConfig
/// Specifies the path to the S3 location where you want to store model artifacts. SageMaker
/// creates subfolders for the artifacts.
@required
OutputDataConfig: OutputDataConfig
/// 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.
@required
ResourceConfig: ResourceConfig
/// 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.
VpcConfig: VpcConfig
/// 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.
@required
StoppingCondition: StoppingCondition
/// 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: TagList
/// 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.
EnableNetworkIsolation: Boolean = 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.
EnableInterContainerTrafficEncryption: Boolean = 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.
EnableManagedSpotTraining: Boolean = false
/// Contains information about the output location for managed spot training checkpoint
/// data.
CheckpointConfig: CheckpointConfig
DebugHookConfig: DebugHookConfig
/// Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
DebugRuleConfigurations: DebugRuleConfigurations
TensorBoardOutputConfig: TensorBoardOutputConfig
ExperimentConfig: ExperimentConfig
ProfilerConfig: ProfilerConfig
/// Configuration information for Amazon SageMaker Debugger rules for profiling system and framework
/// metrics.
ProfilerRuleConfigurations: ProfilerRuleConfigurations
/// The environment variables to set in the Docker container.
Environment: TrainingEnvironmentMap
/// The number of times to retry the job when the job fails due to an
/// InternalServerError.
RetryStrategy: RetryStrategy
}
@output
structure CreateTrainingJobResponse {
/// The Amazon Resource Name (ARN) of the training job.
@required
TrainingJobArn: TrainingJobArn
}
@input
structure CreateTransformJobRequest {
/// The name of the transform job. The name must be unique within an Amazon Web Services Region in an
/// Amazon Web Services account.
@required
TransformJobName: TransformJobName
/// 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.
@required
ModelName: ModelName
/// 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.
MaxConcurrentTransforms: MaxConcurrentTransforms
/// Configures the timeout and maximum number of retries for processing a transform job
/// invocation.
ModelClientConfig: ModelClientConfig
/// 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.
MaxPayloadInMB: MaxPayloadInMB
/// 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.
BatchStrategy: BatchStrategy
/// The environment variables to set in the Docker container. We support up to 16 key and
/// values entries in the map.
Environment: TransformEnvironmentMap
/// Describes the input source and
/// the
/// way the transform job consumes it.
@required
TransformInput: TransformInput
/// Describes the results of the transform job.
@required
TransformOutput: TransformOutput
/// Configuration to control how SageMaker captures inference data.
DataCaptureConfig: BatchDataCaptureConfig
/// Describes the resources, including
/// ML
/// instance types and ML instance count, to use for the transform
/// job.
@required
TransformResources: TransformResources
/// 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.
DataProcessing: DataProcessing
/// (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.
Tags: TagList
ExperimentConfig: ExperimentConfig
}
@output
structure CreateTransformJobResponse {
/// The Amazon Resource Name (ARN) of the transform job.
@required
TransformJobArn: TransformJobArn
}
@input
structure CreateTrialComponentRequest {
/// The name of the component. The name must be unique in your Amazon Web Services account and is not
/// case-sensitive.
@required
TrialComponentName: ExperimentEntityName
/// The name of the component as displayed. The name doesn't need to be unique. If
/// DisplayName isn't specified, TrialComponentName is
/// displayed.
DisplayName: ExperimentEntityName
/// The status of the component. States include:
///
/// -
///
InProgress
///
/// -
///
Completed
///
/// -
///
Failed
///
///
Status: TrialComponentStatus
/// When the component started.
StartTime: Timestamp
/// When the component ended.
EndTime: Timestamp
/// The hyperparameters for the component.
Parameters: TrialComponentParameters
/// The input artifacts for the component. Examples of input artifacts are datasets,
/// algorithms, hyperparameters, source code, and instance types.
InputArtifacts: TrialComponentArtifacts
/// The output artifacts for the component. Examples of output artifacts are metrics,
/// snapshots, logs, and images.
OutputArtifacts: TrialComponentArtifacts
MetadataProperties: MetadataProperties
/// A list of tags to associate with the component. You can use Search API
/// to search on the tags.
Tags: TagList
}
@output
structure CreateTrialComponentResponse {
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
}
@input
structure CreateTrialRequest {
/// The name of the trial. The name must be unique in your Amazon Web Services account and is not
/// case-sensitive.
@required
TrialName: ExperimentEntityName
/// The name of the trial as displayed. The name doesn't need to be unique. If
/// DisplayName isn't specified, TrialName is displayed.
DisplayName: ExperimentEntityName
/// The name of the experiment to associate the trial with.
@required
ExperimentName: ExperimentEntityName
MetadataProperties: MetadataProperties
/// A list of tags to associate with the trial. You can use Search API to
/// search on the tags.
Tags: TagList
}
@output
structure CreateTrialResponse {
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
}
@input
structure CreateUserProfileRequest {
/// The ID of the associated Domain.
@required
DomainId: DomainId
/// A name for the UserProfile. This value is not case sensitive.
@required
UserProfileName: UserProfileName
/// 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.
///
SingleSignOnUserIdentifier: SingleSignOnUserIdentifier
/// 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.
///
SingleSignOnUserValue: String256
/// 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.
Tags: TagList
/// A collection of settings.
UserSettings: UserSettings
}
@output
structure CreateUserProfileResponse {
/// The user profile Amazon Resource Name (ARN).
UserProfileArn: UserProfileArn
}
@input
structure CreateWorkforceRequest {
/// 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.
CognitoConfig: CognitoConfig
/// Use this parameter to configure a private workforce using your own OIDC Identity Provider.
/// Do not use CognitoConfig if you specify values for
/// OidcConfig.
OidcConfig: OidcConfig
SourceIpConfig: SourceIpConfig
/// The name of the private workforce.
@required
WorkforceName: WorkforceName
/// 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.
Tags: TagList
/// Use this parameter to configure a workforce using VPC.
WorkforceVpcConfig: WorkforceVpcConfigRequest
}
@output
structure CreateWorkforceResponse {
/// The Amazon Resource Name (ARN) of the workforce.
@required
WorkforceArn: WorkforceArn
}
@input
structure CreateWorkteamRequest {
/// The name of the work team. Use this name to identify the work team.
@required
WorkteamName: WorkteamName
/// The name of the workforce.
WorkforceName: WorkforceName
/// 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.
@required
MemberDefinitions: MemberDefinitions
/// A description of the work team.
@required
Description: String200
/// Configures notification of workers regarding available or expiring work items.
NotificationConfiguration: NotificationConfiguration
/// 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.
Tags: TagList
}
@output
structure CreateWorkteamResponse {
/// The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the
/// work team.
WorkteamArn: WorkteamArn
}
/// A custom SageMaker image. For more information, see
/// Bring your own SageMaker image.
structure CustomImage {
/// The name of the CustomImage. Must be unique to your account.
@required
ImageName: ImageName
/// The version number of the CustomImage.
ImageVersionNumber: ImageVersionNumber
/// The name of the AppImageConfig.
@required
AppImageConfigName: AppImageConfigName
}
/// Configuration to control how SageMaker captures inference data.
structure DataCaptureConfig {
/// Whether data capture should be enabled or disabled (defaults to enabled).
EnableCapture: EnableCapture = false
/// The percentage of requests SageMaker will capture. A lower value is recommended for
/// Endpoints with high traffic.
@required
InitialSamplingPercentage: SamplingPercentage
/// The Amazon S3 location used to capture the data.
@required
DestinationS3Uri: DestinationS3Uri
/// The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt the
/// captured data at rest using Amazon S3 server-side encryption.
/// 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
///
///
///
KmsKeyId: KmsKeyId
/// Specifies data Model Monitor will capture. You can configure whether to
/// collect only input, only output, or both
@required
CaptureOptions: CaptureOptionList
/// Configuration specifying how to treat different headers. If no headers are specified SageMaker will
/// by default base64 encode when capturing the data.
CaptureContentTypeHeader: CaptureContentTypeHeader
}
/// The currently active data capture configuration used by your Endpoint.
structure DataCaptureConfigSummary {
/// Whether data capture is enabled or disabled.
@required
EnableCapture: EnableCapture = false
/// Whether data capture is currently functional.
@required
CaptureStatus: CaptureStatus
/// The percentage of requests being captured by your Endpoint.
@required
CurrentSamplingPercentage: SamplingPercentage
/// The Amazon S3 location being used to capture the data.
@required
DestinationS3Uri: DestinationS3Uri
/// The KMS key being used to encrypt the data in Amazon S3.
@required
KmsKeyId: KmsKeyId
}
/// The meta data of the Glue table which serves as data catalog for the
/// OfflineStore.
structure DataCatalogConfig {
/// The name of the Glue table.
@required
TableName: TableName
/// The name of the Glue table catalog.
@required
Catalog: Catalog
/// The name of the Glue table database.
@required
Database: Database
}
/// 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.
structure DataProcessing {
/// A JSONPath expression used to select a portion of the input data to pass to
/// the algorithm. Use the InputFilter parameter to exclude fields, such as an
/// ID column, from the input. If you want SageMaker to pass the entire input dataset to the
/// algorithm, accept the default value $.
/// Examples: "$", "$[1:]", "$.features"
///
InputFilter: JsonPath
/// A JSONPath expression used to select a portion of the joined dataset to save
/// in the output file for a batch transform job. If you want SageMaker to store the entire input
/// dataset in the output file, leave the default value, $. If you specify
/// indexes that aren't within the dimension size of the joined dataset, you get an
/// error.
/// Examples: "$", "$[0,5:]",
/// "$['id','SageMakerOutput']"
///
OutputFilter: JsonPath
/// Specifies the source of the data to join with the transformed data. The valid values
/// are None and Input. The default value is None,
/// which specifies not to join the input with the transformed data. If you want the batch
/// transform job to join the original input data with the transformed data, set
/// JoinSource to Input. You can specify
/// OutputFilter as an additional filter to select a portion of the joined
/// dataset and store it in the output file.
/// For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to
/// the input JSON object in an attribute called SageMakerOutput. The joined
/// result for JSON must be a key-value pair object. If the input is not a key-value pair
/// object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored
/// under the SageMakerInput key and the results are stored in
/// SageMakerOutput.
/// For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with
/// the input by appending each transformed row to the end of the input. The joined data has
/// the original input data followed by the transformed data and the output is a CSV
/// file.
/// For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
JoinSource: JoinSource
}
/// Information about the container that a data quality monitoring job runs.
structure DataQualityAppSpecification {
/// The container image that the data quality monitoring job runs.
@required
ImageUri: ImageUri
/// The entrypoint for a container used to run a monitoring job.
ContainerEntrypoint: ContainerEntrypoint
/// The arguments to send to the container that the monitoring job runs.
ContainerArguments: MonitoringContainerArguments
/// An Amazon S3 URI to a script that is called per row prior to running analysis. It can
/// base64 decode the payload and convert it into a flatted json so that the built-in container
/// can use the converted data. Applicable only for the built-in (first party)
/// containers.
RecordPreprocessorSourceUri: S3Uri
/// An Amazon S3 URI to a script that is called after analysis has been performed.
/// Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri: S3Uri
/// Sets the environment variables in the container that the monitoring job runs.
Environment: MonitoringEnvironmentMap
}
/// Configuration for monitoring constraints and monitoring statistics. These baseline
/// resources are compared against the results of the current job from the series of jobs
/// scheduled to collect data periodically.
structure DataQualityBaselineConfig {
/// The name of the job that performs baselining for the data quality monitoring job.
BaseliningJobName: ProcessingJobName
ConstraintsResource: MonitoringConstraintsResource
StatisticsResource: MonitoringStatisticsResource
}
/// The input for the data quality monitoring job. Currently endpoints are supported for
/// input.
structure DataQualityJobInput {
EndpointInput: EndpointInput
/// Input object for the batch transform job.
BatchTransformInput: BatchTransformInput
}
/// Configuration for Dataset Definition inputs. The Dataset Definition input must specify
/// exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition
/// types.
structure DatasetDefinition {
AthenaDatasetDefinition: AthenaDatasetDefinition
RedshiftDatasetDefinition: RedshiftDatasetDefinition
/// The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a
/// processing job. LocalPath is an absolute path to the input data. This is a required
/// parameter when AppManaged is False (default).
LocalPath: ProcessingLocalPath
/// Whether the generated dataset is FullyReplicated or
/// ShardedByS3Key (default).
DataDistributionType: DataDistributionType
/// Whether to use File or Pipe input mode. In File (default) mode,
/// Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store
/// (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used
/// input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your
/// algorithm without using the EBS volume.
InputMode: InputMode
}
/// Describes the location of the channel data.
structure DataSource {
/// The S3 location of the data source that is associated with a channel.
S3DataSource: S3DataSource
/// The file system that is associated with a channel.
FileSystemDataSource: FileSystemDataSource
}
/// 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.
structure DebugHookConfig {
/// Path to local storage location for metrics and tensors. Defaults to
/// /opt/ml/output/tensors/.
LocalPath: DirectoryPath
/// Path to Amazon S3 storage location for metrics and tensors.
@required
S3OutputPath: S3Uri
/// Configuration information for the Amazon SageMaker Debugger hook parameters.
HookParameters: HookParameters
/// Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about
/// how to configure the CollectionConfiguration parameter,
/// see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
///
CollectionConfigurations: CollectionConfigurations
}
/// Configuration information for SageMaker Debugger rules for debugging. To learn more about
/// how to configure the DebugRuleConfiguration parameter,
/// see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
structure DebugRuleConfiguration {
/// The name of the rule configuration. It must be unique relative to other rule
/// configuration names.
@required
RuleConfigurationName: RuleConfigurationName
/// Path to local storage location for output of rules. Defaults to
/// /opt/ml/processing/output/rule/.
LocalPath: DirectoryPath
/// Path to Amazon S3 storage location for rules.
S3OutputPath: S3Uri
/// The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
@required
RuleEvaluatorImage: AlgorithmImage
/// The instance type to deploy a custom rule for debugging a training job.
InstanceType: ProcessingInstanceType
/// The size, in GB, of the ML storage volume attached to the processing instance.
VolumeSizeInGB: OptionalVolumeSizeInGB = 0
/// Runtime configuration for rule container.
RuleParameters: RuleParameters
}
/// Information about the status of the rule evaluation.
structure DebugRuleEvaluationStatus {
/// The name of the rule configuration.
RuleConfigurationName: RuleConfigurationName
/// The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationJobArn: ProcessingJobArn
/// Status of the rule evaluation.
RuleEvaluationStatus: RuleEvaluationStatus
/// Details from the rule evaluation.
StatusDetails: StatusDetails
/// Timestamp when the rule evaluation status was last modified.
LastModifiedTime: Timestamp
}
/// A collection of settings that apply to spaces created in the Domain.
structure DefaultSpaceSettings {
/// The execution role for the space.
ExecutionRole: RoleArn
/// The security groups for the Amazon Virtual Private Cloud that the space uses for communication.
SecurityGroups: SecurityGroupIds
JupyterServerAppSettings: JupyterServerAppSettings
KernelGatewayAppSettings: KernelGatewayAppSettings
}
@input
structure DeleteActionRequest {
/// The name of the action to delete.
@required
ActionName: ExperimentEntityName
}
@output
structure DeleteActionResponse {
/// The Amazon Resource Name (ARN) of the action.
ActionArn: ActionArn
}
@input
structure DeleteAppImageConfigRequest {
/// The name of the AppImageConfig to delete.
@required
AppImageConfigName: AppImageConfigName
}
@input
structure DeleteAppRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name. If this value is not set, then SpaceName must be set.
UserProfileName: UserProfileName
/// The type of app.
@required
AppType: AppType
/// The name of the app.
@required
AppName: AppName
/// The name of the space. If this value is not set, then UserProfileName must be set.
SpaceName: SpaceName
}
@input
structure DeleteArtifactRequest {
/// The Amazon Resource Name (ARN) of the artifact to delete.
ArtifactArn: ArtifactArn
/// The URI of the source.
Source: ArtifactSource
}
@output
structure DeleteArtifactResponse {
/// The Amazon Resource Name (ARN) of the artifact.
ArtifactArn: ArtifactArn
}
@input
structure DeleteAssociationRequest {
/// The ARN of the source.
@required
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination.
@required
DestinationArn: AssociationEntityArn
}
@output
structure DeleteAssociationResponse {
/// The ARN of the source.
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination.
DestinationArn: AssociationEntityArn
}
@input
structure DeleteContextRequest {
/// The name of the context to delete.
@required
ContextName: ExperimentEntityName
}
@output
structure DeleteContextResponse {
/// The Amazon Resource Name (ARN) of the context.
ContextArn: ContextArn
}
@input
structure DeleteDataQualityJobDefinitionRequest {
/// The name of the data quality monitoring job definition to delete.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@input
structure DeleteDeviceFleetRequest {
/// The name of the fleet to delete.
@required
DeviceFleetName: EntityName
}
@input
structure DeleteDomainRequest {
/// The domain ID.
@required
DomainId: DomainId
/// 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).
///
RetentionPolicy: RetentionPolicy
}
@input
structure DeleteEdgeDeploymentPlanRequest {
/// The name of the edge deployment plan to delete.
@required
EdgeDeploymentPlanName: EntityName
}
@input
structure DeleteEdgeDeploymentStageRequest {
/// The name of the edge deployment plan from which the stage will be deleted.
@required
EdgeDeploymentPlanName: EntityName
/// The name of the stage.
@required
StageName: EntityName
}
@input
structure DeleteExperimentRequest {
/// The name of the experiment to delete.
@required
ExperimentName: ExperimentEntityName
}
@output
structure DeleteExperimentResponse {
/// The Amazon Resource Name (ARN) of the experiment that is being deleted.
ExperimentArn: ExperimentArn
}
@input
structure DeleteFeatureGroupRequest {
/// 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.
@required
FeatureGroupName: FeatureGroupName
}
@input
structure DeleteFlowDefinitionRequest {
/// The name of the flow definition you are deleting.
@required
FlowDefinitionName: FlowDefinitionName
}
@output
structure DeleteFlowDefinitionResponse {}
@input
structure DeleteHubContentRequest {
/// The name of the hub that you want to delete content in.
@required
HubName: HubName
/// The type of content that you want to delete from a hub.
@required
HubContentType: HubContentType
/// The name of the content that you want to delete from a hub.
@required
HubContentName: HubContentName
/// The version of the content that you want to delete from a hub.
@required
HubContentVersion: HubContentVersion
}
@input
structure DeleteHubRequest {
/// The name of the hub to delete.
@required
HubName: HubName
}
@input
structure DeleteHumanTaskUiRequest {
/// The name of the human task user interface (work task template) you want to delete.
@required
HumanTaskUiName: HumanTaskUiName
}
@output
structure DeleteHumanTaskUiResponse {}
@input
structure DeleteImageRequest {
/// The name of the image to delete.
@required
ImageName: ImageName
}
@output
structure DeleteImageResponse {}
@input
structure DeleteImageVersionRequest {
/// The name of the image to delete.
@required
ImageName: ImageName
/// The version to delete.
Version: ImageVersionNumber
/// The alias of the image to delete.
Alias: SageMakerImageVersionAlias
}
@output
structure DeleteImageVersionResponse {}
@input
structure DeleteInferenceExperimentRequest {
/// The name of the inference experiment you want to delete.
@required
Name: InferenceExperimentName
}
@output
structure DeleteInferenceExperimentResponse {
/// The ARN of the deleted inference experiment.
@required
InferenceExperimentArn: InferenceExperimentArn
}
@input
structure DeleteModelBiasJobDefinitionRequest {
/// The name of the model bias job definition to delete.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@input
structure DeleteModelCardRequest {
/// The name of the model card to delete.
@required
ModelCardName: EntityName
}
@input
structure DeleteModelExplainabilityJobDefinitionRequest {
/// The name of the model explainability job definition to delete.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@input
structure DeleteModelQualityJobDefinitionRequest {
/// The name of the model quality monitoring job definition to delete.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@input
structure DeleteMonitoringScheduleRequest {
/// The name of the monitoring schedule to delete.
@required
MonitoringScheduleName: MonitoringScheduleName
}
@input
structure DeletePipelineRequest {
/// The name of the pipeline to delete.
@required
PipelineName: PipelineName
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than one time.
@idempotencyToken
@required
ClientRequestToken: IdempotencyToken
}
@output
structure DeletePipelineResponse {
/// The Amazon Resource Name (ARN) of the pipeline to delete.
PipelineArn: PipelineArn
}
@input
structure DeleteSpaceRequest {
/// The ID of the associated Domain.
@required
DomainId: DomainId
/// The name of the space.
@required
SpaceName: SpaceName
}
@input
structure DeleteStudioLifecycleConfigRequest {
/// The name of the Studio Lifecycle Configuration to delete.
@required
StudioLifecycleConfigName: StudioLifecycleConfigName
}
@input
structure DeleteTrialComponentRequest {
/// The name of the component to delete.
@required
TrialComponentName: ExperimentEntityName
}
@output
structure DeleteTrialComponentResponse {
/// The Amazon Resource Name (ARN) of the component is being deleted.
TrialComponentArn: TrialComponentArn
}
@input
structure DeleteTrialRequest {
/// The name of the trial to delete.
@required
TrialName: ExperimentEntityName
}
@output
structure DeleteTrialResponse {
/// The Amazon Resource Name (ARN) of the trial that is being deleted.
TrialArn: TrialArn
}
@input
structure DeleteUserProfileRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name.
@required
UserProfileName: UserProfileName
}
@input
structure DeleteWorkforceRequest {
/// The name of the workforce.
@required
WorkforceName: WorkforceName
}
@output
structure DeleteWorkforceResponse {}
@input
structure DeleteWorkteamRequest {
/// The name of the work team to delete.
@required
WorkteamName: WorkteamName
}
@output
structure DeleteWorkteamResponse {
/// Returns true if the work team was successfully deleted; otherwise,
/// returns false.
@required
Success: Success = false
}
/// Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.
/// If you used the registry/repository[:tag] form to specify the image path
/// of the primary container when you created the model hosted in this
/// ProductionVariant, the path resolves to a path of the form
/// registry/repository[@digest]. A digest is a hash value that identifies
/// a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.
structure DeployedImage {
/// The image path you specified when you created the model.
SpecifiedImage: ContainerImage
/// The specific digest path of the image hosted in this
/// ProductionVariant.
ResolvedImage: ContainerImage
/// The date and time when the image path for the model resolved to the
/// ResolvedImage
///
ResolutionTime: Timestamp
}
/// The deployment configuration for an endpoint, which contains the desired deployment
/// strategy and rollback configurations.
structure DeploymentConfig {
/// Update policy for a blue/green deployment. If this update policy is specified, SageMaker
/// creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips
/// traffic to the new fleet according to the specified traffic routing configuration. Only
/// one update policy should be used in the deployment configuration. If no update policy is
/// specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting
/// by default.
@required
BlueGreenUpdatePolicy: BlueGreenUpdatePolicy
/// Automatic rollback configuration for handling endpoint deployment failures and
/// recovery.
AutoRollbackConfiguration: AutoRollbackConfig
}
/// Contains information about a stage in an edge deployment plan.
structure DeploymentStage {
/// The name of the stage.
@required
StageName: EntityName
/// Configuration of the devices in the stage.
@required
DeviceSelectionConfig: DeviceSelectionConfig
/// Configuration of the deployment details.
DeploymentConfig: EdgeDeploymentConfig
}
/// Contains information summarizing the deployment stage results.
structure DeploymentStageStatusSummary {
/// The name of the stage.
@required
StageName: EntityName
/// Configuration of the devices in the stage.
@required
DeviceSelectionConfig: DeviceSelectionConfig
/// Configuration of the deployment details.
@required
DeploymentConfig: EdgeDeploymentConfig
/// General status of the current state.
@required
DeploymentStatus: EdgeDeploymentStatus
}
@input
structure DeregisterDevicesRequest {
/// The name of the fleet the devices belong to.
@required
DeviceFleetName: EntityName
/// The unique IDs of the devices.
@required
DeviceNames: DeviceNames
}
@input
structure DescribeActionRequest {
/// The name of the action to describe.
@required
ActionName: ExperimentEntityName
}
@output
structure DescribeActionResponse {
/// The name of the action.
ActionName: ExperimentEntityNameOrArn
/// The Amazon Resource Name (ARN) of the action.
ActionArn: ActionArn
/// The source of the action.
Source: ActionSource
/// The type of the action.
ActionType: String256
/// The description of the action.
Description: ExperimentDescription
/// The status of the action.
Status: ActionStatus
/// A list of the action's properties.
Properties: LineageEntityParameters
/// When the action was created.
CreationTime: Timestamp
CreatedBy: UserContext
/// When the action was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
MetadataProperties: MetadataProperties
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
}
@input
structure DescribeAppImageConfigRequest {
/// The name of the AppImageConfig to describe.
@required
AppImageConfigName: AppImageConfigName
}
@output
structure DescribeAppImageConfigResponse {
/// The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigArn: AppImageConfigArn
/// The name of the AppImageConfig.
AppImageConfigName: AppImageConfigName
/// When the AppImageConfig was created.
CreationTime: Timestamp
/// When the AppImageConfig was last modified.
LastModifiedTime: Timestamp
/// The configuration of a KernelGateway app.
KernelGatewayImageConfig: KernelGatewayImageConfig
}
@input
structure DescribeAppRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name. If this value is not set, then SpaceName must be set.
UserProfileName: UserProfileName
/// The type of app.
@required
AppType: AppType
/// The name of the app.
@required
AppName: AppName
/// The name of the space.
SpaceName: SpaceName
}
@output
structure DescribeAppResponse {
/// The Amazon Resource Name (ARN) of the app.
AppArn: AppArn
/// The type of app.
AppType: AppType
/// The name of the app.
AppName: AppName
/// The domain ID.
DomainId: DomainId
/// The user profile name.
UserProfileName: UserProfileName
/// The status.
Status: AppStatus
/// The timestamp of the last health check.
LastHealthCheckTimestamp: Timestamp
/// The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.
LastUserActivityTimestamp: Timestamp
/// The creation time.
CreationTime: CreationTime
/// The failure reason.
FailureReason: FailureReason
/// The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
ResourceSpec: ResourceSpec
/// The name of the space. If this value is not set, then UserProfileName must be set.
SpaceName: SpaceName
}
@input
structure DescribeArtifactRequest {
/// The Amazon Resource Name (ARN) of the artifact to describe.
@required
ArtifactArn: ArtifactArn
}
@output
structure DescribeArtifactResponse {
/// The name of the artifact.
ArtifactName: ExperimentEntityNameOrArn
/// The Amazon Resource Name (ARN) of the artifact.
ArtifactArn: ArtifactArn
/// The source of the artifact.
Source: ArtifactSource
/// The type of the artifact.
ArtifactType: String256
/// A list of the artifact's properties.
Properties: LineageEntityParameters
/// When the artifact was created.
CreationTime: Timestamp
CreatedBy: UserContext
/// When the artifact was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
MetadataProperties: MetadataProperties
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
}
@input
structure DescribeAutoMLJobRequest {
/// Requests information about an AutoML job using its unique name.
@required
AutoMLJobName: AutoMLJobName
}
@output
structure DescribeAutoMLJobResponse {
/// Returns the name of the AutoML job.
@required
AutoMLJobName: AutoMLJobName
/// Returns the ARN of the AutoML job.
@required
AutoMLJobArn: AutoMLJobArn
/// Returns the input data configuration for the AutoML job..
@required
InputDataConfig: AutoMLInputDataConfig
/// Returns the job's output data config.
@required
OutputDataConfig: AutoMLOutputDataConfig
/// The Amazon Resource Name (ARN) of the Identity and Access Management (IAM) role that
/// has read permission to the input data location and write permission to the output data
/// location in Amazon S3.
@required
RoleArn: RoleArn
/// Returns the job's objective.
AutoMLJobObjective: AutoMLJobObjective
/// Returns the job's problem type.
ProblemType: ProblemType
/// Returns the configuration for the AutoML job.
AutoMLJobConfig: AutoMLJobConfig
/// Returns the creation time of the AutoML job.
@required
CreationTime: Timestamp
/// Returns the end time of the AutoML job.
EndTime: Timestamp
/// Returns the job's last modified time.
@required
LastModifiedTime: Timestamp
/// Returns the failure reason for an AutoML job, when applicable.
FailureReason: AutoMLFailureReason
/// Returns a list of reasons for partial failures within an AutoML job.
PartialFailureReasons: AutoMLPartialFailureReasons
/// The best model candidate selected by SageMaker Autopilot using both the best objective metric and
/// lowest InferenceLatency for
/// an experiment.
BestCandidate: AutoMLCandidate
/// Returns the status of the AutoML job.
@required
AutoMLJobStatus: AutoMLJobStatus
/// Returns the secondary status of the AutoML job.
@required
AutoMLJobSecondaryStatus: AutoMLJobSecondaryStatus
/// Indicates whether the output for an AutoML job generates candidate definitions
/// only.
GenerateCandidateDefinitionsOnly: GenerateCandidateDefinitionsOnly = false
/// Returns information on the job's artifacts found in
/// AutoMLJobArtifacts.
AutoMLJobArtifacts: AutoMLJobArtifacts
/// This contains ProblemType, AutoMLJobObjective, and
/// CompletionCriteria. If you do not provide these values, they are
/// auto-inferred. If you do provide them, the values used are the ones you provide.
ResolvedAttributes: ResolvedAttributes
/// Indicates whether the model was deployed automatically to an endpoint and the name of
/// that endpoint if deployed automatically.
ModelDeployConfig: ModelDeployConfig
/// Provides information about endpoint for the model deployment.
ModelDeployResult: ModelDeployResult
}
@input
structure DescribeCompilationJobRequest {
/// The name of the model compilation job that you want information about.
@required
CompilationJobName: EntityName
}
@output
structure DescribeCompilationJobResponse {
/// The name of the model compilation job.
@required
CompilationJobName: EntityName
/// The Amazon Resource Name (ARN) of the model compilation job.
@required
CompilationJobArn: CompilationJobArn
/// The status of the model compilation job.
@required
CompilationJobStatus: CompilationJobStatus
/// The time when the model compilation job started the CompilationJob
/// instances.
/// You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs,
/// the start time might be later than this time. That's because it takes time to download
/// the compilation job, which depends on the size of the compilation job container.
CompilationStartTime: Timestamp
/// The time when the model compilation job on a compilation job instance ended. For a
/// successful or stopped job, this is when the job's model artifacts have finished
/// uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
CompilationEndTime: Timestamp
/// Specifies a limit to how long a model compilation job can run. When the job reaches
/// the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training
/// costs.
@required
StoppingCondition: StoppingCondition
/// The inference image to use when compiling a model.
/// Specify an image only if the target device is a cloud instance.
InferenceImage: InferenceImage
/// The Amazon Resource Name (ARN) of the versioned model package that was
/// provided to SageMaker Neo when you initiated a compilation job.
ModelPackageVersionArn: ModelPackageArn
/// The time that the model compilation job was created.
@required
CreationTime: CreationTime
/// The time that the status
/// of
/// the model compilation job was last modified.
@required
LastModifiedTime: LastModifiedTime
/// If a model compilation job failed, the reason it failed.
@required
FailureReason: FailureReason
/// Information about the location in Amazon S3 that has been configured for storing the model
/// artifacts used in the compilation job.
@required
ModelArtifacts: ModelArtifacts
/// Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
ModelDigests: ModelDigests
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model
/// compilation job.
@required
RoleArn: RoleArn
/// 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.
@required
InputConfig: InputConfig
/// Information about the output location for the compiled model and the target device
/// that the model runs on.
@required
OutputConfig: OutputConfig
/// 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.
VpcConfig: NeoVpcConfig
}
@input
structure DescribeContextRequest {
/// The name of the context to describe.
@required
ContextName: ExperimentEntityNameOrArn
}
@output
structure DescribeContextResponse {
/// The name of the context.
ContextName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the context.
ContextArn: ContextArn
/// The source of the context.
Source: ContextSource
/// The type of the context.
ContextType: String256
/// The description of the context.
Description: ExperimentDescription
/// A list of the context's properties.
Properties: LineageEntityParameters
/// When the context was created.
CreationTime: Timestamp
CreatedBy: UserContext
/// When the context was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
}
@input
structure DescribeDataQualityJobDefinitionRequest {
/// The name of the data quality monitoring job definition to describe.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@output
structure DescribeDataQualityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the data quality monitoring job definition.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
/// The name of the data quality monitoring job definition.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The time that the data quality monitoring job definition was created.
@required
CreationTime: Timestamp
/// The constraints and baselines for the data quality monitoring job definition.
DataQualityBaselineConfig: DataQualityBaselineConfig
/// Information about the container that runs the data quality monitoring job.
@required
DataQualityAppSpecification: DataQualityAppSpecification
/// The list of inputs for the data quality monitoring job. Currently endpoints are
/// supported.
@required
DataQualityJobInput: DataQualityJobInput
@required
DataQualityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// The networking configuration for the data quality monitoring job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
}
@input
structure DescribeDeviceFleetRequest {
/// The name of the fleet.
@required
DeviceFleetName: EntityName
}
@output
structure DescribeDeviceFleetResponse {
/// The name of the fleet.
@required
DeviceFleetName: EntityName
/// The The Amazon Resource Name (ARN) of the fleet.
@required
DeviceFleetArn: DeviceFleetArn
/// The output configuration for storing sampled data.
@required
OutputConfig: EdgeOutputConfig
/// A description of the fleet.
Description: DeviceFleetDescription
/// Timestamp of when the device fleet was created.
@required
CreationTime: Timestamp
/// Timestamp of when the device fleet was last updated.
@required
LastModifiedTime: Timestamp
/// The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
RoleArn: RoleArn
/// The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
IotRoleAlias: IotRoleAlias
}
@input
structure DescribeDeviceRequest {
/// Next token of device description.
NextToken: NextToken
/// The unique ID of the device.
@required
DeviceName: EntityName
/// The name of the fleet the devices belong to.
@required
DeviceFleetName: EntityName
}
@output
structure DescribeDeviceResponse {
/// The Amazon Resource Name (ARN) of the device.
DeviceArn: DeviceArn
/// The unique identifier of the device.
@required
DeviceName: EntityName
/// A description of the device.
Description: DeviceDescription
/// The name of the fleet the device belongs to.
@required
DeviceFleetName: EntityName
/// The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
IotThingName: ThingName
/// The timestamp of the last registration or de-reregistration.
@required
RegistrationTime: Timestamp
/// The last heartbeat received from the device.
LatestHeartbeat: Timestamp
/// Models on the device.
Models: EdgeModels
/// The maximum number of models.
MaxModels: Integer = 0
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
/// Edge Manager agent version.
AgentVersion: EdgeVersion
}
@input
structure DescribeDomainRequest {
/// The domain ID.
@required
DomainId: DomainId
}
@output
structure DescribeDomainResponse {
/// The domain's Amazon Resource Name (ARN).
DomainArn: DomainArn
/// The domain ID.
DomainId: DomainId
/// The domain name.
DomainName: DomainName
/// The ID of the Amazon Elastic File System (EFS) managed by this Domain.
HomeEfsFileSystemId: ResourceId
/// The IAM Identity Center managed application instance ID.
SingleSignOnManagedApplicationInstanceId: String256
/// The status.
Status: DomainStatus
/// The creation time.
CreationTime: CreationTime
/// The last modified time.
LastModifiedTime: LastModifiedTime
/// The failure reason.
FailureReason: FailureReason
/// The domain's authentication mode.
AuthMode: AuthMode
/// Settings which are applied to UserProfiles in this domain if settings are not explicitly specified
/// in a given UserProfile.
///
DefaultUserSettings: UserSettings
/// Specifies the VPC used for non-EFS traffic. The default value is
/// PublicInternetOnly.
///
/// -
///
/// PublicInternetOnly - Non-EFS traffic is through a VPC managed by
/// Amazon SageMaker, which allows direct internet access
///
/// -
///
/// VpcOnly - All Studio traffic is through the specified VPC and subnets
///
///
AppNetworkAccessType: AppNetworkAccessType
/// Use KmsKeyId.
@deprecated(
message: "This property is deprecated, use KmsKeyId instead."
)
HomeEfsFileSystemKmsKeyId: KmsKeyId
/// The VPC subnets that Studio uses for communication.
SubnetIds: Subnets
/// The domain's URL.
Url: String1024
/// The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
VpcId: VpcId
/// The Amazon Web Services KMS customer managed key used to encrypt
/// the EFS volume attached to the domain.
KmsKeyId: KmsKeyId
/// A collection of Domain settings.
DomainSettings: DomainSettings
/// 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.
AppSecurityGroupManagement: AppSecurityGroupManagement
/// The ID of the security group that authorizes traffic between the
/// RSessionGateway apps and the RStudioServerPro app.
SecurityGroupIdForDomainBoundary: SecurityGroupId
/// The default settings used to create a space.
DefaultSpaceSettings: DefaultSpaceSettings
}
@input
structure DescribeEdgeDeploymentPlanRequest {
/// The name of the deployment plan to describe.
@required
EdgeDeploymentPlanName: EntityName
/// If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.
NextToken: NextToken
/// The maximum number of results to select (50 by default).
MaxResults: DeploymentStageMaxResults = 0
}
@output
structure DescribeEdgeDeploymentPlanResponse {
/// The ARN of edge deployment plan.
@required
EdgeDeploymentPlanArn: EdgeDeploymentPlanArn
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// List of models associated with the edge deployment plan.
@required
ModelConfigs: EdgeDeploymentModelConfigs
/// The device fleet used for this edge deployment plan.
@required
DeviceFleetName: EntityName
/// The number of edge devices with the successful deployment.
EdgeDeploymentSuccess: Integer = 0
/// The number of edge devices yet to pick up deployment, or in progress.
EdgeDeploymentPending: Integer = 0
/// The number of edge devices that failed the deployment.
EdgeDeploymentFailed: Integer = 0
/// List of stages in the edge deployment plan.
@required
Stages: DeploymentStageStatusSummaries
/// Token to use when calling the next set of stages in the edge deployment plan.
NextToken: NextToken
/// The time when the edge deployment plan was created.
CreationTime: Timestamp
/// The time when the edge deployment plan was last updated.
LastModifiedTime: Timestamp
}
@input
structure DescribeEdgePackagingJobRequest {
/// The name of the edge packaging job.
@required
EdgePackagingJobName: EntityName
}
@output
structure DescribeEdgePackagingJobResponse {
/// The Amazon Resource Name (ARN) of the edge packaging job.
@required
EdgePackagingJobArn: EdgePackagingJobArn
/// The name of the edge packaging job.
@required
EdgePackagingJobName: EntityName
/// The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
CompilationJobName: EntityName
/// The name of the model.
ModelName: EntityName
/// The version of the model.
ModelVersion: EdgeVersion
/// The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
RoleArn: RoleArn
/// The output configuration for the edge packaging job.
OutputConfig: EdgeOutputConfig
/// The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.
ResourceKey: KmsKeyId
/// The current status of the packaging job.
@required
EdgePackagingJobStatus: EdgePackagingJobStatus
/// Returns a message describing the job status and error messages.
EdgePackagingJobStatusMessage: String
/// The timestamp of when the packaging job was created.
CreationTime: Timestamp
/// The timestamp of when the job was last updated.
LastModifiedTime: Timestamp
/// The Amazon Simple Storage (S3) URI where model artifacts ares stored.
ModelArtifact: S3Uri
/// The signature document of files in the model artifact.
ModelSignature: String
/// The output of a SageMaker Edge Manager deployable resource.
PresetDeploymentOutput: EdgePresetDeploymentOutput
}
@input
structure DescribeExperimentRequest {
/// The name of the experiment to describe.
@required
ExperimentName: ExperimentEntityName
}
@output
structure DescribeExperimentResponse {
/// The name of the experiment.
ExperimentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the experiment.
ExperimentArn: ExperimentArn
/// The name of the experiment as displayed. If DisplayName isn't specified,
/// ExperimentName is displayed.
DisplayName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the source and, optionally, the type.
Source: ExperimentSource
/// The description of the experiment.
Description: ExperimentDescription
/// When the experiment was created.
CreationTime: Timestamp
/// Who created the experiment.
CreatedBy: UserContext
/// When the experiment was last modified.
LastModifiedTime: Timestamp
/// Who last modified the experiment.
LastModifiedBy: UserContext
}
@input
structure DescribeFeatureGroupRequest {
/// The name of the FeatureGroup you want described.
@required
FeatureGroupName: FeatureGroupName
/// A token to resume pagination of the list of Features
/// (FeatureDefinitions). 2,500 Features are returned by
/// default.
NextToken: NextToken
}
@output
structure DescribeFeatureGroupResponse {
/// The Amazon Resource Name (ARN) of the FeatureGroup.
@required
FeatureGroupArn: FeatureGroupArn
/// he name of the FeatureGroup.
@required
FeatureGroupName: FeatureGroupName
/// The name of the Feature used for RecordIdentifier, whose value
/// uniquely identifies a record stored in the feature store.
@required
RecordIdentifierFeatureName: FeatureName
/// 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.
@required
EventTimeFeatureName: FeatureName
/// A list of the Features in the FeatureGroup.
/// Each feature is defined by a FeatureName and FeatureType.
@required
FeatureDefinitions: FeatureDefinitions
/// A timestamp indicating when SageMaker created the FeatureGroup.
@required
CreationTime: CreationTime
/// A timestamp indicating when the feature group was last updated.
LastModifiedTime: LastModifiedTime
/// The configuration for the OnlineStore.
OnlineStoreConfig: OnlineStoreConfig
/// 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.
///
///
OfflineStoreConfig: OfflineStoreConfig
/// The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
RoleArn: RoleArn
/// The status of the feature group.
FeatureGroupStatus: FeatureGroupStatus
/// The status of the OfflineStore. Notifies you if replicating data into the
/// OfflineStore has failed. Returns either: Active or
/// Blocked
///
OfflineStoreStatus: OfflineStoreStatus
/// A value indicating whether the update made to the feature group was successful.
LastUpdateStatus: LastUpdateStatus
/// 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.
///
///
FailureReason: FailureReason
/// A free form description of the feature group.
Description: Description
/// A token to resume pagination of the list of Features
/// (FeatureDefinitions).
@required
NextToken: NextToken
/// The size of the OnlineStore in bytes.
OnlineStoreTotalSizeBytes: OnlineStoreTotalSizeBytes
}
@input
structure DescribeFeatureMetadataRequest {
/// The name of the feature group containing the feature.
@required
FeatureGroupName: FeatureGroupName
/// The name of the feature.
@required
FeatureName: FeatureName
}
@output
structure DescribeFeatureMetadataResponse {
/// The Amazon Resource Number (ARN) of the feature group that contains the feature.
@required
FeatureGroupArn: FeatureGroupArn
/// The name of the feature group that you've specified.
@required
FeatureGroupName: FeatureGroupName
/// The name of the feature that you've specified.
@required
FeatureName: FeatureName
/// The data type of the feature.
@required
FeatureType: FeatureType
/// A timestamp indicating when the feature was created.
@required
CreationTime: CreationTime
/// 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
@required
LastModifiedTime: LastModifiedTime
/// The description you added to describe the feature.
Description: FeatureDescription
/// The key-value pairs that you added to describe the feature.
Parameters: FeatureParameters
}
@input
structure DescribeFlowDefinitionRequest {
/// The name of the flow definition.
@required
FlowDefinitionName: FlowDefinitionName
}
@output
structure DescribeFlowDefinitionResponse {
/// The Amazon Resource Name (ARN) of the flow defintion.
@required
FlowDefinitionArn: FlowDefinitionArn
/// The Amazon Resource Name (ARN) of the flow definition.
@required
FlowDefinitionName: FlowDefinitionName
/// The status of the flow definition. Valid values are listed below.
@required
FlowDefinitionStatus: FlowDefinitionStatus
/// The timestamp when the flow definition was created.
@required
CreationTime: Timestamp
/// Container for configuring the source of human task requests. Used to specify if
/// Amazon Rekognition or Amazon Textract is used as an integration source.
HumanLoopRequestSource: HumanLoopRequestSource
/// An object containing information about what triggers a human review workflow.
HumanLoopActivationConfig: HumanLoopActivationConfig
/// An object containing information about who works on the task, the workforce task price, and other task details.
@required
HumanLoopConfig: HumanLoopConfig
/// An object containing information about the output file.
@required
OutputConfig: FlowDefinitionOutputConfig
/// The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
@required
RoleArn: RoleArn
/// The reason your flow definition failed.
FailureReason: FailureReason
}
@input
structure DescribeHubContentRequest {
/// The name of the hub that contains the content to describe.
@required
HubName: HubName
/// The type of content in the hub.
@required
HubContentType: HubContentType
/// The name of the content to describe.
@required
HubContentName: HubContentName
/// The version of the content to describe.
HubContentVersion: HubContentVersion
}
@output
structure DescribeHubContentResponse {
/// The name of the hub content.
@required
HubContentName: HubContentName
/// The Amazon Resource Name (ARN) of the hub content.
@required
HubContentArn: HubContentArn
/// The version of the hub content.
@required
HubContentVersion: HubContentVersion
/// The type of hub content.
@required
HubContentType: HubContentType
/// The document schema version for the hub content.
@required
DocumentSchemaVersion: DocumentSchemaVersion
/// The name of the hub that contains the content.
@required
HubName: HubName
/// The Amazon Resource Name (ARN) of the hub that contains the content.
@required
HubArn: HubArn
/// The display name of the hub content.
HubContentDisplayName: HubContentDisplayName
/// A description of the hub content.
HubContentDescription: HubContentDescription
/// Markdown files associated with the hub content to import.
HubContentMarkdown: HubContentMarkdown
/// The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
@required
HubContentDocument: HubContentDocument
/// The searchable keywords for the hub content.
HubContentSearchKeywords: HubContentSearchKeywordList
/// The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.
HubContentDependencies: HubContentDependencyList
/// The status of the hub content.
@required
HubContentStatus: HubContentStatus
/// The failure reason if importing hub content failed.
FailureReason: FailureReason
/// The date and time that hub content was created.
@required
CreationTime: Timestamp
}
@input
structure DescribeHubRequest {
/// The name of the hub to describe.
@required
HubName: HubName
}
@output
structure DescribeHubResponse {
/// The name of the hub.
@required
HubName: HubName
/// The Amazon Resource Name (ARN) of the hub.
@required
HubArn: HubArn
/// The display name of the hub.
HubDisplayName: HubDisplayName
/// A description of the hub.
HubDescription: HubDescription
/// The searchable keywords for the hub.
HubSearchKeywords: HubSearchKeywordList
/// The Amazon S3 storage configuration for the hub.
S3StorageConfig: HubS3StorageConfig
/// The status of the hub.
@required
HubStatus: HubStatus
/// The failure reason if importing hub content failed.
FailureReason: FailureReason
/// The date and time that the hub was created.
@required
CreationTime: Timestamp
/// The date and time that the hub was last modified.
@required
LastModifiedTime: Timestamp
}
@input
structure DescribeHumanTaskUiRequest {
/// The name of the human task user interface
/// (worker task template) you want information about.
@required
HumanTaskUiName: HumanTaskUiName
}
@output
structure DescribeHumanTaskUiResponse {
/// The Amazon Resource Name (ARN) of the human task user interface (worker task template).
@required
HumanTaskUiArn: HumanTaskUiArn
/// The name of the human task user interface (worker task template).
@required
HumanTaskUiName: HumanTaskUiName
/// The status of the human task user interface (worker task template). Valid values are listed below.
HumanTaskUiStatus: HumanTaskUiStatus
/// The timestamp when the human task user interface was created.
@required
CreationTime: Timestamp
@required
UiTemplate: UiTemplateInfo
}
@input
structure DescribeHyperParameterTuningJobRequest {
/// The name of the tuning job.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
}
@output
structure DescribeHyperParameterTuningJobResponse {
/// The name of the tuning job.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
/// The Amazon Resource Name (ARN) of the tuning job.
@required
HyperParameterTuningJobArn: HyperParameterTuningJobArn
/// The HyperParameterTuningJobConfig object that specifies the
/// configuration of the tuning job.
@required
HyperParameterTuningJobConfig: HyperParameterTuningJobConfig
/// The HyperParameterTrainingJobDefinition object that specifies the
/// definition of the training jobs that this tuning job launches.
TrainingJobDefinition: HyperParameterTrainingJobDefinition
/// A list of the HyperParameterTrainingJobDefinition objects launched
/// for this tuning job.
TrainingJobDefinitions: HyperParameterTrainingJobDefinitions
/// The status of the tuning job: InProgress, Completed, Failed, Stopping, or
/// Stopped.
@required
HyperParameterTuningJobStatus: HyperParameterTuningJobStatus
/// The date and time that the tuning job started.
@required
CreationTime: Timestamp
/// The date and time that the tuning job ended.
HyperParameterTuningEndTime: Timestamp
/// The date and time that the status of the tuning job was modified.
LastModifiedTime: Timestamp
/// The TrainingJobStatusCounters object that specifies the number of
/// training jobs, categorized by status, that this tuning job launched.
@required
TrainingJobStatusCounters: TrainingJobStatusCounters
/// The ObjectiveStatusCounters object that specifies the number of
/// training jobs, categorized by the status of their final objective metric, that this
/// tuning job launched.
@required
ObjectiveStatusCounters: ObjectiveStatusCounters
/// A TrainingJobSummary object that describes the training job that
/// completed with the best current HyperParameterTuningJobObjective.
BestTrainingJob: 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.
OverallBestTrainingJob: HyperParameterTrainingJobSummary
/// 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.
WarmStartConfig: HyperParameterTuningJobWarmStartConfig
/// If the tuning job failed, the reason it failed.
FailureReason: FailureReason
/// 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.
TuningJobCompletionDetails: HyperParameterTuningJobCompletionDetails
ConsumedResources: HyperParameterTuningJobConsumedResources
}
@input
structure DescribeImageRequest {
/// The name of the image to describe.
@required
ImageName: ImageName
}
@output
structure DescribeImageResponse {
/// When the image was created.
CreationTime: Timestamp
/// The description of the image.
Description: ImageDescription
/// The name of the image as displayed.
DisplayName: ImageDisplayName
/// When a create, update, or delete operation fails, the reason for the failure.
FailureReason: FailureReason
/// The ARN of the image.
ImageArn: ImageArn
/// The name of the image.
ImageName: ImageName
/// The status of the image.
ImageStatus: ImageStatus
/// When the image was last modified.
LastModifiedTime: Timestamp
/// The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
RoleArn: RoleArn
}
@input
structure DescribeImageVersionRequest {
/// The name of the image.
@required
ImageName: ImageName
/// The version of the image. If not specified, the latest version is described.
Version: ImageVersionNumber
/// The alias of the image version.
Alias: SageMakerImageVersionAlias
}
@output
structure DescribeImageVersionResponse {
/// The registry path of the container image on which this image version is based.
BaseImage: ImageBaseImage
/// The registry path of the container image that contains this image version.
ContainerImage: ImageContainerImage
/// When the version was created.
CreationTime: Timestamp
/// When a create or delete operation fails, the reason for the failure.
FailureReason: FailureReason
/// The ARN of the image the version is based on.
ImageArn: ImageArn
/// The ARN of the version.
ImageVersionArn: ImageVersionArn
/// The status of the version.
ImageVersionStatus: ImageVersionStatus
/// When the version was last modified.
LastModifiedTime: Timestamp
/// The version number.
Version: ImageVersionNumber
/// 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.
///
///
VendorGuidance: VendorGuidance
/// Indicates SageMaker job type compatibility.
///
/// -
///
/// TRAINING: The image version is compatible with SageMaker training jobs.
///
/// -
///
/// INFERENCE: The image version is compatible with SageMaker inference jobs.
///
/// -
///
/// NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.
///
///
JobType: JobType
/// The machine learning framework vended in the image version.
MLFramework: MLFramework
/// The supported programming language and its version.
ProgrammingLang: ProgrammingLang
/// Indicates CPU or GPU compatibility.
///
/// -
///
/// CPU: The image version is compatible with CPU.
///
/// -
///
/// GPU: The image version is compatible with GPU.
///
///
Processor: Processor
/// Indicates Horovod compatibility.
Horovod: Horovod = false
/// The maintainer description of the image version.
ReleaseNotes: ReleaseNotes
}
@input
structure DescribeInferenceExperimentRequest {
/// The name of the inference experiment to describe.
@required
Name: InferenceExperimentName
}
@output
structure DescribeInferenceExperimentResponse {
/// The ARN of the inference experiment being described.
@required
Arn: InferenceExperimentArn
/// The name of the inference experiment.
@required
Name: InferenceExperimentName
/// The type of the inference experiment.
@required
Type: InferenceExperimentType
/// The duration for which the inference experiment ran or will run.
Schedule: InferenceExperimentSchedule
///
/// 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.
///
///
///
@required
Status: InferenceExperimentStatus
///
/// The error message or client-specified Reason from the StopInferenceExperiment
/// API, that explains the status of the inference experiment.
///
StatusReason: InferenceExperimentStatusReason
/// The description of the inference experiment.
Description: InferenceExperimentDescription
/// The timestamp at which you created the inference experiment.
CreationTime: Timestamp
///
/// The timestamp at which the inference experiment was completed.
///
CompletionTime: Timestamp
/// The timestamp at which you last modified the inference experiment.
LastModifiedTime: Timestamp
///
/// 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.
///
RoleArn: RoleArn
/// The metadata of the endpoint on which the inference experiment ran.
@required
EndpointMetadata: EndpointMetadata
///
/// 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.
///
@required
ModelVariants: ModelVariantConfigSummaryList
/// The Amazon S3 location and configuration for storing inference request and response data.
DataStorageConfig: InferenceExperimentDataStorageConfig
///
/// 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.
///
ShadowModeConfig: ShadowModeConfig
///
/// 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
/// CreateInferenceExperimentRequest$KmsKey.
///
KmsKey: KmsKeyId
}
@input
structure DescribeInferenceRecommendationsJobRequest {
/// The name of the job. The name must be unique within an
/// Amazon Web Services Region in the Amazon Web Services account.
@required
JobName: RecommendationJobName
}
@output
structure DescribeInferenceRecommendationsJobResponse {
/// The name of the job. The name must be unique within an
/// Amazon Web Services Region in the Amazon Web Services account.
@required
JobName: RecommendationJobName
/// The job description that you provided when you initiated the job.
JobDescription: RecommendationJobDescription
/// The job type that you provided when you initiated the job.
@required
JobType: RecommendationJobType
/// The Amazon Resource Name (ARN) of the job.
@required
JobArn: RecommendationJobArn
/// The Amazon Resource Name (ARN) of the Amazon Web Services
/// Identity and Access Management (IAM) role you provided when you initiated the job.
@required
RoleArn: RoleArn
/// The status of the job.
@required
Status: RecommendationJobStatus
/// A timestamp that shows when the job was created.
@required
CreationTime: CreationTime
/// A timestamp that shows when the job completed.
CompletionTime: Timestamp
/// A timestamp that shows when the job was last modified.
@required
LastModifiedTime: LastModifiedTime
/// If the job fails, provides information why the job failed.
FailureReason: FailureReason
/// Returns information about the versioned model package Amazon Resource Name (ARN),
/// the traffic pattern, and endpoint configurations you provided when you initiated the job.
@required
InputConfig: RecommendationJobInputConfig
/// The stopping conditions that you provided when you initiated the job.
StoppingConditions: RecommendationJobStoppingConditions
/// The recommendations made by Inference Recommender.
InferenceRecommendations: InferenceRecommendations
/// The performance results from running an Inference Recommender job on an existing endpoint.
EndpointPerformances: EndpointPerformances
}
@input
structure DescribeLabelingJobRequest {
/// The name of the labeling job to return information for.
@required
LabelingJobName: LabelingJobName
}
@output
structure DescribeLabelingJobResponse {
/// The processing status of the labeling job.
@required
LabelingJobStatus: LabelingJobStatus
/// 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.
@required
LabelCounters: LabelCounters
/// If the job failed, the reason that it failed.
FailureReason: FailureReason
/// The date and time that the labeling job was created.
@required
CreationTime: Timestamp
/// The date and time that the labeling job was last updated.
@required
LastModifiedTime: Timestamp
/// A unique identifier for work done as part of a labeling job.
@required
JobReferenceCode: JobReferenceCode
/// The name assigned to the labeling job when it was created.
@required
LabelingJobName: LabelingJobName
/// The Amazon Resource Name (ARN) of the labeling job.
@required
LabelingJobArn: LabelingJobArn
/// The attribute used as the label in the output manifest file.
LabelAttributeName: LabelAttributeName
/// 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.
@required
InputConfig: LabelingJobInputConfig
/// 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.
@required
OutputConfig: LabelingJobOutputConfig
/// The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf
/// during data labeling.
@required
RoleArn: RoleArn
/// 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"
///
///
/// }
///
///
/// ]
///
///
/// }
///
LabelCategoryConfigS3Uri: S3Uri
/// A set of conditions for stopping a labeling job. If any of the conditions are met, the
/// job is automatically stopped.
StoppingConditions: LabelingJobStoppingConditions
/// Configuration information for automated data labeling.
LabelingJobAlgorithmsConfig: LabelingJobAlgorithmsConfig
/// Configuration information required for human workers to complete a labeling
/// task.
@required
HumanTaskConfig: HumanTaskConfig
/// 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: TagList
/// The location of the output produced by the labeling job.
LabelingJobOutput: LabelingJobOutput
}
@input
structure DescribeLineageGroupRequest {
/// The name of the lineage group.
@required
LineageGroupName: ExperimentEntityName
}
@output
structure DescribeLineageGroupResponse {
/// The name of the lineage group.
LineageGroupName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
/// The display name of the lineage group.
DisplayName: ExperimentEntityName
/// The description of the lineage group.
Description: ExperimentDescription
/// The creation time of lineage group.
CreationTime: Timestamp
CreatedBy: UserContext
/// The last modified time of the lineage group.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
}
@input
structure DescribeModelBiasJobDefinitionRequest {
/// 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.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@output
structure DescribeModelBiasJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model bias job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
/// The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The time at which the model bias job was created.
@required
CreationTime: Timestamp
/// The baseline configuration for a model bias job.
ModelBiasBaselineConfig: ModelBiasBaselineConfig
/// Configures the model bias job to run a specified Docker container image.
@required
ModelBiasAppSpecification: ModelBiasAppSpecification
/// Inputs for the model bias job.
@required
ModelBiasJobInput: ModelBiasJobInput
@required
ModelBiasJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Networking options for a model bias job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that
/// has read permission to the input data location and write permission to the output data
/// location in Amazon S3.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
}
@input
structure DescribeModelCardExportJobRequest {
/// The Amazon Resource Name (ARN) of the model card export job to describe.
@required
ModelCardExportJobArn: ModelCardExportJobArn
}
@output
structure DescribeModelCardExportJobResponse {
/// The name of the model card export job to describe.
@required
ModelCardExportJobName: EntityName
/// The Amazon Resource Name (ARN) of the model card export job.
@required
ModelCardExportJobArn: ModelCardExportJobArn
/// 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.
///
///
@required
Status: ModelCardExportJobStatus
/// The name of the model card that the model export job exports.
@required
ModelCardName: EntityName
/// The version of the model card that the model export job exports.
@required
ModelCardVersion: Integer = 0
/// The export output details for the model card.
@required
OutputConfig: ModelCardExportOutputConfig
/// The date and time that the model export job was created.
@required
CreatedAt: Timestamp
/// The date and time that the model export job was last modified.
@required
LastModifiedAt: Timestamp
/// The failure reason if the model export job fails.
FailureReason: FailureReason
/// The exported model card artifacts.
ExportArtifacts: ModelCardExportArtifacts
}
@input
structure DescribeModelCardRequest {
/// The name of the model card to describe.
@required
ModelCardName: EntityName
/// The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.
ModelCardVersion: Integer = 0
}
@output
structure DescribeModelCardResponse {
/// The Amazon Resource Name (ARN) of the model card.
@required
ModelCardArn: ModelCardArn
/// The name of the model card.
@required
ModelCardName: EntityName
/// The version of the model card.
@required
ModelCardVersion: Integer = 0
/// The content of the model card.
@required
Content: ModelCardContent
/// 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.
///
///
@required
ModelCardStatus: ModelCardStatus
/// The security configuration used to protect model card content.
SecurityConfig: ModelCardSecurityConfig
/// The date and time the model card was created.
@required
CreationTime: Timestamp
@required
CreatedBy: UserContext
/// The date and time the model card was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// 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.
///
///
ModelCardProcessingStatus: ModelCardProcessingStatus
}
@input
structure DescribeModelExplainabilityJobDefinitionRequest {
/// 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.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@output
structure DescribeModelExplainabilityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model explainability job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
/// The name of the explainability job definition. The name must be unique within an Amazon Web Services
/// Region in the Amazon Web Services account.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The time at which the model explainability job was created.
@required
CreationTime: Timestamp
/// The baseline configuration for a model explainability job.
ModelExplainabilityBaselineConfig: ModelExplainabilityBaselineConfig
/// Configures the model explainability job to run a specified Docker container
/// image.
@required
ModelExplainabilityAppSpecification: ModelExplainabilityAppSpecification
/// Inputs for the model explainability job.
@required
ModelExplainabilityJobInput: ModelExplainabilityJobInput
@required
ModelExplainabilityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Networking options for a model explainability job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that
/// has read permission to the input data location and write permission to the output data
/// location in Amazon S3.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
}
@input
structure DescribeModelQualityJobDefinitionRequest {
/// The name of the model quality job. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
JobDefinitionName: MonitoringJobDefinitionName
}
@output
structure DescribeModelQualityJobDefinitionResponse {
/// The Amazon Resource Name (ARN) of the model quality job.
@required
JobDefinitionArn: MonitoringJobDefinitionArn
/// The name of the quality job definition. The name must be unique within an Amazon Web Services Region in
/// the Amazon Web Services account.
@required
JobDefinitionName: MonitoringJobDefinitionName
/// The time at which the model quality job was created.
@required
CreationTime: Timestamp
/// The baseline configuration for a model quality job.
ModelQualityBaselineConfig: ModelQualityBaselineConfig
/// Configures the model quality job to run a specified Docker container image.
@required
ModelQualityAppSpecification: ModelQualityAppSpecification
/// Inputs for the model quality job.
@required
ModelQualityJobInput: ModelQualityJobInput
@required
ModelQualityJobOutputConfig: MonitoringOutputConfig
@required
JobResources: MonitoringResources
/// Networking options for a model quality job.
NetworkConfig: MonitoringNetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to
/// perform tasks on your behalf.
@required
RoleArn: RoleArn
StoppingCondition: MonitoringStoppingCondition
}
@input
structure DescribeMonitoringScheduleRequest {
/// Name of a previously created monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
}
@output
structure DescribeMonitoringScheduleResponse {
/// The Amazon Resource Name (ARN) of the monitoring schedule.
@required
MonitoringScheduleArn: MonitoringScheduleArn
/// Name of the monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The status of an monitoring job.
@required
MonitoringScheduleStatus: ScheduleStatus
/// 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.
///
///
MonitoringType: MonitoringType
/// A string, up to one KB in size, that contains the reason a monitoring job failed, if it
/// failed.
FailureReason: FailureReason
/// The time at which the monitoring job was created.
@required
CreationTime: Timestamp
/// The time at which the monitoring job was last modified.
@required
LastModifiedTime: Timestamp
/// The configuration object that specifies the monitoring schedule and defines the
/// monitoring job.
@required
MonitoringScheduleConfig: MonitoringScheduleConfig
/// The name of the endpoint for the monitoring job.
EndpointName: EndpointName
/// Describes metadata on the last execution to run, if there was one.
LastMonitoringExecutionSummary: MonitoringExecutionSummary
}
@input
structure DescribePipelineDefinitionForExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
}
@output
structure DescribePipelineDefinitionForExecutionResponse {
/// The JSON pipeline definition.
PipelineDefinition: PipelineDefinition
/// The time when the pipeline was created.
CreationTime: Timestamp
}
@input
structure DescribePipelineExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
}
@output
structure DescribePipelineExecutionResponse {
/// The Amazon Resource Name (ARN) of the pipeline.
PipelineArn: PipelineArn
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
/// The display name of the pipeline execution.
PipelineExecutionDisplayName: PipelineExecutionName
/// The status of the pipeline execution.
PipelineExecutionStatus: PipelineExecutionStatus
/// The description of the pipeline execution.
PipelineExecutionDescription: PipelineExecutionDescription
PipelineExperimentConfig: PipelineExperimentConfig
/// If the execution failed, a message describing why.
FailureReason: PipelineExecutionFailureReason
/// The time when the pipeline execution was created.
CreationTime: Timestamp
/// The time when the pipeline execution was modified last.
LastModifiedTime: Timestamp
CreatedBy: UserContext
LastModifiedBy: UserContext
/// The parallelism configuration applied to the pipeline.
ParallelismConfiguration: ParallelismConfiguration
}
@input
structure DescribePipelineRequest {
/// The name of the pipeline to describe.
@required
PipelineName: PipelineNameOrArn
}
@output
structure DescribePipelineResponse {
/// The Amazon Resource Name (ARN) of the pipeline.
PipelineArn: PipelineArn
/// The name of the pipeline.
PipelineName: PipelineName
/// The display name of the pipeline.
PipelineDisplayName: PipelineName
/// The JSON pipeline definition.
PipelineDefinition: PipelineDefinition
/// The description of the pipeline.
PipelineDescription: PipelineDescription
/// The Amazon Resource Name (ARN) that the pipeline uses to execute.
RoleArn: RoleArn
/// The status of the pipeline execution.
PipelineStatus: PipelineStatus
/// The time when the pipeline was created.
CreationTime: Timestamp
/// The time when the pipeline was last modified.
LastModifiedTime: Timestamp
/// The time when the pipeline was last run.
LastRunTime: Timestamp
CreatedBy: UserContext
LastModifiedBy: UserContext
/// Lists the parallelism configuration applied to the pipeline.
ParallelismConfiguration: ParallelismConfiguration
}
@input
structure DescribeProcessingJobRequest {
/// The name of the processing job. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
ProcessingJobName: ProcessingJobName
}
@output
structure DescribeProcessingJobResponse {
/// The inputs for a processing job.
ProcessingInputs: ProcessingInputs
/// Output configuration for the processing job.
ProcessingOutputConfig: ProcessingOutputConfig
/// The name of the processing job. The name must be unique within an Amazon Web Services Region in the
/// Amazon Web Services account.
@required
ProcessingJobName: ProcessingJobName
/// 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.
@required
ProcessingResources: ProcessingResources
/// The time limit for how long the processing job is allowed to run.
StoppingCondition: ProcessingStoppingCondition
/// Configures the processing job to run a specified container image.
@required
AppSpecification: AppSpecification
/// The environment variables set in the Docker container.
Environment: ProcessingEnvironmentMap
/// Networking options for a processing job.
NetworkConfig: NetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on
/// your behalf.
RoleArn: RoleArn
/// The configuration information used to create an experiment.
ExperimentConfig: ExperimentConfig
/// The Amazon Resource Name (ARN) of the processing job.
@required
ProcessingJobArn: ProcessingJobArn
/// Provides the status of a processing job.
@required
ProcessingJobStatus: ProcessingJobStatus
/// An optional string, up to one KB in size, that contains metadata from the processing
/// container when the processing job exits.
ExitMessage: ExitMessage
/// A string, up to one KB in size, that contains the reason a processing job failed, if
/// it failed.
FailureReason: FailureReason
/// The time at which the processing job completed.
ProcessingEndTime: Timestamp
/// The time at which the processing job started.
ProcessingStartTime: Timestamp
/// The time at which the processing job was last modified.
LastModifiedTime: Timestamp
/// The time at which the processing job was created.
@required
CreationTime: Timestamp
/// The ARN of a monitoring schedule for an endpoint associated with this processing
/// job.
MonitoringScheduleArn: MonitoringScheduleArn
/// The ARN of an AutoML job associated with this processing job.
AutoMLJobArn: AutoMLJobArn
/// The ARN of a training job associated with this processing job.
TrainingJobArn: TrainingJobArn
}
@input
structure DescribeSpaceRequest {
/// The ID of the associated Domain.
@required
DomainId: DomainId
/// The name of the space.
@required
SpaceName: SpaceName
}
@output
structure DescribeSpaceResponse {
/// The ID of the associated Domain.
DomainId: DomainId
/// The space's Amazon Resource Name (ARN).
SpaceArn: SpaceArn
/// The name of the space.
SpaceName: SpaceName
/// The ID of the space's profile in the Amazon Elastic File System volume.
HomeEfsFileSystemUid: EfsUid
/// The status.
Status: SpaceStatus
/// The last modified time.
LastModifiedTime: LastModifiedTime
/// The creation time.
CreationTime: CreationTime
/// The failure reason.
FailureReason: FailureReason
/// A collection of space settings.
SpaceSettings: SpaceSettings
}
@input
structure DescribeStudioLifecycleConfigRequest {
/// The name of the Studio Lifecycle Configuration to describe.
@required
StudioLifecycleConfigName: StudioLifecycleConfigName
}
@output
structure DescribeStudioLifecycleConfigResponse {
/// The ARN of the Lifecycle Configuration to describe.
StudioLifecycleConfigArn: StudioLifecycleConfigArn
/// The name of the Studio Lifecycle Configuration that is described.
StudioLifecycleConfigName: StudioLifecycleConfigName
/// The creation time of the Studio Lifecycle Configuration.
CreationTime: Timestamp
/// This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.
LastModifiedTime: Timestamp
/// The content of your Studio Lifecycle Configuration script.
StudioLifecycleConfigContent: StudioLifecycleConfigContent
/// The App type that the Lifecycle Configuration is attached to.
StudioLifecycleConfigAppType: StudioLifecycleConfigAppType
}
@input
structure DescribeSubscribedWorkteamRequest {
/// The Amazon Resource Name (ARN) of the subscribed work team to describe.
@required
WorkteamArn: WorkteamArn
}
@output
structure DescribeSubscribedWorkteamResponse {
/// A Workteam instance that contains information about the work team.
@required
SubscribedWorkteam: SubscribedWorkteam
}
@input
structure DescribeTrainingJobRequest {
/// The name of the training job.
@required
TrainingJobName: TrainingJobName
}
@output
structure DescribeTrainingJobResponse {
/// Name of the model training job.
@required
TrainingJobName: TrainingJobName
/// The Amazon Resource Name (ARN) of the training job.
@required
TrainingJobArn: TrainingJobArn
/// The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the
/// training job was launched by a hyperparameter tuning job.
TuningJobArn: HyperParameterTuningJobArn
/// The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the
/// transform or training job.
LabelingJobArn: LabelingJobArn
/// The Amazon Resource Name (ARN) of an AutoML job.
AutoMLJobArn: AutoMLJobArn
/// Information about the Amazon S3 location that is configured for storing model artifacts.
///
@required
ModelArtifacts: ModelArtifacts
/// 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.
@required
TrainingJobStatus: TrainingJobStatus
/// 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
///
///
///
@required
SecondaryStatus: SecondaryStatus
/// If the training job failed, the reason it failed.
FailureReason: FailureReason
/// Algorithm-specific parameters.
HyperParameters: HyperParameters
/// Information about the algorithm used for training, and algorithm metadata.
///
@required
AlgorithmSpecification: AlgorithmSpecification
/// The Amazon Web Services Identity and Access Management (IAM) role configured for
/// the training job.
RoleArn: RoleArn
/// An array of Channel objects that describes each data input channel.
///
InputDataConfig: InputDataConfig
/// The S3 path where model artifacts that you configured when creating the job are
/// stored. SageMaker creates subfolders for model artifacts.
OutputDataConfig: OutputDataConfig
/// Resources, including ML compute instances and ML storage volumes, that are
/// configured for model training.
@required
ResourceConfig: ResourceConfig
/// 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.
VpcConfig: VpcConfig
/// 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.
@required
StoppingCondition: StoppingCondition
/// A timestamp that indicates when the training job was created.
@required
CreationTime: Timestamp
/// 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.
TrainingStartTime: Timestamp
/// 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.
TrainingEndTime: Timestamp
/// A timestamp that indicates when the status of the training job was last
/// modified.
LastModifiedTime: Timestamp
/// A history of all of the secondary statuses that the training job has transitioned
/// through.
SecondaryStatusTransitions: SecondaryStatusTransitions
/// A collection of MetricData objects that specify the names, values, and
/// dates and times that the training algorithm emitted to Amazon CloudWatch.
FinalMetricDataList: FinalMetricDataList
/// 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.
EnableNetworkIsolation: Boolean = 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 algorithms in
/// distributed training.
EnableInterContainerTrafficEncryption: Boolean = false
/// A Boolean indicating whether managed spot training is enabled (True) or
/// not (False).
EnableManagedSpotTraining: Boolean = false
CheckpointConfig: CheckpointConfig
/// The training time in seconds.
TrainingTimeInSeconds: TrainingTimeInSeconds
/// 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%.
BillableTimeInSeconds: BillableTimeInSeconds
DebugHookConfig: DebugHookConfig
ExperimentConfig: ExperimentConfig
/// Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
DebugRuleConfigurations: DebugRuleConfigurations
TensorBoardOutputConfig: TensorBoardOutputConfig
/// Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
DebugRuleEvaluationStatuses: DebugRuleEvaluationStatuses
ProfilerConfig: ProfilerConfig
/// Configuration information for Amazon SageMaker Debugger rules for profiling system and framework
/// metrics.
ProfilerRuleConfigurations: ProfilerRuleConfigurations
/// Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
ProfilerRuleEvaluationStatuses: ProfilerRuleEvaluationStatuses
/// Profiling status of a training job.
ProfilingStatus: ProfilingStatus
/// The number of times to retry the job when the job fails due to an
/// InternalServerError.
RetryStrategy: RetryStrategy
/// The environment variables to set in the Docker container.
Environment: TrainingEnvironmentMap
/// The status of the warm pool associated with the training job.
WarmPoolStatus: WarmPoolStatus
}
@input
structure DescribeTransformJobRequest {
/// The name of the transform job that you want to view details of.
@required
TransformJobName: TransformJobName
}
@output
structure DescribeTransformJobResponse {
/// The name of the transform job.
@required
TransformJobName: TransformJobName
/// The Amazon Resource Name (ARN) of the transform job.
@required
TransformJobArn: TransformJobArn
/// The
/// status of the transform job. If the transform job failed, the reason
/// is returned in the FailureReason field.
@required
TransformJobStatus: TransformJobStatus
/// 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.
FailureReason: FailureReason
/// The name of the model used in the transform job.
@required
ModelName: ModelName
/// The
/// maximum number
/// of
/// parallel requests on each instance node
/// that can be launched in a transform job. The default value is 1.
MaxConcurrentTransforms: MaxConcurrentTransforms
/// The timeout and maximum number of retries for processing a transform job
/// invocation.
ModelClientConfig: ModelClientConfig
/// The
/// maximum
/// payload size, in MB, used in the
/// transform job.
MaxPayloadInMB: MaxPayloadInMB
/// 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.
BatchStrategy: BatchStrategy
/// The
/// environment variables to set in the Docker container. We support up to 16 key and values
/// entries in the map.
Environment: TransformEnvironmentMap
/// Describes the dataset to be transformed and the Amazon S3 location where it is
/// stored.
@required
TransformInput: TransformInput
/// Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the
/// transform job.
TransformOutput: TransformOutput
/// Configuration to control how SageMaker captures inference data.
DataCaptureConfig: BatchDataCaptureConfig
/// Describes
/// the resources, including ML instance types and ML instance count, to
/// use for the transform job.
@required
TransformResources: TransformResources
/// A timestamp that shows when the transform Job was created.
@required
CreationTime: Timestamp
/// Indicates when the transform job starts
/// on
/// ML instances. You are billed for the time interval between this time
/// and the value of TransformEndTime.
TransformStartTime: Timestamp
/// 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.
TransformEndTime: Timestamp
/// The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the
/// transform or training job.
LabelingJobArn: LabelingJobArn
/// The Amazon Resource Name (ARN) of the AutoML transform job.
AutoMLJobArn: AutoMLJobArn
DataProcessing: DataProcessing
ExperimentConfig: ExperimentConfig
}
@input
structure DescribeTrialComponentRequest {
/// The name of the trial component to describe.
@required
TrialComponentName: ExperimentEntityNameOrArn
}
@output
structure DescribeTrialComponentResponse {
/// The name of the trial component.
TrialComponentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
/// The name of the component as displayed. If DisplayName isn't specified,
/// TrialComponentName is displayed.
DisplayName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the source and, optionally, the job type.
Source: TrialComponentSource
/// The status of the component. States include:
///
/// -
///
InProgress
///
/// -
///
Completed
///
/// -
///
Failed
///
///
Status: TrialComponentStatus
/// When the component started.
StartTime: Timestamp
/// When the component ended.
EndTime: Timestamp
/// When the component was created.
CreationTime: Timestamp
/// Who created the trial component.
CreatedBy: UserContext
/// When the component was last modified.
LastModifiedTime: Timestamp
/// Who last modified the component.
LastModifiedBy: UserContext
/// The hyperparameters of the component.
Parameters: TrialComponentParameters
/// The input artifacts of the component.
InputArtifacts: TrialComponentArtifacts
/// The output artifacts of the component.
OutputArtifacts: TrialComponentArtifacts
MetadataProperties: MetadataProperties
/// The metrics for the component.
Metrics: TrialComponentMetricSummaries
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
/// A list of ARNs and, if applicable, job types for multiple sources of an experiment
/// run.
Sources: TrialComponentSources
}
@input
structure DescribeTrialRequest {
/// The name of the trial to describe.
@required
TrialName: ExperimentEntityName
}
@output
structure DescribeTrialResponse {
/// The name of the trial.
TrialName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
/// The name of the trial as displayed. If DisplayName isn't specified,
/// TrialName is displayed.
DisplayName: ExperimentEntityName
/// The name of the experiment the trial is part of.
ExperimentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the source and, optionally, the job type.
Source: TrialSource
/// When the trial was created.
CreationTime: Timestamp
/// Who created the trial.
CreatedBy: UserContext
/// When the trial was last modified.
LastModifiedTime: Timestamp
/// Who last modified the trial.
LastModifiedBy: UserContext
MetadataProperties: MetadataProperties
}
@input
structure DescribeUserProfileRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name. This value is not case sensitive.
@required
UserProfileName: UserProfileName
}
@output
structure DescribeUserProfileResponse {
/// The ID of the domain that contains the profile.
DomainId: DomainId
/// The user profile Amazon Resource Name (ARN).
UserProfileArn: UserProfileArn
/// The user profile name.
UserProfileName: UserProfileName
/// The ID of the user's profile in the Amazon Elastic File System (EFS) volume.
HomeEfsFileSystemUid: EfsUid
/// The status.
Status: UserProfileStatus
/// The last modified time.
LastModifiedTime: LastModifiedTime
/// The creation time.
CreationTime: CreationTime
/// The failure reason.
FailureReason: FailureReason
/// The IAM Identity Center user identifier.
SingleSignOnUserIdentifier: SingleSignOnUserIdentifier
/// The IAM Identity Center user value.
SingleSignOnUserValue: String256
/// A collection of settings.
UserSettings: UserSettings
}
@input
structure DescribeWorkforceRequest {
/// 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.
@required
WorkforceName: WorkforceName
}
@output
structure DescribeWorkforceResponse {
/// 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.
@required
Workforce: Workforce
}
@input
structure DescribeWorkteamRequest {
/// The name of the work team to return a description of.
@required
WorkteamName: WorkteamName
}
@output
structure DescribeWorkteamResponse {
/// A Workteam instance that contains information about the work team.
///
@required
Workteam: Workteam
}
/// Specifies weight and capacity values for a production variant.
structure DesiredWeightAndCapacity {
/// The name of the variant to update.
@required
VariantName: VariantName
/// The variant's weight.
DesiredWeight: VariantWeight
/// The variant's capacity.
DesiredInstanceCount: TaskCount
}
/// Information of a particular device.
structure Device {
/// The name of the device.
@required
DeviceName: DeviceName
/// Description of the device.
Description: DeviceDescription
/// Amazon Web Services Internet of Things (IoT) object name.
IotThingName: ThingName
}
/// Contains information summarizing device details and deployment status.
structure DeviceDeploymentSummary {
/// The ARN of the edge deployment plan.
@required
EdgeDeploymentPlanArn: EdgeDeploymentPlanArn
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// The name of the stage in the edge deployment plan.
@required
StageName: EntityName
/// The name of the deployed stage.
DeployedStageName: EntityName
/// The name of the fleet to which the device belongs to.
DeviceFleetName: EntityName
/// The name of the device.
@required
DeviceName: DeviceName
/// The ARN of the device.
@required
DeviceArn: DeviceArn
/// The deployment status of the device.
DeviceDeploymentStatus: DeviceDeploymentStatus
/// The detailed error message for the deployoment status result.
DeviceDeploymentStatusMessage: String
/// The description of the device.
Description: DeviceDescription
/// The time when the deployment on the device started.
DeploymentStartTime: Timestamp
}
/// Summary of the device fleet.
structure DeviceFleetSummary {
/// Amazon Resource Name (ARN) of the device fleet.
@required
DeviceFleetArn: DeviceFleetArn
/// Name of the device fleet.
@required
DeviceFleetName: EntityName
/// Timestamp of when the device fleet was created.
CreationTime: Timestamp
/// Timestamp of when the device fleet was last updated.
LastModifiedTime: Timestamp
}
/// Contains information about the configurations of selected devices.
structure DeviceSelectionConfig {
/// Type of device subsets to deploy to the current stage.
@required
DeviceSubsetType: DeviceSubsetType
/// Percentage of devices in the fleet to deploy to the current stage.
Percentage: Percentage = 0
/// List of devices chosen to deploy.
DeviceNames: DeviceNames
/// A filter to select devices with names containing this name.
DeviceNameContains: DeviceName
}
/// Status of devices.
structure DeviceStats {
/// The number of devices connected with a heartbeat.
@required
ConnectedDeviceCount: Long = 0
/// The number of registered devices.
@required
RegisteredDeviceCount: Long = 0
}
/// Summary of the device.
structure DeviceSummary {
/// The unique identifier of the device.
@required
DeviceName: EntityName
/// Amazon Resource Name (ARN) of the device.
@required
DeviceArn: DeviceArn
/// A description of the device.
Description: DeviceDescription
/// The name of the fleet the device belongs to.
DeviceFleetName: EntityName
/// The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
IotThingName: ThingName
/// The timestamp of the last registration or de-reregistration.
RegistrationTime: Timestamp
/// The last heartbeat received from the device.
LatestHeartbeat: Timestamp
/// Models on the device.
Models: EdgeModelSummaries
/// Edge Manager agent version.
AgentVersion: EdgeVersion
}
@input
structure DisassociateTrialComponentRequest {
/// The name of the component to disassociate from the trial.
@required
TrialComponentName: ExperimentEntityName
/// The name of the trial to disassociate from.
@required
TrialName: ExperimentEntityName
}
@output
structure DisassociateTrialComponentResponse {
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
}
/// The domain's details.
structure DomainDetails {
/// The domain's Amazon Resource Name (ARN).
DomainArn: DomainArn
/// The domain ID.
DomainId: DomainId
/// The domain name.
DomainName: DomainName
/// The status.
Status: DomainStatus
/// The creation time.
CreationTime: CreationTime
/// The last modified time.
LastModifiedTime: LastModifiedTime
/// The domain's URL.
Url: String1024
}
/// A collection of settings that apply to the SageMaker Domain. These
/// settings are specified through the CreateDomain API call.
structure DomainSettings {
/// The security groups for the Amazon Virtual Private Cloud that the Domain uses for
/// communication between Domain-level apps and user apps.
SecurityGroupIds: DomainSecurityGroupIds
/// A collection of settings that configure the RStudioServerPro Domain-level
/// app.
RStudioServerProDomainSettings: RStudioServerProDomainSettings
/// The configuration for attaching a SageMaker user profile name to the execution role as a
/// sts:SourceIdentity key.
ExecutionRoleIdentityConfig: ExecutionRoleIdentityConfig
}
/// A collection of Domain configuration settings to update.
structure DomainSettingsForUpdate {
/// A collection of RStudioServerPro Domain-level app settings to
/// update.
RStudioServerProDomainSettingsForUpdate: RStudioServerProDomainSettingsForUpdate
/// The configuration for attaching a SageMaker user profile name to the execution role as a
/// sts:SourceIdentity key. This configuration can only be modified if there
/// are no apps in the InService or Pending state.
ExecutionRoleIdentityConfig: ExecutionRoleIdentityConfig
/// The security groups for the Amazon Virtual Private Cloud that the Domain uses for
/// communication between Domain-level apps and user apps.
SecurityGroupIds: DomainSecurityGroupIds
}
/// Represents the drift check baselines that can be used when the model monitor is set using the model
/// package.
structure DriftCheckBaselines {
/// Represents the drift check bias baselines that can be used when the model monitor is set using the model
/// package.
Bias: DriftCheckBias
/// Represents the drift check explainability baselines that can be used when the model monitor is set using
/// the model package.
Explainability: DriftCheckExplainability
/// Represents the drift check model quality baselines that can be used when the model monitor is set using
/// the model package.
ModelQuality: DriftCheckModelQuality
/// Represents the drift check model data quality baselines that can be used when the model monitor is set
/// using the model package.
ModelDataQuality: DriftCheckModelDataQuality
}
/// Represents the drift check bias baselines that can be used when the model monitor is set using the
/// model package.
structure DriftCheckBias {
/// The bias config file for a model.
ConfigFile: FileSource
/// The pre-training constraints.
PreTrainingConstraints: MetricsSource
/// The post-training constraints.
PostTrainingConstraints: MetricsSource
}
/// Represents the drift check explainability baselines that can be used when the model monitor is set
/// using the model package.
structure DriftCheckExplainability {
/// The drift check explainability constraints.
Constraints: MetricsSource
/// The explainability config file for the model.
ConfigFile: FileSource
}
/// Represents the drift check data quality baselines that can be used when the model monitor is set using
/// the model package.
structure DriftCheckModelDataQuality {
/// The drift check model data quality statistics.
Statistics: MetricsSource
/// The drift check model data quality constraints.
Constraints: MetricsSource
}
/// Represents the drift check model quality baselines that can be used when the model monitor is set using
/// the model package.
structure DriftCheckModelQuality {
/// The drift check model quality statistics.
Statistics: MetricsSource
/// The drift check model quality constraints.
Constraints: MetricsSource
}
/// A directed edge connecting two lineage entities.
structure Edge {
/// The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
SourceArn: AssociationEntityArn
/// The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
DestinationArn: AssociationEntityArn
/// The type of the Association(Edge) between the source and destination. For example ContributedTo,
/// Produced, or DerivedFrom.
AssociationType: AssociationEdgeType
}
/// Contains information about the configuration of a deployment.
structure EdgeDeploymentConfig {
/// Toggle that determines whether to rollback to previous configuration if the current deployment fails.
/// By default this is turned on. You may turn this off if you want to investigate the errors yourself.
@required
FailureHandlingPolicy: FailureHandlingPolicy
}
/// Contains information about the configuration of a model in a deployment.
structure EdgeDeploymentModelConfig {
/// The name the device application uses to reference this model.
@required
ModelHandle: EntityName
/// The edge packaging job associated with this deployment.
@required
EdgePackagingJobName: EntityName
}
/// Contains information summarizing an edge deployment plan.
structure EdgeDeploymentPlanSummary {
/// The ARN of the edge deployment plan.
@required
EdgeDeploymentPlanArn: EdgeDeploymentPlanArn
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// The name of the device fleet used for the deployment.
@required
DeviceFleetName: EntityName
/// The number of edge devices with the successful deployment.
@required
EdgeDeploymentSuccess: Integer = 0
/// The number of edge devices yet to pick up the deployment, or in progress.
@required
EdgeDeploymentPending: Integer = 0
/// The number of edge devices that failed the deployment.
@required
EdgeDeploymentFailed: Integer = 0
/// The time when the edge deployment plan was created.
CreationTime: Timestamp
/// The time when the edge deployment plan was last updated.
LastModifiedTime: Timestamp
}
/// Contains information summarizing the deployment stage results.
structure EdgeDeploymentStatus {
/// The general status of the current stage.
@required
StageStatus: StageStatus
/// The number of edge devices with the successful deployment in the current stage.
@required
EdgeDeploymentSuccessInStage: Integer = 0
/// The number of edge devices yet to pick up the deployment in current stage, or in progress.
@required
EdgeDeploymentPendingInStage: Integer = 0
/// The number of edge devices that failed the deployment in current stage.
@required
EdgeDeploymentFailedInStage: Integer = 0
/// A detailed message about deployment status in current stage.
EdgeDeploymentStatusMessage: String
/// The time when the deployment API started.
EdgeDeploymentStageStartTime: Timestamp
}
/// The model on the edge device.
structure EdgeModel {
/// The name of the model.
@required
ModelName: EntityName
/// The model version.
@required
ModelVersion: EdgeVersion
/// The timestamp of the last data sample taken.
LatestSampleTime: Timestamp
/// The timestamp of the last inference that was made.
LatestInference: Timestamp
}
/// Status of edge devices with this model.
structure EdgeModelStat {
/// The name of the model.
@required
ModelName: EntityName
/// The model version.
@required
ModelVersion: EdgeVersion
/// The number of devices that have this model version and do not have a heart beat.
@required
OfflineDeviceCount: Long = 0
/// The number of devices that have this model version and have a heart beat.
@required
ConnectedDeviceCount: Long = 0
/// The number of devices that have this model version, a heart beat, and are currently running.
@required
ActiveDeviceCount: Long = 0
/// The number of devices with this model version and are producing sample data.
@required
SamplingDeviceCount: Long = 0
}
/// Summary of model on edge device.
structure EdgeModelSummary {
/// The name of the model.
@required
ModelName: EntityName
/// The version model.
@required
ModelVersion: EdgeVersion
}
/// The output configuration.
structure EdgeOutputConfig {
/// The Amazon Simple Storage (S3) bucker URI.
@required
S3OutputLocation: S3Uri
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job.
/// If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
KmsKeyId: KmsKeyId
/// The deployment type SageMaker Edge Manager will create.
/// Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
PresetDeploymentType: EdgePresetDeploymentType
/// The configuration used to create deployment artifacts.
/// Specify configuration options with a JSON string. The available configuration options for each type are:
///
/// -
///
/// ComponentName (optional) - Name of the GreenGrass V2 component. If not specified,
/// the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager
/// packaging job.
///
/// -
///
/// ComponentDescription (optional) - Description of the component.
///
/// -
///
/// ComponentVersion (optional) - The version of the component.
///
/// Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a
/// major.minor.patch number system. For example, version 1.0.0 represents the first
/// major release for a component. For more information, see the semantic version specification.
///
///
/// -
///
/// PlatformOS (optional) - The name of the operating system for the platform.
/// Supported platforms include Windows and Linux.
///
/// -
///
/// PlatformArchitecture (optional) - The processor architecture for the platform.
/// Supported architectures Windows include: Windows32_x86, Windows64_x64.
/// Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
///
///
PresetDeploymentConfig: String
}
/// Summary of edge packaging job.
structure EdgePackagingJobSummary {
/// The Amazon Resource Name (ARN) of the edge packaging job.
@required
EdgePackagingJobArn: EdgePackagingJobArn
/// The name of the edge packaging job.
@required
EdgePackagingJobName: EntityName
/// The status of the edge packaging job.
@required
EdgePackagingJobStatus: EdgePackagingJobStatus
/// The name of the SageMaker Neo compilation job.
CompilationJobName: EntityName
/// The name of the model.
ModelName: EntityName
/// The version of the model.
ModelVersion: EdgeVersion
/// The timestamp of when the job was created.
CreationTime: Timestamp
/// The timestamp of when the edge packaging job was last updated.
LastModifiedTime: Timestamp
}
/// The output of a SageMaker Edge Manager deployable resource.
structure EdgePresetDeploymentOutput {
/// The deployment type created by SageMaker Edge Manager. Currently only
/// supports Amazon Web Services IoT Greengrass Version 2 components.
@required
Type: EdgePresetDeploymentType
/// The Amazon Resource Name (ARN) of the generated deployable resource.
Artifact: EdgePresetDeploymentArtifact
/// The status of the deployable resource.
Status: EdgePresetDeploymentStatus
/// Returns a message describing the status of the deployed resource.
StatusMessage: String
}
/// The configurations and outcomes of an Amazon EMR step execution.
structure EMRStepMetadata {
/// The identifier of the EMR cluster.
ClusterId: String256
/// The identifier of the EMR cluster step.
StepId: String256
/// The name of the EMR cluster step.
StepName: String256
/// The path to the log file where the cluster step's failure root cause
/// is recorded.
LogFilePath: String1024
}
/// A hosted endpoint for real-time inference.
structure Endpoint {
/// The name of the endpoint.
@required
EndpointName: EndpointName
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
/// The endpoint configuration associated with the endpoint.
@required
EndpointConfigName: EndpointConfigName
/// A list of the production variants hosted on the endpoint. Each production variant is a
/// model.
ProductionVariants: ProductionVariantSummaryList
DataCaptureConfig: DataCaptureConfigSummary
/// The status of the endpoint.
@required
EndpointStatus: EndpointStatus
/// If the endpoint failed, the reason it failed.
FailureReason: FailureReason
/// The time that the endpoint was created.
@required
CreationTime: Timestamp
/// The last time the endpoint was modified.
@required
LastModifiedTime: Timestamp
/// A list of monitoring schedules for the endpoint. For information about model
/// monitoring, see Amazon SageMaker Model Monitor.
MonitoringSchedules: MonitoringScheduleList
/// A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General
/// Reference Guide.
Tags: TagList
/// A list of the shadow variants hosted on the endpoint. Each shadow variant is a model
/// in shadow mode with production traffic replicated from the production variant.
ShadowProductionVariants: ProductionVariantSummaryList
}
/// Provides summary information for an endpoint configuration.
structure EndpointConfigSummary {
/// The name of the endpoint configuration.
@required
EndpointConfigName: EndpointConfigName
/// The Amazon Resource Name (ARN) of the endpoint configuration.
@required
EndpointConfigArn: EndpointConfigArn
/// A timestamp that shows when the endpoint configuration was created.
@required
CreationTime: Timestamp
}
/// Details about a customer endpoint that was compared in an Inference Recommender job.
structure EndpointInfo {
/// The name of a customer's endpoint.
@required
EndpointName: EndpointName
}
/// Input object for the endpoint
structure EndpointInput {
/// An endpoint in customer's account which has enabled DataCaptureConfig
/// enabled.
@required
EndpointName: EndpointName
/// Path to the filesystem where the endpoint data is available to the container.
@required
LocalPath: ProcessingLocalPath
/// Whether the Pipe or File is used as the input mode for
/// transferring data for the monitoring job. Pipe mode is recommended for large
/// datasets. File mode is useful for small files that fit in memory. Defaults to
/// File.
S3InputMode: ProcessingS3InputMode
/// Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key.
/// Defaults to FullyReplicated
///
S3DataDistributionType: ProcessingS3DataDistributionType
/// The attributes of the input data that are the input features.
FeaturesAttribute: String
/// The attribute of the input data that represents the ground truth label.
InferenceAttribute: String
/// In a classification problem, the attribute that represents the class probability.
ProbabilityAttribute: String
/// The threshold for the class probability to be evaluated as a positive result.
ProbabilityThresholdAttribute: ProbabilityThresholdAttribute
/// If specified, monitoring jobs substract this time from the start time. For information
/// about using offsets for scheduling monitoring jobs, see Schedule Model
/// Quality Monitoring Jobs.
StartTimeOffset: MonitoringTimeOffsetString
/// If specified, monitoring jobs substract this time from the end time. For information
/// about using offsets for scheduling monitoring jobs, see Schedule Model
/// Quality Monitoring Jobs.
EndTimeOffset: MonitoringTimeOffsetString
}
/// The endpoint configuration for the load test.
structure EndpointInputConfiguration {
/// The instance types to use for the load test.
@required
InstanceType: ProductionVariantInstanceType
/// The inference specification name in the model package version.
InferenceSpecificationName: InferenceSpecificationName
/// The parameter you want to benchmark against.
EnvironmentParameterRanges: EnvironmentParameterRanges
}
/// The metadata of the endpoint.
structure EndpointMetadata {
/// The name of the endpoint.
@required
EndpointName: EndpointName
/// The name of the endpoint configuration.
EndpointConfigName: EndpointConfigName
///
/// The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary$EndpointStatus.
///
EndpointStatus: EndpointStatus
///
/// If the status of the endpoint is Failed, or the status is InService but update
/// operation fails, this provides the reason why it failed.
///
FailureReason: FailureReason
}
/// The endpoint configuration made by Inference Recommender during a recommendation job.
structure EndpointOutputConfiguration {
/// The name of the endpoint made during a recommendation job.
@required
EndpointName: String
/// The name of the production variant (deployed model) made during a recommendation job.
@required
VariantName: String
/// The instance type recommended by Amazon SageMaker Inference Recommender.
@required
InstanceType: ProductionVariantInstanceType
/// The number of instances recommended to launch initially.
@required
InitialInstanceCount: Integer = 0
}
/// The performance results from running an Inference Recommender job on an existing endpoint.
structure EndpointPerformance {
/// The metrics for an existing endpoint.
@required
Metrics: InferenceMetrics
@required
EndpointInfo: EndpointInfo
}
/// Provides summary information for an endpoint.
structure EndpointSummary {
/// The name of the endpoint.
@required
EndpointName: EndpointName
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
/// A timestamp that shows when the endpoint was created.
@required
CreationTime: Timestamp
/// A timestamp that shows when the endpoint was last modified.
@required
LastModifiedTime: Timestamp
/// The status of the endpoint.
///
/// -
///
/// OutOfService: Endpoint is not available to take incoming
/// requests.
///
/// -
///
/// Creating: CreateEndpoint is executing.
///
/// -
///
/// Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
///
/// -
///
/// SystemUpdating: Endpoint is undergoing maintenance and cannot be
/// updated or deleted or re-scaled until it has completed. This maintenance
/// operation does not change any customer-specified values such as VPC config, KMS
/// encryption, model, instance type, or instance count.
///
/// -
///
/// RollingBack: Endpoint fails to scale up or down or change its
/// variant weight and is in the process of rolling back to its previous
/// configuration. Once the rollback completes, endpoint returns to an
/// InService status. This transitional status only applies to an
/// endpoint that has autoscaling enabled and is undergoing variant weight or
/// capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called
/// explicitly.
///
/// -
///
/// InService: Endpoint is available to process incoming
/// requests.
///
/// -
///
/// Deleting: DeleteEndpoint is executing.
///
/// -
///
/// Failed: Endpoint could not be created, updated, or re-scaled. Use
/// DescribeEndpointOutput$FailureReason for information about
/// the failure. DeleteEndpoint is the only operation that can be
/// performed on a failed endpoint.
///
///
/// To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.
@required
EndpointStatus: EndpointStatus
}
/// A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
structure EnvironmentParameter {
/// The environment key suggested by the Amazon SageMaker Inference Recommender.
@required
Key: String
/// The value type suggested by the Amazon SageMaker Inference Recommender.
@required
ValueType: String
/// The value suggested by the Amazon SageMaker Inference Recommender.
@required
Value: String
}
/// Specifies the range of environment parameters
structure EnvironmentParameterRanges {
/// Specified a list of parameters for each category.
CategoricalParameterRanges: CategoricalParameters
}
/// The properties of an experiment as returned by the Search API.
structure Experiment {
/// The name of the experiment.
ExperimentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the experiment.
ExperimentArn: ExperimentArn
/// The name of the experiment as displayed. If DisplayName isn't specified,
/// ExperimentName is displayed.
DisplayName: ExperimentEntityName
Source: ExperimentSource
/// The description of the experiment.
Description: ExperimentDescription
/// When the experiment was created.
CreationTime: Timestamp
/// Who created the experiment.
CreatedBy: UserContext
/// When the experiment was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// The list of tags that are associated with the experiment. You can use Search API to search on the tags.
Tags: TagList
}
/// Associates a SageMaker job as a trial component with an experiment and trial. Specified when
/// you call the following APIs:
///
/// -
///
/// CreateProcessingJob
///
///
/// -
///
/// CreateTrainingJob
///
///
/// -
///
/// CreateTransformJob
///
///
///
structure ExperimentConfig {
/// The name of an existing experiment to associate with the trial component.
ExperimentName: ExperimentEntityName
/// The name of an existing trial to associate the trial component with. If not specified, a
/// new trial is created.
TrialName: ExperimentEntityName
/// The display name for the trial component. If this key isn't specified, the display name is
/// the trial component name.
TrialComponentDisplayName: ExperimentEntityName
/// The name of the experiment run to associate with the trial component.
RunName: ExperimentEntityName
}
/// The source of the experiment.
structure ExperimentSource {
/// The Amazon Resource Name (ARN) of the source.
@required
SourceArn: ExperimentSourceArn
/// The source type.
SourceType: SourceType
}
/// A summary of the properties of an experiment. To get the complete set of properties, call
/// the DescribeExperiment API and provide the
/// ExperimentName.
structure ExperimentSummary {
/// The Amazon Resource Name (ARN) of the experiment.
ExperimentArn: ExperimentArn
/// The name of the experiment.
ExperimentName: ExperimentEntityName
/// The name of the experiment as displayed. If DisplayName isn't specified,
/// ExperimentName is displayed.
DisplayName: ExperimentEntityName
ExperimentSource: ExperimentSource
/// When the experiment was created.
CreationTime: Timestamp
/// When the experiment was last modified.
LastModifiedTime: Timestamp
}
/// Contains explainability metrics for a model.
structure Explainability {
/// The explainability report for a model.
Report: MetricsSource
}
/// A parameter to activate explainers.
structure ExplainerConfig {
/// A member of ExplainerConfig that contains configuration parameters for
/// the SageMaker Clarify explainer.
ClarifyExplainerConfig: ClarifyExplainerConfig
}
/// The container for the metadata for Fail step.
structure FailStepMetadata {
/// A message that you define and then is processed and rendered by
/// the Fail step when the error occurs.
ErrorMessage: String3072
}
/// A list of features. You must include FeatureName and
/// FeatureType. Valid feature FeatureTypes are
/// Integral, Fractional and String.
structure FeatureDefinition {
/// The name of a feature. The type must be a string. FeatureName cannot be any
/// of the following: is_deleted, write_time,
/// api_invocation_time.
FeatureName: FeatureName
/// The value type of a feature. Valid values are Integral, Fractional, or String.
FeatureType: FeatureType
}
/// Amazon SageMaker Feature Store stores features in a collection called Feature Group.
/// A Feature Group can be visualized as a table which has rows,
/// with a unique identifier for each row where each column in the table is a feature.
/// In principle, a Feature Group is composed of features and values per features.
structure FeatureGroup {
/// The Amazon Resource Name (ARN) of a FeatureGroup.
FeatureGroupArn: FeatureGroupArn
/// The name of the FeatureGroup.
FeatureGroupName: FeatureGroupName
/// The name of the Feature whose value uniquely identifies a
/// Record defined in the FeatureGroup
/// FeatureDefinitions.
RecordIdentifierFeatureName: FeatureName
/// The name of the feature that stores the EventTime of a Record in a
/// FeatureGroup.
/// A EventTime is point in time when a new event
/// occurs that corresponds to the creation or update of a Record in
/// FeatureGroup. All Records in the FeatureGroup
/// must have a corresponding EventTime.
EventTimeFeatureName: FeatureName
/// A list of Features. Each Feature must include a
/// FeatureName and a FeatureType.
/// Valid 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.
FeatureDefinitions: FeatureDefinitions
/// The time a FeatureGroup was created.
CreationTime: CreationTime
/// A timestamp indicating the last time you updated the feature group.
LastModifiedTime: LastModifiedTime
OnlineStoreConfig: OnlineStoreConfig
OfflineStoreConfig: OfflineStoreConfig
/// The Amazon Resource Name (ARN) of the IAM execution role used to create the feature
/// group.
RoleArn: RoleArn
/// A FeatureGroup status.
FeatureGroupStatus: FeatureGroupStatus
OfflineStoreStatus: OfflineStoreStatus
/// A value that indicates whether the feature group was updated successfully.
LastUpdateStatus: LastUpdateStatus
/// The reason that the FeatureGroup failed to
/// be replicated in the OfflineStore. This is
/// failure may be due to a failure to create a FeatureGroup in
/// or delete a FeatureGroup from the OfflineStore.
FailureReason: FailureReason
/// A free form description of a FeatureGroup.
Description: Description
/// Tags used to define a FeatureGroup.
Tags: TagList
}
/// The name, Arn, CreationTime, FeatureGroup values,
/// LastUpdatedTime and EnableOnlineStorage status of a
/// FeatureGroup.
structure FeatureGroupSummary {
/// The name of FeatureGroup.
@required
FeatureGroupName: FeatureGroupName
/// Unique identifier for the FeatureGroup.
@required
FeatureGroupArn: FeatureGroupArn
/// A timestamp indicating the time of creation time of the FeatureGroup.
@required
CreationTime: Timestamp
/// The status of a FeatureGroup. The status can be any of the following:
/// Creating, Created, CreateFail,
/// Deleting or DetailFail.
FeatureGroupStatus: FeatureGroupStatus
/// Notifies you if replicating data into the OfflineStore has failed. Returns
/// either: Active or Blocked.
OfflineStoreStatus: OfflineStoreStatus
}
/// The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.
structure FeatureMetadata {
/// The Amazon Resource Number (ARN) of the feature group.
FeatureGroupArn: FeatureGroupArn
/// The name of the feature group containing the feature.
FeatureGroupName: FeatureGroupName
/// The name of feature.
FeatureName: FeatureName
/// The data type of the feature.
FeatureType: FeatureType
/// A timestamp indicating when the feature was created.
CreationTime: CreationTime
/// A timestamp indicating when the feature was last modified.
LastModifiedTime: LastModifiedTime
/// An optional description that you specify to better describe the feature.
Description: FeatureDescription
/// Optional key-value pairs that you specify to better describe the feature.
Parameters: FeatureParameters
}
/// A key-value pair that you specify to describe the feature.
structure FeatureParameter {
/// A key that must contain a value to describe the feature.
Key: FeatureParameterKey
/// The value that belongs to a key.
Value: FeatureParameterValue
}
/// Contains details regarding the file source.
structure FileSource {
/// The type of content stored in the file source.
ContentType: ContentType
/// The digest of the file source.
ContentDigest: ContentDigest
/// The Amazon S3 URI for the file source.
@required
S3Uri: S3Uri
}
/// The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
structure FileSystemConfig {
/// The path within the image to mount the user's EFS home directory. The directory
/// should be empty. If not specified, defaults to /home/sagemaker-user.
MountPath: MountPath
/// The default POSIX user ID (UID). If not specified, defaults to 1000.
DefaultUid: DefaultUid = null
/// The default POSIX group ID (GID). If not specified, defaults to 100.
DefaultGid: DefaultGid = null
}
/// Specifies a file system data source for a channel.
structure FileSystemDataSource {
/// The file system id.
@required
FileSystemId: FileSystemId
/// The access mode of the mount of the directory associated with the channel. A directory
/// can be mounted either in ro (read-only) or rw (read-write)
/// mode.
@required
FileSystemAccessMode: FileSystemAccessMode
/// The file system type.
@required
FileSystemType: FileSystemType
/// The full path to the directory to associate with the channel.
@required
DirectoryPath: DirectoryPath
}
/// A conditional statement for a search expression that includes a resource property, a
/// Boolean operator, and a value. Resources that match the statement are returned in the
/// results from the Search API.
/// If you specify a Value, but not an Operator, Amazon SageMaker uses the
/// equals operator.
/// In search, there are several property types:
///
/// - Metrics
/// -
///
To define a metric filter, enter a value using the form
/// "Metrics." , where "accuracy" metric greater than
/// "0.9":
///
/// {
///
///
/// "Name": "Metrics.accuracy",
///
///
/// "Operator": "GreaterThan",
///
///
/// "Value": "0.9"
///
///
/// }
///
///
/// - HyperParameters
/// -
///
To define a hyperparameter filter, enter a value with the form
/// "HyperParameters." . Decimal hyperparameter
/// values are treated as a decimal in a comparison if the specified
/// Value is also a decimal value. If the specified
/// Value is an integer, the decimal hyperparameter values are
/// treated as integers. For example, the following filter is satisfied by
/// training jobs with a "learning_rate" hyperparameter that is
/// less than "0.5":
///
/// {
///
///
/// "Name": "HyperParameters.learning_rate",
///
///
/// "Operator": "LessThan",
///
///
/// "Value": "0.5"
///
///
/// }
///
///
/// - Tags
/// -
///
To define a tag filter, enter a value with the form
/// Tags..
///
///
structure Filter {
/// A resource property name. For example, TrainingJobName. For
/// valid property names, see SearchRecord.
/// You must specify a valid property for the resource.
@required
Name: ResourcePropertyName
/// A Boolean binary operator that is used to evaluate the filter. The operator field
/// contains one of the following values:
///
/// - Equals
/// -
///
The value of Name equals Value.
///
/// - NotEquals
/// -
///
The value of Name doesn't equal Value.
///
/// - Exists
/// -
///
The Name property exists.
///
/// - NotExists
/// -
///
The Name property does not exist.
///
/// - GreaterThan
/// -
///
The value of Name is greater than Value.
/// Not supported for text properties.
///
/// - GreaterThanOrEqualTo
/// -
///
The value of Name is greater than or equal to Value.
/// Not supported for text properties.
///
/// - LessThan
/// -
///
The value of Name is less than Value.
/// Not supported for text properties.
///
/// - LessThanOrEqualTo
/// -
///
The value of Name is less than or equal to Value.
/// Not supported for text properties.
///
/// - In
/// -
///
The value of Name is one of the comma delimited strings in
/// Value. Only supported for text properties.
///
/// - Contains
/// -
///
The value of Name contains the string Value.
/// Only supported for text properties.
/// A SearchExpression can include the Contains operator
/// multiple times when the value of Name is one of the following:
///
/// -
///
/// Experiment.DisplayName
///
///
/// -
///
/// Experiment.ExperimentName
///
///
/// -
///
/// Experiment.Tags
///
///
/// -
///
/// Trial.DisplayName
///
///
/// -
///
/// Trial.TrialName
///
///
/// -
///
/// Trial.Tags
///
///
/// -
///
/// TrialComponent.DisplayName
///
///
/// -
///
/// TrialComponent.TrialComponentName
///
///
/// -
///
/// TrialComponent.Tags
///
///
/// -
///
/// TrialComponent.InputArtifacts
///
///
/// -
///
/// TrialComponent.OutputArtifacts
///
///
///
/// A SearchExpression can include only one Contains operator
/// for all other values of Name. In these cases, if you include multiple
/// Contains operators in the SearchExpression, the result is
/// the following error message: "'CONTAINS' operator usage limit of 1
/// exceeded."
///
///
Operator: Operator
/// A value used with Name and Operator to determine which
/// resources satisfy the filter's condition. For numerical properties, Value
/// must be an integer or floating-point decimal. For timestamp properties,
/// Value must be an ISO 8601 date-time string of the following format:
/// YYYY-mm-dd'T'HH:MM:SS.
Value: FilterValue
}
/// The best candidate result from an AutoML training job.
structure FinalAutoMLJobObjectiveMetric {
/// The type of metric with the best result.
Type: AutoMLJobObjectiveType
/// The name of the metric with the best result. For a description of the possible objective
/// metrics, see AutoMLJobObjective$MetricName.
@required
MetricName: AutoMLMetricEnum
/// The value of the metric with the best result.
@required
Value: MetricValue = 0
}
/// Shows the final value for the
/// objective
/// metric for a training job that was launched by a hyperparameter
/// tuning job. You define the objective metric in the
/// HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.
structure FinalHyperParameterTuningJobObjectiveMetric {
/// Whether to
/// minimize
/// or maximize the objective metric. Valid values are Minimize and
/// Maximize.
Type: HyperParameterTuningJobObjectiveType
/// The name of the
/// objective
/// metric.
@required
MetricName: MetricName
/// The value of the objective metric.
@required
Value: MetricValue = 0
}
/// Contains information about where human output will be stored.
structure FlowDefinitionOutputConfig {
/// The Amazon S3 path where the object containing human output will be made available.
/// To learn more about the format of Amazon A2I output data, see Amazon A2I
/// Output Data.
@required
S3OutputPath: S3Uri
/// The Amazon Key Management Service (KMS) key ID for server-side encryption.
KmsKeyId: KmsKeyId
}
/// Contains summary information about the flow definition.
structure FlowDefinitionSummary {
/// The name of the flow definition.
@required
FlowDefinitionName: FlowDefinitionName
/// The Amazon Resource Name (ARN) of the flow definition.
@required
FlowDefinitionArn: FlowDefinitionArn
/// The status of the flow definition. Valid values:
@required
FlowDefinitionStatus: FlowDefinitionStatus
/// The timestamp when SageMaker created the flow definition.
@required
CreationTime: Timestamp
/// The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.
FailureReason: FailureReason
}
@input
structure GetDeviceFleetReportRequest {
/// The name of the fleet.
@required
DeviceFleetName: EntityName
}
@output
structure GetDeviceFleetReportResponse {
/// The Amazon Resource Name (ARN) of the device.
@required
DeviceFleetArn: DeviceFleetArn
/// The name of the fleet.
@required
DeviceFleetName: EntityName
/// The output configuration for storing sample data collected by the fleet.
OutputConfig: EdgeOutputConfig
/// Description of the fleet.
Description: DeviceFleetDescription
/// Timestamp of when the report was generated.
ReportGenerated: Timestamp
/// Status of devices.
DeviceStats: DeviceStats
/// The versions of Edge Manager agent deployed on the fleet.
AgentVersions: AgentVersions
/// Status of model on device.
ModelStats: EdgeModelStats
}
@input
structure GetLineageGroupPolicyRequest {
/// The name or Amazon Resource Name (ARN) of the lineage group.
@required
LineageGroupName: LineageGroupNameOrArn
}
@output
structure GetLineageGroupPolicyResponse {
/// The Amazon Resource Name (ARN) of the lineage group.
LineageGroupArn: LineageGroupArn
/// The resource policy that gives access to the lineage group in another account.
ResourcePolicy: ResourcePolicyString
}
@input
structure GetSearchSuggestionsRequest {
/// The name of the Amazon SageMaker resource to search for.
@required
Resource: ResourceType
/// Limits the property names that are included in the response.
SuggestionQuery: SuggestionQuery
}
@output
structure GetSearchSuggestionsResponse {
/// A list of property names for a Resource that match a
/// SuggestionQuery.
PropertyNameSuggestions: PropertyNameSuggestionList
}
/// Specifies configuration details for a Git repository in your Amazon Web Services
/// account.
structure GitConfig {
/// The URL where the Git repository is located.
@required
RepositoryUrl: GitConfigUrl
/// The default branch for the Git repository.
Branch: Branch
/// The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that
/// contains the credentials used to access the git repository. The secret must have a
/// staging label of AWSCURRENT and must be in the following format:
///
/// {"username": UserName, "password":
/// Password}
///
SecretArn: SecretArn
}
/// Specifies configuration details for a Git repository when the repository is
/// updated.
structure GitConfigForUpdate {
/// The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that
/// contains the credentials used to access the git repository. The secret must have a
/// staging label of AWSCURRENT and must be in the following format:
///
/// {"username": UserName, "password":
/// Password}
///
SecretArn: SecretArn
}
/// Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.
structure HubContentDependency {
/// The hub content dependency origin path.
DependencyOriginPath: DependencyOriginPath
/// The hub content dependency copy path.
DependencyCopyPath: DependencyCopyPath
}
/// Information about hub content.
structure HubContentInfo {
/// The name of the hub content.
@required
HubContentName: HubContentName
/// The Amazon Resource Name (ARN) of the hub content.
@required
HubContentArn: HubContentArn
/// The version of the hub content.
@required
HubContentVersion: HubContentVersion
/// The type of hub content.
@required
HubContentType: HubContentType
/// The version of the hub content document schema.
@required
DocumentSchemaVersion: DocumentSchemaVersion
/// The display name of the hub content.
HubContentDisplayName: HubContentDisplayName
/// A description of the hub content.
HubContentDescription: HubContentDescription
/// The searchable keywords for the hub content.
HubContentSearchKeywords: HubContentSearchKeywordList
/// The status of the hub content.
@required
HubContentStatus: HubContentStatus
/// The date and time that the hub content was created.
@required
CreationTime: Timestamp
}
/// Information about a hub.
structure HubInfo {
/// The name of the hub.
@required
HubName: HubName
/// The Amazon Resource Name (ARN) of the hub.
@required
HubArn: HubArn
/// The display name of the hub.
HubDisplayName: HubDisplayName
/// A description of the hub.
HubDescription: HubDescription
/// The searchable keywords for the hub.
HubSearchKeywords: HubSearchKeywordList
/// The status of the hub.
@required
HubStatus: HubStatus
/// The date and time that the hub was created.
@required
CreationTime: Timestamp
/// The date and time that the hub was last modified.
@required
LastModifiedTime: Timestamp
}
/// The Amazon S3 storage configuration of a hub.
structure HubS3StorageConfig {
/// The Amazon S3 output path for the hub.
S3OutputPath: S3OutputPath
}
/// Defines under what conditions SageMaker creates a human loop. Used within . See for the required
/// format of activation conditions.
structure HumanLoopActivationConditionsConfig {
/// JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team.
/// The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see
/// JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI
/// in the Amazon SageMaker Developer Guide.
@required
HumanLoopActivationConditions: SynthesizedJsonHumanLoopActivationConditions
}
/// Provides information about how and under what conditions SageMaker creates a human loop. If HumanLoopActivationConfig is not given, then all requests go to humans.
structure HumanLoopActivationConfig {
/// Container structure for defining under what conditions SageMaker creates a human
/// loop.
@required
HumanLoopActivationConditionsConfig: HumanLoopActivationConditionsConfig
}
/// Describes the work to be performed by human workers.
structure HumanLoopConfig {
/// Amazon Resource Name (ARN) of a team of workers. To learn more about the types of
/// workforces and work teams you can create and use with Amazon A2I, see Create
/// and Manage Workforces.
@required
WorkteamArn: WorkteamArn
/// The Amazon Resource Name (ARN) of the human task user interface.
/// You can use standard HTML and Crowd HTML Elements to create a custom worker task
/// template. You use this template to create a human task UI.
/// To learn how to create a custom HTML template, see Create Custom Worker
/// Task Template.
/// To learn how to create a human task UI, which is a worker task template that can be used
/// in a flow definition, see Create and Delete a Worker Task Templates.
@required
HumanTaskUiArn: HumanTaskUiArn
/// A title for the human worker task.
@required
TaskTitle: FlowDefinitionTaskTitle
/// A description for the human worker task.
@required
TaskDescription: FlowDefinitionTaskDescription
/// The number of distinct workers who will perform the same task on each object.
/// For example, if TaskCount is set to 3 for an image classification
/// labeling job, three workers will classify each input image.
/// Increasing TaskCount can improve label accuracy.
@required
TaskCount: FlowDefinitionTaskCount
/// The length of time that a task remains available for review by human workers.
TaskAvailabilityLifetimeInSeconds: FlowDefinitionTaskAvailabilityLifetimeInSeconds
/// The amount of time that a worker has to complete a task. The default value is 3,600
/// seconds (1 hour).
TaskTimeLimitInSeconds: FlowDefinitionTaskTimeLimitInSeconds
/// Keywords used to describe the task so that workers can discover the task.
TaskKeywords: FlowDefinitionTaskKeywords
PublicWorkforceTaskPrice: PublicWorkforceTaskPrice
}
/// Container for configuring the source of human task requests.
structure HumanLoopRequestSource {
/// Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source.
/// The default field settings and JSON parsing rules are different based on the integration source. Valid values:
@required
AwsManagedHumanLoopRequestSource: AwsManagedHumanLoopRequestSource
}
/// Information required for human workers to complete a labeling task.
structure HumanTaskConfig {
/// The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
@required
WorkteamArn: WorkteamArn
/// Information about the user interface that workers use to complete the labeling
/// task.
@required
UiConfig: UiConfig
/// The Amazon Resource Name (ARN) of a Lambda function that is run before a data object
/// is sent to a human worker. Use this function to provide input to a custom labeling
/// job.
/// For built-in
/// task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
/// PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda.
///
/// Bounding box - Finds the most similar boxes from
/// different workers based on the Jaccard index of the boxes.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
///
///
///
///
/// Image classification - Uses a variant of the Expectation
/// Maximization approach to estimate the true class of an image based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
///
///
///
///
/// Multi-label image classification - Uses a variant of the Expectation
/// Maximization approach to estimate the true classes of an image based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
///
///
///
///
/// Semantic segmentation - Treats each pixel in an image as
/// a multi-class classification and treats pixel annotations from workers as
/// "votes" for the correct label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
///
///
///
///
/// Text classification - Uses a variant of the Expectation
/// Maximization approach to estimate the true class of text based on annotations
/// from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
///
///
///
///
/// Multi-label text classification - Uses a variant of the
/// Expectation Maximization approach to estimate the true classes of text based on
/// annotations from individual workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
///
///
///
///
/// Named entity recognition - Groups similar selections and
/// calculates aggregate boundaries, resolving to most-assigned label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
///
///
///
///
/// Video Classification - Use this task type when you need workers to classify videos using
/// predefined labels that you specify. Workers are shown videos and are asked to choose one
/// label for each video.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
///
///
///
///
/// Video Frame Object Detection - Use this task type to
/// have workers identify and locate objects in a sequence of video frames (images extracted
/// from a video) using bounding boxes. For example, you can use this task to ask workers to
/// identify and localize various objects in a series of video frames, such as cars, bikes,
/// and pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
///
///
///
///
/// Video Frame Object Tracking - Use this task type to
/// have workers track the movement of objects in a sequence of video frames (images
/// extracted from a video) using bounding boxes. For example, you can use this task to ask
/// workers to track the movement of objects, such as cars, bikes, and pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
///
///
///
///
/// 3D Point Cloud Modalities
///
/// Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks.
/// See 3D Point Cloud Task types
/// to learn more.
///
/// 3D Point Cloud Object Detection -
/// Use this task type when you want workers to classify objects in a 3D point cloud by
/// drawing 3D cuboids around objects. For example, you can use this task type to ask workers
/// to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
///
///
///
///
/// 3D Point Cloud Object Tracking -
/// Use this task type when you want workers to draw 3D cuboids around objects
/// that appear in a sequence of 3D point cloud frames.
/// For example, you can use this task type to ask workers to track
/// the movement of vehicles across multiple point cloud frames.
///
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
///
///
///
///
/// 3D Point Cloud Semantic Segmentation -
/// Use this task type when you want workers to create a point-level semantic segmentation masks by
/// painting objects in a 3D point cloud using different colors where each color is assigned to one of
/// the classes you specify.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
///
///
///
///
/// Use the following ARNs for Label Verification and Adjustment Jobs
///
/// Use label verification and adjustment jobs to review and adjust labels. To learn more,
/// see Verify and Adjust Labels .
///
/// Bounding box verification - Uses a variant of the
/// Expectation Maximization approach to estimate the true class of verification
/// judgement for bounding box labels based on annotations from individual
/// workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
///
///
///
///
/// Bounding box adjustment - Finds the most similar boxes
/// from different workers based on the Jaccard index of the adjusted
/// annotations.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
///
///
///
///
/// Semantic segmentation verification - Uses a variant of
/// the Expectation Maximization approach to estimate the true class of verification
/// judgment for semantic segmentation labels based on annotations from individual
/// workers.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
///
///
///
///
/// Semantic segmentation adjustment - Treats each pixel in
/// an image as a multi-class classification and treats pixel adjusted annotations
/// from workers as "votes" for the correct label.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
///
///
///
///
/// Video Frame Object Detection Adjustment -
/// Use this task type when you want workers to adjust bounding boxes that workers have added
/// to video frames to classify and localize objects in a sequence of video frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
///
///
///
///
/// Video Frame Object Tracking Adjustment -
/// Use this task type when you want workers to adjust bounding boxes that workers have added
/// to video frames to track object movement across a sequence of video frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
///
///
///
///
/// 3D point cloud object detection adjustment - Adjust
/// 3D cuboids in a point cloud frame.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
///
///
///
///
/// 3D point cloud object tracking adjustment - Adjust 3D
/// cuboids across a sequence of point cloud frames.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
///
///
///
///
/// 3D point cloud semantic segmentation adjustment -
/// Adjust semantic segmentation masks in a 3D point cloud.
///
/// -
///
/// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
/// -
///
/// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
///
///
///
@required
PreHumanTaskLambdaArn: LambdaFunctionArn
/// Keywords used to describe the task so that workers on Amazon Mechanical Turk can
/// discover the task.
TaskKeywords: TaskKeywords
/// A title for the task for your human workers.
@required
TaskTitle: TaskTitle
/// A description of the task for your human workers.
@required
TaskDescription: TaskDescription
/// The number of human workers that will label an object.
@required
NumberOfHumanWorkersPerDataObject: NumberOfHumanWorkersPerDataObject
/// The amount of time that a worker has to complete a task.
/// If you create a custom labeling job, the maximum value for this parameter is 8 hours
/// (28,800 seconds).
/// If you create a labeling job using a built-in task type the maximum
/// for this parameter depends on the task type you use:
///
/// -
///
For image and
/// text labeling jobs,
/// the maximum is 8 hours (28,800 seconds).
///
/// -
///
For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
///
///
@required
TaskTimeLimitInSeconds: TaskTimeLimitInSeconds
/// The length of time that a task remains available for labeling by human workers. The
/// default and maximum values for this parameter depend on the type of workforce you
/// use.
///
/// -
///
If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds).
/// The default is 6 hours (21,600 seconds).
///
/// -
///
If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
///
///
TaskAvailabilityLifetimeInSeconds: TaskAvailabilityLifetimeInSeconds
/// Defines the maximum number of data objects that can be labeled by human workers at the
/// same time. Also referred to as batch size. Each object may have more than one worker at one time.
/// The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.
MaxConcurrentTaskCount: MaxConcurrentTaskCount
/// Configures how labels are consolidated across human workers.
@required
AnnotationConsolidationConfig: AnnotationConsolidationConfig
/// The price that you pay for each task performed by an Amazon Mechanical Turk worker.
PublicWorkforceTaskPrice: PublicWorkforceTaskPrice
}
/// Container for human task user interface information.
structure HumanTaskUiSummary {
/// The name of the human task user interface.
@required
HumanTaskUiName: HumanTaskUiName
/// The Amazon Resource Name (ARN) of the human task user interface.
@required
HumanTaskUiArn: HumanTaskUiArn
/// A timestamp when SageMaker created the human task user interface.
@required
CreationTime: Timestamp
}
/// The configuration for Hyperband, a multi-fidelity based hyperparameter
/// tuning strategy. Hyperband uses the final and intermediate results of a
/// training job to dynamically allocate resources to utilized hyperparameter configurations
/// while automatically stopping under-performing configurations. This parameter should be
/// provided only if Hyperband is selected as the StrategyConfig
/// under the HyperParameterTuningJobConfig API.
structure HyperbandStrategyConfig {
/// The minimum number of resources (such as epochs) that can be used by a training job
/// launched by a hyperparameter tuning job. If the value for MinResource has not
/// been reached, the training job will not be stopped by Hyperband.
MinResource: HyperbandStrategyMinResource
/// The maximum number of resources (such as epochs) that can be used by a training job
/// launched by a hyperparameter tuning job. Once a job reaches the MaxResource
/// value, it is stopped. If a value for MaxResource is not provided, and
/// Hyperband is selected as the hyperparameter tuning strategy,
/// HyperbandTrainingJ attempts to infer MaxResource from the
/// following keys (if present) in StaticsHyperParameters:
///
/// -
///
/// epochs
///
///
/// -
///
/// numepochs
///
///
/// -
///
/// n-epochs
///
///
/// -
///
/// n_epochs
///
///
/// -
///
/// num_epochs
///
///
///
/// If HyperbandStrategyConfig is unable to infer a value for
/// MaxResource, it generates a validation error. The maximum value is 20,000
/// epochs. All metrics that correspond to an objective metric are used to derive early stopping
/// decisions. For distributive training jobs,
/// ensure that duplicate metrics are not printed in the logs across the individual nodes in a
/// training job. If multiple nodes are publishing duplicate or incorrect metrics, training
/// jobs may make an incorrect stopping decision and stop the job prematurely.
MaxResource: HyperbandStrategyMaxResource
}
/// Specifies
/// which
/// training algorithm to use for training jobs that a hyperparameter
/// tuning job launches and the metrics to monitor.
structure HyperParameterAlgorithmSpecification {
/// The registry path of the Docker image that contains the training algorithm. For
/// information about Docker registry paths for built-in algorithms, see Algorithms
/// Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
/// registry/repository[:tag] and registry/repository[@digest]
/// image path formats. For more information, see Using Your Own Algorithms with Amazon
/// SageMaker.
TrainingImage: AlgorithmImage
@required
TrainingInputMode: TrainingInputMode
/// The name of the resource algorithm to use for the hyperparameter tuning job. If you
/// specify a value for this parameter, do not specify a value for
/// TrainingImage.
AlgorithmName: ArnOrName
/// An array of MetricDefinition objects that specify the
/// metrics
/// that the algorithm emits.
MetricDefinitions: MetricDefinitionList
}
/// Defines a hyperparameter to be used by an algorithm.
structure HyperParameterSpecification {
/// The name of this hyperparameter. The name must be unique.
@required
Name: ParameterName
/// A brief description of the hyperparameter.
Description: EntityDescription
/// The type of this hyperparameter. The valid types are Integer,
/// Continuous, Categorical, and FreeText.
@required
Type: ParameterType
/// The allowed range for this hyperparameter.
Range: ParameterRange
/// Indicates whether this hyperparameter is tunable in a hyperparameter tuning
/// job.
IsTunable: Boolean = false
/// Indicates whether this hyperparameter is required.
IsRequired: Boolean = false
/// The default value for this hyperparameter. If a default value is specified, a
/// hyperparameter cannot be required.
DefaultValue: HyperParameterValue
}
/// Defines
/// the training jobs launched by a hyperparameter tuning job.
structure HyperParameterTrainingJobDefinition {
/// The job definition name.
DefinitionName: HyperParameterTrainingJobDefinitionName
TuningObjective: HyperParameterTuningJobObjective
HyperParameterRanges: ParameterRanges
/// Specifies the values of hyperparameters
/// that
/// do not change for the tuning job.
StaticHyperParameters: HyperParameters
/// The HyperParameterAlgorithmSpecification object that
/// specifies
/// the resource algorithm to use for the training jobs that the tuning
/// job launches.
@required
AlgorithmSpecification: HyperParameterAlgorithmSpecification
/// The Amazon Resource Name (ARN) of the
/// IAM
/// role associated with the training jobs that the tuning job
/// launches.
@required
RoleArn: RoleArn
/// An array of Channel objects that specify
/// the
/// input for the training jobs that the tuning job launches.
InputDataConfig: InputDataConfig
/// The VpcConfig object that specifies the VPC that you want the
/// training jobs that this hyperparameter tuning job launches 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.
VpcConfig: VpcConfig
/// Specifies the path to the Amazon S3 bucket where you
/// store
/// model artifacts from the training jobs that the tuning job
/// launches.
@required
OutputDataConfig: OutputDataConfig
/// The resources,
/// including
/// the compute instances and storage volumes, to use for the training
/// jobs that the tuning job launches.
/// Storage volumes store model artifacts and
/// incremental
/// states. Training algorithms might also use storage volumes for
/// scratch
/// space. If you want SageMaker to use the 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.
///
/// If you want to use hyperparameter optimization with instance type flexibility, use
/// HyperParameterTuningResourceConfig instead.
///
ResourceConfig: ResourceConfig
/// Specifies a limit to how long a model hyperparameter 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.
@required
StoppingCondition: StoppingCondition
/// 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
/// network isolation is used 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.
EnableNetworkIsolation: Boolean = 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.
EnableInterContainerTrafficEncryption: Boolean = false
/// A Boolean indicating whether managed spot training is enabled (True) or
/// not (False).
EnableManagedSpotTraining: Boolean = false
CheckpointConfig: CheckpointConfig
/// The number of times to retry the job when the job fails due to an
/// InternalServerError.
RetryStrategy: RetryStrategy
/// The configuration for the hyperparameter tuning resources, including the compute
/// instances and storage volumes, used for training jobs launched by the tuning job. By
/// default, storage volumes hold model artifacts and incremental states. Choose
/// File for TrainingInputMode in the
/// AlgorithmSpecification parameter to additionally store training data in
/// the storage volume (optional).
HyperParameterTuningResourceConfig: HyperParameterTuningResourceConfig
/// An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See
/// Define metrics
/// and variables for more information.
///
/// The maximum number of items specified for Map Entries refers to the
/// maximum number of environment variables for each TrainingJobDefinition
/// and also the maximum for the hyperparameter tuning job itself. That is, the sum of
/// the number of environment variables for all the training job definitions can't
/// exceed the maximum number specified.
///
Environment: HyperParameterTrainingJobEnvironmentMap
}
/// The container for the summary information about a training job.
structure HyperParameterTrainingJobSummary {
/// The training job definition name.
TrainingJobDefinitionName: HyperParameterTrainingJobDefinitionName
/// The name of the training job.
@required
TrainingJobName: TrainingJobName
/// The Amazon Resource Name (ARN) of the training job.
@required
TrainingJobArn: TrainingJobArn
/// The HyperParameter tuning job that launched the training job.
TuningJobName: HyperParameterTuningJobName
/// The date and time that the training job was created.
@required
CreationTime: Timestamp
/// The date and time that the training job started.
TrainingStartTime: Timestamp
/// Specifies 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.
TrainingEndTime: Timestamp
/// The
/// status
/// of the training job.
@required
TrainingJobStatus: TrainingJobStatus
/// A
/// list of the hyperparameters for which you specified ranges to
/// search.
@required
TunedHyperParameters: HyperParameters
/// The
/// reason that the training job failed.
///
FailureReason: FailureReason
/// The FinalHyperParameterTuningJobObjectiveMetric object that
/// specifies the
/// value
/// of the
/// objective
/// metric of the tuning job that launched this training job.
FinalHyperParameterTuningJobObjectiveMetric: FinalHyperParameterTuningJobObjectiveMetric
/// The status of the objective metric for the training job:
///
/// -
///
Succeeded: The
/// final
/// objective metric for the training job was evaluated by the
/// hyperparameter tuning job and
/// used
/// in the hyperparameter tuning process.
///
///
///
/// -
///
Pending: The training job is in progress and evaluation of its final objective
/// metric is pending.
///
///
///
/// -
///
Failed:
/// The final objective metric for the training job was not evaluated, and was not
/// used in the hyperparameter tuning process. This typically occurs when the
/// training job failed or did not emit an objective
/// metric.
///
///
ObjectiveStatus: ObjectiveStatus
}
/// The configuration for hyperparameter tuning resources for use in training jobs
/// launched by the tuning job. These resources include compute instances and storage
/// volumes. Specify one or more compute instance configurations and allocation strategies
/// to select resources (optional).
structure HyperParameterTuningInstanceConfig {
/// The instance type used for processing of hyperparameter optimization jobs. Choose from
/// general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge
/// or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more
/// information about instance types, see instance type
/// descriptions.
@required
InstanceType: TrainingInstanceType
/// The number of instances of the type specified by InstanceType. Choose an
/// instance count larger than 1 for distributed training algorithms. See SageMaker distributed training
/// jobs for more information.
@required
InstanceCount: TrainingInstanceCount = 0
/// The volume size in GB of the data to be processed for hyperparameter optimization
/// (optional).
@required
VolumeSizeInGB: VolumeSizeInGB = 0
}
/// A structure that contains runtime information about both current and completed hyperparameter tuning jobs.
structure HyperParameterTuningJobCompletionDetails {
/// The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
NumberOfTrainingJobsObjectiveNotImproving: Integer = 0
/// The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConvergenceDetectedTime: Timestamp
}
/// Configures a hyperparameter tuning job.
structure HyperParameterTuningJobConfig {
/// Specifies how hyperparameter tuning chooses the combinations of hyperparameter values
/// to use for the training job it launches. For information about search strategies, see
/// How
/// Hyperparameter Tuning Works.
@required
Strategy: HyperParameterTuningJobStrategyType
/// The configuration for the Hyperband optimization strategy. This parameter
/// should be provided only if Hyperband is selected as the strategy for
/// HyperParameterTuningJobConfig.
StrategyConfig: HyperParameterTuningJobStrategyConfig
/// The HyperParameterTuningJobObjective specifies the objective metric
/// used to evaluate the performance of training jobs launched by this tuning job.
HyperParameterTuningJobObjective: HyperParameterTuningJobObjective
/// The ResourceLimits object that specifies the maximum number of
/// training and parallel training jobs that can be used for this hyperparameter tuning
/// job.
@required
ResourceLimits: ResourceLimits
/// The ParameterRanges object that specifies the ranges of
/// hyperparameters that this tuning job searches over to find the optimal configuration for
/// the highest model performance against your chosen objective metric.
ParameterRanges: ParameterRanges
/// Specifies whether to use early stopping for training jobs launched by the
/// hyperparameter tuning job. Because the Hyperband strategy has its own
/// advanced internal early stopping mechanism, TrainingJobEarlyStoppingType
/// must be OFF to use Hyperband. This parameter can take on one
/// of the following values (the default value is OFF):
///
/// - OFF
/// -
///
Training jobs launched by the hyperparameter tuning job do not use early
/// stopping.
///
/// - AUTO
/// -
///
SageMaker stops training jobs launched by the hyperparameter tuning job when
/// they are unlikely to perform better than previously completed training jobs.
/// For more information, see Stop Training Jobs Early.
///
///
TrainingJobEarlyStoppingType: TrainingJobEarlyStoppingType
/// The tuning job's completion criteria.
TuningJobCompletionCriteria: TuningJobCompletionCriteria
/// A value used to initialize a pseudo-random number generator. Setting a random seed and
/// using the same seed later for the same tuning job will allow hyperparameter optimization
/// to find more a consistent hyperparameter configuration between the two runs.
RandomSeed: RandomSeed
}
/// The total resources consumed by your hyperparameter tuning job.
structure HyperParameterTuningJobConsumedResources {
/// The wall clock runtime in seconds used by your hyperparameter tuning job.
RuntimeInSeconds: Integer = 0
}
/// Defines the objective metric for a hyperparameter tuning job.
/// Hyperparameter
/// tuning uses the value of this metric to evaluate the training jobs it launches, and
/// returns the training job that results in either the highest or lowest value for this
/// metric, depending on the value you specify for the Type
/// parameter.
structure HyperParameterTuningJobObjective {
/// Whether to
/// minimize
/// or maximize the objective metric.
@required
Type: HyperParameterTuningJobObjectiveType
/// The
/// name of the metric to use for the objective metric.
@required
MetricName: MetricName
}
/// An entity returned by the SearchRecord API
/// containing the properties of a hyperparameter tuning job.
structure HyperParameterTuningJobSearchEntity {
/// The name of a hyperparameter tuning job.
HyperParameterTuningJobName: HyperParameterTuningJobName
/// The Amazon Resource Name (ARN) of a hyperparameter tuning job.
HyperParameterTuningJobArn: HyperParameterTuningJobArn
HyperParameterTuningJobConfig: HyperParameterTuningJobConfig
TrainingJobDefinition: HyperParameterTrainingJobDefinition
/// The job definitions included in a hyperparameter tuning job.
TrainingJobDefinitions: HyperParameterTrainingJobDefinitions
/// The status of a hyperparameter tuning job.
HyperParameterTuningJobStatus: HyperParameterTuningJobStatus
/// The time that a hyperparameter tuning job was created.
CreationTime: Timestamp
/// The time that a hyperparameter tuning job ended.
HyperParameterTuningEndTime: Timestamp
/// The time that a hyperparameter tuning job was last modified.
LastModifiedTime: Timestamp
TrainingJobStatusCounters: TrainingJobStatusCounters
ObjectiveStatusCounters: ObjectiveStatusCounters
BestTrainingJob: HyperParameterTrainingJobSummary
OverallBestTrainingJob: HyperParameterTrainingJobSummary
WarmStartConfig: HyperParameterTuningJobWarmStartConfig
/// The error that was created when a hyperparameter tuning job failed.
FailureReason: FailureReason
/// The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.
Tags: TagList
/// Information about either a current or completed hyperparameter tuning job.
TuningJobCompletionDetails: HyperParameterTuningJobCompletionDetails
/// The total amount of resources consumed by a hyperparameter tuning job.
ConsumedResources: HyperParameterTuningJobConsumedResources
}
/// The configuration for a training job launched by a hyperparameter tuning job. Choose
/// Bayesian for Bayesian optimization, and Random for random
/// search optimization. For more advanced use cases, use Hyperband, which
/// evaluates objective metrics for training jobs after every epoch. For more information about
/// strategies, see How Hyperparameter
/// Tuning Works.
structure HyperParameterTuningJobStrategyConfig {
/// The configuration for the object that specifies the Hyperband strategy.
/// This parameter is only supported for the Hyperband selection for
/// Strategy within the HyperParameterTuningJobConfig API.
HyperbandStrategyConfig: HyperbandStrategyConfig
}
/// Provides summary information about a hyperparameter tuning job.
structure HyperParameterTuningJobSummary {
/// The name of the tuning job.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
/// The
/// Amazon
/// Resource Name (ARN) of the tuning job.
@required
HyperParameterTuningJobArn: HyperParameterTuningJobArn
/// The status of the
/// tuning
/// job.
@required
HyperParameterTuningJobStatus: HyperParameterTuningJobStatus
/// Specifies the search strategy hyperparameter tuning uses to choose which
/// hyperparameters to
/// evaluate
/// at each iteration.
@required
Strategy: HyperParameterTuningJobStrategyType
/// The date and time that the tuning job was created.
@required
CreationTime: Timestamp
/// The date and time that the tuning job ended.
HyperParameterTuningEndTime: Timestamp
/// The date and time that the tuning job was
/// modified.
LastModifiedTime: Timestamp
/// The TrainingJobStatusCounters object that specifies the numbers of
/// training jobs, categorized by status, that this tuning job launched.
@required
TrainingJobStatusCounters: TrainingJobStatusCounters
/// The ObjectiveStatusCounters object that specifies the numbers of
/// training jobs, categorized by objective metric status, that this tuning job
/// launched.
@required
ObjectiveStatusCounters: ObjectiveStatusCounters
/// The ResourceLimits object that specifies the maximum number of
/// training jobs and parallel training jobs allowed for this tuning job.
ResourceLimits: ResourceLimits
}
/// Specifies the configuration for a hyperparameter tuning job that uses one or more
/// previous hyperparameter 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, and the training job that performs the best 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.
///
structure HyperParameterTuningJobWarmStartConfig {
/// An array of hyperparameter tuning jobs that are used as the starting point for the new
/// hyperparameter tuning job. For more information about warm starting a hyperparameter
/// tuning job, see Using a Previous
/// Hyperparameter Tuning Job as a Starting Point.
/// Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent
/// jobs for warm start tuning jobs.
@required
ParentHyperParameterTuningJobs: ParentHyperParameterTuningJobs
/// Specifies one of the following:
///
/// - IDENTICAL_DATA_AND_ALGORITHM
/// -
///
The new hyperparameter tuning job uses the same input data and training
/// image as the parent tuning jobs. You can change the hyperparameter ranges to
/// search and the maximum number of training jobs that the hyperparameter
/// tuning job launches. You cannot use a new version of the training algorithm,
/// unless the changes in the new version do not affect the algorithm itself.
/// For example, changes that improve logging or adding support for a different
/// data format are allowed. You can also change hyperparameters from tunable to
/// static, and from static to tunable, but the total number of static plus
/// tunable hyperparameters must remain the same as it is in all parent jobs.
/// The objective metric for the new tuning job must be the same as for all
/// parent jobs.
///
/// - TRANSFER_LEARNING
/// -
///
The new hyperparameter tuning job can include input data, hyperparameter
/// ranges, maximum number of concurrent training jobs, and maximum number of
/// training jobs that are different than those of its parent hyperparameter
/// tuning jobs. The training image can also be a different version from the
/// version used in the parent hyperparameter tuning job. You can also change
/// hyperparameters from tunable to static, and from static to tunable, but the
/// total number of static plus tunable hyperparameters must remain the same as
/// it is in all parent jobs. The objective metric for the new tuning job must
/// be the same as for all parent jobs.
///
///
@required
WarmStartType: HyperParameterTuningJobWarmStartType
}
/// The configuration of resources, including compute instances and storage volumes for
/// use in training jobs launched by hyperparameter tuning jobs.
/// HyperParameterTuningResourceConfig is similar to
/// ResourceConfig, but has the additional InstanceConfigs and
/// AllocationStrategy fields to allow for flexible instance management.
/// Specify one or more instance types, count, and the allocation strategy for instance
/// selection.
///
///
/// HyperParameterTuningResourceConfig supports the capabilities of
/// ResourceConfig with the exception of
/// KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools
/// by default, which reuse clusters between training jobs.
///
structure HyperParameterTuningResourceConfig {
/// The instance type used to run hyperparameter optimization tuning jobs. See descriptions of
/// instance types for more information.
InstanceType: TrainingInstanceType
/// The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
InstanceCount: TrainingInstanceCount = 0
/// The volume size in GB for the storage volume to be used in processing hyperparameter
/// optimization jobs (optional). These volumes store model artifacts, incremental states
/// and optionally, scratch space for training algorithms. Do not provide a value for this
/// parameter if a value for InstanceConfigs is also specified.
/// Some instance types have a fixed total local storage size. If you select one of these
/// instances for training, VolumeSizeInGB cannot be greater than this total
/// size. For a list of instance types with local instance storage and their sizes, see
/// instance store volumes.
///
/// SageMaker supports only the General Purpose SSD
/// (gp2) storage volume type.
///
VolumeSizeInGB: OptionalVolumeSizeInGB = 0
/// A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume
/// attached to the compute instances used to run the training job. You can use either of
/// the following formats to specify a key.
/// 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"
///
/// Some instances use local storage, which use a hardware module to
/// encrypt storage volumes. If you choose one of these instance types, you
/// cannot request a VolumeKmsKeyId. For a list of instance types that use
/// local storage, see instance store
/// volumes. For more information about Amazon Web Services Key Management Service, see KMS
/// encryption for more information.
VolumeKmsKeyId: KmsKeyId
/// The strategy that determines the order of preference for resources specified in
/// InstanceConfigs used in hyperparameter optimization.
AllocationStrategy: HyperParameterTuningAllocationStrategy
/// A list containing the configuration(s) for one or more resources for processing
/// hyperparameter jobs. These resources include compute instances and storage volumes to
/// use in model training jobs launched by hyperparameter tuning jobs. The
/// AllocationStrategy controls the order in which multiple configurations
/// provided in InstanceConfigs are used.
///
/// If you only want to use a single instance configuration inside the
/// HyperParameterTuningResourceConfig API, do not provide a value for
/// InstanceConfigs. Instead, use InstanceType,
/// VolumeSizeInGB and InstanceCount. If you use
/// InstanceConfigs, do not provide values for
/// InstanceType, VolumeSizeInGB or
/// InstanceCount.
///
InstanceConfigs: HyperParameterTuningInstanceConfigs
}
/// A SageMaker image. A SageMaker image represents a set of container images that are derived from
/// a common base container image. Each of these container images is represented by a SageMaker
/// ImageVersion.
structure Image {
/// When the image was created.
@required
CreationTime: Timestamp
/// The description of the image.
Description: ImageDescription
/// The name of the image as displayed.
DisplayName: ImageDisplayName
/// When a create, update, or delete operation fails, the reason for the failure.
FailureReason: FailureReason
/// The ARN of the image.
@required
ImageArn: ImageArn
/// The name of the image.
@required
ImageName: ImageName
/// The status of the image.
@required
ImageStatus: ImageStatus
/// When the image was last modified.
@required
LastModifiedTime: Timestamp
}
/// Specifies whether the model container is in Amazon ECR or a private Docker registry
/// accessible from your Amazon Virtual Private Cloud (VPC).
structure ImageConfig {
/// Set this to one of the following values:
///
/// -
///
/// Platform - The model image is hosted in Amazon ECR.
///
/// -
///
/// Vpc - The model image is hosted in a private Docker registry in
/// your VPC.
///
///
@required
RepositoryAccessMode: RepositoryAccessMode
/// (Optional) Specifies an authentication configuration for the private docker registry
/// where your model image is hosted. Specify a value for this property only if you
/// specified Vpc as the value for the RepositoryAccessMode field,
/// and the private Docker registry where the model image is hosted requires
/// authentication.
RepositoryAuthConfig: RepositoryAuthConfig
}
/// A version of a SageMaker Image. A version represents an existing container
/// image.
structure ImageVersion {
/// When the version was created.
@required
CreationTime: Timestamp
/// When a create or delete operation fails, the reason for the failure.
FailureReason: FailureReason
/// The ARN of the image the version is based on.
@required
ImageArn: ImageArn
/// The ARN of the version.
@required
ImageVersionArn: ImageVersionArn
/// The status of the version.
@required
ImageVersionStatus: ImageVersionStatus
/// When the version was last modified.
@required
LastModifiedTime: Timestamp
/// The version number.
@required
Version: ImageVersionNumber
}
@input
structure ImportHubContentRequest {
/// The name of the hub content to import.
@required
HubContentName: HubContentName
/// The version of the hub content to import.
HubContentVersion: HubContentVersion
/// The type of hub content to import.
@required
HubContentType: HubContentType
/// The version of the hub content schema to import.
@required
DocumentSchemaVersion: DocumentSchemaVersion
/// The name of the hub to import content into.
@required
HubName: HubName
/// The display name of the hub content to import.
HubContentDisplayName: HubContentDisplayName
/// A description of the hub content to import.
HubContentDescription: HubContentDescription
/// Markdown files associated with the hub content to import.
HubContentMarkdown: HubContentMarkdown
/// The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
@required
HubContentDocument: HubContentDocument
/// The searchable keywords of the hub content.
HubContentSearchKeywords: HubContentSearchKeywordList
/// Any tags associated with the hub content.
Tags: TagList
}
@output
structure ImportHubContentResponse {
/// The ARN of the hub that the content was imported into.
@required
HubArn: HubArn
/// The ARN of the hub content that was imported.
@required
HubContentArn: HubContentArn
}
/// Specifies details about how containers in a multi-container endpoint are run.
structure InferenceExecutionConfig {
/// How containers in a multi-container are run. The following values are valid.
///
/// -
///
/// SERIAL - Containers run as a serial pipeline.
///
/// -
///
/// DIRECT - Only the individual container that you specify is
/// run.
///
///
@required
Mode: InferenceExecutionMode
}
/// The Amazon S3 location and configuration for storing inference request and response data.
structure InferenceExperimentDataStorageConfig {
/// The Amazon S3 bucket where the inference request and response data is stored.
@required
Destination: DestinationS3Uri
///
/// The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3
/// server-side encryption.
///
KmsKey: KmsKeyId
ContentType: CaptureContentTypeHeader
}
/// The start and end times of an inference experiment.
/// The maximum duration that you can set for an inference experiment is 30 days.
structure InferenceExperimentSchedule {
/// The timestamp at which the inference experiment started or will start.
StartTime: Timestamp
/// The timestamp at which the inference experiment ended or will end.
EndTime: Timestamp
}
/// Lists a summary of properties of an inference experiment.
structure InferenceExperimentSummary {
/// The name of the inference experiment.
@required
Name: InferenceExperimentName
/// The type of the inference experiment.
@required
Type: InferenceExperimentType
/// The duration for which the inference experiment ran or will run.
/// The maximum duration that you can set for an inference experiment is 30 days.
Schedule: InferenceExperimentSchedule
/// The status of the inference experiment.
@required
Status: InferenceExperimentStatus
/// The error message for the inference experiment status result.
StatusReason: InferenceExperimentStatusReason
/// The description of the inference experiment.
Description: InferenceExperimentDescription
/// The timestamp at which the inference experiment was created.
@required
CreationTime: Timestamp
/// The timestamp at which the inference experiment was completed.
CompletionTime: Timestamp
/// The timestamp when you last modified the inference experiment.
@required
LastModifiedTime: Timestamp
///
/// 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.
///
RoleArn: RoleArn
}
/// The metrics for an existing endpoint compared in an Inference Recommender job.
structure InferenceMetrics {
/// The expected maximum number of requests per minute for the instance.
@required
MaxInvocations: Integer = 0
/// The expected model latency at maximum invocations per minute for the instance.
@required
ModelLatency: Integer = 0
}
/// A list of recommendations made by Amazon SageMaker Inference Recommender.
structure InferenceRecommendation {
/// The metrics used to decide what recommendation to make.
@required
Metrics: RecommendationMetrics
/// Defines the endpoint configuration parameters.
@required
EndpointConfiguration: EndpointOutputConfiguration
/// Defines the model configuration.
@required
ModelConfiguration: ModelConfiguration
/// The recommendation ID which uniquely identifies each recommendation.
RecommendationId: String
}
/// A structure that contains a list of recommendation jobs.
structure InferenceRecommendationsJob {
/// The name of the job.
@required
JobName: RecommendationJobName
/// The job description.
@required
JobDescription: RecommendationJobDescription
/// The recommendation job type.
@required
JobType: RecommendationJobType
/// The Amazon Resource Name (ARN) of the recommendation job.
@required
JobArn: RecommendationJobArn
/// The status of the job.
@required
Status: RecommendationJobStatus
/// A timestamp that shows when the job was created.
@required
CreationTime: CreationTime
/// A timestamp that shows when the job completed.
CompletionTime: Timestamp
/// The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker
/// to perform tasks on your behalf.
@required
RoleArn: RoleArn
/// A timestamp that shows when the job was last modified.
@required
LastModifiedTime: LastModifiedTime
/// If the job fails, provides information why the job failed.
FailureReason: FailureReason
}
/// A returned array object for the Steps response field in the
/// ListInferenceRecommendationsJobSteps API command.
structure InferenceRecommendationsJobStep {
/// The type of the subtask.
///
/// BENCHMARK: Evaluate the performance of your model on different instance types.
@required
StepType: RecommendationStepType
/// The name of the Inference Recommender job.
@required
JobName: RecommendationJobName
/// The current status of the benchmark.
@required
Status: RecommendationJobStatus
/// The details for a specific benchmark.
InferenceBenchmark: RecommendationJobInferenceBenchmark
}
/// Defines how to perform inference generation after a training job is run.
structure InferenceSpecification {
/// The Amazon ECR registry path of the Docker image that contains the inference code.
@required
Containers: ModelPackageContainerDefinitionList
/// A list of the instance types on which a transformation job can be run or on which an
/// endpoint can be deployed.
/// This parameter is required for unversioned models, and optional for versioned
/// models.
SupportedTransformInstanceTypes: TransformInstanceTypes
/// A list of the instance types that are used to generate inferences in real-time.
/// This parameter is required for unversioned models, and optional for versioned
/// models.
SupportedRealtimeInferenceInstanceTypes: RealtimeInferenceInstanceTypes
/// The supported MIME types for the input data.
@required
SupportedContentTypes: ContentTypes
/// The supported MIME types for the output data.
@required
SupportedResponseMIMETypes: ResponseMIMETypes
}
/// Contains 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.
structure InputConfig {
/// The S3 path where the model artifacts, which result from model training, are stored.
/// This path must point to a single gzip compressed tar archive (.tar.gz suffix).
@required
S3Uri: S3Uri
/// Specifies the name and shape of the expected data inputs for your trained model with a
/// JSON dictionary form. The data inputs are InputConfig$Framework
/// specific.
///
/// -
///
/// TensorFlow: You must specify the name and shape (NHWC format) of
/// the expected data inputs using a dictionary format for your trained model. The
/// dictionary formats required for the console and CLI are different.
///
/// -
///
Examples for one input:
///
/// -
///
If using the console,
/// {"input":[1,1024,1024,3]}
///
///
/// -
///
If using the CLI,
/// {\"input\":[1,1024,1024,3]}
///
///
///
///
/// -
///
Examples for two inputs:
///
/// -
///
If using the console, {"data1": [1,28,28,1],
/// "data2":[1,28,28,1]}
///
///
/// -
///
If using the CLI, {\"data1\": [1,28,28,1],
/// \"data2\":[1,28,28,1]}
///
///
///
///
///
///
/// -
///
/// KERAS: You must specify the name and shape (NCHW format) of
/// expected data inputs using a dictionary format for your trained model. Note that
/// while Keras model artifacts should be uploaded in NHWC (channel-last) format,
/// DataInputConfig should be specified in NCHW (channel-first)
/// format. The dictionary formats required for the console and CLI are
/// different.
///
/// -
///
Examples for one input:
///
/// -
///
If using the console,
/// {"input_1":[1,3,224,224]}
///
///
/// -
///
If using the CLI,
/// {\"input_1\":[1,3,224,224]}
///
///
///
///
/// -
///
Examples for two inputs:
///
/// -
///
If using the console, {"input_1": [1,3,224,224],
/// "input_2":[1,3,224,224]}
///
///
/// -
///
If using the CLI, {\"input_1\": [1,3,224,224],
/// \"input_2\":[1,3,224,224]}
///
///
///
///
///
///
/// -
///
/// MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of
/// the expected data inputs in order using a dictionary format for your trained
/// model. The dictionary formats required for the console and CLI are
/// different.
///
/// -
///
Examples for one input:
///
/// -
///
If using the console,
/// {"data":[1,3,1024,1024]}
///
///
/// -
///
If using the CLI,
/// {\"data\":[1,3,1024,1024]}
///
///
///
///
/// -
///
Examples for two inputs:
///
/// -
///
If using the console, {"var1": [1,1,28,28],
/// "var2":[1,1,28,28]}
///
///
/// -
///
If using the CLI, {\"var1\": [1,1,28,28],
/// \"var2\":[1,1,28,28]}
///
///
///
///
///
///
/// -
///
/// PyTorch: You can either specify the name and shape (NCHW format)
/// of expected data inputs in order using a dictionary format for your trained
/// model or you can specify the shape only using a list format. The dictionary
/// formats required for the console and CLI are different. The list formats for the
/// console and CLI are the same.
///
/// -
///
Examples for one input in dictionary format:
///
/// -
///
If using the console,
/// {"input0":[1,3,224,224]}
///
///
/// -
///
If using the CLI,
/// {\"input0\":[1,3,224,224]}
///
///
///
///
/// -
///
Example for one input in list format:
/// [[1,3,224,224]]
///
///
/// -
///
Examples for two inputs in dictionary format:
///
/// -
///
If using the console, {"input0":[1,3,224,224],
/// "input1":[1,3,224,224]}
///
///
/// -
///
If using the CLI, {\"input0\":[1,3,224,224],
/// \"input1\":[1,3,224,224]}
///
///
///
///
/// -
///
Example for two inputs in list format: [[1,3,224,224],
/// [1,3,224,224]]
///
///
///
///
/// -
///
/// XGBOOST: input data name and shape are not needed.
///
///
///
/// DataInputConfig supports the following parameters for CoreML
/// OutputConfig$TargetDevice (ML Model format):
///
/// -
///
/// shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}.
/// In addition to static input shapes, CoreML converter supports Flexible input shapes:
///
/// -
///
Range Dimension. You can use the Range Dimension feature if you know the input shape
/// will be within some specific interval in that dimension,
/// for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
///
///
/// -
///
Enumerated shapes. Sometimes, the models are trained to work only on a select
/// set of inputs. You can enumerate all supported input shapes,
/// for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
///
///
///
///
/// -
///
/// default_shape: Default input shape. You can set a default shape during
/// conversion for both Range Dimension and Enumerated Shapes. For example
/// {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
///
///
/// -
///
/// type: Input type. Allowed values: Image and Tensor.
/// By default, the converter generates an ML Model with inputs of type Tensor (MultiArray).
/// User can set input type to be Image. Image input type requires additional input parameters
/// such as bias and scale.
///
/// -
///
/// bias: If the input type is an Image, you need to provide the bias vector.
///
/// -
///
/// scale: If the input type is an Image, you need to provide a scale factor.
///
///
/// CoreML ClassifierConfig parameters can be specified using
/// OutputConfig$CompilerOptions. CoreML converter supports Tensorflow and PyTorch models.
/// CoreML conversion examples:
///
/// -
///
Tensor type input:
///
/// -
///
/// "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape":
/// [1,224,224,3]}}
///
///
///
///
/// -
///
Tensor type input without input name (PyTorch):
///
/// -
///
/// "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape":
/// [1,3,224,224]}]
///
///
///
///
/// -
///
Image type input:
///
/// -
///
/// "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape":
/// [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
///
///
/// -
///
/// "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
///
///
///
///
/// -
///
Image type input without input name (PyTorch):
///
/// -
///
/// "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape":
/// [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
///
///
/// -
///
/// "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
///
///
///
///
///
/// Depending on the model format, DataInputConfig requires the following parameters for
/// ml_eia2
/// OutputConfig:TargetDevice.
///
/// -
///
For TensorFlow models saved in the SavedModel format, specify the input names
/// from signature_def_key and the input model shapes for DataInputConfig.
/// Specify the signature_def_key in
///
/// OutputConfig:CompilerOptions
/// if
/// the model does not use TensorFlow's default signature def key. For example:
///
/// -
///
/// "DataInputConfig": {"inputs": [1, 224, 224, 3]}
///
///
/// -
///
/// "CompilerOptions": {"signature_def_key": "serving_custom"}
///
///
///
///
/// -
///
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes
/// in DataInputConfig and the output tensor names for output_names in
///
/// OutputConfig:CompilerOptions
/// .
/// For example:
///
/// -
///
/// "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
///
///
/// -
///
/// "CompilerOptions": {"output_names": ["output_tensor:0"]}
///
///
///
///
///
@required
DataInputConfig: DataInputConfig
/// Identifies the framework in which the model was trained. For example:
/// TENSORFLOW.
@required
Framework: Framework
/// Specifies the framework version to use. This API field is only supported for the MXNet,
/// PyTorch, TensorFlow and TensorFlow Lite frameworks.
/// For information about framework versions supported for cloud targets and edge devices, see
/// Cloud Supported Instance Types and Frameworks and
/// Edge Supported Frameworks.
FrameworkVersion: FrameworkVersion
}
/// Defines an instance group for heterogeneous cluster training. When requesting a
/// training job using the CreateTrainingJob API, you can configure multiple instance groups .
structure InstanceGroup {
/// Specifies the instance type of the instance group.
@required
InstanceType: TrainingInstanceType
/// Specifies the number of instances of the instance group.
@required
InstanceCount: TrainingInstanceCount = 0
/// Specifies the name of the instance group.
@required
InstanceGroupName: InstanceGroupName
}
/// Information on the IMDS configuration of the notebook instance
structure InstanceMetadataServiceConfiguration {
/// Indicates the minimum IMDS version that the notebook instance supports. When passed as part of CreateNotebookInstance, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part of UpdateNotebookInstance, there is no default.
@required
MinimumInstanceMetadataServiceVersion: MinimumInstanceMetadataServiceVersion
}
/// For a hyperparameter of the integer type, specifies the range
/// that
/// a hyperparameter tuning job searches.
structure IntegerParameterRange {
/// The name of the hyperparameter to search.
@required
Name: ParameterKey
/// The minimum
/// value
/// of the hyperparameter to search.
@required
MinValue: ParameterValue
/// The maximum
/// value
/// of the hyperparameter to search.
@required
MaxValue: ParameterValue
/// The scale that hyperparameter tuning uses to search the hyperparameter range. For
/// information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
///
/// - Auto
/// -
///
SageMaker hyperparameter tuning chooses the best scale for the
/// hyperparameter.
///
/// - Linear
/// -
///
Hyperparameter tuning searches the values in the hyperparameter range by
/// using a linear scale.
///
/// - Logarithmic
/// -
///
Hyperparameter tuning searches the values in the hyperparameter range by
/// using a logarithmic scale.
/// Logarithmic scaling works only for ranges that have only values greater
/// than 0.
///
///
ScalingType: HyperParameterScalingType
}
/// Defines the possible values for an integer hyperparameter.
structure IntegerParameterRangeSpecification {
/// The minimum integer value allowed.
@required
MinValue: ParameterValue
/// The maximum integer value allowed.
@required
MaxValue: ParameterValue
}
/// The JupyterServer app settings.
structure JupyterServerAppSettings {
/// The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
DefaultResourceSpec: ResourceSpec
/// The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
///
/// To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
///
LifecycleConfigArns: LifecycleConfigArns
/// A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
CodeRepositories: CodeRepositories
}
/// The KernelGateway app settings.
structure KernelGatewayAppSettings {
/// The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
///
/// The Amazon SageMaker Studio UI does not use the default instance type value set here. The default
/// instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation
/// and the instance type parameter value is not passed.
///
DefaultResourceSpec: ResourceSpec
/// A list of custom SageMaker images that are configured to run as a KernelGateway app.
CustomImages: CustomImages
/// The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
///
/// To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
///
LifecycleConfigArns: LifecycleConfigArns
}
/// The configuration for the file system and kernels in a SageMaker image running as a
/// KernelGateway app.
structure KernelGatewayImageConfig {
/// The specification of the Jupyter kernels in the image.
@required
KernelSpecs: KernelSpecs
/// The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
FileSystemConfig: FileSystemConfig
}
/// The specification of a Jupyter kernel.
structure KernelSpec {
/// The name of the Jupyter kernel in the image. This value is case sensitive.
@required
Name: KernelName
/// The display name of the kernel.
DisplayName: KernelDisplayName
}
/// Provides a breakdown of the number of objects labeled.
structure LabelCounters {
/// The total number of objects labeled.
TotalLabeled: LabelCounter = 0
/// The total number of objects labeled by a human worker.
HumanLabeled: LabelCounter = 0
/// The total number of objects labeled by automated data labeling.
MachineLabeled: LabelCounter = 0
/// The total number of objects that could not be labeled due to an error.
FailedNonRetryableError: LabelCounter = 0
/// The total number of objects not yet labeled.
Unlabeled: LabelCounter = 0
}
/// Provides counts for human-labeled tasks in the labeling job.
structure LabelCountersForWorkteam {
/// The total number of data objects labeled by a human worker.
HumanLabeled: LabelCounter = 0
/// The total number of data objects that need to be labeled by a human worker.
PendingHuman: LabelCounter = 0
/// The total number of tasks in the labeling job.
Total: LabelCounter = 0
}
/// Provides configuration information for auto-labeling of your data objects. A
/// LabelingJobAlgorithmsConfig object must be supplied in order to use
/// auto-labeling.
structure LabelingJobAlgorithmsConfig {
/// Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You
/// must select one of the following ARNs:
///
/// -
///
/// Image classification
///
///
/// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
///
///
/// -
///
/// Text classification
///
///
/// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
///
///
/// -
///
/// Object detection
///
///
/// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
///
///
/// -
///
/// Semantic Segmentation
///
///
/// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
///
///
///
@required
LabelingJobAlgorithmSpecificationArn: LabelingJobAlgorithmSpecificationArn
/// At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final
/// model used for auto-labeling. You can use this model as the starting point for
/// subsequent similar jobs by providing the ARN of the model here.
InitialActiveLearningModelArn: ModelArn
/// Provides configuration information for a labeling job.
LabelingJobResourceConfig: LabelingJobResourceConfig
}
/// Attributes of the data specified by the customer. Use these to describe the data to be
/// labeled.
structure LabelingJobDataAttributes {
/// Declares that your content is free of personally identifiable information or adult
/// content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task
/// based on this information.
ContentClassifiers: ContentClassifiers
}
/// Provides information about the location of input data.
/// 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.
/// 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.
structure LabelingJobDataSource {
/// The Amazon S3 location of the input data objects.
S3DataSource: LabelingJobS3DataSource
/// An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
SnsDataSource: LabelingJobSnsDataSource
}
/// Provides summary information for a work team.
structure LabelingJobForWorkteamSummary {
/// The name of the labeling job that the work team is assigned to.
LabelingJobName: LabelingJobName
/// A unique identifier for a labeling job. You can use this to refer to a specific
/// labeling job.
@required
JobReferenceCode: JobReferenceCode
/// The Amazon Web Services account ID of the account used to start the labeling
/// job.
@required
WorkRequesterAccountId: AccountId
/// The date and time that the labeling job was created.
@required
CreationTime: Timestamp
/// Provides information about the progress of a labeling job.
LabelCounters: LabelCountersForWorkteam
/// The configured number of workers per data object.
NumberOfHumanWorkersPerDataObject: NumberOfHumanWorkersPerDataObject
}
/// Input configuration information for a labeling job.
structure LabelingJobInputConfig {
/// The location of the input data.
@required
DataSource: LabelingJobDataSource
/// Attributes of the data specified by the customer.
DataAttributes: LabelingJobDataAttributes
}
/// Specifies the location of the output produced by the labeling job.
structure LabelingJobOutput {
/// The Amazon S3 bucket location of the manifest file for labeled data.
@required
OutputDatasetS3Uri: S3Uri
/// The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of
/// automated data labeling.
FinalActiveLearningModelArn: ModelArn
}
/// Output configuration information for a labeling job.
structure LabelingJobOutputConfig {
/// The Amazon S3 location to write output data.
@required
S3OutputPath: S3Uri
/// The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
/// If you provide your own KMS key ID, you must add the required permissions to your KMS
/// key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.
/// If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your
/// role's account to encrypt your output data.
/// 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.
///
KmsKeyId: KmsKeyId
/// An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to
/// do real time chaining to another streaming job and receive an Amazon SNS notifications each
/// time a data object is submitted by a worker.
/// If you provide an SnsTopicArn in OutputConfig, when workers
/// complete labeling tasks, Ground Truth will send labeling task output data to the SNS output
/// topic you specify here.
/// To learn more, see Receive Output Data from a Streaming Labeling
/// Job.
SnsTopicArn: SnsTopicArn
}
/// Configure encryption on the storage volume attached to the ML compute instance used to
/// run automated data labeling model training and inference.
structure LabelingJobResourceConfig {
/// 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(s) that run the training and inference jobs used for
/// automated data labeling.
/// You can only specify a VolumeKmsKeyId when you create a labeling job with
/// automated data labeling enabled using the API operation CreateLabelingJob.
/// You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for
/// automated data labeling model training and inference when you create a labeling job
/// using the console. To learn more, see Output Data and Storage Volume
/// Encryption.
/// The VolumeKmsKeyId 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"
///
///
///
VolumeKmsKeyId: KmsKeyId
VpcConfig: VpcConfig
}
/// The Amazon S3 location of the input data objects.
structure LabelingJobS3DataSource {
/// The Amazon S3 location of the manifest file that describes the input data objects.
/// The input manifest file referenced in ManifestS3Uri must contain one of
/// the following keys: source-ref or source. The value of the
/// keys are interpreted as follows:
///
/// -
///
/// source-ref: The source of the object is the Amazon S3 object
/// specified in the value. Use this value when the object is a binary object, such
/// as an image.
///
/// -
///
/// source: The source of the object is the value. Use this
/// value when the object is a text value.
///
///
/// If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to
/// create an input manifest file.
@required
ManifestS3Uri: S3Uri
}
/// An Amazon SNS data source used for streaming labeling jobs.
structure LabelingJobSnsDataSource {
/// The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic
/// you will use to send new data objects to a streaming labeling job.
@required
SnsTopicArn: SnsTopicArn
}
/// A set of conditions for stopping a 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 jobs fail after 30 days with an appropriate client error message.
///
structure LabelingJobStoppingConditions {
/// The maximum number of objects that can be labeled by human workers.
MaxHumanLabeledObjectCount: MaxHumanLabeledObjectCount
/// The maximum number of input data objects that should be labeled.
MaxPercentageOfInputDatasetLabeled: MaxPercentageOfInputDatasetLabeled
}
/// Provides summary information about a labeling job.
structure LabelingJobSummary {
/// The name of the labeling job.
@required
LabelingJobName: LabelingJobName
/// The Amazon Resource Name (ARN) assigned to the labeling job when it was
/// created.
@required
LabelingJobArn: LabelingJobArn
/// The date and time that the job was created (timestamp).
@required
CreationTime: Timestamp
/// The date and time that the job was last modified (timestamp).
@required
LastModifiedTime: Timestamp
/// The current status of the labeling job.
@required
LabelingJobStatus: LabelingJobStatus
/// Counts showing the progress of the labeling job.
@required
LabelCounters: LabelCounters
/// The Amazon Resource Name (ARN) of the work team assigned to the job.
@required
WorkteamArn: WorkteamArn
/// The Amazon Resource Name (ARN) of a Lambda function. The function is run before each
/// data object is sent to a worker.
@required
PreHumanTaskLambdaArn: LambdaFunctionArn
/// The Amazon Resource Name (ARN) of the Lambda function used to consolidate the
/// annotations from individual workers into a label for a data object. For more
/// information, see Annotation
/// Consolidation.
AnnotationConsolidationLambdaArn: LambdaFunctionArn
/// If the LabelingJobStatus field is Failed, this field
/// contains a description of the error.
FailureReason: FailureReason
/// The location of the output produced by the labeling job.
LabelingJobOutput: LabelingJobOutput
/// Input configuration for the labeling job.
InputConfig: LabelingJobInputConfig
}
/// Metadata for a Lambda step.
structure LambdaStepMetadata {
/// The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.
Arn: String256
/// A list of the output parameters of the Lambda step.
OutputParameters: OutputParameterList
}
/// A value that indicates whether the update was successful.
structure LastUpdateStatus {
/// A value that indicates whether the update was made successful.
@required
Status: LastUpdateStatusValue
/// If the update wasn't successful, indicates the reason why it failed.
FailureReason: FailureReason
}
/// Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity
/// resources.
structure LineageGroupSummary {
/// The Amazon Resource Name (ARN) of the lineage group resource.
LineageGroupArn: LineageGroupArn
/// The name or Amazon Resource Name (ARN) of the lineage group.
LineageGroupName: ExperimentEntityName
/// The display name of the lineage group summary.
DisplayName: ExperimentEntityName
/// The creation time of the lineage group summary.
CreationTime: Timestamp
/// The last modified time of the lineage group summary.
LastModifiedTime: Timestamp
}
@input
structure ListActionsRequest {
/// A filter that returns only actions with the specified source URI.
SourceUri: SourceUri
/// A filter that returns only actions of the specified type.
ActionType: String256
/// A filter that returns only actions created on or after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only actions created on or before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortActionsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of actions to return in the response. The default value is 10.
MaxResults: MaxResults
}
@output
structure ListActionsResponse {
/// A list of actions and their properties.
ActionSummaries: ActionSummaries
/// A token for getting the next set of actions, if there are any.
NextToken: NextToken
}
@input
structure ListAliasesRequest {
/// The name of the image.
@required
ImageName: ImageName
/// The alias of the image version.
Alias: SageMakerImageVersionAlias
/// The version of the image. If image version is not specified, the aliases of all versions of the image are listed.
Version: ImageVersionNumber
/// The maximum number of aliases to return.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListAliasesResponse {
/// A list of SageMaker image version aliases.
SageMakerImageVersionAliases: SageMakerImageVersionAliases
/// A token for getting the next set of aliases, if more aliases exist.
NextToken: NextToken
}
@input
structure ListAppImageConfigsRequest {
/// The maximum number of AppImageConfigs to return in the response. The default value is
/// 10.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
/// A filter that returns only AppImageConfigs whose name contains the specified string.
NameContains: AppImageConfigName
/// A filter that returns only AppImageConfigs created on or before the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only AppImageConfigs created on or after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only AppImageConfigs modified on or before the specified time.
ModifiedTimeBefore: Timestamp
/// A filter that returns only AppImageConfigs modified on or after the specified time.
ModifiedTimeAfter: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: AppImageConfigSortKey
/// The sort order. The default value is Descending.
SortOrder: SortOrder
}
@output
structure ListAppImageConfigsResponse {
/// A token for getting the next set of AppImageConfigs, if there are any.
NextToken: NextToken
/// A list of AppImageConfigs and their properties.
AppImageConfigs: AppImageConfigList
}
@input
structure ListAppsRequest {
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
/// Returns a list up to a specified limit.
MaxResults: MaxResults
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
/// The parameter by which to sort the results. The default is CreationTime.
SortBy: AppSortKey
/// A parameter to search for the domain ID.
DomainIdEquals: DomainId
/// A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.
UserProfileNameEquals: UserProfileName
/// A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.
SpaceNameEquals: SpaceName
}
@output
structure ListAppsResponse {
/// The list of apps.
Apps: AppList
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListArtifactsRequest {
/// A filter that returns only artifacts with the specified source URI.
SourceUri: SourceUri
/// A filter that returns only artifacts of the specified type.
ArtifactType: String256
/// A filter that returns only artifacts created on or after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only artifacts created on or before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortArtifactsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of artifacts to return in the response. The default value is 10.
MaxResults: MaxResults
}
@output
structure ListArtifactsResponse {
/// A list of artifacts and their properties.
ArtifactSummaries: ArtifactSummaries
/// A token for getting the next set of artifacts, if there are any.
NextToken: NextToken
}
@input
structure ListAssociationsRequest {
/// A filter that returns only associations with the specified source ARN.
SourceArn: AssociationEntityArn
/// A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
DestinationArn: AssociationEntityArn
/// A filter that returns only associations with the specified source type.
SourceType: String256
/// A filter that returns only associations with the specified destination type.
DestinationType: String256
/// A filter that returns only associations of the specified type.
AssociationType: AssociationEdgeType
/// A filter that returns only associations created on or after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only associations created on or before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortAssociationsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of associations to return in the response. The default value is 10.
MaxResults: MaxResults
}
@output
structure ListAssociationsResponse {
/// A list of associations and their properties.
AssociationSummaries: AssociationSummaries
/// A token for getting the next set of associations, if there are any.
NextToken: NextToken
}
@input
structure ListAutoMLJobsRequest {
/// Request a list of jobs, using a filter for time.
CreationTimeAfter: Timestamp
/// Request a list of jobs, using a filter for time.
CreationTimeBefore: Timestamp
/// Request a list of jobs, using a filter for time.
LastModifiedTimeAfter: Timestamp
/// Request a list of jobs, using a filter for time.
LastModifiedTimeBefore: Timestamp
/// Request a list of jobs, using a search filter for name.
NameContains: AutoMLNameContains
/// Request a list of jobs, using a filter for status.
StatusEquals: AutoMLJobStatus
/// The sort order for the results. The default is Descending.
SortOrder: AutoMLSortOrder
/// The parameter by which to sort the results. The default is Name.
SortBy: AutoMLSortBy
/// Request a list of jobs up to a specified limit.
MaxResults: AutoMLMaxResults = null
/// If the previous response was truncated, you receive this token. Use it in your next
/// request to receive the next set of results.
NextToken: NextToken
}
@output
structure ListAutoMLJobsResponse {
/// Returns a summary list of jobs.
@required
AutoMLJobSummaries: AutoMLJobSummaries
/// If the previous response was truncated, you receive this token. Use it in your next
/// request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListCandidatesForAutoMLJobRequest {
/// List the candidates created for the job by providing the job's name.
@required
AutoMLJobName: AutoMLJobName
/// List the candidates for the job and filter by status.
StatusEquals: CandidateStatus
/// List the candidates for the job and filter by candidate name.
CandidateNameEquals: CandidateName
/// The sort order for the results. The default is Ascending.
SortOrder: AutoMLSortOrder
/// The parameter by which to sort the results. The default is
/// Descending.
SortBy: CandidateSortBy
/// List the job's candidates up to a specified limit.
MaxResults: AutoMLMaxResults = null
/// If the previous response was truncated, you receive this token. Use it in your next
/// request to receive the next set of results.
NextToken: NextToken
}
@output
structure ListCandidatesForAutoMLJobResponse {
/// Summaries about the AutoMLCandidates.
@required
Candidates: AutoMLCandidates
/// If the previous response was truncated, you receive this token. Use it in your next
/// request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListCompilationJobsRequest {
/// 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.
NextToken: NextToken
/// The maximum number of model compilation jobs to return in the response.
MaxResults: MaxResults
/// A filter that returns the model compilation jobs that were created after a specified
/// time.
CreationTimeAfter: CreationTime
/// A filter that returns the model compilation jobs that were created before a specified
/// time.
CreationTimeBefore: CreationTime
/// A filter that returns the model compilation jobs that were modified after a specified
/// time.
LastModifiedTimeAfter: LastModifiedTime
/// A filter that returns the model compilation jobs that were modified before a specified
/// time.
LastModifiedTimeBefore: LastModifiedTime
/// A filter that returns the model compilation jobs whose name contains a specified
/// string.
NameContains: NameContains
/// A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
StatusEquals: CompilationJobStatus
/// The field by which to sort results. The default is CreationTime.
SortBy: ListCompilationJobsSortBy
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
}
@output
structure ListCompilationJobsResponse {
/// An array of CompilationJobSummary objects, each describing a model
/// compilation job.
@required
CompilationJobSummaries: CompilationJobSummaries
/// If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve
/// the next set of model compilation jobs, use this token in the next request.
NextToken: NextToken
}
@input
structure ListContextsRequest {
/// A filter that returns only contexts with the specified source URI.
SourceUri: SourceUri
/// A filter that returns only contexts of the specified type.
ContextType: String256
/// A filter that returns only contexts created on or after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only contexts created on or before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortContextsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of contexts to return in the response. The default value is 10.
MaxResults: MaxResults
}
@output
structure ListContextsResponse {
/// A list of contexts and their properties.
ContextSummaries: ContextSummaries
/// A token for getting the next set of contexts, if there are any.
NextToken: NextToken
}
@input
structure ListDataQualityJobDefinitionsRequest {
/// A filter that lists the data quality job definitions associated with the specified
/// endpoint.
EndpointName: EndpointName
/// The field to sort results by. The default is CreationTime.
SortBy: MonitoringJobDefinitionSortKey
/// The sort order for results. The default is Descending.
SortOrder: SortOrder
/// 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.>
NextToken: NextToken
/// The maximum number of data quality monitoring job definitions to return in the
/// response.
MaxResults: MaxResults
/// 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.
NameContains: NameContains
/// A filter that returns only data quality monitoring job definitions created before the
/// specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only data quality monitoring job definitions created after the
/// specified time.
CreationTimeAfter: Timestamp
}
@output
structure ListDataQualityJobDefinitionsResponse {
/// A list of data quality monitoring job definitions.
@required
JobDefinitionSummaries: MonitoringJobDefinitionSummaryList
/// 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.
NextToken: NextToken
}
@input
structure ListDeviceFleetsRequest {
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
/// The maximum number of results to select.
MaxResults: ListMaxResults = null
/// Filter fleets where packaging job was created after specified time.
CreationTimeAfter: Timestamp
/// Filter fleets where the edge packaging job was created before specified time.
CreationTimeBefore: Timestamp
/// Select fleets where the job was updated after X
LastModifiedTimeAfter: Timestamp
/// Select fleets where the job was updated before X
LastModifiedTimeBefore: Timestamp
/// Filter for fleets containing this name in their fleet device name.
NameContains: NameContains
/// The column to sort by.
SortBy: ListDeviceFleetsSortBy
/// What direction to sort in.
SortOrder: SortOrder
}
@output
structure ListDeviceFleetsResponse {
/// Summary of the device fleet.
@required
DeviceFleetSummaries: DeviceFleetSummaries
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
}
@input
structure ListDevicesRequest {
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
/// Maximum number of results to select.
MaxResults: ListMaxResults = null
/// Select fleets where the job was updated after X
LatestHeartbeatAfter: Timestamp
/// A filter that searches devices that contains this name in any of their models.
ModelName: EntityName
/// Filter for fleets containing this name in their device fleet name.
DeviceFleetName: EntityName
}
@output
structure ListDevicesResponse {
/// Summary of devices.
@required
DeviceSummaries: DeviceSummaries
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
}
@input
structure ListDomainsRequest {
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
/// Returns a list up to a specified limit.
MaxResults: MaxResults
}
@output
structure ListDomainsResponse {
/// The list of domains.
Domains: DomainList
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListEdgeDeploymentPlansRequest {
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
/// The maximum number of results to select (50 by default).
MaxResults: ListMaxResults = null
/// Selects edge deployment plans created after this time.
CreationTimeAfter: Timestamp
/// Selects edge deployment plans created before this time.
CreationTimeBefore: Timestamp
/// Selects edge deployment plans that were last updated after this time.
LastModifiedTimeAfter: Timestamp
/// Selects edge deployment plans that were last updated before this time.
LastModifiedTimeBefore: Timestamp
/// Selects edge deployment plans with names containing this name.
NameContains: NameContains
/// Selects edge deployment plans with a device fleet name containing this name.
DeviceFleetNameContains: NameContains
/// The column by which to sort the edge deployment plans. Can be one of NAME, DEVICEFLEETNAME, CREATIONTIME, LASTMODIFIEDTIME.
SortBy: ListEdgeDeploymentPlansSortBy
/// The direction of the sorting (ascending or descending).
SortOrder: SortOrder
}
@output
structure ListEdgeDeploymentPlansResponse {
/// List of summaries of edge deployment plans.
@required
EdgeDeploymentPlanSummaries: EdgeDeploymentPlanSummaries
/// The token to use when calling the next page of results.
NextToken: NextToken
}
@input
structure ListEdgePackagingJobsRequest {
/// The response from the last list when returning a list large enough to need tokening.
NextToken: NextToken
/// Maximum number of results to select.
MaxResults: ListMaxResults = null
/// Select jobs where the job was created after specified time.
CreationTimeAfter: Timestamp
/// Select jobs where the job was created before specified time.
CreationTimeBefore: Timestamp
/// Select jobs where the job was updated after specified time.
LastModifiedTimeAfter: Timestamp
/// Select jobs where the job was updated before specified time.
LastModifiedTimeBefore: Timestamp
/// Filter for jobs containing this name in their packaging job name.
NameContains: NameContains
/// Filter for jobs where the model name contains this string.
ModelNameContains: NameContains
/// The job status to filter for.
StatusEquals: EdgePackagingJobStatus
/// Use to specify what column to sort by.
SortBy: ListEdgePackagingJobsSortBy
/// What direction to sort by.
SortOrder: SortOrder
}
@output
structure ListEdgePackagingJobsResponse {
/// Summaries of edge packaging jobs.
@required
EdgePackagingJobSummaries: EdgePackagingJobSummaries
/// Token to use when calling the next page of results.
NextToken: NextToken
}
@input
structure ListExperimentsRequest {
/// A filter that returns only experiments created after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only experiments created before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortExperimentsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of experiments to return in the response. The default value is
/// 10.
MaxResults: MaxResults
}
@output
structure ListExperimentsResponse {
/// A list of the summaries of your experiments.
ExperimentSummaries: ExperimentSummaries
/// A token for getting the next set of experiments, if there are any.
NextToken: NextToken
}
@input
structure ListFeatureGroupsRequest {
/// A string that partially matches one or more FeatureGroups names. Filters
/// FeatureGroups by name.
NameContains: FeatureGroupNameContains
/// A FeatureGroup status. Filters by FeatureGroup status.
FeatureGroupStatusEquals: FeatureGroupStatus
/// An OfflineStore status. Filters by OfflineStore status.
OfflineStoreStatusEquals: OfflineStoreStatusValue
/// Use this parameter to search for FeatureGroupss created after a specific
/// date and time.
CreationTimeAfter: CreationTime
/// Use this parameter to search for FeatureGroupss created before a specific
/// date and time.
CreationTimeBefore: CreationTime
/// The order in which feature groups are listed.
SortOrder: FeatureGroupSortOrder
/// The value on which the feature group list is sorted.
SortBy: FeatureGroupSortBy
/// The maximum number of results returned by ListFeatureGroups.
MaxResults: FeatureGroupMaxResults
/// A token to resume pagination of ListFeatureGroups results.
NextToken: NextToken
}
@output
structure ListFeatureGroupsResponse {
/// A summary of feature groups.
@required
FeatureGroupSummaries: FeatureGroupSummaries
/// A token to resume pagination of ListFeatureGroups results.
@required
NextToken: NextToken
}
@input
structure ListFlowDefinitionsRequest {
/// A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
CreationTimeAfter: Timestamp
/// A filter that returns only flow definitions that were created before the specified timestamp.
CreationTimeBefore: Timestamp
/// An optional value that specifies whether you want the results sorted in Ascending or Descending order.
SortOrder: SortOrder
/// A token to resume pagination.
NextToken: NextToken
/// 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.
MaxResults: MaxResults
}
@output
structure ListFlowDefinitionsResponse {
/// An array of objects describing the flow definitions.
@required
FlowDefinitionSummaries: FlowDefinitionSummaries
/// A token to resume pagination.
NextToken: NextToken
}
@input
structure ListHubContentsRequest {
/// The name of the hub to list the contents of.
@required
HubName: HubName
/// The type of hub content to list.
@required
HubContentType: HubContentType
/// Only list hub content if the name contains the specified string.
NameContains: NameContains
/// The upper bound of the hub content schema verion.
MaxSchemaVersion: DocumentSchemaVersion
/// Only list hub content that was created before the time specified.
CreationTimeBefore: Timestamp
/// Only list hub content that was created after the time specified.
CreationTimeAfter: Timestamp
/// Sort hub content versions by either name or creation time.
SortBy: HubContentSortBy
/// Sort hubs by ascending or descending order.
SortOrder: SortOrder
/// The maximum amount of hub content to list.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListHubContentsResponse {
/// The summaries of the listed hub content.
@required
HubContentSummaries: HubContentInfoList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListHubContentVersionsRequest {
/// The name of the hub to list the content versions of.
@required
HubName: HubName
/// The type of hub content to list versions of.
@required
HubContentType: HubContentType
/// The name of the hub content.
@required
HubContentName: HubContentName
/// The lower bound of the hub content versions to list.
MinVersion: HubContentVersion
/// The upper bound of the hub content schema version.
MaxSchemaVersion: DocumentSchemaVersion
/// Only list hub content versions that were created before the time specified.
CreationTimeBefore: Timestamp
/// Only list hub content versions that were created after the time specified.
CreationTimeAfter: Timestamp
/// Sort hub content versions by either name or creation time.
SortBy: HubContentSortBy
/// Sort hub content versions by ascending or descending order.
SortOrder: SortOrder
/// The maximum number of hub content versions to list.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListHubContentVersionsResponse {
/// The summaries of the listed hub content versions.
@required
HubContentSummaries: HubContentInfoList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content versions, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListHubsRequest {
/// Only list hubs with names that contain the specified string.
NameContains: NameContains
/// Only list hubs that were created before the time specified.
CreationTimeBefore: Timestamp
/// Only list hubs that were created after the time specified.
CreationTimeAfter: Timestamp
/// Only list hubs that were last modified before the time specified.
LastModifiedTimeBefore: Timestamp
/// Only list hubs that were last modified after the time specified.
LastModifiedTimeAfter: Timestamp
/// Sort hubs by either name or creation time.
SortBy: HubSortBy
/// Sort hubs by ascending or descending order.
SortOrder: SortOrder
/// The maximum number of hubs to list.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListHubsResponse {
/// The summaries of the listed hubs.
@required
HubSummaries: HubInfoList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of hubs, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListHumanTaskUisRequest {
/// A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
CreationTimeAfter: Timestamp
/// A filter that returns only human task user interfaces that were created before the specified timestamp.
CreationTimeBefore: Timestamp
/// An optional value that specifies whether you want the results sorted in Ascending or Descending order.
SortOrder: SortOrder
/// A token to resume pagination.
NextToken: NextToken
/// 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.
MaxResults: MaxResults
}
@output
structure ListHumanTaskUisResponse {
/// An array of objects describing the human task user interfaces.
@required
HumanTaskUiSummaries: HumanTaskUiSummaries
/// A token to resume pagination.
NextToken: NextToken
}
@input
structure ListHyperParameterTuningJobsRequest {
/// 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.
NextToken: NextToken
/// The
/// maximum number of tuning jobs to return. The default value is
/// 10.
MaxResults: MaxResults
/// The field to sort results by. The default is Name.
SortBy: HyperParameterTuningJobSortByOptions
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
/// A string in the tuning job name. This filter returns only tuning jobs whose name
/// contains the specified string.
NameContains: NameContains
/// A filter that returns only tuning jobs that were created after the specified
/// time.
CreationTimeAfter: Timestamp
/// A filter that returns only tuning jobs that were created before the specified
/// time.
CreationTimeBefore: Timestamp
/// A filter that returns only tuning jobs that were modified after the specified
/// time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only tuning jobs that were modified before the specified
/// time.
LastModifiedTimeBefore: Timestamp
/// A filter that returns only tuning jobs with the specified status.
StatusEquals: HyperParameterTuningJobStatus
}
@output
structure ListHyperParameterTuningJobsResponse {
/// A list of HyperParameterTuningJobSummary objects that
/// describe
/// the tuning jobs that the ListHyperParameterTuningJobs
/// request returned.
@required
HyperParameterTuningJobSummaries: HyperParameterTuningJobSummaries
/// 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.
NextToken: NextToken
}
@input
structure ListImagesRequest {
/// A filter that returns only images created on or after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only images created on or before the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only images modified on or after the specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only images modified on or before the specified time.
LastModifiedTimeBefore: Timestamp
/// The maximum number of images to return in the response. The default value is 10.
MaxResults: MaxResults
/// A filter that returns only images whose name contains the specified string.
NameContains: ImageNameContains
/// 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.
NextToken: NextToken
/// The property used to sort results. The default value is CREATION_TIME.
SortBy: ImageSortBy
/// The sort order. The default value is DESCENDING.
SortOrder: ImageSortOrder
}
@output
structure ListImagesResponse {
/// A list of images and their properties.
Images: Images
/// A token for getting the next set of images, if there are any.
NextToken: NextToken
}
@input
structure ListImageVersionsRequest {
/// A filter that returns only versions created on or after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only versions created on or before the specified time.
CreationTimeBefore: Timestamp
/// The name of the image to list the versions of.
@required
ImageName: ImageName
/// A filter that returns only versions modified on or after the specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only versions modified on or before the specified time.
LastModifiedTimeBefore: Timestamp
/// The maximum number of versions to return in the response. The default value is 10.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
/// The property used to sort results. The default value is CREATION_TIME.
SortBy: ImageVersionSortBy
/// The sort order. The default value is DESCENDING.
SortOrder: ImageVersionSortOrder
}
@output
structure ListImageVersionsResponse {
/// A list of versions and their properties.
ImageVersions: ImageVersions
/// A token for getting the next set of versions, if there are any.
NextToken: NextToken
}
@input
structure ListInferenceExperimentsRequest {
/// Selects inference experiments whose names contain this name.
NameContains: NameContains
///
/// Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperimentRequest$Type.
///
Type: InferenceExperimentType
///
/// Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperimentResponse$Status.
///
StatusEquals: InferenceExperimentStatus
/// Selects inference experiments which were created after this timestamp.
CreationTimeAfter: Timestamp
/// Selects inference experiments which were created before this timestamp.
CreationTimeBefore: Timestamp
/// Selects inference experiments which were last modified after this timestamp.
LastModifiedTimeAfter: Timestamp
/// Selects inference experiments which were last modified before this timestamp.
LastModifiedTimeBefore: Timestamp
/// The column by which to sort the listed inference experiments.
SortBy: SortInferenceExperimentsBy
/// The direction of sorting (ascending or descending).
SortOrder: SortOrder
///
/// The response from the last list when returning a list large enough to need tokening.
///
NextToken: NextToken
/// The maximum number of results to select.
MaxResults: MaxResults
}
@output
structure ListInferenceExperimentsResponse {
/// List of inference experiments.
InferenceExperiments: InferenceExperimentList
/// The token to use when calling the next page of results.
NextToken: NextToken
}
@input
structure ListInferenceRecommendationsJobsRequest {
/// A filter that returns only jobs created after the specified time (timestamp).
CreationTimeAfter: CreationTime
/// A filter that returns only jobs created before the specified time (timestamp).
CreationTimeBefore: CreationTime
/// A filter that returns only jobs that were last modified after the specified time (timestamp).
LastModifiedTimeAfter: LastModifiedTime
/// A filter that returns only jobs that were last modified before the specified time (timestamp).
LastModifiedTimeBefore: LastModifiedTime
/// A string in the job name. This filter returns only recommendations whose name contains the specified string.
NameContains: NameContains
/// A filter that retrieves only inference recommendations jobs with a specific status.
StatusEquals: RecommendationJobStatus
/// The parameter by which to sort the results.
SortBy: ListInferenceRecommendationsJobsSortBy
/// The sort order for the results.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of recommendations to return in the response.
MaxResults: MaxResults
}
@output
structure ListInferenceRecommendationsJobsResponse {
/// The recommendations created from the Amazon SageMaker Inference Recommender job.
@required
InferenceRecommendationsJobs: InferenceRecommendationsJobs
/// A token for getting the next set of recommendations, if there are any.
NextToken: NextToken
}
@input
structure ListInferenceRecommendationsJobStepsRequest {
/// The name for the Inference Recommender job.
@required
JobName: RecommendationJobName
/// A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.
Status: RecommendationJobStatus
/// A filter to return details about the specified type of subtask.
///
/// BENCHMARK: Evaluate the performance of your model on different instance types.
StepType: RecommendationStepType
/// The maximum number of results to return.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListInferenceRecommendationsJobStepsResponse {
/// A list of all subtask details in Inference Recommender.
Steps: InferenceRecommendationsJobSteps
/// A token that you can specify in your next request to return more results from the list.
NextToken: NextToken
}
@input
structure ListLabelingJobsForWorkteamRequest {
/// The Amazon Resource Name (ARN) of the work team for which you want to see labeling
/// jobs for.
@required
WorkteamArn: WorkteamArn
/// The maximum number of labeling jobs to return in each page of the response.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
/// A filter that returns only labeling jobs created after the specified time
/// (timestamp).
CreationTimeAfter: Timestamp
/// A filter that returns only labeling jobs created before the specified time
/// (timestamp).
CreationTimeBefore: Timestamp
/// A filter the limits jobs to only the ones whose job reference code contains the
/// specified string.
JobReferenceCodeContains: JobReferenceCodeContains
/// The field to sort results by. The default is CreationTime.
SortBy: ListLabelingJobsForWorkteamSortByOptions
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
}
@output
structure ListLabelingJobsForWorkteamResponse {
/// An array of LabelingJobSummary objects, each describing a labeling
/// job.
@required
LabelingJobSummaryList: LabelingJobForWorkteamSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// labeling jobs, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListLabelingJobsRequest {
/// A filter that returns only labeling jobs created after the specified time
/// (timestamp).
CreationTimeAfter: Timestamp
/// A filter that returns only labeling jobs created before the specified time
/// (timestamp).
CreationTimeBefore: Timestamp
/// A filter that returns only labeling jobs modified after the specified time
/// (timestamp).
LastModifiedTimeAfter: Timestamp
/// A filter that returns only labeling jobs modified before the specified time
/// (timestamp).
LastModifiedTimeBefore: Timestamp
/// The maximum number of labeling jobs to return in each page of the response.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
/// A string in the labeling job name. This filter returns only labeling jobs whose name
/// contains the specified string.
NameContains: NameContains
/// The field to sort results by. The default is CreationTime.
SortBy: SortBy
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
/// A filter that retrieves only labeling jobs with a specific status.
StatusEquals: LabelingJobStatus
}
@output
structure ListLabelingJobsResponse {
/// An array of LabelingJobSummary objects, each describing a labeling
/// job.
LabelingJobSummaryList: LabelingJobSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// labeling jobs, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListLineageGroupsRequest {
/// A timestamp to filter against lineage groups created after a certain point in time.
CreatedAfter: Timestamp
/// A timestamp to filter against lineage groups created before a certain point in time.
CreatedBefore: Timestamp
/// The parameter by which to sort the results. The default is
/// CreationTime.
SortBy: SortLineageGroupsBy
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// algorithms, use it in the subsequent request.
NextToken: NextToken
/// The maximum number of endpoints to return in the response. This value defaults to
/// 10.
MaxResults: MaxResults
}
@output
structure ListLineageGroupsResponse {
/// A list of lineage groups and their properties.
LineageGroupSummaries: LineageGroupSummaries
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// algorithms, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListModelBiasJobDefinitionsRequest {
/// Name of the endpoint to monitor for model bias.
EndpointName: EndpointName
/// Whether to sort results by the Name or CreationTime field. The
/// default is CreationTime.
SortBy: MonitoringJobDefinitionSortKey
/// Whether to sort the results in Ascending or Descending order.
/// The default is Descending.
SortOrder: SortOrder
/// The token returned if the response is truncated. To retrieve the next set of job
/// executions, use it in the next request.
NextToken: NextToken
/// The maximum number of model bias jobs to return in the response. The default value is
/// 10.
MaxResults: MaxResults
/// Filter for model bias jobs whose name contains a specified string.
NameContains: NameContains
/// A filter that returns only model bias jobs created before a specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only model bias jobs created after a specified time.
CreationTimeAfter: Timestamp
}
@output
structure ListModelBiasJobDefinitionsResponse {
/// A JSON array in which each element is a summary for a model bias jobs.
@required
JobDefinitionSummaries: MonitoringJobDefinitionSummaryList
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs,
/// use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListModelCardExportJobsRequest {
/// List export jobs for the model card with the specified name.
@required
ModelCardName: EntityName
/// List export jobs for the model card with the specified version.
ModelCardVersion: Integer = 0
/// Only list model card export jobs that were created after the time specified.
CreationTimeAfter: Timestamp
/// Only list model card export jobs that were created before the time specified.
CreationTimeBefore: Timestamp
/// Only list model card export jobs with names that contain the specified string.
ModelCardExportJobNameContains: EntityName
/// Only list model card export jobs with the specified status.
StatusEquals: ModelCardExportJobStatus
/// Sort model card export jobs by either name or creation time. Sorts by creation time by default.
SortBy: ModelCardExportJobSortBy
/// Sort model card export jobs by ascending or descending order.
SortOrder: ModelCardExportJobSortOrder
/// 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.
NextToken: NextToken
/// The maximum number of model card export jobs to list.
MaxResults: MaxResults
}
@output
structure ListModelCardExportJobsResponse {
/// The summaries of the listed model card export jobs.
@required
ModelCardExportJobSummaries: ModelCardExportJobSummaryList
/// 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.
NextToken: NextToken
}
@input
structure ListModelCardsRequest {
/// Only list model cards that were created after the time specified.
CreationTimeAfter: Timestamp
/// Only list model cards that were created before the time specified.
CreationTimeBefore: Timestamp
/// The maximum number of model cards to list.
MaxResults: MaxResults
/// Only list model cards with names that contain the specified string.
NameContains: EntityName
/// Only list model cards with the specified approval status.
ModelCardStatus: ModelCardStatus
/// 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.
NextToken: NextToken
/// Sort model cards by either name or creation time. Sorts by creation time by default.
SortBy: ModelCardSortBy
/// Sort model cards by ascending or descending order.
SortOrder: ModelCardSortOrder
}
@output
structure ListModelCardsResponse {
/// The summaries of the listed model cards.
@required
ModelCardSummaries: ModelCardSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of model
/// cards, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListModelCardVersionsRequest {
/// Only list model card versions that were created after the time specified.
CreationTimeAfter: Timestamp
/// Only list model card versions that were created before the time specified.
CreationTimeBefore: Timestamp
/// The maximum number of model card versions to list.
MaxResults: MaxResults
/// List model card versions for the model card with the specified name.
@required
ModelCardName: EntityName
/// Only list model card versions with the specified approval status.
ModelCardStatus: ModelCardStatus
/// 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.
NextToken: NextToken
/// Sort listed model card versions by version. Sorts by version by default.
SortBy: ModelCardVersionSortBy
/// Sort model card versions by ascending or descending order.
SortOrder: ModelCardSortOrder
}
@output
structure ListModelCardVersionsResponse {
/// The summaries of the listed versions of the model card.
@required
ModelCardVersionSummaryList: ModelCardVersionSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of model
/// card versions, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListModelExplainabilityJobDefinitionsRequest {
/// Name of the endpoint to monitor for model explainability.
EndpointName: EndpointName
/// Whether to sort results by the Name or CreationTime field. The
/// default is CreationTime.
SortBy: MonitoringJobDefinitionSortKey
/// Whether to sort the results in Ascending or Descending order.
/// The default is Descending.
SortOrder: SortOrder
/// The token returned if the response is truncated. To retrieve the next set of job
/// executions, use it in the next request.
NextToken: NextToken
/// The maximum number of jobs to return in the response. The default value is 10.
MaxResults: MaxResults
/// Filter for model explainability jobs whose name contains a specified string.
NameContains: NameContains
/// A filter that returns only model explainability jobs created before a specified
/// time.
CreationTimeBefore: Timestamp
/// A filter that returns only model explainability jobs created after a specified
/// time.
CreationTimeAfter: Timestamp
}
@output
structure ListModelExplainabilityJobDefinitionsResponse {
/// A JSON array in which each element is a summary for a explainability bias jobs.
@required
JobDefinitionSummaries: MonitoringJobDefinitionSummaryList
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs,
/// use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListModelMetadataRequest {
/// 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.
SearchExpression: ModelMetadataSearchExpression
/// 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.
NextToken: NextToken
/// The maximum number of models to return in the response.
MaxResults: MaxResults
}
@output
structure ListModelMetadataResponse {
/// A structure that holds model metadata.
@required
ModelMetadataSummaries: ModelMetadataSummaries
/// A token for getting the next set of recommendations, if there are any.
NextToken: NextToken
}
@input
structure ListModelQualityJobDefinitionsRequest {
/// A filter that returns only model quality monitoring job definitions that are associated
/// with the specified endpoint.
EndpointName: EndpointName
/// The field to sort results by. The default is CreationTime.
SortBy: MonitoringJobDefinitionSortKey
/// The sort order for results. The default is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of results to return in a call to
/// ListModelQualityJobDefinitions.
MaxResults: MaxResults
/// A string in the transform job name. This filter returns only model quality monitoring
/// job definitions whose name contains the specified string.
NameContains: NameContains
/// A filter that returns only model quality monitoring job definitions created before the
/// specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only model quality monitoring job definitions created after the
/// specified time.
CreationTimeAfter: Timestamp
}
@output
structure ListModelQualityJobDefinitionsResponse {
/// A list of summaries of model quality monitoring job definitions.
@required
JobDefinitionSummaries: MonitoringJobDefinitionSummaryList
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model
/// quality monitoring job definitions, use it in the next request.
NextToken: NextToken
}
@input
structure ListMonitoringAlertHistoryRequest {
/// The name of a monitoring schedule.
MonitoringScheduleName: MonitoringScheduleName
/// The name of a monitoring alert.
MonitoringAlertName: MonitoringAlertName
/// The field used to sort results. The default is CreationTime.
SortBy: MonitoringAlertHistorySortKey
/// The sort order, whether Ascending or Descending, of the alert
/// history. The default is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of results to display. The default is 100.
MaxResults: MaxResults
/// A filter that returns only alerts created on or before the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only alerts created on or after the specified time.
CreationTimeAfter: Timestamp
/// A filter that retrieves only alerts with a specific status.
StatusEquals: MonitoringAlertStatus
}
@output
structure ListMonitoringAlertHistoryResponse {
/// An alert history for a model monitoring schedule.
MonitoringAlertHistory: MonitoringAlertHistoryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// alerts, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListMonitoringAlertsRequest {
/// The name of a monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// 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.
NextToken: NextToken
/// The maximum number of results to display. The default is 100.
MaxResults: MaxResults
}
@output
structure ListMonitoringAlertsResponse {
/// A JSON array where each element is a summary for a monitoring alert.
MonitoringAlertSummaries: MonitoringAlertSummaryList
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// alerts, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListMonitoringExecutionsRequest {
/// Name of a specific schedule to fetch jobs for.
MonitoringScheduleName: MonitoringScheduleName
/// Name of a specific endpoint to fetch jobs for.
EndpointName: EndpointName
/// Whether to sort results by Status, CreationTime,
/// ScheduledTime field. The default is CreationTime.
SortBy: MonitoringExecutionSortKey
/// Whether to sort the results in Ascending or Descending order.
/// The default is Descending.
SortOrder: SortOrder
/// The token returned if the response is truncated. To retrieve the next set of job
/// executions, use it in the next request.
NextToken: NextToken
/// The maximum number of jobs to return in the response. The default value is 10.
MaxResults: MaxResults
/// Filter for jobs scheduled before a specified time.
ScheduledTimeBefore: Timestamp
/// Filter for jobs scheduled after a specified time.
ScheduledTimeAfter: Timestamp
/// A filter that returns only jobs created before a specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only jobs created after a specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only jobs modified after a specified time.
LastModifiedTimeBefore: Timestamp
/// A filter that returns only jobs modified before a specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that retrieves only jobs with a specific status.
StatusEquals: ExecutionStatus
/// Gets a list of the monitoring job runs of the specified monitoring job
/// definitions.
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// A filter that returns only the monitoring job runs of the specified monitoring
/// type.
MonitoringTypeEquals: MonitoringType
}
@output
structure ListMonitoringExecutionsResponse {
/// A JSON array in which each element is a summary for a monitoring execution.
@required
MonitoringExecutionSummaries: MonitoringExecutionSummaryList
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs,
/// use it in the subsequent reques
NextToken: NextToken
}
@input
structure ListMonitoringSchedulesRequest {
/// Name of a specific endpoint to fetch schedules for.
EndpointName: EndpointName
/// Whether to sort results by Status, CreationTime,
/// ScheduledTime field. The default is CreationTime.
SortBy: MonitoringScheduleSortKey
/// Whether to sort the results in Ascending or Descending order.
/// The default is Descending.
SortOrder: SortOrder
/// The token returned if the response is truncated. To retrieve the next set of job
/// executions, use it in the next request.
NextToken: NextToken
/// The maximum number of jobs to return in the response. The default value is 10.
MaxResults: MaxResults
/// Filter for monitoring schedules whose name contains a specified string.
NameContains: NameContains
/// A filter that returns only monitoring schedules created before a specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only monitoring schedules created after a specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only monitoring schedules modified before a specified time.
LastModifiedTimeBefore: Timestamp
/// A filter that returns only monitoring schedules modified after a specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only monitoring schedules modified before a specified time.
StatusEquals: ScheduleStatus
/// Gets a list of the monitoring schedules for the specified monitoring job
/// definition.
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// A filter that returns only the monitoring schedules for the specified monitoring
/// type.
MonitoringTypeEquals: MonitoringType
}
@output
structure ListMonitoringSchedulesResponse {
/// A JSON array in which each element is a summary for a monitoring schedule.
@required
MonitoringScheduleSummaries: MonitoringScheduleSummaryList
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs,
/// use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListPipelineExecutionsRequest {
/// The name of the pipeline.
@required
PipelineName: PipelineNameOrArn
/// A filter that returns the pipeline executions that were created after a specified
/// time.
CreatedAfter: Timestamp
/// A filter that returns the pipeline executions that were created before a specified
/// time.
CreatedBefore: Timestamp
/// The field by which to sort results. The default is CreatedTime.
SortBy: SortPipelineExecutionsBy
/// The sort order for results.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of pipeline executions to return in the response.
MaxResults: MaxResults
}
@output
structure ListPipelineExecutionsResponse {
/// 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.
PipelineExecutionSummaries: PipelineExecutionSummaryList
/// 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.
NextToken: NextToken
}
@input
structure ListPipelineExecutionStepsRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
/// 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.
NextToken: NextToken
/// The maximum number of pipeline execution steps to return in the response.
MaxResults: MaxResults
/// The field by which to sort results. The default is CreatedTime.
SortOrder: SortOrder
}
@output
structure ListPipelineExecutionStepsResponse {
/// 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.
PipelineExecutionSteps: PipelineExecutionStepList
/// 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.
NextToken: NextToken
}
@input
structure ListPipelineParametersForExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
/// 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.
NextToken: NextToken
/// The maximum number of parameters to return in the response.
MaxResults: MaxResults
}
@output
structure ListPipelineParametersForExecutionResponse {
/// Contains a list of pipeline parameters. This list can be empty.
PipelineParameters: ParameterList
/// 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.
NextToken: NextToken
}
@input
structure ListPipelinesRequest {
/// The prefix of the pipeline name.
PipelineNamePrefix: PipelineName
/// A filter that returns the pipelines that were created after a specified
/// time.
CreatedAfter: Timestamp
/// A filter that returns the pipelines that were created before a specified
/// time.
CreatedBefore: Timestamp
/// The field by which to sort results. The default is CreatedTime.
SortBy: SortPipelinesBy
/// The sort order for results.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of pipelines to return in the response.
MaxResults: MaxResults
}
@output
structure ListPipelinesResponse {
/// 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.
PipelineSummaries: PipelineSummaryList
/// 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.
NextToken: NextToken
}
@input
structure ListProcessingJobsRequest {
/// A filter that returns only processing jobs created after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only processing jobs created after the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only processing jobs modified after the specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only processing jobs modified before the specified time.
LastModifiedTimeBefore: Timestamp
/// A string in the processing job name. This filter returns only processing jobs whose
/// name contains the specified string.
NameContains: String
/// A filter that retrieves only processing jobs with a specific status.
StatusEquals: ProcessingJobStatus
/// The field to sort results by. The default is CreationTime.
SortBy: SortBy
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of processing jobs to return in the response.
MaxResults: MaxResults
}
@output
structure ListProcessingJobsResponse {
/// An array of ProcessingJobSummary objects, each listing a processing
/// job.
@required
ProcessingJobSummaries: ProcessingJobSummaries
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of
/// processing jobs, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListSpacesRequest {
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
/// Returns a list up to a specified limit.
MaxResults: MaxResults
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
/// The parameter by which to sort the results. The default is CreationTime.
SortBy: SpaceSortKey
/// A parameter to search for the Domain ID.
DomainIdEquals: DomainId
/// A parameter by which to filter the results.
SpaceNameContains: SpaceName
}
@output
structure ListSpacesResponse {
/// The list of spaces.
Spaces: SpaceList
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListStageDevicesRequest {
/// The response from the last list when returning a list large enough to neeed tokening.
NextToken: NextToken
/// The maximum number of requests to select.
MaxResults: ListMaxResults = null
/// The name of the edge deployment plan.
@required
EdgeDeploymentPlanName: EntityName
/// Toggle for excluding devices deployed in other stages.
ExcludeDevicesDeployedInOtherStage: Boolean = false
/// The name of the stage in the deployment.
@required
StageName: EntityName
}
@output
structure ListStageDevicesResponse {
/// List of summaries of devices allocated to the stage.
@required
DeviceDeploymentSummaries: DeviceDeploymentSummaries
/// The token to use when calling the next page of results.
NextToken: NextToken
}
@input
structure ListStudioLifecycleConfigsRequest {
/// The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
/// A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
NameContains: StudioLifecycleConfigName
/// A parameter to search for the App Type to which the Lifecycle Configuration is attached.
AppTypeEquals: StudioLifecycleConfigAppType
/// A filter that returns only Lifecycle Configurations created on or before the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only Lifecycle Configurations created on or after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only Lifecycle Configurations modified before the specified time.
ModifiedTimeBefore: Timestamp
/// A filter that returns only Lifecycle Configurations modified after the specified time.
ModifiedTimeAfter: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: StudioLifecycleConfigSortKey
/// The sort order. The default value is Descending.
SortOrder: SortOrder
}
@output
structure ListStudioLifecycleConfigsResponse {
/// A token for getting the next set of actions, if there are any.
NextToken: NextToken
/// A list of Lifecycle Configurations and their properties.
StudioLifecycleConfigs: StudioLifecycleConfigsList
}
@input
structure ListSubscribedWorkteamsRequest {
/// A string in the work team name. This filter returns only work teams whose name
/// contains the specified string.
NameContains: WorkteamName
/// 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.
NextToken: NextToken
/// The maximum number of work teams to return in each page of the response.
MaxResults: MaxResults
}
@output
structure ListSubscribedWorkteamsResponse {
/// An array of Workteam objects, each describing a work team.
@required
SubscribedWorkteams: SubscribedWorkteams
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of
/// work teams, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListTrainingJobsForHyperParameterTuningJobRequest {
/// The name of the tuning job whose training jobs you want to list.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
/// 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.
NextToken: NextToken
/// The maximum number of training jobs to return. The default value is 10.
MaxResults: MaxResults
/// A filter that returns only training jobs with the specified status.
StatusEquals: TrainingJobStatus
/// 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.
SortBy: TrainingJobSortByOptions
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
}
@output
structure ListTrainingJobsForHyperParameterTuningJobResponse {
/// A list of TrainingJobSummary objects that
/// describe
/// the training jobs that the
/// ListTrainingJobsForHyperParameterTuningJob request returned.
@required
TrainingJobSummaries: HyperParameterTrainingJobSummaries
/// 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.
NextToken: NextToken
}
@input
structure ListTrainingJobsRequest {
/// 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.
NextToken: NextToken
/// The maximum number of training jobs to return in the response.
MaxResults: MaxResults
/// A filter that returns only training jobs created after the specified time
/// (timestamp).
CreationTimeAfter: Timestamp
/// A filter that returns only training jobs created before the specified time
/// (timestamp).
CreationTimeBefore: Timestamp
/// A filter that returns only training jobs modified after the specified time
/// (timestamp).
LastModifiedTimeAfter: Timestamp
/// A filter that returns only training jobs modified before the specified time
/// (timestamp).
LastModifiedTimeBefore: Timestamp
/// A string in the training job name. This filter returns only training jobs whose
/// name contains the specified string.
NameContains: NameContains
/// A filter that retrieves only training jobs with a specific status.
StatusEquals: TrainingJobStatus
/// The field to sort results by. The default is CreationTime.
SortBy: SortBy
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
/// A filter that retrieves only training jobs with a specific warm pool status.
WarmPoolStatusEquals: WarmPoolResourceStatus
}
@output
structure ListTrainingJobsResponse {
/// An array of TrainingJobSummary objects, each listing a training
/// job.
@required
TrainingJobSummaries: TrainingJobSummaries
/// If the response is truncated, SageMaker returns this token. To retrieve the next set of
/// training jobs, use it in the subsequent request.
NextToken: NextToken
}
@input
structure ListTransformJobsRequest {
/// A filter that returns only transform jobs created after the specified time.
CreationTimeAfter: Timestamp
/// A filter that returns only transform jobs created before the specified time.
CreationTimeBefore: Timestamp
/// A filter that returns only transform jobs modified after the specified time.
LastModifiedTimeAfter: Timestamp
/// A filter that returns only transform jobs modified before the specified time.
LastModifiedTimeBefore: Timestamp
/// A string in the transform job name. This filter returns only transform jobs whose name
/// contains the specified string.
NameContains: NameContains
/// A filter that retrieves only transform jobs with a specific status.
StatusEquals: TransformJobStatus
/// The field to sort results by. The default is CreationTime.
SortBy: SortBy
/// The sort order for results. The default is Descending.
SortOrder: SortOrder
/// 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.
NextToken: NextToken
/// The maximum number of transform jobs to return in the response. The default value is 10.
MaxResults: MaxResults
}
@output
structure ListTransformJobsResponse {
/// An array of
/// TransformJobSummary
/// objects.
@required
TransformJobSummaries: TransformJobSummaries
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of
/// transform jobs, use it in the next request.
NextToken: NextToken
}
@input
structure ListTrialComponentsRequest {
/// 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.
ExperimentName: ExperimentEntityName
/// 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.
TrialName: ExperimentEntityName
/// 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.
SourceArn: String256
/// A filter that returns only components created after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only components created before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortTrialComponentsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// The maximum number of components to return in the response. The default value is
/// 10.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListTrialComponentsResponse {
/// A list of the summaries of your trial components.
TrialComponentSummaries: TrialComponentSummaries
/// A token for getting the next set of components, if there are any.
NextToken: NextToken
}
@input
structure ListTrialsRequest {
/// A filter that returns only trials that are part of the specified experiment.
ExperimentName: ExperimentEntityName
/// A filter that returns only trials that are associated with the specified trial
/// component.
TrialComponentName: ExperimentEntityName
/// A filter that returns only trials created after the specified time.
CreatedAfter: Timestamp
/// A filter that returns only trials created before the specified time.
CreatedBefore: Timestamp
/// The property used to sort results. The default value is CreationTime.
SortBy: SortTrialsBy
/// The sort order. The default value is Descending.
SortOrder: SortOrder
/// The maximum number of trials to return in the response. The default value is 10.
MaxResults: MaxResults
/// 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.
NextToken: NextToken
}
@output
structure ListTrialsResponse {
/// A list of the summaries of your trials.
TrialSummaries: TrialSummaries
/// A token for getting the next set of trials, if there are any.
NextToken: NextToken
}
@input
structure ListUserProfilesRequest {
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
/// Returns a list up to a specified limit.
MaxResults: MaxResults
/// The sort order for the results. The default is Ascending.
SortOrder: SortOrder
/// The parameter by which to sort the results. The default is CreationTime.
SortBy: UserProfileSortKey
/// A parameter by which to filter the results.
DomainIdEquals: DomainId
/// A parameter by which to filter the results.
UserProfileNameContains: UserProfileName
}
@output
structure ListUserProfilesResponse {
/// The list of user profiles.
UserProfiles: UserProfileList
/// If the previous response was truncated, you will receive this token.
/// Use it in your next request to receive the next set of results.
NextToken: NextToken
}
@input
structure ListWorkforcesRequest {
/// Sort workforces using the workforce name or creation date.
SortBy: ListWorkforcesSortByOptions
/// Sort workforces in ascending or descending order.
SortOrder: SortOrder
/// A filter you can use to search for workforces using part of the workforce name.
NameContains: WorkforceName
/// A token to resume pagination.
NextToken: NextToken
/// The maximum number of workforces returned in the response.
MaxResults: MaxResults
}
@output
structure ListWorkforcesResponse {
/// A list containing information about your workforce.
@required
Workforces: Workforces
/// A token to resume pagination.
NextToken: NextToken
}
@input
structure ListWorkteamsRequest {
/// The field to sort results by. The default is CreationTime.
SortBy: ListWorkteamsSortByOptions
/// The sort order for results. The default is Ascending.
SortOrder: SortOrder
/// A string in the work team's name. This filter returns only work teams whose name
/// contains the specified string.
NameContains: WorkteamName
/// 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.
NextToken: NextToken
/// The maximum number of work teams to return in each page of the response.
MaxResults: MaxResults
}
@output
structure ListWorkteamsResponse {
/// An array of Workteam objects, each describing a work team.
@required
Workteams: Workteams
/// If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of
/// work teams, use it in the subsequent request.
NextToken: NextToken
}
/// Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
structure MemberDefinition {
/// The Amazon Cognito user group that is part of the work team.
CognitoMemberDefinition: CognitoMemberDefinition
/// A list user groups that exist in your OIDC Identity Provider (IdP).
/// One to ten groups can be used to create a single private work team.
/// When you add a user group to the list of Groups, you can add that user group to one or more
/// private work teams. If you add a user group to a private work team, all workers in that user group
/// are added to the work team.
OidcMemberDefinition: OidcMemberDefinition
}
/// Metadata properties of the tracking entity, trial, or trial component.
structure MetadataProperties {
/// The commit ID.
CommitId: MetadataPropertyValue
/// The repository.
Repository: MetadataPropertyValue
/// The entity this entity was generated by.
GeneratedBy: MetadataPropertyValue
/// The project ID.
ProjectId: MetadataPropertyValue
}
/// The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
structure MetricData {
/// The name of the metric.
MetricName: MetricName
/// The value of the metric.
Value: Float = 0
/// The date and time that the algorithm emitted the metric.
Timestamp: Timestamp
}
/// Information about the metric for a candidate produced by an AutoML job.
structure MetricDatum {
/// The name of the metric.
MetricName: AutoMLMetricEnum
/// The value of the metric.
Value: Float = 0
/// The dataset split from which the AutoML job produced the metric.
Set: MetricSetSource
/// The name of the standard metric.
///
/// For definitions of the standard metrics, see
/// Autopilot candidate metrics
/// .
///
StandardMetricName: AutoMLMetricExtendedEnum
}
/// Specifies a metric that the training algorithm
/// writes
/// to stderr or stdout. SageMakerhyperparameter
/// tuning captures
/// all
/// defined metrics.
/// You
/// specify one metric that a hyperparameter tuning job uses as its
/// objective metric to choose the best training job.
structure MetricDefinition {
/// The name of the metric.
@required
Name: MetricName
/// A regular expression that searches the output of a training job and gets the value of
/// the metric. For more information about using regular expressions to define metrics, see
/// Defining
/// Objective Metrics.
@required
Regex: MetricRegex
}
/// Details about the metrics source.
structure MetricsSource {
/// The metric source content type.
@required
ContentType: ContentType
/// The hash key used for the metrics source.
ContentDigest: ContentDigest
/// The S3 URI for the metrics source.
@required
S3Uri: S3Uri
}
/// The properties of a model as returned by the Search API.
structure Model {
/// The name of the model.
ModelName: ModelName
PrimaryContainer: ContainerDefinition
/// The containers in the inference pipeline.
Containers: ContainerDefinitionList
InferenceExecutionConfig: InferenceExecutionConfig
/// The Amazon Resource Name (ARN) of the IAM role that you specified for the
/// model.
ExecutionRoleArn: RoleArn
VpcConfig: VpcConfig
/// A timestamp that indicates when the model was created.
CreationTime: Timestamp
/// The Amazon Resource Name (ARN) of the model.
ModelArn: ModelArn
/// Isolates the model container. No inbound or outbound network calls can be made to or
/// from the model container.
EnableNetworkIsolation: Boolean = 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.
Tags: TagList
}
/// Provides information about the location that is configured for storing model
/// artifacts.
/// Model artifacts are the output that results from training a model, and typically
/// consist of trained parameters, a model definition that describes how to compute
/// inferences, and other metadata.
structure ModelArtifacts {
/// The path of the S3 object that contains the model artifacts. For example,
/// s3://bucket-name/keynameprefix/model.tar.gz.
@required
S3ModelArtifacts: S3Uri
}
/// Docker container image configuration object for the model bias job.
structure ModelBiasAppSpecification {
/// The container image to be run by the model bias job.
@required
ImageUri: ImageUri
/// JSON formatted S3 file that defines bias parameters. For more information on this JSON
/// configuration file, see Configure bias
/// parameters.
@required
ConfigUri: S3Uri
/// Sets the environment variables in the Docker container.
Environment: MonitoringEnvironmentMap
}
/// The configuration for a baseline model bias job.
structure ModelBiasBaselineConfig {
/// The name of the baseline model bias job.
BaseliningJobName: ProcessingJobName
ConstraintsResource: MonitoringConstraintsResource
}
/// Inputs for the model bias job.
structure ModelBiasJobInput {
EndpointInput: EndpointInput
/// Input object for the batch transform job.
BatchTransformInput: BatchTransformInput
/// Location of ground truth labels to use in model bias job.
@required
GroundTruthS3Input: MonitoringGroundTruthS3Input
}
/// An Amazon SageMaker Model Card.
structure ModelCard {
/// The Amazon Resource Name (ARN) of the model card.
ModelCardArn: ModelCardArn
/// The unique name of the model card.
ModelCardName: EntityName
/// The version of the model card.
ModelCardVersion: Integer = 0
/// The content of the model card. Content uses the model card JSON schema and provided as a string.
Content: ModelCardContent
/// 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.
///
///
ModelCardStatus: ModelCardStatus
/// The security configuration used to protect model card data.
SecurityConfig: ModelCardSecurityConfig
/// The date and time that the model card was created.
CreationTime: Timestamp
CreatedBy: UserContext
/// The date and time that the model card was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// Key-value pairs used to manage metadata for the model card.
Tags: TagList
/// The unique name (ID) of the model.
ModelId: String
/// The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings.
RiskRating: String
}
/// The artifacts of the model card export job.
structure ModelCardExportArtifacts {
/// The Amazon S3 URI of the exported model artifacts.
@required
S3ExportArtifacts: S3Uri
}
/// The summary of the Amazon SageMaker Model Card export job.
structure ModelCardExportJobSummary {
/// The name of the model card export job.
@required
ModelCardExportJobName: EntityName
/// The Amazon Resource Name (ARN) of the model card export job.
@required
ModelCardExportJobArn: ModelCardExportJobArn
/// The completion status of the model card export job.
@required
Status: ModelCardExportJobStatus
/// The name of the model card that the export job exports.
@required
ModelCardName: EntityName
/// The version of the model card that the export job exports.
@required
ModelCardVersion: Integer = 0
/// The date and time that the model card export job was created.
@required
CreatedAt: Timestamp
/// The date and time that the model card export job was last modified..
@required
LastModifiedAt: Timestamp
}
/// Configure the export output details for an Amazon SageMaker Model Card.
structure ModelCardExportOutputConfig {
/// The Amazon S3 output path to export your model card PDF.
@required
S3OutputPath: S3Uri
}
/// Configure the security settings to protect model card data.
structure ModelCardSecurityConfig {
/// A Key Management Service
/// key
/// ID to use for encrypting a model card.
KmsKeyId: KmsKeyId
}
/// A summary of the model card.
structure ModelCardSummary {
/// The name of the model card.
@required
ModelCardName: EntityName
/// The Amazon Resource Name (ARN) of the model card.
@required
ModelCardArn: ModelCardArn
/// 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.
///
///
@required
ModelCardStatus: ModelCardStatus
/// The date and time that the model card was created.
@required
CreationTime: Timestamp
/// The date and time that the model card was last modified.
LastModifiedTime: Timestamp
}
/// A summary of a specific version of the model card.
structure ModelCardVersionSummary {
/// The name of the model card.
@required
ModelCardName: EntityName
/// The Amazon Resource Name (ARN) of the model card.
@required
ModelCardArn: ModelCardArn
/// The approval status of the model card version 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.
///
///
@required
ModelCardStatus: ModelCardStatus
/// A version of the model card.
@required
ModelCardVersion: Integer = 0
/// The date and time that the model card version was created.
@required
CreationTime: Timestamp
/// The time date and time that the model card version was last modified.
LastModifiedTime: Timestamp
}
/// Configures the timeout and maximum number of retries for processing a transform job
/// invocation.
structure ModelClientConfig {
/// The timeout value in seconds for an invocation request. The default value is
/// 600.
InvocationsTimeoutInSeconds: InvocationsTimeoutInSeconds
/// The maximum number of retries when invocation requests are failing. The default value
/// is 3.
InvocationsMaxRetries: InvocationsMaxRetries
}
/// Defines the model configuration. Includes the specification name and environment parameters.
structure ModelConfiguration {
/// The inference specification name in the model package version.
InferenceSpecificationName: InferenceSpecificationName
/// Defines the environment parameters that includes key, value types, and values.
EnvironmentParameters: EnvironmentParameters
/// The name of the compilation job used to create the recommended model artifacts.
CompilationJobName: RecommendationJobCompilationJobName
}
/// An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
structure ModelDashboardEndpoint {
/// The endpoint name.
@required
EndpointName: EndpointName
/// The Amazon Resource Name (ARN) of the endpoint.
@required
EndpointArn: EndpointArn
/// A timestamp that indicates when the endpoint was created.
@required
CreationTime: Timestamp
/// The last time the endpoint was modified.
@required
LastModifiedTime: Timestamp
/// The endpoint status.
@required
EndpointStatus: EndpointStatus
}
/// An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into
/// InAlert status.
structure ModelDashboardIndicatorAction {
/// Indicates whether the alert action is turned on.
Enabled: Boolean = false
}
/// A model displayed in the Amazon SageMaker Model Dashboard.
structure ModelDashboardModel {
/// A model displayed in the Model Dashboard.
Model: Model
/// The endpoints that host a model.
Endpoints: ModelDashboardEndpoints
LastBatchTransformJob: TransformJob
/// The monitoring schedules for a model.
MonitoringSchedules: ModelDashboardMonitoringSchedules
/// The model card for a model.
ModelCard: ModelDashboardModelCard
}
/// The model card for a model displayed in the Amazon SageMaker Model Dashboard.
structure ModelDashboardModelCard {
/// The Amazon Resource Name (ARN) for a model card.
ModelCardArn: ModelCardArn
/// The name of a model card.
ModelCardName: EntityName
/// The model card version.
ModelCardVersion: Integer = 0
/// The model card status.
ModelCardStatus: ModelCardStatus
/// The KMS Key ID (KMSKeyId) for encryption of model card information.
SecurityConfig: ModelCardSecurityConfig
/// A timestamp that indicates when the model card was created.
CreationTime: Timestamp
CreatedBy: UserContext
/// A timestamp that indicates when the model card was last updated.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// The tags associated with a model card.
Tags: TagList
/// For models created in SageMaker, this is the model ARN. For models created
/// outside of SageMaker, this is a user-customized string.
ModelId: String
/// A model card's risk rating. Can be low, medium, or high.
RiskRating: String
}
/// A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
structure ModelDashboardMonitoringSchedule {
/// The Amazon Resource Name (ARN) of a monitoring schedule.
MonitoringScheduleArn: MonitoringScheduleArn
/// The name of a monitoring schedule.
MonitoringScheduleName: MonitoringScheduleName
/// The status of the monitoring schedule.
MonitoringScheduleStatus: ScheduleStatus
/// The monitor type of a model monitor.
MonitoringType: MonitoringType
/// If a monitoring job failed, provides the reason.
FailureReason: FailureReason
/// A timestamp that indicates when the monitoring schedule was created.
CreationTime: Timestamp
/// A timestamp that indicates when the monitoring schedule was last updated.
LastModifiedTime: Timestamp
MonitoringScheduleConfig: MonitoringScheduleConfig
/// The endpoint which is monitored.
EndpointName: EndpointName
/// A JSON array where each element is a summary for a monitoring alert.
MonitoringAlertSummaries: MonitoringAlertSummaryList
LastMonitoringExecutionSummary: MonitoringExecutionSummary
}
/// Data quality constraints and statistics for a model.
structure ModelDataQuality {
/// Data quality statistics for a model.
Statistics: MetricsSource
/// Data quality constraints for a model.
Constraints: MetricsSource
}
/// Specifies how to generate the endpoint name for an automatic one-click Autopilot model
/// deployment.
structure ModelDeployConfig {
/// Set to True to automatically generate an endpoint name for a one-click
/// Autopilot model deployment; set to False otherwise. The default value is
/// False.
///
/// If you set AutoGenerateEndpointName to True, do not specify
/// the EndpointName; otherwise a 400 error is thrown.
///
AutoGenerateEndpointName: AutoGenerateEndpointName = false
/// Specifies the endpoint name to use for a one-click Autopilot model deployment if the
/// endpoint name is not generated automatically.
///
/// Specify the EndpointName if and only if you set
/// AutoGenerateEndpointName to False; otherwise a 400 error is
/// thrown.
///
EndpointName: EndpointName
}
/// Provides information about the endpoint of the model deployment.
structure ModelDeployResult {
/// The name of the endpoint to which the model has been deployed.
///
/// If model deployment fails, this field is omitted from the response.
///
EndpointName: EndpointName
}
/// Provides information to verify the integrity of stored model artifacts.
structure ModelDigests {
/// Provides a hash value that uniquely identifies the stored model
/// artifacts.
ArtifactDigest: ArtifactDigest
}
/// Docker container image configuration object for the model explainability job.
structure ModelExplainabilityAppSpecification {
/// The container image to be run by the model explainability job.
@required
ImageUri: ImageUri
/// JSON formatted S3 file that defines explainability parameters. For more information on
/// this JSON configuration file, see Configure model
/// explainability parameters.
@required
ConfigUri: S3Uri
/// Sets the environment variables in the Docker container.
Environment: MonitoringEnvironmentMap
}
/// The configuration for a baseline model explainability job.
structure ModelExplainabilityBaselineConfig {
/// The name of the baseline model explainability job.
BaseliningJobName: ProcessingJobName
ConstraintsResource: MonitoringConstraintsResource
}
/// Inputs for the model explainability job.
structure ModelExplainabilityJobInput {
EndpointInput: EndpointInput
/// Input object for the batch transform job.
BatchTransformInput: BatchTransformInput
}
/// The configuration for the infrastructure that the model will be deployed to.
structure ModelInfrastructureConfig {
/// The inference option to which to deploy your model. Possible values are the following:
///
/// -
///
/// RealTime: Deploy to real-time inference.
///
///
@required
InfrastructureType: ModelInfrastructureType
/// The infrastructure configuration for deploying the model to real-time inference.
@required
RealTimeInferenceConfig: RealTimeInferenceConfig
}
/// Input object for the model.
structure ModelInput {
/// The input configuration object for the model.
@required
DataInputConfig: DataInputConfig
}
/// The model latency threshold.
structure ModelLatencyThreshold {
/// The model latency percentile threshold.
Percentile: String64
/// The model latency percentile value in milliseconds.
ValueInMilliseconds: Integer = 0
}
/// Part of the search expression. You can specify the name and value
/// (domain, task, framework, framework version, task, and model).
structure ModelMetadataFilter {
/// The name of the of the model to filter by.
@required
Name: ModelMetadataFilterType
/// The value to filter the model metadata.
@required
Value: String256
}
/// 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
structure ModelMetadataSearchExpression {
/// A list of filter objects.
Filters: ModelMetadataFilters
}
/// A summary of the model metadata.
structure ModelMetadataSummary {
/// The machine learning domain of the model.
@required
Domain: String
/// The machine learning framework of the model.
@required
Framework: String
/// The machine learning task of the model.
@required
Task: String
/// The name of the model.
@required
Model: String
/// The framework version of the model.
@required
FrameworkVersion: String
}
/// Contains metrics captured from a model.
structure ModelMetrics {
/// Metrics that measure the quality of a model.
ModelQuality: ModelQuality
/// Metrics that measure the quality of the input data for a model.
ModelDataQuality: ModelDataQuality
/// Metrics that measure bais in a model.
Bias: Bias
/// Metrics that help explain a model.
Explainability: Explainability
}
/// A versioned model that can be deployed for SageMaker inference.
structure ModelPackage {
/// The name of the model.
ModelPackageName: EntityName
/// The model group to which the model belongs.
ModelPackageGroupName: EntityName
/// The version number of a versioned model.
ModelPackageVersion: ModelPackageVersion
/// The Amazon Resource Name (ARN) of the model package.
ModelPackageArn: ModelPackageArn
/// The description of the model package.
ModelPackageDescription: EntityDescription
/// The time that the model package was created.
CreationTime: CreationTime
/// Defines how to perform inference generation after a training job is run.
InferenceSpecification: InferenceSpecification
/// A list of algorithms that were used to create a model package.
SourceAlgorithmSpecification: SourceAlgorithmSpecification
/// Specifies batch transform jobs that SageMaker runs to validate your model package.
ValidationSpecification: ModelPackageValidationSpecification
/// The status of the model package. This can be one of the following values.
///
/// -
///
/// PENDING - The model package is pending being created.
///
/// -
///
/// IN_PROGRESS - The model package is in the process of being
/// created.
///
/// -
///
/// COMPLETED - The model package was successfully created.
///
/// -
///
/// FAILED - The model package failed.
///
/// -
///
/// DELETING - The model package is in the process of being deleted.
///
///
ModelPackageStatus: ModelPackageStatus
/// Specifies the validation and image scan statuses of the model package.
ModelPackageStatusDetails: ModelPackageStatusDetails
/// Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For
/// information about listing model packages on Amazon Web Services Marketplace, see List Your
/// Algorithm or Model Package on Amazon Web Services Marketplace.
CertifyForMarketplace: CertifyForMarketplace = false
/// The approval status of the model. This can be one of the following values.
///
/// -
///
/// APPROVED - The model is approved
///
/// -
///
/// REJECTED - The model is rejected.
///
/// -
///
/// PENDING_MANUAL_APPROVAL - The model is waiting for manual
/// approval.
///
///
ModelApprovalStatus: ModelApprovalStatus
/// Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
CreatedBy: UserContext
/// Metadata properties of the tracking entity, trial, or trial component.
MetadataProperties: MetadataProperties
/// Metrics for the model.
ModelMetrics: ModelMetrics
/// The last time the model package was modified.
LastModifiedTime: Timestamp
/// Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
LastModifiedBy: UserContext
/// A description provided when the model approval is set.
ApprovalDescription: ApprovalDescription
/// The machine learning domain of your model package and its components. Common
/// machine learning domains include computer vision and natural language processing.
Domain: String
/// The machine learning task your model package accomplishes. Common machine
/// learning tasks include object detection and image classification.
Task: String
/// The Amazon Simple Storage Service path where the sample payload are stored. This path must point to
/// a single gzip compressed tar archive (.tar.gz suffix).
SamplePayloadUrl: String
/// An array of additional Inference Specification objects.
AdditionalInferenceSpecifications: AdditionalInferenceSpecifications
/// A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services
/// resources in the Amazon Web Services General Reference Guide.
Tags: TagList
/// The metadata properties for the model package.
CustomerMetadataProperties: CustomerMetadataMap
/// Represents the drift check baselines that can be used when the model monitor is set using the model package.
DriftCheckBaselines: DriftCheckBaselines
}
/// Describes the Docker container for the model package.
structure ModelPackageContainerDefinition {
/// The DNS host name for the Docker container.
ContainerHostname: ContainerHostname
/// The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
/// If you are using your own custom algorithm instead of an algorithm provided by SageMaker,
/// the inference code must meet SageMaker requirements. SageMaker supports both
/// registry/repository[:tag] and registry/repository[@digest]
/// image path formats. For more information, see Using Your Own Algorithms with Amazon
/// SageMaker.
@required
Image: ContainerImage
/// An MD5 hash of the training algorithm that identifies the Docker image used for
/// training.
ImageDigest: ImageDigest
/// The Amazon S3 path where the model artifacts, which result from model training, are stored.
/// This path must point to a single gzip compressed tar archive
/// (.tar.gz suffix).
///
/// The model artifacts must be in an S3 bucket that is in the same region as the
/// model package.
///
ModelDataUrl: Url
/// The Amazon Web Services Marketplace product ID of the model package.
ProductId: ProductId
/// The environment variables to set in the Docker container. Each key and value in the
/// Environment string to string map can have length of up to 1024. We
/// support up to 16 entries in the map.
Environment: EnvironmentMap
/// A structure with Model Input details.
ModelInput: ModelInput
/// The machine learning framework of the model package container image.
Framework: String
/// The framework version of the Model Package Container Image.
FrameworkVersion: ModelPackageFrameworkVersion
/// The name of a pre-trained machine learning benchmarked by
/// Amazon SageMaker Inference Recommender model that matches your model.
/// You can find a list of benchmarked models by calling ListModelMetadata.
NearestModelName: String
}
/// A group of versioned models in the model registry.
structure ModelPackageGroup {
/// The name of the model group.
ModelPackageGroupName: EntityName
/// The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupArn: ModelPackageGroupArn
/// The description for the model group.
ModelPackageGroupDescription: EntityDescription
/// The time that the model group was created.
CreationTime: CreationTime
CreatedBy: UserContext
/// The status of the model group. This can be one of the following values.
///
/// -
///
/// PENDING - The model group is pending being created.
///
/// -
///
/// IN_PROGRESS - The model group is in the process of being
/// created.
///
/// -
///
/// COMPLETED - The model group was successfully created.
///
/// -
///
/// FAILED - The model group failed.
///
/// -
///
/// DELETING - The model group is in the process of being deleted.
///
/// -
///
/// DELETE_FAILED - SageMaker failed to delete the model group.
///
///
ModelPackageGroupStatus: ModelPackageGroupStatus
/// A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services
/// resources in the Amazon Web Services General Reference Guide.
Tags: TagList
}
/// Summary information about a model group.
structure ModelPackageGroupSummary {
/// The name of the model group.
@required
ModelPackageGroupName: EntityName
/// The Amazon Resource Name (ARN) of the model group.
@required
ModelPackageGroupArn: ModelPackageGroupArn
/// A description of the model group.
ModelPackageGroupDescription: EntityDescription
/// The time that the model group was created.
@required
CreationTime: CreationTime
/// The status of the model group.
@required
ModelPackageGroupStatus: ModelPackageGroupStatus
}
/// Specifies the validation and image scan statuses of the model package.
structure ModelPackageStatusDetails {
/// The validation status of the model package.
@required
ValidationStatuses: ModelPackageStatusItemList
/// The status of the scan of the Docker image container for the model package.
ImageScanStatuses: ModelPackageStatusItemList
}
/// Represents the overall status of a model package.
structure ModelPackageStatusItem {
/// The name of the model package for which the overall status is being reported.
@required
Name: EntityName
/// The current status.
@required
Status: DetailedModelPackageStatus
/// if the overall status is Failed, the reason for the failure.
FailureReason: String
}
/// Provides summary information about a model package.
structure ModelPackageSummary {
/// The name of the model package.
@required
ModelPackageName: EntityName
/// If the model package is a versioned model, the model group that the versioned model
/// belongs to.
ModelPackageGroupName: EntityName
/// If the model package is a versioned model, the version of the model.
ModelPackageVersion: ModelPackageVersion
/// The Amazon Resource Name (ARN) of the model package.
@required
ModelPackageArn: ModelPackageArn
/// A brief description of the model package.
ModelPackageDescription: EntityDescription
/// A timestamp that shows when the model package was created.
@required
CreationTime: CreationTime
/// The overall status of the model package.
@required
ModelPackageStatus: ModelPackageStatus
/// The approval status of the model. This can be one of the following values.
///
/// -
///
/// APPROVED - The model is approved
///
/// -
///
/// REJECTED - The model is rejected.
///
/// -
///
/// PENDING_MANUAL_APPROVAL - The model is waiting for manual
/// approval.
///
///
ModelApprovalStatus: ModelApprovalStatus
}
/// Contains data, such as the inputs and targeted instance types that are used in the
/// process of validating the model package.
/// The data provided in the validation profile is made available to your buyers on Amazon Web Services
/// Marketplace.
structure ModelPackageValidationProfile {
/// The name of the profile for the model package.
@required
ProfileName: EntityName
/// The TransformJobDefinition object that describes the transform job used
/// for the validation of the model package.
@required
TransformJobDefinition: TransformJobDefinition
}
/// Specifies batch transform jobs that SageMaker runs to validate your model package.
structure ModelPackageValidationSpecification {
/// The IAM roles to be used for the validation of the model package.
@required
ValidationRole: RoleArn
/// An array of ModelPackageValidationProfile objects, each of which
/// specifies a batch transform job that SageMaker runs to validate your model package.
@required
ValidationProfiles: ModelPackageValidationProfiles
}
/// Model quality statistics and constraints.
structure ModelQuality {
/// Model quality statistics.
Statistics: MetricsSource
/// Model quality constraints.
Constraints: MetricsSource
}
/// Container image configuration object for the monitoring job.
structure ModelQualityAppSpecification {
/// The address of the container image that the monitoring job runs.
@required
ImageUri: ImageUri
/// Specifies the entrypoint for a container that the monitoring job runs.
ContainerEntrypoint: ContainerEntrypoint
/// An array of arguments for the container used to run the monitoring job.
ContainerArguments: MonitoringContainerArguments
/// An Amazon S3 URI to a script that is called per row prior to running analysis. It can
/// base64 decode the payload and convert it into a flatted json so that the built-in container
/// can use the converted data. Applicable only for the built-in (first party)
/// containers.
RecordPreprocessorSourceUri: S3Uri
/// An Amazon S3 URI to a script that is called after analysis has been performed.
/// Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri: S3Uri
/// The machine learning problem type of the model that the monitoring job monitors.
ProblemType: MonitoringProblemType
/// Sets the environment variables in the container that the monitoring job runs.
Environment: MonitoringEnvironmentMap
}
/// Configuration for monitoring constraints and monitoring statistics. These baseline
/// resources are compared against the results of the current job from the series of jobs
/// scheduled to collect data periodically.
structure ModelQualityBaselineConfig {
/// The name of the job that performs baselining for the monitoring job.
BaseliningJobName: ProcessingJobName
ConstraintsResource: MonitoringConstraintsResource
}
/// The input for the model quality monitoring job. Currently endponts are supported for
/// input for model quality monitoring jobs.
structure ModelQualityJobInput {
EndpointInput: EndpointInput
/// Input object for the batch transform job.
BatchTransformInput: BatchTransformInput
/// The ground truth label provided for the model.
@required
GroundTruthS3Input: MonitoringGroundTruthS3Input
}
/// Metadata for Model steps.
structure ModelStepMetadata {
/// The Amazon Resource Name (ARN) of the created model.
Arn: String256
}
/// Provides summary information about a model.
structure ModelSummary {
/// The name of the model that you want a summary for.
@required
ModelName: ModelName
/// The Amazon Resource Name (ARN) of the model.
@required
ModelArn: ModelArn
/// A timestamp that indicates when the model was created.
@required
CreationTime: Timestamp
}
/// Contains information about the deployment options of a model.
structure ModelVariantConfig {
/// The name of the Amazon SageMaker Model entity.
@required
ModelName: ModelName
/// The name of the variant.
@required
VariantName: ModelVariantName
/// The configuration for the infrastructure that the model will be deployed to.
@required
InfrastructureConfig: ModelInfrastructureConfig
}
/// Summary of the deployment configuration of a model.
structure ModelVariantConfigSummary {
/// The name of the Amazon SageMaker Model entity.
@required
ModelName: ModelName
/// The name of the variant.
@required
VariantName: ModelVariantName
/// The configuration of the infrastructure that the model has been deployed to.
@required
InfrastructureConfig: ModelInfrastructureConfig
/// The status of deployment for the model variant on the hosted inference endpoint.
///
/// -
///
/// Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint.
///
///
/// -
///
/// InService - The model variant is running on the hosted inference endpoint.
///
///
/// -
///
/// Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint.
///
///
/// -
///
/// Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint.
///
///
/// -
///
/// Deleted - The model variant has been deleted on the hosted inference endpoint. This
/// can only happen after stopping the experiment.
///
///
///
@required
Status: ModelVariantStatus
}
/// A list of alert actions taken in response to an alert going into
/// InAlert status.
structure MonitoringAlertActions {
/// An alert action taken to light up an icon on the Model Dashboard when an alert goes into
/// InAlert status.
ModelDashboardIndicator: ModelDashboardIndicatorAction
}
/// Provides summary information of an alert's history.
structure MonitoringAlertHistorySummary {
/// The name of a monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The name of a monitoring alert.
@required
MonitoringAlertName: MonitoringAlertName
/// A timestamp that indicates when the first alert transition occurred in an alert history.
/// An alert transition can be from status InAlert to OK,
/// or from OK to InAlert.
@required
CreationTime: Timestamp
/// The current alert status of an alert.
@required
AlertStatus: MonitoringAlertStatus
}
/// Provides summary information about a monitor alert.
structure MonitoringAlertSummary {
/// The name of a monitoring alert.
@required
MonitoringAlertName: MonitoringAlertName
/// A timestamp that indicates when a monitor alert was created.
@required
CreationTime: Timestamp
/// A timestamp that indicates when a monitor alert was last updated.
@required
LastModifiedTime: Timestamp
/// The current status of an alert.
@required
AlertStatus: MonitoringAlertStatus
/// Within EvaluationPeriod, how many execution failures will raise an
/// alert.
@required
DatapointsToAlert: MonitoringDatapointsToAlert
/// The number of most recent monitoring executions to consider when evaluating alert
/// status.
@required
EvaluationPeriod: MonitoringEvaluationPeriod
/// A list of alert actions taken in response to an alert going into
/// InAlert status.
@required
Actions: MonitoringAlertActions
}
/// Container image configuration object for the monitoring job.
structure MonitoringAppSpecification {
/// The container image to be run by the monitoring job.
@required
ImageUri: ImageUri
/// Specifies the entrypoint for a container used to run the monitoring job.
ContainerEntrypoint: ContainerEntrypoint
/// An array of arguments for the container used to run the monitoring job.
ContainerArguments: MonitoringContainerArguments
/// An Amazon S3 URI to a script that is called per row prior to running analysis. It can
/// base64 decode the payload and convert it into a flatted json so that the built-in container
/// can use the converted data. Applicable only for the built-in (first party)
/// containers.
RecordPreprocessorSourceUri: S3Uri
/// An Amazon S3 URI to a script that is called after analysis has been performed.
/// Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri: S3Uri
}
/// Configuration for monitoring constraints and monitoring statistics. These baseline
/// resources are compared against the results of the current job from the series of jobs
/// scheduled to collect data periodically.
structure MonitoringBaselineConfig {
/// The name of the job that performs baselining for the monitoring job.
BaseliningJobName: ProcessingJobName
/// The baseline constraint file in Amazon S3 that the current monitoring job should
/// validated against.
ConstraintsResource: MonitoringConstraintsResource
/// The baseline statistics file in Amazon S3 that the current monitoring job should be
/// validated against.
StatisticsResource: MonitoringStatisticsResource
}
/// Configuration for the cluster used to run model monitoring jobs.
structure MonitoringClusterConfig {
/// The number of ML compute instances to use in the model monitoring job. For distributed
/// processing jobs, specify a value greater than 1. The default value is 1.
@required
InstanceCount: ProcessingInstanceCount
/// The ML compute instance type for the processing job.
@required
InstanceType: ProcessingInstanceType
/// The size of the ML storage volume, in gigabytes, that you want to provision. You must
/// specify sufficient ML storage for your scenario.
@required
VolumeSizeInGB: ProcessingVolumeSizeInGB
/// 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(s) that run the model monitoring
/// job.
VolumeKmsKeyId: KmsKeyId
}
/// The constraints resource for a monitoring job.
structure MonitoringConstraintsResource {
/// The Amazon S3 URI for the constraints resource.
S3Uri: S3Uri
}
/// Represents the CSV dataset format used when running a monitoring job.
structure MonitoringCsvDatasetFormat {
/// Indicates if the CSV data has a header.
Header: Boolean = false
}
/// Represents the dataset format used when running a monitoring job.
structure MonitoringDatasetFormat {
/// The CSV dataset used in the monitoring job.
Csv: MonitoringCsvDatasetFormat
/// The JSON dataset used in the monitoring job
Json: MonitoringJsonDatasetFormat
/// The Parquet dataset used in the monitoring job
Parquet: MonitoringParquetDatasetFormat
}
/// Summary of information about the last monitoring job to run.
structure MonitoringExecutionSummary {
/// The name of the monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The time the monitoring job was scheduled.
@required
ScheduledTime: Timestamp
/// The time at which the monitoring job was created.
@required
CreationTime: Timestamp
/// A timestamp that indicates the last time the monitoring job was modified.
@required
LastModifiedTime: Timestamp
/// The status of the monitoring job.
@required
MonitoringExecutionStatus: ExecutionStatus
/// The Amazon Resource Name (ARN) of the monitoring job.
ProcessingJobArn: ProcessingJobArn
/// The name of the endpoint used to run the monitoring job.
EndpointName: EndpointName
/// Contains the reason a monitoring job failed, if it failed.
FailureReason: FailureReason
/// The name of the monitoring job.
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// The type of the monitoring job.
MonitoringType: MonitoringType
}
/// The ground truth labels for the dataset used for the monitoring job.
structure MonitoringGroundTruthS3Input {
/// The address of the Amazon S3 location of the ground truth labels.
S3Uri: MonitoringS3Uri
}
/// The inputs for a monitoring job.
structure MonitoringInput {
/// The endpoint for a monitoring job.
EndpointInput: EndpointInput
/// Input object for the batch transform job.
BatchTransformInput: BatchTransformInput
}
/// Defines the monitoring job.
structure MonitoringJobDefinition {
/// Baseline configuration used to validate that the data conforms to the specified
/// constraints and statistics
BaselineConfig: MonitoringBaselineConfig
/// The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker
/// Endpoint.
@required
MonitoringInputs: MonitoringInputs
/// The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage
/// Service (Amazon S3).
@required
MonitoringOutputConfig: MonitoringOutputConfig
/// Identifies the resources, ML compute instances, and ML storage volumes to deploy for a
/// monitoring job. In distributed processing, you specify more than one instance.
@required
MonitoringResources: MonitoringResources
/// Configures the monitoring job to run a specified Docker container image.
@required
MonitoringAppSpecification: MonitoringAppSpecification
/// Specifies a time limit for how long the monitoring job is allowed to run.
StoppingCondition: MonitoringStoppingCondition
/// Sets the environment variables in the Docker container.
Environment: MonitoringEnvironmentMap
/// Specifies networking options for an monitoring job.
NetworkConfig: NetworkConfig
/// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on
/// your behalf.
@required
RoleArn: RoleArn
}
/// Summary information about a monitoring job.
structure MonitoringJobDefinitionSummary {
/// The name of the monitoring job.
@required
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// The Amazon Resource Name (ARN) of the monitoring job.
@required
MonitoringJobDefinitionArn: MonitoringJobDefinitionArn
/// The time that the monitoring job was created.
@required
CreationTime: Timestamp
/// The name of the endpoint that the job monitors.
@required
EndpointName: EndpointName
}
/// Represents the JSON dataset format used when running a monitoring job.
structure MonitoringJsonDatasetFormat {
/// Indicates if the file should be read as a json object per line.
///
Line: Boolean = false
}
/// The networking configuration for the monitoring job.
structure MonitoringNetworkConfig {
/// Whether to encrypt all communications between the instances used for the monitoring
/// jobs. Choose True to encrypt communications. Encryption provides greater
/// security for distributed jobs, but the processing might take longer.
EnableInterContainerTrafficEncryption: Boolean = false
/// Whether to allow inbound and outbound network calls to and from the containers used for
/// the monitoring job.
EnableNetworkIsolation: Boolean = false
VpcConfig: VpcConfig
}
/// The output object for a monitoring job.
structure MonitoringOutput {
/// The Amazon S3 storage location where the results of a monitoring job are saved.
@required
S3Output: MonitoringS3Output
}
/// The output configuration for monitoring jobs.
structure MonitoringOutputConfig {
/// Monitoring outputs for monitoring jobs. This is where the output of the periodic
/// monitoring jobs is uploaded.
@required
MonitoringOutputs: MonitoringOutputs
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model
/// artifacts at rest using Amazon S3 server-side encryption.
KmsKeyId: KmsKeyId
}
/// Represents the Parquet dataset format used when running a monitoring job.
structure MonitoringParquetDatasetFormat {}
/// Identifies the resources to deploy for a monitoring job.
structure MonitoringResources {
/// The configuration for the cluster resources used to run the processing job.
@required
ClusterConfig: MonitoringClusterConfig
}
/// Information about where and how you want to store the results of a monitoring
/// job.
structure MonitoringS3Output {
/// A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a
/// monitoring job.
@required
S3Uri: MonitoringS3Uri
/// The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a
/// monitoring job. LocalPath is an absolute path for the output data.
@required
LocalPath: ProcessingLocalPath
/// Whether to upload the results of the monitoring job continuously or after the job
/// completes.
S3UploadMode: ProcessingS3UploadMode
}
/// A schedule for a model monitoring job. For information about model monitor, see
/// Amazon SageMaker Model
/// Monitor.
structure MonitoringSchedule {
/// The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleArn: MonitoringScheduleArn
/// The name of the monitoring schedule.
MonitoringScheduleName: MonitoringScheduleName
/// The status of the monitoring schedule. This can be one of the following values.
///
/// -
///
/// PENDING - The schedule is pending being created.
///
/// -
///
/// FAILED - The schedule failed.
///
/// -
///
/// SCHEDULED - The schedule was successfully created.
///
/// -
///
/// STOPPED - The schedule was stopped.
///
///
MonitoringScheduleStatus: ScheduleStatus
/// The type of the monitoring job definition to schedule.
MonitoringType: MonitoringType
/// If the monitoring schedule failed, the reason it failed.
FailureReason: FailureReason
/// The time that the monitoring schedule was created.
CreationTime: Timestamp
/// The last time the monitoring schedule was changed.
LastModifiedTime: Timestamp
MonitoringScheduleConfig: MonitoringScheduleConfig
/// The endpoint that hosts the model being monitored.
EndpointName: EndpointName
LastMonitoringExecutionSummary: MonitoringExecutionSummary
/// A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services
/// resources in the Amazon Web Services General Reference Guide.
Tags: TagList
}
/// Configures the monitoring schedule and defines the monitoring job.
structure MonitoringScheduleConfig {
/// Configures the monitoring schedule.
ScheduleConfig: ScheduleConfig
/// Defines the monitoring job.
MonitoringJobDefinition: MonitoringJobDefinition
/// The name of the monitoring job definition to schedule.
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// The type of the monitoring job definition to schedule.
MonitoringType: MonitoringType
}
/// Summarizes the monitoring schedule.
structure MonitoringScheduleSummary {
/// The name of the monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The Amazon Resource Name (ARN) of the monitoring schedule.
@required
MonitoringScheduleArn: MonitoringScheduleArn
/// The creation time of the monitoring schedule.
@required
CreationTime: Timestamp
/// The last time the monitoring schedule was modified.
@required
LastModifiedTime: Timestamp
/// The status of the monitoring schedule.
@required
MonitoringScheduleStatus: ScheduleStatus
/// The name of the endpoint using the monitoring schedule.
EndpointName: EndpointName
/// The name of the monitoring job definition that the schedule is for.
MonitoringJobDefinitionName: MonitoringJobDefinitionName
/// The type of the monitoring job definition that the schedule is for.
MonitoringType: MonitoringType
}
/// The statistics resource for a monitoring job.
structure MonitoringStatisticsResource {
/// The Amazon S3 URI for the statistics resource.
S3Uri: S3Uri
}
/// A time limit for how long the monitoring job is allowed to run before stopping.
structure MonitoringStoppingCondition {
/// The maximum runtime allowed in seconds.
///
/// The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and
/// model explainability, this can be up to 3600 seconds for an hourly schedule. For model
/// bias and model quality hourly schedules, this can be up to 1800 seconds.
///
@required
MaxRuntimeInSeconds: MonitoringMaxRuntimeInSeconds = 0
}
/// Specifies additional configuration for hosting multi-model endpoints.
structure MultiModelConfig {
/// Whether to cache models for a multi-model endpoint. By default, multi-model endpoints
/// cache models so that a model does not have to be loaded into memory each time it is
/// invoked. Some use cases do not benefit from model caching. For example, if an endpoint
/// hosts a large number of models that are each invoked infrequently, the endpoint might
/// perform better if you disable model caching. To disable model caching, set the value of
/// this parameter to Disabled.
ModelCacheSetting: ModelCacheSetting
}
/// The VpcConfig configuration object that specifies the VPC that you
/// want the compilation jobs to connect to. For more information on
/// controlling access to your Amazon S3 buckets used for compilation job, see
/// Give Amazon SageMaker Compilation Jobs Access to Resources in Your Amazon VPC.
structure NeoVpcConfig {
/// The VPC security group IDs. IDs have the form of sg-xxxxxxxx.
/// Specify the security groups for the VPC that is specified in the Subnets field.
@required
SecurityGroupIds: NeoVpcSecurityGroupIds
/// The ID of the subnets in the VPC that you want to connect the
/// compilation job to for accessing the model in Amazon S3.
@required
Subnets: NeoVpcSubnets
}
/// A list of nested Filter objects. A resource must satisfy the conditions
/// of all filters to be included in the results returned from the Search API.
/// For example, to filter on a training job's InputDataConfig property with a
/// specific channel name and S3Uri prefix, define the following filters:
///
/// -
///
/// '{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',
///
///
/// -
///
/// '{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains",
/// "Value":"mybucket/catdata"}'
///
///
///
structure NestedFilters {
/// The name of the property to use in the nested filters. The value must match a listed property name,
/// such as InputDataConfig.
@required
NestedPropertyName: ResourcePropertyName
/// A list of filters. Each filter acts on a property. Filters must contain at least one
/// Filters value. For example, a NestedFilters call might
/// include a filter on the PropertyName parameter of the
/// InputDataConfig property:
/// InputDataConfig.DataSource.S3DataSource.S3Uri.
@required
Filters: FilterList
}
/// Networking options for a job, such as network traffic encryption between containers,
/// whether to allow inbound and outbound network calls to and from containers, and the VPC
/// subnets and security groups to use for VPC-enabled jobs.
structure NetworkConfig {
/// Whether to encrypt all communications between distributed processing jobs. Choose
/// True to encrypt communications. Encryption provides greater security for distributed
/// processing jobs, but the processing might take longer.
EnableInterContainerTrafficEncryption: Boolean = false
/// Whether to allow inbound and outbound network calls to and from the containers used for
/// the processing job.
EnableNetworkIsolation: Boolean = false
VpcConfig: VpcConfig
}
/// Provides a summary of a notebook instance lifecycle configuration.
structure NotebookInstanceLifecycleConfigSummary {
/// The name of the lifecycle configuration.
@required
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// The Amazon Resource Name (ARN) of the lifecycle configuration.
@required
NotebookInstanceLifecycleConfigArn: NotebookInstanceLifecycleConfigArn
/// A timestamp that tells when the lifecycle configuration was created.
CreationTime: CreationTime
/// A timestamp that tells when the lifecycle configuration was last modified.
LastModifiedTime: LastModifiedTime
}
/// Contains the notebook instance lifecycle configuration script.
/// Each lifecycle configuration script has a limit of 16384 characters.
/// The value of the $PATH environment variable that is available to both
/// scripts is /sbin:bin:/usr/sbin:/usr/bin.
/// View CloudWatch Logs for notebook instance lifecycle configurations in log group
/// /aws/sagemaker/NotebookInstances in log stream
/// [notebook-instance-name]/[LifecycleConfigHook].
/// Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs
/// for longer than 5 minutes, it fails and the notebook instance is not created or
/// started.
/// For information about notebook instance lifestyle configurations, see Step
/// 2.1: (Optional) Customize a Notebook Instance.
structure NotebookInstanceLifecycleHook {
/// A base64-encoded string that contains a shell script for a notebook instance lifecycle
/// configuration.
Content: NotebookInstanceLifecycleConfigContent
}
/// Provides summary information for an SageMaker notebook instance.
structure NotebookInstanceSummary {
/// The name of the notebook instance that you want a summary for.
@required
NotebookInstanceName: NotebookInstanceName
/// The Amazon Resource Name (ARN) of the notebook instance.
@required
NotebookInstanceArn: NotebookInstanceArn
/// The status of the notebook instance.
NotebookInstanceStatus: NotebookInstanceStatus
/// The URL that you use to connect to the Jupyter notebook running in your notebook
/// instance.
Url: NotebookInstanceUrl
/// The type of ML compute instance that the notebook instance is running on.
InstanceType: InstanceType
/// A timestamp that shows when the notebook instance was created.
CreationTime: CreationTime
/// A timestamp that shows when the notebook instance was last modified.
LastModifiedTime: LastModifiedTime
/// The name of a notebook instance lifecycle configuration associated with this notebook
/// instance.
/// For information about notebook instance lifestyle configurations, see Step
/// 2.1: (Optional) Customize a Notebook Instance.
NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName
/// 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 Notebook Instances.
DefaultCodeRepository: CodeRepositoryNameOrUrl
/// 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 Notebook Instances.
AdditionalCodeRepositories: AdditionalCodeRepositoryNamesOrUrls
}
/// Configures Amazon SNS notifications of available or expiring work items for work
/// teams.
structure NotificationConfiguration {
/// The ARN for the Amazon SNS topic to which notifications should be published.
NotificationTopicArn: NotificationTopicArn
}
/// Specifies the number of training jobs that this hyperparameter tuning job launched,
/// categorized by the status of their objective metric. The objective metric status shows
/// whether the
/// final
/// objective metric for the training job has been evaluated by the
/// tuning job and used in the hyperparameter tuning process.
structure ObjectiveStatusCounters {
/// The number of training jobs whose final objective metric was evaluated by the
/// hyperparameter tuning job and used in the hyperparameter tuning process.
Succeeded: ObjectiveStatusCounter = 0
/// The number of training jobs that are in progress and pending evaluation of their final
/// objective metric.
Pending: ObjectiveStatusCounter = 0
/// The number of training jobs whose final objective metric was not evaluated and used in
/// the hyperparameter tuning process. This typically occurs when the training job failed or
/// did not emit an objective metric.
Failed: ObjectiveStatusCounter = 0
}
/// The configuration of an OfflineStore.
/// Provide an OfflineStoreConfig in a request to
/// CreateFeatureGroup to create an OfflineStore.
/// To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key
/// Management Service (KMS) key ID, or KMSKeyId, in
/// S3StorageConfig.
structure OfflineStoreConfig {
/// The Amazon Simple Storage (Amazon S3) location of OfflineStore.
@required
S3StorageConfig: S3StorageConfig
/// Set to True to disable the automatic creation of an Amazon Web Services Glue table when
/// configuring an OfflineStore.
DisableGlueTableCreation: Boolean = false
/// The meta data of the Glue table that is autogenerated when an OfflineStore
/// is created.
DataCatalogConfig: DataCatalogConfig
/// Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
TableFormat: TableFormat
}
/// The status of OfflineStore.
structure OfflineStoreStatus {
/// An OfflineStore status.
@required
Status: OfflineStoreStatusValue
/// The justification for why the OfflineStoreStatus is Blocked (if applicable).
BlockedReason: BlockedReason
}
/// Use this parameter to configure your OIDC Identity Provider (IdP).
structure OidcConfig {
/// The OIDC IdP client ID used to configure your private workforce.
@required
ClientId: ClientId
/// The OIDC IdP client secret used to configure your private workforce.
@required
ClientSecret: ClientSecret
/// The OIDC IdP issuer used to configure your private workforce.
@required
Issuer: OidcEndpoint
/// The OIDC IdP authorization endpoint used to configure your private workforce.
@required
AuthorizationEndpoint: OidcEndpoint
/// The OIDC IdP token endpoint used to configure your private workforce.
@required
TokenEndpoint: OidcEndpoint
/// The OIDC IdP user information endpoint used to configure your private workforce.
@required
UserInfoEndpoint: OidcEndpoint
/// The OIDC IdP logout endpoint used to configure your private workforce.
@required
LogoutEndpoint: OidcEndpoint
/// The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
@required
JwksUri: OidcEndpoint
}
/// Your OIDC IdP workforce configuration.
structure OidcConfigForResponse {
/// The OIDC IdP client ID used to configure your private workforce.
ClientId: ClientId
/// The OIDC IdP issuer used to configure your private workforce.
Issuer: OidcEndpoint
/// The OIDC IdP authorization endpoint used to configure your private workforce.
AuthorizationEndpoint: OidcEndpoint
/// The OIDC IdP token endpoint used to configure your private workforce.
TokenEndpoint: OidcEndpoint
/// The OIDC IdP user information endpoint used to configure your private workforce.
UserInfoEndpoint: OidcEndpoint
/// The OIDC IdP logout endpoint used to configure your private workforce.
LogoutEndpoint: OidcEndpoint
/// The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
JwksUri: OidcEndpoint
}
/// A list of user groups that exist in your OIDC Identity Provider (IdP).
/// One to ten groups can be used to create a single private work team.
/// When you add a user group to the list of Groups, you can add that user group to one or more
/// private work teams. If you add a user group to a private work team, all workers in that user group
/// are added to the work team.
structure OidcMemberDefinition {
/// A list of comma seperated strings that identifies
/// user groups in your OIDC IdP. Each user group is
/// made up of a group of private workers.
@required
Groups: Groups
}
/// Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or
/// KMSKeyId, for at rest data encryption. You can turn
/// OnlineStore on or off by specifying the EnableOnlineStore flag
/// at General Assembly; the default value is False.
structure OnlineStoreConfig {
/// Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your
/// OnlineStore.
SecurityConfig: OnlineStoreSecurityConfig
/// Turn OnlineStore off by specifying False
/// for the EnableOnlineStore flag. Turn OnlineStore
/// on by specifying True
/// for the EnableOnlineStore flag.
/// The default value is False.
EnableOnlineStore: Boolean = false
}
/// The security configuration for OnlineStore.
structure OnlineStoreSecurityConfig {
/// The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses
/// to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
/// The caller (either IAM user or IAM role) of CreateFeatureGroup must have
/// below permissions to the OnlineStore
/// KmsKeyId:
///
/// -
///
/// "kms:Encrypt"
///
///
/// -
///
/// "kms:Decrypt"
///
///
/// -
///
/// "kms:DescribeKey"
///
///
/// -
///
/// "kms:CreateGrant"
///
///
/// -
///
/// "kms:RetireGrant"
///
///
/// -
///
/// "kms:ReEncryptFrom"
///
///
/// -
///
/// "kms:ReEncryptTo"
///
///
/// -
///
/// "kms:GenerateDataKey"
///
///
/// -
///
/// "kms:ListAliases"
///
///
/// -
///
/// "kms:ListGrants"
///
///
/// -
///
/// "kms:RevokeGrant"
///
///
///
/// The caller (either IAM user or IAM role) to all DataPlane operations
/// (PutRecord, GetRecord, DeleteRecord) must have
/// the following permissions to the KmsKeyId:
///
/// -
///
/// "kms:Decrypt"
///
///
///
KmsKeyId: KmsKeyId
}
/// Contains information about the output location for the compiled model and the target
/// device that the model runs on. TargetDevice and TargetPlatform
/// are mutually exclusive, so you need to choose one between the two to specify your target
/// device or platform. If you cannot find your device you want to use from the
/// TargetDevice list, use TargetPlatform to describe the
/// platform of your edge device and CompilerOptions if there are specific
/// settings that are required or recommended to use for particular TargetPlatform.
structure OutputConfig {
/// Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For
/// example, s3://bucket-name/key-name-prefix.
@required
S3OutputLocation: S3Uri
/// Identifies the target device or the machine learning instance that you want to run
/// your model on after the compilation has completed. Alternatively, you can specify OS,
/// architecture, and accelerator using TargetPlatform fields. It can be
/// used instead of TargetPlatform.
TargetDevice: TargetDevice
/// Contains information about a target platform that you want your model to run on, such
/// as OS, architecture, and accelerators. It is an alternative of
/// TargetDevice.
/// The following examples show how to configure the TargetPlatform and
/// CompilerOptions JSON strings for popular target platforms:
///
/// -
///
Raspberry Pi 3 Model B+
///
/// "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
///
///
/// "CompilerOptions": {'mattr': ['+neon']}
///
///
/// -
///
Jetson TX2
///
/// "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator":
/// "NVIDIA"},
///
///
/// "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1',
/// 'cuda-ver': '10.0'}
///
///
/// -
///
EC2 m5.2xlarge instance OS
///
/// "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator":
/// "NVIDIA"},
///
///
/// "CompilerOptions": {'mcpu': 'skylake-avx512'}
///
///
/// -
///
RK3399
///
/// "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator":
/// "MALI"}
///
///
/// -
///
ARMv7 phone (CPU)
///
/// "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
///
///
/// "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr':
/// ['+neon']}
///
///
/// -
///
ARMv8 phone (CPU)
///
/// "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
///
///
/// "CompilerOptions": {'ANDROID_PLATFORM': 29}
///
///
///
TargetPlatform: TargetPlatform
/// Specifies additional parameters for compiler options in JSON format. The compiler
/// options are TargetPlatform specific. It is required for NVIDIA accelerators
/// and highly recommended for CPU compilations. For any other cases, it is optional to
/// specify CompilerOptions.
///
///
/// -
///
/// DTYPE: Specifies the data type for the input. When compiling for
/// ml_* (except for ml_inf) instances using PyTorch
/// framework, provide the data type (dtype) of the model's input.
/// "float32" is used if "DTYPE" is not specified.
/// Options for data type are:
///
/// -
///
float32: Use either "float" or "float32".
///
/// -
///
int64: Use either "int64" or "long".
///
///
/// For example, {"dtype" : "float32"}.
///
/// -
///
/// CPU: Compilation for CPU supports the following compiler
/// options.
///
/// -
///
/// mcpu: CPU micro-architecture. For example, {'mcpu':
/// 'skylake-avx512'}
///
///
/// -
///
/// mattr: CPU flags. For example, {'mattr': ['+neon',
/// '+vfpv4']}
///
///
///
///
/// -
///
/// ARM: Details of ARM CPU compilations.
///
/// -
///
/// NEON: NEON is an implementation of the Advanced SIMD
/// extension used in ARMv7 processors.
/// For example, add {'mattr': ['+neon']} to the compiler
/// options if compiling for ARM 32-bit platform with the NEON
/// support.
///
///
///
/// -
///
/// NVIDIA: Compilation for NVIDIA GPU supports the following
/// compiler options.
///
/// -
///
/// gpu_code: Specifies the targeted architecture.
///
/// -
///
/// trt-ver: Specifies the TensorRT versions in x.y.z.
/// format.
///
/// -
///
/// cuda-ver: Specifies the CUDA version in x.y
/// format.
///
///
/// For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver':
/// '10.1'}
///
///
/// -
///
/// ANDROID: Compilation for the Android OS supports the following
/// compiler options:
///
/// -
///
/// ANDROID_PLATFORM: Specifies the Android API levels.
/// Available levels range from 21 to 29. For example,
/// {'ANDROID_PLATFORM': 28}.
///
/// -
///
/// mattr: Add {'mattr': ['+neon']} to compiler
/// options if compiling for ARM 32-bit platform with NEON support.
///
///
///
/// -
///
/// INFERENTIA: Compilation for target ml_inf1 uses compiler options
/// passed in as a JSON string. For example,
/// "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
///
/// For information about supported compiler options, see
///
/// Neuron Compiler CLI.
///
///
/// -
///
/// CoreML: Compilation for the CoreML OutputConfig$TargetDevice
/// supports the following compiler options:
///
/// -
///
/// class_labels: Specifies the classification labels file
/// name inside input tar.gz file. For example,
/// {"class_labels": "imagenet_labels_1000.txt"}.
/// Labels inside the txt file should be separated by newlines.
///
///
///
/// -
///
/// EIA: Compilation for the Elastic Inference Accelerator supports the following
/// compiler options:
///
/// -
///
/// precision_mode: Specifies the precision of compiled artifacts. Supported values
/// are "FP16" and "FP32". Default is
/// "FP32".
///
/// -
///
/// signature_def_key: Specifies the signature to use for models in SavedModel
/// format. Defaults is TensorFlow's default signature def key.
///
/// -
///
/// output_names: Specifies a list of output tensor names for
/// models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
///
///
/// For example:
/// {"precision_mode": "FP32", "output_names": ["output:0"]}
///
///
///
CompilerOptions: CompilerOptions
/// The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption
/// after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
/// For more information, see
/// KMS-Managed Encryption
/// Keys in the Amazon Simple Storage Service Developer Guide.
///
/// 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
///
///
///
KmsKeyId: KmsKeyId
}
/// Provides information about how to store model training results (model
/// artifacts).
structure OutputDataConfig {
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker
/// uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
/// KmsKeyId 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 SageMaker execution role must
/// include permissions to call kms:Encrypt. If you don't provide a KMS key ID,
/// SageMaker uses the default KMS key for Amazon S3 for your role's account. 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
/// CreateTrainingJob, CreateTransformJob, or
/// CreateHyperParameterTuningJob requests. For more information, see
/// Using
/// Key Policies in Amazon Web Services KMS in the Amazon Web Services
/// Key Management Service Developer Guide.
KmsKeyId: KmsKeyId
/// Identifies the S3 path where you want SageMaker to store the model artifacts. For
/// example, s3://bucket-name/key-name-prefix.
@required
S3OutputPath: S3Uri
}
/// An output parameter of a pipeline step.
structure OutputParameter {
/// The name of the output parameter.
@required
Name: String256
/// The value of the output parameter.
@required
Value: String1024
}
/// Configuration that controls the parallelism of the pipeline.
/// By default, the parallelism configuration specified applies to all
/// executions of the pipeline unless overridden.
structure ParallelismConfiguration {
/// The max number of steps that can be executed in parallel.
@required
MaxParallelExecutionSteps: MaxParallelExecutionSteps = 0
}
/// Assigns a value to a named Pipeline parameter.
structure Parameter {
/// The name of the parameter to assign a value to. This
/// parameter name must match a named parameter in the
/// pipeline definition.
@required
Name: PipelineParameterName
/// The literal value for the parameter.
@required
Value: String1024
}
/// Defines the possible values for categorical, continuous, and integer hyperparameters
/// to be used by an algorithm.
structure ParameterRange {
/// A IntegerParameterRangeSpecification object that defines the possible
/// values for an integer hyperparameter.
IntegerParameterRangeSpecification: IntegerParameterRangeSpecification
/// A ContinuousParameterRangeSpecification object that defines the possible
/// values for a continuous hyperparameter.
ContinuousParameterRangeSpecification: ContinuousParameterRangeSpecification
/// A CategoricalParameterRangeSpecification object that defines the possible
/// values for a categorical hyperparameter.
CategoricalParameterRangeSpecification: CategoricalParameterRangeSpecification
}
/// Specifies ranges of integer, continuous, and categorical hyperparameters that a
/// hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs
/// with hyperparameter values within these ranges to find the combination of values that
/// result in the training job with the best performance as measured by the objective metric
/// of the hyperparameter tuning job.
///
/// The maximum number of items specified for Array Members refers to the
/// maximum number of hyperparameters for each range and also the maximum for the
/// hyperparameter tuning job itself. That is, the sum of the number of hyperparameters
/// for all the ranges can't exceed the maximum number specified.
///
structure ParameterRanges {
/// The array of IntegerParameterRange objects that specify ranges of
/// integer hyperparameters that a hyperparameter tuning job searches.
IntegerParameterRanges: IntegerParameterRanges
/// The array of ContinuousParameterRange objects that specify ranges of
/// continuous hyperparameters that a hyperparameter tuning job searches.
ContinuousParameterRanges: ContinuousParameterRanges
/// The array of CategoricalParameterRange objects that specify ranges
/// of categorical hyperparameters that a hyperparameter tuning job searches.
CategoricalParameterRanges: CategoricalParameterRanges
}
/// The trial that a trial component is associated with and the experiment the trial is part
/// of. A component might not be associated with a trial. A component can be associated with
/// multiple trials.
structure Parent {
/// The name of the trial.
TrialName: ExperimentEntityName
/// The name of the experiment.
ExperimentName: ExperimentEntityName
}
/// A previously completed or stopped hyperparameter tuning job to be used as a starting
/// point for a new hyperparameter tuning job.
structure ParentHyperParameterTuningJob {
/// The name of the hyperparameter tuning job to be used as a starting point for a new
/// hyperparameter tuning job.
HyperParameterTuningJobName: HyperParameterTuningJobName
}
/// The summary of an in-progress deployment when an endpoint is creating or updating with
/// a new endpoint configuration.
structure PendingDeploymentSummary {
/// The name of the endpoint configuration used in the deployment.
@required
EndpointConfigName: EndpointConfigName
/// An array of PendingProductionVariantSummary objects, one for each
/// model hosted behind this endpoint for the in-progress deployment.
ProductionVariants: PendingProductionVariantSummaryList
/// The start time of the deployment.
StartTime: Timestamp
/// An array of PendingProductionVariantSummary objects, one for each
/// model hosted behind this endpoint in shadow mode with production traffic replicated from
/// the model specified on ProductionVariants for the in-progress
/// deployment.
ShadowProductionVariants: PendingProductionVariantSummaryList
}
/// The production variant summary for a deployment when an endpoint is creating or
/// updating with the
/// CreateEndpoint
/// or
/// UpdateEndpoint
/// operations. Describes the VariantStatus
/// , weight and capacity for a production variant associated with an endpoint.
///
structure PendingProductionVariantSummary {
/// The name of the variant.
@required
VariantName: VariantName
/// An array of DeployedImage objects that specify the Amazon EC2 Container
/// Registry paths of the inference images deployed on instances of this
/// ProductionVariant.
DeployedImages: DeployedImages
/// The weight associated with the variant.
CurrentWeight: VariantWeight
/// The requested weight for the variant in this deployment, as specified in the endpoint
/// configuration for the endpoint. The value is taken from the request to the
/// CreateEndpointConfig
/// operation.
DesiredWeight: VariantWeight
/// The number of instances associated with the variant.
CurrentInstanceCount: TaskCount
/// The number of instances requested in this deployment, as specified in the endpoint
/// configuration for the endpoint. The value is taken from the request to the
/// CreateEndpointConfig
/// operation.
DesiredInstanceCount: TaskCount
/// The type of instances associated with the variant.
InstanceType: ProductionVariantInstanceType
/// The size of the Elastic Inference (EI) instance to use for the production variant. EI
/// instances provide on-demand GPU computing for inference. For more information, see
/// Using Elastic
/// Inference in Amazon SageMaker.
AcceleratorType: ProductionVariantAcceleratorType
/// The endpoint variant status which describes the current deployment stage status or
/// operational status.
VariantStatus: ProductionVariantStatusList
/// The serverless configuration for the endpoint.
CurrentServerlessConfig: ProductionVariantServerlessConfig
/// The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
DesiredServerlessConfig: ProductionVariantServerlessConfig
}
/// Defines the traffic pattern.
structure Phase {
/// Specifies how many concurrent users to start with.
InitialNumberOfUsers: InitialNumberOfUsers
/// Specified how many new users to spawn in a minute.
SpawnRate: SpawnRate
/// Specifies how long traffic phase should be.
DurationInSeconds: TrafficDurationInSeconds
}
/// A SageMaker Model Building Pipeline instance.
structure Pipeline {
/// The Amazon Resource Name (ARN) of the pipeline.
PipelineArn: PipelineArn
/// The name of the pipeline.
PipelineName: PipelineName
/// The display name of the pipeline.
PipelineDisplayName: PipelineName
/// The description of the pipeline.
PipelineDescription: PipelineDescription
/// The Amazon Resource Name (ARN) of the role that created the pipeline.
RoleArn: RoleArn
/// The status of the pipeline.
PipelineStatus: PipelineStatus
/// The creation time of the pipeline.
CreationTime: Timestamp
/// The time that the pipeline was last modified.
LastModifiedTime: Timestamp
/// The time when the pipeline was last run.
LastRunTime: Timestamp
CreatedBy: UserContext
LastModifiedBy: UserContext
/// The parallelism configuration applied to the pipeline.
ParallelismConfiguration: ParallelismConfiguration
/// A list of tags that apply to the pipeline.
Tags: TagList
}
/// The location of the pipeline definition stored in Amazon S3.
structure PipelineDefinitionS3Location {
/// Name of the S3 bucket.
@required
Bucket: BucketName
/// The object key (or key name) uniquely identifies the
/// object in an S3 bucket.
@required
ObjectKey: Key
/// Version Id of the pipeline definition file. If not specified, Amazon SageMaker
/// will retrieve the latest version.
VersionId: VersionId
}
/// An execution of a pipeline.
structure PipelineExecution {
/// The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineArn: PipelineArn
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
/// The display name of the pipeline execution.
PipelineExecutionDisplayName: PipelineExecutionName
/// The status of the pipeline status.
PipelineExecutionStatus: PipelineExecutionStatus
/// The description of the pipeline execution.
PipelineExecutionDescription: PipelineExecutionDescription
PipelineExperimentConfig: PipelineExperimentConfig
/// If the execution failed, a message describing why.
FailureReason: PipelineExecutionFailureReason
/// The creation time of the pipeline execution.
CreationTime: Timestamp
/// The time that the pipeline execution was last modified.
LastModifiedTime: Timestamp
CreatedBy: UserContext
LastModifiedBy: UserContext
/// The parallelism configuration applied to the pipeline execution.
ParallelismConfiguration: ParallelismConfiguration
/// Contains a list of pipeline parameters. This list can be empty.
PipelineParameters: ParameterList
}
/// An execution of a step in a pipeline.
structure PipelineExecutionStep {
/// The name of the step that is executed.
StepName: StepName
/// The display name of the step.
StepDisplayName: StepDisplayName
/// The description of the step.
StepDescription: StepDescription
/// The time that the step started executing.
StartTime: Timestamp
/// The time that the step stopped executing.
EndTime: Timestamp
/// The status of the step execution.
StepStatus: StepStatus
/// If this pipeline execution step was cached, details on the cache hit.
CacheHitResult: CacheHitResult
/// The current attempt of the execution step. For more information, see Retry Policy for SageMaker Pipelines steps.
AttemptCount: IntegerValue = 0
/// The reason why the step failed execution. This is only returned if the step failed its execution.
FailureReason: FailureReason
/// Metadata to run the pipeline step.
Metadata: PipelineExecutionStepMetadata
}
/// Metadata for a step execution.
structure PipelineExecutionStepMetadata {
/// The Amazon Resource Name (ARN) of the training job that was run by this step execution.
TrainingJob: TrainingJobStepMetadata
/// The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
ProcessingJob: ProcessingJobStepMetadata
/// The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
TransformJob: TransformJobStepMetadata
/// The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
TuningJob: TuningJobStepMetaData
/// The Amazon Resource Name (ARN) of the model that was created by this step execution.
Model: ModelStepMetadata
/// The Amazon Resource Name (ARN) of the model package that the model was registered to by this step execution.
RegisterModel: RegisterModelStepMetadata
/// The outcome of the condition evaluation that was run by this step execution.
Condition: ConditionStepMetadata
/// The URL of the Amazon SQS queue used by this step execution, the pipeline generated token,
/// and a list of output parameters.
Callback: CallbackStepMetadata
/// The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of
/// output parameters.
Lambda: LambdaStepMetadata
/// The configurations and outcomes of the check step execution. This includes:
///
/// -
///
The type of the check conducted.
///
/// -
///
The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
///
/// -
///
The Amazon S3 URIs of newly calculated baseline constraints and statistics.
///
/// -
///
The model package group name provided.
///
/// -
///
The Amazon S3 URI of the violation report if violations detected.
///
/// -
///
The Amazon Resource Name (ARN) of check processing job initiated by the step execution.
///
/// -
///
The Boolean flags indicating if the drift check is skipped.
///
/// -
///
If step property BaselineUsedForDriftCheck is set the same as
/// CalculatedBaseline.
///
///
QualityCheck: QualityCheckStepMetadata
/// Container for the metadata for a Clarify check step. The configurations
/// and outcomes of the check step execution. This includes:
///
/// -
///
The type of the check conducted,
///
/// -
///
The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
///
/// -
///
The Amazon S3 URIs of newly calculated baseline constraints and statistics.
///
/// -
///
The model package group name provided.
///
/// -
///
The Amazon S3 URI of the violation report if violations detected.
///
/// -
///
The Amazon Resource Name (ARN) of check processing job initiated by the step execution.
///
/// -
///
The boolean flags indicating if the drift check is skipped.
///
/// -
///
If step property BaselineUsedForDriftCheck is set the same as
/// CalculatedBaseline.
///
///
ClarifyCheck: ClarifyCheckStepMetadata
/// The configurations and outcomes of an Amazon EMR step execution.
EMR: EMRStepMetadata
/// The configurations and outcomes of a Fail step execution.
Fail: FailStepMetadata
/// The Amazon Resource Name (ARN) of the AutoML job that was run by this step.
AutoMLJob: AutoMLJobStepMetadata
}
/// A pipeline execution summary.
structure PipelineExecutionSummary {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
/// The start time of the pipeline execution.
StartTime: Timestamp
/// The status of the pipeline execution.
PipelineExecutionStatus: PipelineExecutionStatus
/// The description of the pipeline execution.
PipelineExecutionDescription: PipelineExecutionDescription
/// The display name of the pipeline execution.
PipelineExecutionDisplayName: PipelineExecutionName
/// A message generated by SageMaker Pipelines describing why the pipeline execution failed.
PipelineExecutionFailureReason: String3072
}
/// Specifies the names of the experiment and trial created by a pipeline.
structure PipelineExperimentConfig {
/// The name of the experiment.
ExperimentName: ExperimentEntityName
/// The name of the trial.
TrialName: ExperimentEntityName
}
/// A summary of a pipeline.
structure PipelineSummary {
/// The Amazon Resource Name (ARN) of the pipeline.
PipelineArn: PipelineArn
/// The name of the pipeline.
PipelineName: PipelineName
/// The display name of the pipeline.
PipelineDisplayName: PipelineName
/// The description of the pipeline.
PipelineDescription: PipelineDescription
/// The Amazon Resource Name (ARN) that the pipeline used to execute.
RoleArn: RoleArn
/// The creation time of the pipeline.
CreationTime: Timestamp
/// The time that the pipeline was last modified.
LastModifiedTime: Timestamp
/// The last time that a pipeline execution began.
LastExecutionTime: Timestamp
}
/// Configuration for the cluster used to run a processing job.
structure ProcessingClusterConfig {
/// The number of ML compute instances to use in the processing job. For distributed
/// processing jobs, specify a value greater than 1. The default value is 1.
@required
InstanceCount: ProcessingInstanceCount
/// The ML compute instance type for the processing job.
@required
InstanceType: ProcessingInstanceType
/// The size of the ML storage volume in gigabytes that you want to provision. You must
/// specify sufficient ML storage for your scenario.
///
/// Certain Nitro-based instances include local storage with a fixed total size,
/// dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts
/// the local instance storage instead of Amazon EBS gp2 storage. You can't request a
/// VolumeSizeInGB greater than the total size of the local instance
/// storage.
/// For a list of instance types that support local instance storage, including the
/// total size per instance type, see Instance Store Volumes.
///
@required
VolumeSizeInGB: ProcessingVolumeSizeInGB
/// 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(s) that run the processing job.
///
///
/// 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 VolumeKmsKeyId when using an instance type with
/// local storage.
/// 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.
///
VolumeKmsKeyId: KmsKeyId
}
/// Configuration for processing job outputs in Amazon SageMaker Feature Store.
structure ProcessingFeatureStoreOutput {
/// The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your
/// processing script is responsible for putting records into your Feature Store.
@required
FeatureGroupName: FeatureGroupName
}
/// The inputs for a processing job. The processing input must specify exactly one of either
/// S3Input or DatasetDefinition types.
structure ProcessingInput {
/// The name for the processing job input.
@required
InputName: String
/// When True, input operations such as data download are managed natively by the
/// processing job application. When False (default), input operations are managed by Amazon SageMaker.
AppManaged: AppManaged = false
/// Configuration for downloading input data from Amazon S3 into the processing container.
S3Input: ProcessingS3Input
/// Configuration for a Dataset Definition input.
DatasetDefinition: DatasetDefinition
}
/// An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information,
/// see Process
/// Data and Evaluate Models.
structure ProcessingJob {
/// List of input configurations for the processing job.
ProcessingInputs: ProcessingInputs
ProcessingOutputConfig: ProcessingOutputConfig
/// The name of the processing job.
ProcessingJobName: ProcessingJobName
ProcessingResources: ProcessingResources
StoppingCondition: ProcessingStoppingCondition
AppSpecification: AppSpecification
/// Sets the environment variables in the Docker container.
Environment: ProcessingEnvironmentMap
NetworkConfig: NetworkConfig
/// The ARN of the role used to create the processing job.
RoleArn: RoleArn
ExperimentConfig: ExperimentConfig
/// The ARN of the processing job.
ProcessingJobArn: ProcessingJobArn
/// The status of the processing job.
ProcessingJobStatus: ProcessingJobStatus
/// A string, up to one KB in size, that contains metadata from the processing
/// container when the processing job exits.
ExitMessage: ExitMessage
/// A string, up to one KB in size, that contains the reason a processing job failed, if
/// it failed.
FailureReason: FailureReason
/// The time that the processing job ended.
ProcessingEndTime: Timestamp
/// The time that the processing job started.
ProcessingStartTime: Timestamp
/// The time the processing job was last modified.
LastModifiedTime: Timestamp
/// The time the processing job was created.
CreationTime: Timestamp
/// The ARN of a monitoring schedule for an endpoint associated with this processing
/// job.
MonitoringScheduleArn: MonitoringScheduleArn
/// The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
AutoMLJobArn: AutoMLJobArn
/// The ARN of the training job associated with this processing job.
TrainingJobArn: TrainingJobArn
/// 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.
Tags: TagList
}
/// Metadata for a processing job step.
structure ProcessingJobStepMetadata {
/// The Amazon Resource Name (ARN) of the processing job.
Arn: ProcessingJobArn
}
/// Summary of information about a processing job.
structure ProcessingJobSummary {
/// The name of the processing job.
@required
ProcessingJobName: ProcessingJobName
/// The Amazon Resource Name (ARN) of the processing job..
@required
ProcessingJobArn: ProcessingJobArn
/// The time at which the processing job was created.
@required
CreationTime: Timestamp
/// The time at which the processing job completed.
ProcessingEndTime: Timestamp
/// A timestamp that indicates the last time the processing job was modified.
LastModifiedTime: Timestamp
/// The status of the processing job.
@required
ProcessingJobStatus: ProcessingJobStatus
/// A string, up to one KB in size, that contains the reason a processing job failed, if
/// it failed.
FailureReason: FailureReason
/// An optional string, up to one KB in size, that contains metadata from the processing
/// container when the processing job exits.
ExitMessage: ExitMessage
}
/// Describes the results of a processing job. The processing output must specify exactly one of
/// either S3Output or FeatureStoreOutput types.
structure ProcessingOutput {
/// The name for the processing job output.
@required
OutputName: String
/// Configuration for processing job outputs in Amazon S3.
S3Output: ProcessingS3Output
/// Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output
/// type is only supported when AppManaged is specified.
FeatureStoreOutput: ProcessingFeatureStoreOutput
/// When True, output operations such as data upload are managed natively by the
/// processing job application. When False (default), output operations are managed by
/// Amazon SageMaker.
AppManaged: AppManaged = false
}
/// Configuration for uploading output from the processing container.
structure ProcessingOutputConfig {
/// An array of outputs configuring the data to upload from the processing container.
@required
Outputs: ProcessingOutputs
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing
/// job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of
/// a KMS key, or alias of a KMS key. The KmsKeyId is applied to all
/// outputs.
KmsKeyId: KmsKeyId
}
/// 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.
structure ProcessingResources {
/// The configuration for the resources in a cluster used to run the processing
/// job.
@required
ClusterConfig: ProcessingClusterConfig
}
/// Configuration for downloading input data from Amazon S3 into the processing container.
structure ProcessingS3Input {
/// The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
@required
S3Uri: S3Uri
/// The local path in your container where you want Amazon SageMaker to write input data to.
/// LocalPath is an absolute path to the input data and must begin with
/// /opt/ml/processing/. LocalPath is a required
/// parameter when AppManaged is False (default).
LocalPath: ProcessingLocalPath
/// Whether you use an S3Prefix or a ManifestFile for
/// the data type. If you choose S3Prefix, S3Uri identifies a key
/// name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing
/// job. If you choose ManifestFile, S3Uri identifies an object
/// that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for
/// the processing job.
@required
S3DataType: ProcessingS3DataType
/// Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data
/// from the input source onto the local ML storage volume before starting your processing
/// container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker
/// streams input data from the source directly to your processing container into named
/// pipes without using the ML storage volume.
S3InputMode: ProcessingS3InputMode
/// Whether to distribute the data from Amazon S3 to all processing instances with
/// FullyReplicated, or whether the data from Amazon S3 is shared by Amazon S3 key,
/// downloading one shard of data to each processing instance.
S3DataDistributionType: ProcessingS3DataDistributionType
/// Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing
/// container. Gzip can only be used when Pipe mode is
/// specified as the S3InputMode. In Pipe mode, Amazon SageMaker streams input
/// data from the source directly to your container without using the EBS volume.
S3CompressionType: ProcessingS3CompressionType
}
/// Configuration for uploading output data to Amazon S3 from the processing container.
structure ProcessingS3Output {
/// A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of
/// a processing job.
@required
S3Uri: S3Uri
/// The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
/// LocalPath is an absolute path to a directory containing output files.
/// This directory will be created by the platform and exist when your container's
/// entrypoint is invoked.
@required
LocalPath: ProcessingLocalPath
/// Whether to upload the results of the processing job continuously or after the job
/// completes.
@required
S3UploadMode: ProcessingS3UploadMode
}
/// Configures conditions under which the processing job should be stopped, such as how long
/// the processing job has been running. After the condition is met, the processing job is stopped.
structure ProcessingStoppingCondition {
/// Specifies the maximum runtime in seconds.
@required
MaxRuntimeInSeconds: ProcessingMaxRuntimeInSeconds = 0
}
/// Identifies a model that you want to host and the resources chosen to deploy for
/// hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic
/// among the models by specifying variant weights.
structure ProductionVariant {
/// The name of the production variant.
@required
VariantName: VariantName
/// The name of the model that you want to host. This is the name that you specified
/// when creating the model.
@required
ModelName: ModelName
/// Number of instances to launch initially.
InitialInstanceCount: InitialTaskCount
/// The ML compute instance type.
InstanceType: ProductionVariantInstanceType
/// Determines initial traffic distribution among all of the models that you specify in
/// the endpoint configuration. The traffic to a production variant is determined by the
/// ratio of the VariantWeight to the sum of all VariantWeight
/// values across all ProductionVariants. If unspecified, it defaults to 1.0.
///
InitialVariantWeight: VariantWeight
/// The size of the Elastic Inference (EI) instance to use for the production variant. EI
/// instances provide on-demand GPU computing for inference. For more information, see
/// Using Elastic
/// Inference in Amazon SageMaker.
AcceleratorType: ProductionVariantAcceleratorType
/// Specifies configuration for a core dump from the model container when the process
/// crashes.
CoreDumpConfig: ProductionVariantCoreDumpConfig
/// The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
ServerlessConfig: ProductionVariantServerlessConfig
/// The size, in GB, of the ML storage volume attached to individual inference instance
/// associated with the production variant. Currently only Amazon EBS gp2 storage volumes are
/// supported.
VolumeSizeInGB: ProductionVariantVolumeSizeInGB
/// The timeout value, in seconds, to download and extract the model that you want to host
/// from Amazon S3 to the individual inference instance associated with this production
/// variant.
ModelDataDownloadTimeoutInSeconds: ProductionVariantModelDataDownloadTimeoutInSeconds
/// The timeout value, in seconds, for your inference container to pass health check by
/// SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
ContainerStartupHealthCheckTimeoutInSeconds: ProductionVariantContainerStartupHealthCheckTimeoutInSeconds
}
/// Specifies configuration for a core dump from the model container when the process
/// crashes.
structure ProductionVariantCoreDumpConfig {
/// The Amazon S3 bucket to send the core dump to.
@required
DestinationS3Uri: DestinationS3Uri
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker
/// uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
/// KmsKeyId 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 SageMaker execution role must
/// include permissions to call kms:Encrypt. If you don't provide a KMS key ID,
/// SageMaker uses the default KMS key for Amazon S3 for your role's account. 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.
KmsKeyId: KmsKeyId
}
/// Specifies the serverless configuration for an endpoint variant.
structure ProductionVariantServerlessConfig {
/// The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
@required
MemorySizeInMB: ServerlessMemorySizeInMB
/// The maximum number of concurrent invocations your serverless endpoint can process.
@required
MaxConcurrency: ServerlessMaxConcurrency
}
/// Describes the status of the production variant.
structure ProductionVariantStatus {
/// The endpoint variant status which describes the current deployment stage status or
/// operational status.
///
/// -
///
/// Creating: Creating inference resources for the production
/// variant.
///
/// -
///
/// Deleting: Terminating inference resources for the production
/// variant.
///
/// -
///
/// Updating: Updating capacity for the production variant.
///
/// -
///
/// ActivatingTraffic: Turning on traffic for the production
/// variant.
///
/// -
///
/// Baking: Waiting period to monitor the CloudWatch alarms in the
/// automatic rollback configuration.
///
///
@required
Status: VariantStatus
/// A message that describes the status of the production variant.
StatusMessage: VariantStatusMessage
/// The start time of the current status change.
StartTime: Timestamp
}
/// Describes weight and capacities for a production variant associated with an
/// endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities
/// API and the endpoint status is Updating, you get different desired and
/// current values.
structure ProductionVariantSummary {
/// The name of the variant.
@required
VariantName: VariantName
/// An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the
/// inference images deployed on instances of this ProductionVariant.
DeployedImages: DeployedImages
/// The weight associated with the variant.
CurrentWeight: VariantWeight
/// The requested weight, as specified in the
/// UpdateEndpointWeightsAndCapacities request.
DesiredWeight: VariantWeight
/// The number of instances associated with the variant.
CurrentInstanceCount: TaskCount
/// The number of instances requested in the
/// UpdateEndpointWeightsAndCapacities request.
DesiredInstanceCount: TaskCount
/// The endpoint variant status which describes the current deployment stage status or
/// operational status.
VariantStatus: ProductionVariantStatusList
/// The serverless configuration for the endpoint.
CurrentServerlessConfig: ProductionVariantServerlessConfig
/// The serverless configuration requested for the endpoint update.
DesiredServerlessConfig: ProductionVariantServerlessConfig
}
/// Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and
/// storage paths.
structure ProfilerConfig {
/// Path to Amazon S3 storage location for system and framework metrics.
S3OutputPath: S3Uri
/// A time interval for capturing system metrics in milliseconds. Available values are
/// 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingIntervalInMilliseconds: ProfilingIntervalInMilliseconds
/// Configuration information for capturing framework metrics. Available key strings for different profiling options are
/// DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig.
/// The following codes are configuration structures for the ProfilingParameters parameter. To learn more about
/// how to configure the ProfilingParameters parameter,
/// see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
///
ProfilingParameters: ProfilingParameters
/// Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True.
DisableProfiler: DisableProfiler = false
}
/// Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and
/// storage paths.
structure ProfilerConfigForUpdate {
/// Path to Amazon S3 storage location for system and framework metrics.
S3OutputPath: S3Uri
/// A time interval for capturing system metrics in milliseconds. Available values are
/// 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingIntervalInMilliseconds: ProfilingIntervalInMilliseconds
/// Configuration information for capturing framework metrics. Available key strings for different profiling options are
/// DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig.
/// The following codes are configuration structures for the ProfilingParameters parameter. To learn more about
/// how to configure the ProfilingParameters parameter,
/// see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
///
ProfilingParameters: ProfilingParameters
/// To turn off Amazon SageMaker Debugger monitoring and profiling while a training job is in progress, set to True.
DisableProfiler: DisableProfiler = false
}
/// Configuration information for profiling rules.
structure ProfilerRuleConfiguration {
/// The name of the rule configuration. It must be unique relative to other rule configuration names.
@required
RuleConfigurationName: RuleConfigurationName
/// Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
LocalPath: DirectoryPath
/// Path to Amazon S3 storage location for rules.
S3OutputPath: S3Uri
/// The Amazon Elastic Container Registry Image for the managed rule evaluation.
@required
RuleEvaluatorImage: AlgorithmImage
/// The instance type to deploy a custom rule for profiling a training job.
InstanceType: ProcessingInstanceType
/// The size, in GB, of the ML storage volume attached to the processing instance.
VolumeSizeInGB: OptionalVolumeSizeInGB = 0
/// Runtime configuration for rule container.
RuleParameters: RuleParameters
}
/// Information about the status of the rule evaluation.
structure ProfilerRuleEvaluationStatus {
/// The name of the rule configuration.
RuleConfigurationName: RuleConfigurationName
/// The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationJobArn: ProcessingJobArn
/// Status of the rule evaluation.
RuleEvaluationStatus: RuleEvaluationStatus
/// Details from the rule evaluation.
StatusDetails: StatusDetails
/// Timestamp when the rule evaluation status was last modified.
LastModifiedTime: Timestamp
}
/// The properties of a project as returned by the Search API.
structure Project {
/// The Amazon Resource Name (ARN) of the project.
ProjectArn: ProjectArn
/// The name of the project.
ProjectName: ProjectEntityName
/// The ID of the project.
ProjectId: ProjectId
/// The description of the project.
ProjectDescription: EntityDescription
ServiceCatalogProvisioningDetails: ServiceCatalogProvisioningDetails
ServiceCatalogProvisionedProductDetails: ServiceCatalogProvisionedProductDetails
/// The status of the project.
ProjectStatus: ProjectStatus
/// Who created the project.
CreatedBy: UserContext
/// A timestamp specifying when the project was created.
CreationTime: Timestamp
/// 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: TagList
/// A timestamp container for when the project was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
}
/// Information about a project.
structure ProjectSummary {
/// The name of the project.
@required
ProjectName: ProjectEntityName
/// The description of the project.
ProjectDescription: EntityDescription
/// The Amazon Resource Name (ARN) of the project.
@required
ProjectArn: ProjectArn
/// The ID of the project.
@required
ProjectId: ProjectId
/// The time that the project was created.
@required
CreationTime: Timestamp
/// The status of the project.
@required
ProjectStatus: ProjectStatus
}
/// Part of the SuggestionQuery type. Specifies a hint for retrieving property
/// names that begin with the specified text.
structure PropertyNameQuery {
/// Text that begins a property's name.
@required
PropertyNameHint: PropertyNameHint
}
/// A property name returned from a GetSearchSuggestions call that specifies
/// a value in the PropertyNameQuery field.
structure PropertyNameSuggestion {
/// A suggested property name based on what you entered in the search textbox in the Amazon SageMaker
/// console.
PropertyName: ResourcePropertyName
}
/// A key value pair used when you provision a project as a service catalog product. For
/// information, see What is Amazon Web Services Service
/// Catalog.
structure ProvisioningParameter {
/// The key that identifies a provisioning parameter.
Key: ProvisioningParameterKey
/// The value of the provisioning parameter.
Value: ProvisioningParameterValue
}
/// Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
/// Use one of the following prices for bounding box tasks. Prices are in US dollars and
/// should be based on the complexity of the task; the longer it takes in your initial
/// testing, the more you should offer.
///
/// -
///
0.036
///
/// -
///
0.048
///
/// -
///
0.060
///
/// -
///
0.072
///
/// -
///
0.120
///
/// -
///
0.240
///
/// -
///
0.360
///
/// -
///
0.480
///
/// -
///
0.600
///
/// -
///
0.720
///
/// -
///
0.840
///
/// -
///
0.960
///
/// -
///
1.080
///
/// -
///
1.200
///
///
/// Use one of the following prices for image classification, text classification, and
/// custom tasks. Prices are in US dollars.
///
/// -
///
0.012
///
/// -
///
0.024
///
/// -
///
0.036
///
/// -
///
0.048
///
/// -
///
0.060
///
/// -
///
0.072
///
/// -
///
0.120
///
/// -
///
0.240
///
/// -
///
0.360
///
/// -
///
0.480
///
/// -
///
0.600
///
/// -
///
0.720
///
/// -
///
0.840
///
/// -
///
0.960
///
/// -
///
1.080
///
/// -
///
1.200
///
///
/// Use one of the following prices for semantic segmentation tasks. Prices are in US
/// dollars.
///
/// -
///
0.840
///
/// -
///
0.960
///
/// -
///
1.080
///
/// -
///
1.200
///
///
/// Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon
/// Augmented AI review tasks. Prices are in US dollars.
///
/// -
///
2.400
///
/// -
///
2.280
///
/// -
///
2.160
///
/// -
///
2.040
///
/// -
///
1.920
///
/// -
///
1.800
///
/// -
///
1.680
///
/// -
///
1.560
///
/// -
///
1.440
///
/// -
///
1.320
///
/// -
///
1.200
///
/// -
///
1.080
///
/// -
///
0.960
///
/// -
///
0.840
///
/// -
///
0.720
///
/// -
///
0.600
///
/// -
///
0.480
///
/// -
///
0.360
///
/// -
///
0.240
///
/// -
///
0.120
///
/// -
///
0.072
///
/// -
///
0.060
///
/// -
///
0.048
///
/// -
///
0.036
///
/// -
///
0.024
///
/// -
///
0.012
///
///
/// Use one of the following prices for Rekognition DetectModerationLabels Amazon
/// Augmented AI review tasks. Prices are in US dollars.
///
/// -
///
1.200
///
/// -
///
1.080
///
/// -
///
0.960
///
/// -
///
0.840
///
/// -
///
0.720
///
/// -
///
0.600
///
/// -
///
0.480
///
/// -
///
0.360
///
/// -
///
0.240
///
/// -
///
0.120
///
/// -
///
0.072
///
/// -
///
0.060
///
/// -
///
0.048
///
/// -
///
0.036
///
/// -
///
0.024
///
/// -
///
0.012
///
///
/// Use one of the following prices for Amazon Augmented AI custom human review tasks.
/// Prices are in US dollars.
///
/// -
///
1.200
///
/// -
///
1.080
///
/// -
///
0.960
///
/// -
///
0.840
///
/// -
///
0.720
///
/// -
///
0.600
///
/// -
///
0.480
///
/// -
///
0.360
///
/// -
///
0.240
///
/// -
///
0.120
///
/// -
///
0.072
///
/// -
///
0.060
///
/// -
///
0.048
///
/// -
///
0.036
///
/// -
///
0.024
///
/// -
///
0.012
///
///
structure PublicWorkforceTaskPrice {
/// Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
AmountInUsd: USD
}
/// Container for the metadata for a Quality check step. For more information, see
/// the topic on QualityCheck step in the Amazon SageMaker Developer Guide.
///
structure QualityCheckStepMetadata {
/// The type of the Quality check step.
CheckType: String256
/// The Amazon S3 URI of the baseline statistics file used for the drift check.
BaselineUsedForDriftCheckStatistics: String1024
/// The Amazon S3 URI of the baseline constraints file used for the drift check.
BaselineUsedForDriftCheckConstraints: String1024
/// The Amazon S3 URI of the newly calculated baseline statistics file.
CalculatedBaselineStatistics: String1024
/// The Amazon S3 URI of the newly calculated baseline constraints file.
CalculatedBaselineConstraints: String1024
/// The model package group name.
ModelPackageGroupName: String256
/// The Amazon S3 URI of violation report if violations are detected.
ViolationReport: String1024
/// The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.
CheckJobArn: String256
/// This flag indicates if the drift check against the previous baseline will be skipped or not.
/// If it is set to False, the previous baseline of the configured check type must be available.
SkipCheck: Boolean = false
/// This flag indicates if a newly calculated baseline can be accessed through step properties
/// BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics.
/// If it is set to False, the previous baseline of the configured check type must also be available.
/// These can be accessed through the BaselineUsedForDriftCheckConstraints and
/// BaselineUsedForDriftCheckStatistics properties.
RegisterNewBaseline: Boolean = false
}
/// A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the
/// QueryLineage API action.
structure QueryFilters {
/// Filter the lineage entities connected to the StartArn by type. For example: DataSet,
/// Model, Endpoint, or ModelDeployment.
Types: QueryTypes
/// Filter the lineage entities connected to the StartArn(s) by the type of the lineage entity.
LineageTypes: QueryLineageTypes
/// Filter the lineage entities connected to the StartArn(s) by created date.
CreatedBefore: Timestamp
/// Filter the lineage entities connected to the StartArn(s) after the create date.
CreatedAfter: Timestamp
/// Filter the lineage entities connected to the StartArn(s) before the last modified date.
ModifiedBefore: Timestamp
/// Filter the lineage entities connected to the StartArn(s) after the last modified date.
ModifiedAfter: Timestamp
/// Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs.
/// If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
Properties: QueryProperties
}
@input
structure QueryLineageRequest {
/// A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
StartArns: QueryLineageStartArns
/// Associations between lineage entities have a direction. This parameter determines the direction from the
/// StartArn(s) that the query traverses.
Direction: Direction
/// 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.
IncludeEdges: Boolean = 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.
///
///
Filters: QueryFilters
/// 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.
MaxDepth: QueryLineageMaxDepth
/// Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the next page of results.
MaxResults: QueryLineageMaxResults
/// Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the next page of results.
NextToken: String8192
}
@output
structure QueryLineageResponse {
/// A list of vertices connected to the start entity(ies) in the lineage graph.
Vertices: Vertices
/// A list of edges that connect vertices in the response.
Edges: Edges
/// Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the next page of results.
NextToken: String8192
}
/// The infrastructure configuration for deploying the model to a real-time inference endpoint.
structure RealTimeInferenceConfig {
/// The instance type the model is deployed to.
@required
InstanceType: InstanceType
/// The number of instances of the type specified by InstanceType.
@required
InstanceCount: TaskCount
}
/// Provides information about the output configuration for the compiled
/// model.
structure RecommendationJobCompiledOutputConfig {
/// Identifies the Amazon S3 bucket where you want SageMaker to store the
/// compiled model artifacts.
S3OutputUri: S3Uri
}
/// Specifies mandatory fields for running an Inference Recommender job directly in the
/// CreateInferenceRecommendationsJob
/// API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use
/// ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.
structure RecommendationJobContainerConfig {
/// The machine learning domain of the model and its components.
/// Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING |
/// MACHINE_LEARNING
///
Domain: String
/// The machine learning task that the model accomplishes.
/// Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION
/// | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION |
/// REGRESSION | OTHER
///
Task: String
/// The machine learning framework of the container image.
/// Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
///
Framework: String
/// The framework version of the container image.
FrameworkVersion: String
/// Specifies the SamplePayloadUrl and all other sample payload-related fields.
PayloadConfig: RecommendationJobPayloadConfig
/// The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
/// Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn |
/// densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
///
NearestModelName: String
/// A list of the instance types that are used to generate inferences in real-time.
SupportedInstanceTypes: RecommendationJobSupportedInstanceTypes
/// Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
/// This field is used for optimizing your model using SageMaker Neo. For more information, see
/// DataInputConfig.
DataInputConfig: RecommendationJobDataInputConfig
}
/// The details for a specific benchmark from an Inference Recommender job.
structure RecommendationJobInferenceBenchmark {
Metrics: RecommendationMetrics
EndpointConfiguration: EndpointOutputConfiguration
@required
ModelConfiguration: ModelConfiguration
/// The reason why a benchmark failed.
FailureReason: RecommendationFailureReason
}
/// The input configuration of the recommendation job.
structure RecommendationJobInputConfig {
/// The Amazon Resource Name (ARN) of a versioned model package.
ModelPackageVersionArn: ModelPackageArn
/// Specifies the maximum duration of the job, in seconds.>
JobDurationInSeconds: JobDurationInSeconds
/// Specifies the traffic pattern of the job.
TrafficPattern: TrafficPattern
/// Defines the resource limit of the job.
ResourceLimit: RecommendationJobResourceLimit
/// Specifies the endpoint configuration to use for a job.
EndpointConfigurations: EndpointInputConfigurations
/// The Amazon Resource Name (ARN) of a 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.
/// This key will be passed to SageMaker Hosting for endpoint creation.
/// The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage
/// volume of the endpoints created for inference recommendation. The inference recommendation job will fail
/// asynchronously during endpoint configuration creation if the role passed does not have
/// kms:CreateGrant permission.
/// The KmsKeyId 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:::key/"
///
///
/// -
///
// KMS Key Alias
///
/// "alias/ExampleAlias"
///
///
/// -
///
// Amazon Resource Name (ARN) of a KMS Key Alias
///
/// "arn:aws:kms:::alias/"
///
///
///
/// For more information about key identifiers, see
/// Key identifiers (KeyID) in the
/// Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
VolumeKmsKeyId: KmsKeyId
/// Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig
/// override the corresponding fields in the model package.
ContainerConfig: RecommendationJobContainerConfig
/// Existing customer endpoints on which to run an Inference Recommender job.
Endpoints: Endpoints
/// Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
VpcConfig: RecommendationJobVpcConfig
/// The name of the created model.
ModelName: ModelName
}
/// Provides information about the output configuration for the compiled model.
structure RecommendationJobOutputConfig {
/// The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key
/// that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption.
/// The SageMaker execution role must have kms:GenerateDataKey permission.
/// The KmsKeyId 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:::key/"
///
///
/// -
///
// KMS Key Alias
///
/// "alias/ExampleAlias"
///
///
/// -
///
// Amazon Resource Name (ARN) of a KMS Key Alias
///
/// "arn:aws:kms:::alias/"
///
///
///
/// For more information about key identifiers, see
/// Key identifiers (KeyID) in the
/// Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
KmsKeyId: KmsKeyId
/// Provides information about the output configuration for the compiled
/// model.
CompiledOutputConfig: RecommendationJobCompiledOutputConfig
}
/// The configuration for the payload for a recommendation job.
structure RecommendationJobPayloadConfig {
/// 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).
SamplePayloadUrl: String
/// The supported MIME types for the input data.
SupportedContentTypes: RecommendationJobSupportedContentTypes
}
/// Specifies the maximum number of jobs that can run in parallel
/// and the maximum number of jobs that can run.
structure RecommendationJobResourceLimit {
/// Defines the maximum number of load tests.
MaxNumberOfTests: MaxNumberOfTests
/// Defines the maximum number of parallel load tests.
MaxParallelOfTests: MaxParallelOfTests
}
/// Specifies conditions for stopping a job. When a job reaches a
/// stopping condition limit, SageMaker ends the job.
structure RecommendationJobStoppingConditions {
/// The maximum number of requests per minute expected for the endpoint.
MaxInvocations: Integer = 0
/// The interval of time taken by a model to respond as viewed from SageMaker.
/// The interval includes the local communication time taken to send the request
/// and to fetch the response from the container of a model and the time taken to
/// complete the inference in the container.
ModelLatencyThresholds: ModelLatencyThresholds
}
/// Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
structure RecommendationJobVpcConfig {
/// The VPC security group IDs. IDs have the form of sg-xxxxxxxx.
/// Specify the security groups for the VPC that is specified in the Subnets field.
@required
SecurityGroupIds: RecommendationJobVpcSecurityGroupIds
/// The ID of the subnets in the VPC to which you want to connect your model.
@required
Subnets: RecommendationJobVpcSubnets
}
/// The metrics of recommendations.
structure RecommendationMetrics {
/// Defines the cost per hour for the instance.
@required
CostPerHour: Float = 0
/// Defines the cost per inference for the instance .
@required
CostPerInference: Float = 0
/// The expected maximum number of requests per minute for the instance.
@required
MaxInvocations: Integer = 0
/// The expected model latency at maximum invocation per minute for the instance.
@required
ModelLatency: Integer = 0
/// The expected CPU utilization at maximum invocations per minute for the instance.
///
/// NaN indicates that the value is not available.
CpuUtilization: UtilizationMetric
/// The expected memory utilization at maximum invocations per minute for the instance.
///
/// NaN indicates that the value is not available.
MemoryUtilization: UtilizationMetric
}
/// Configuration for Redshift Dataset Definition input.
structure RedshiftDatasetDefinition {
@required
ClusterId: RedshiftClusterId
@required
Database: RedshiftDatabase
@required
DbUser: RedshiftUserName
@required
QueryString: RedshiftQueryString
/// The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
@required
ClusterRoleArn: RoleArn
/// The location in Amazon S3 where the Redshift query results are stored.
@required
OutputS3Uri: S3Uri
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a
/// Redshift execution.
KmsKeyId: KmsKeyId
@required
OutputFormat: RedshiftResultFormat
OutputCompression: RedshiftResultCompressionType
}
@input
structure RegisterDevicesRequest {
/// The name of the fleet.
@required
DeviceFleetName: EntityName
/// A list of devices to register with SageMaker Edge Manager.
@required
Devices: Devices
/// The tags associated with devices.
Tags: TagList
}
/// Metadata for a register model job step.
structure RegisterModelStepMetadata {
/// The Amazon Resource Name (ARN) of the model package.
Arn: String256
}
/// Contains input values for a task.
structure RenderableTask {
/// A JSON object that contains values for the variables defined in the template. It is
/// made available to the template under the substitution variable task.input.
/// For example, if you define a variable task.input.text in your template, you
/// can supply the variable in the JSON object as "text": "sample text".
@required
Input: TaskInput
}
/// A description of an error that occurred while rendering the template.
structure RenderingError {
/// A unique identifier for a specific class of errors.
@required
Code: String
/// A human-readable message describing the error.
@required
Message: String
}
@input
structure RenderUiTemplateRequest {
/// A Template object containing the worker UI template to render.
UiTemplate: UiTemplate
/// A RenderableTask object containing a representative task to
/// render.
@required
Task: RenderableTask
/// The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the
/// template.
@required
RoleArn: RoleArn
/// 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.
HumanTaskUiArn: HumanTaskUiArn
}
@output
structure RenderUiTemplateResponse {
/// A Liquid template that renders the HTML for the worker UI.
@required
RenderedContent: String
/// 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.
@required
Errors: RenderingErrorList
}
/// Specifies an authentication configuration for the private docker registry where your
/// model image is hosted. Specify a value for this property only if you specified
/// Vpc as the value for the RepositoryAccessMode field of the
/// ImageConfig object that you passed to a call to
/// CreateModel and the private Docker registry where the model image is
/// hosted requires authentication.
structure RepositoryAuthConfig {
/// The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides
/// credentials to authenticate to the private Docker registry where your model image is
/// hosted. For information about how to create an Amazon Web Services Lambda function, see
/// Create a Lambda function
/// with the console in the Amazon Web Services Lambda Developer
/// Guide.
@required
RepositoryCredentialsProviderArn: RepositoryCredentialsProviderArn
}
/// The resolved attributes.
structure ResolvedAttributes {
AutoMLJobObjective: AutoMLJobObjective
/// The problem type.
ProblemType: ProblemType
CompletionCriteria: AutoMLJobCompletionCriteria
}
/// Describes the resources, including machine learning (ML) compute instances and ML
/// storage volumes, to use for model training.
structure ResourceConfig {
/// The ML compute instance type.
///
/// SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting
/// December 9th, 2022.
///
/// Amazon EC2 P4de instances
/// (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance
/// HBM2e GPU memory, which accelerate the speed of training ML models that need to be
/// trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker
/// supports ML training jobs on P4de instances (ml.p4de.24xlarge) to
/// reduce model training time. The ml.p4de.24xlarge instances are
/// available in the following Amazon Web Services Regions.
///
/// -
///
US East (N. Virginia) (us-east-1)
///
/// -
///
US West (Oregon) (us-west-2)
///
///
/// To request quota limit increase and start using P4de instances, contact the SageMaker
/// Training service team through your account team.
///
InstanceType: TrainingInstanceType
/// The number of ML compute instances to use. For distributed training, provide a
/// value greater than 1.
InstanceCount: TrainingInstanceCount = 0
/// The size of the ML storage volume that you want to provision.
/// ML storage volumes store model artifacts and incremental states. Training
/// algorithms might also use the ML storage volume for scratch space. If you want to store
/// the training data in the ML storage volume, choose File as the
/// TrainingInputMode in the algorithm specification.
/// When using an ML instance with NVMe SSD
/// volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage.
/// Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures
/// storage paths for training datasets, checkpoints, model artifacts, and outputs to use
/// the entire capacity of the instance storage. For example, ML instance families with the
/// NVMe-type instance storage include ml.p4d, ml.g4dn, and
/// ml.g5.
/// When using an ML instance with the EBS-only storage option and without instance
/// storage, you must define the size of EBS volume through VolumeSizeInGB in
/// the ResourceConfig API. For example, ML instance families that use EBS
/// volumes include ml.c5 and ml.p2.
/// To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
/// To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker
/// Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and
/// Outputs.
@required
VolumeSizeInGB: VolumeSizeInGB = 0
/// The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume
/// attached to the ML compute instance(s) that run the training job.
///
/// 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 VolumeKmsKeyId when using an instance type with
/// local storage.
/// 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.
///
/// The VolumeKmsKeyId can be in 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"
///
///
///
VolumeKmsKeyId: KmsKeyId
/// The configuration of a heterogeneous cluster in JSON format.
InstanceGroups: InstanceGroups
/// The duration of time in seconds to retain configured resources in a warm pool for
/// subsequent training jobs.
KeepAlivePeriodInSeconds: KeepAlivePeriodInSeconds
}
/// The ResourceConfig to update KeepAlivePeriodInSeconds. Other
/// fields in the ResourceConfig cannot be updated.
structure ResourceConfigForUpdate {
/// The KeepAlivePeriodInSeconds value specified in the
/// ResourceConfig to update.
@required
KeepAlivePeriodInSeconds: KeepAlivePeriodInSeconds
}
/// Resource being accessed is in use.
@error("client")
structure ResourceInUse {
Message: FailureReason
}
/// You have exceeded an SageMaker resource limit. For example, you might have too many
/// training jobs created.
@error("client")
structure ResourceLimitExceeded {
Message: FailureReason
}
/// Specifies the maximum number of training jobs and parallel training jobs that a
/// hyperparameter tuning job can launch.
structure ResourceLimits {
/// The maximum number of training jobs that a hyperparameter tuning job can
/// launch.
MaxNumberOfTrainingJobs: MaxNumberOfTrainingJobs
/// The maximum number of concurrent training jobs that a hyperparameter tuning job can
/// launch.
@required
MaxParallelTrainingJobs: MaxParallelTrainingJobs = 0
/// The maximum time in seconds that a training job launched by a hyperparameter tuning job can run.
MaxRuntimeInSeconds: HyperParameterTuningMaxRuntimeInSeconds
}
/// Resource being access is not found.
@error("client")
structure ResourceNotFound {
Message: FailureReason
}
/// Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that
/// the version runs on.
structure ResourceSpec {
/// The ARN of the SageMaker image that the image version belongs to.
SageMakerImageArn: ImageArn
/// The ARN of the image version created on the instance.
SageMakerImageVersionArn: ImageVersionArn
/// The instance type that the image version runs on.
///
///
/// JupyterServer apps only support the system value.
/// For KernelGateway apps, the system
/// value is translated to ml.t3.medium. KernelGateway apps also support all other values for available
/// instance types.
///
InstanceType: AppInstanceType
/// The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArn: StudioLifecycleConfigArn
}
/// The retention policy for data stored on an Amazon Elastic File System (EFS) volume.
structure RetentionPolicy {
/// The default is Retain, which specifies to keep the data stored on the EFS volume.
/// Specify Delete to delete the data stored on the EFS volume.
HomeEfsFileSystem: RetentionType
}
@input
structure RetryPipelineExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than once.
@idempotencyToken
@required
ClientRequestToken: IdempotencyToken
/// This configuration, if specified, overrides the parallelism configuration
/// of the parent pipeline.
ParallelismConfiguration: ParallelismConfiguration
}
@output
structure RetryPipelineExecutionResponse {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
/// The retry strategy to use when a training job fails due to an
/// InternalServerError. RetryStrategy is specified as part of
/// the CreateTrainingJob and CreateHyperParameterTuningJob
/// requests. You can add the StoppingCondition parameter to the request to
/// limit the training time for the complete job.
structure RetryStrategy {
/// The number of times to retry the job. When the job is retried, it's
/// SecondaryStatus is changed to STARTING.
@required
MaximumRetryAttempts: MaximumRetryAttempts = 0
}
/// A collection of settings that apply to an RSessionGateway app.
structure RSessionAppSettings {
DefaultResourceSpec: ResourceSpec
/// A list of custom SageMaker images that are configured to run as a RSession app.
CustomImages: CustomImages
}
/// A collection of settings that configure user interaction with the
/// RStudioServerPro app. RStudioServerProAppSettings cannot
/// be updated. The RStudioServerPro app must be deleted and a new one created
/// to make any changes.
structure RStudioServerProAppSettings {
/// Indicates whether the current user has access to the RStudioServerPro
/// app.
AccessStatus: RStudioServerProAccessStatus
/// The level of permissions that the user has within the RStudioServerPro
/// app. This value defaults to `User`. The `Admin` value allows the user access to the
/// RStudio Administrative Dashboard.
UserGroup: RStudioServerProUserGroup
}
/// A collection of settings that configure the RStudioServerPro Domain-level
/// app.
structure RStudioServerProDomainSettings {
/// The ARN of the execution role for the RStudioServerPro Domain-level
/// app.
@required
DomainExecutionRoleArn: RoleArn
/// A URL pointing to an RStudio Connect server.
RStudioConnectUrl: String
/// A URL pointing to an RStudio Package Manager server.
RStudioPackageManagerUrl: String
DefaultResourceSpec: ResourceSpec
}
/// A collection of settings that update the current configuration for the
/// RStudioServerPro Domain-level app.
structure RStudioServerProDomainSettingsForUpdate {
/// The execution role for the RStudioServerPro Domain-level app.
@required
DomainExecutionRoleArn: RoleArn
DefaultResourceSpec: ResourceSpec
/// A URL pointing to an RStudio Connect server.
RStudioConnectUrl: String
/// A URL pointing to an RStudio Package Manager server.
RStudioPackageManagerUrl: String
}
/// Describes the S3 data source.
structure S3DataSource {
/// If you choose S3Prefix, S3Uri identifies a key name prefix.
/// SageMaker uses all objects that match the specified key name prefix for model training.
/// If you choose ManifestFile, S3Uri identifies an object that
/// is a manifest file containing a list of object keys that you want SageMaker to use for model
/// training.
/// If you choose AugmentedManifestFile, S3Uri identifies an object that is
/// an augmented manifest file in JSON lines format. This file contains the data you want to
/// use for model training. AugmentedManifestFile can only be used if the
/// Channel's input mode is Pipe.
@required
S3DataType: S3DataType
/// Depending on the value specified for the S3DataType, identifies either
/// a key name prefix or a manifest. For example:
///
/// -
///
A key name prefix might look like this:
/// s3://bucketname/exampleprefix
///
///
/// -
///
A manifest might look like this:
/// s3://bucketname/example.manifest
///
/// A manifest is an S3 object which is a JSON file consisting of an array of
/// elements. The first element is a prefix which is followed by one or more
/// suffixes. SageMaker appends the suffix elements to the prefix to get a full set
/// of S3Uri. Note that the prefix must be a valid non-empty
/// S3Uri that precludes users from specifying a manifest whose
/// individual S3Uri is sourced from different S3 buckets.
/// The following code example shows a valid manifest format:
///
/// [ {"prefix": "s3://customer_bucket/some/prefix/"},
///
///
/// "relative/path/to/custdata-1",
///
///
/// "relative/path/custdata-2",
///
///
/// ...
///
///
/// "relative/path/custdata-N"
///
///
/// ]
///
/// This JSON is equivalent to the following S3Uri
/// list:
///
/// s3://customer_bucket/some/prefix/relative/path/to/custdata-1
///
///
/// s3://customer_bucket/some/prefix/relative/path/custdata-2
///
///
/// ...
///
///
/// s3://customer_bucket/some/prefix/relative/path/custdata-N
///
/// The complete set of S3Uri in this manifest is the input data
/// for the channel for this data source. The object that each S3Uri
/// points to must be readable by the IAM role that SageMaker uses to perform tasks on
/// your behalf.
///
///
@required
S3Uri: S3Uri
/// If you want SageMaker to replicate the entire dataset on each ML compute instance that
/// is launched for model training, specify FullyReplicated.
/// If you want SageMaker to replicate a subset of data on each ML compute instance that is
/// launched for model training, specify ShardedByS3Key. If there are
/// n ML compute instances launched for a training job, each
/// instance gets approximately 1/n of the number of S3 objects. In
/// this case, model training on each machine uses only the subset of training data.
/// Don't choose more ML compute instances for training than available S3 objects. If
/// you do, some nodes won't get any data and you will pay for nodes that aren't getting any
/// training data. This applies in both File and Pipe modes. Keep this in mind when
/// developing algorithms.
/// In distributed training, where you use multiple ML compute EC2 instances, you might
/// choose ShardedByS3Key. If the algorithm requires copying training data to
/// the ML storage volume (when TrainingInputMode is set to File),
/// this copies 1/n of the number of objects.
S3DataDistributionType: S3DataDistribution
/// A list of one or more attribute names to use that are found in a specified augmented
/// manifest file.
AttributeNames: AttributeNames
/// A list of names of instance groups that get data from the S3 data source.
InstanceGroupNames: InstanceGroupNames
}
/// The Amazon Simple Storage (Amazon S3) location and and security configuration for OfflineStore.
structure S3StorageConfig {
/// The S3 URI, or location in Amazon S3, of OfflineStore.
/// S3 URIs have a format similar to the following: s3://example-bucket/prefix/.
@required
S3Uri: S3Uri
/// The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects
/// written into the OfflineStore S3 location.
/// The IAM roleARN that is passed as a parameter to
/// CreateFeatureGroup must have below permissions to the
/// KmsKeyId:
///
/// -
///
/// "kms:GenerateDataKey"
///
///
///
KmsKeyId: KmsKeyId
/// The S3 path where offline records are written.
ResolvedOutputS3Uri: S3Uri
}
/// Configuration details about the monitoring schedule.
structure ScheduleConfig {
/// A cron expression that describes details about the monitoring schedule.
/// Currently the only supported cron expressions are:
///
/// -
///
If you want to set the job to start every hour, please use the following:
///
/// Hourly: cron(0 * ? * * *)
///
///
/// -
///
If you want to start the job daily:
///
/// cron(0 [00-23] ? * * *)
///
///
///
/// For example, the following are valid cron expressions:
///
/// -
///
Daily at noon UTC: cron(0 12 ? * * *)
///
///
/// -
///
Daily at midnight UTC: cron(0 0 ? * * *)
///
///
///
/// To support running every 6, 12 hours, the following are also supported:
///
/// cron(0 [00-23]/[01-24] ? * * *)
///
/// For example, the following are valid cron expressions:
///
/// -
///
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
///
///
/// -
///
Every two hours starting at midnight: cron(0 0/2 ? * * *)
///
///
///
///
///
/// -
///
Even though the cron expression is set to start at 5PM UTC, note that there
/// could be a delay of 0-20 minutes from the actual requested time to run the
/// execution.
///
/// -
///
We recommend that if you would like a daily schedule, you do not provide this
/// parameter. Amazon SageMaker will pick a time for running every day.
///
///
///
@required
ScheduleExpression: ScheduleExpression
}
/// A multi-expression 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. You must specify at
/// least one subexpression, filter, or nested filter. A SearchExpression can contain up to
/// twenty elements.
/// A SearchExpression contains the following components:
///
/// -
///
A list of Filter objects. Each filter defines a simple Boolean
/// expression comprised of a resource property name, Boolean operator, and
/// value.
///
/// -
///
A list of NestedFilter objects. Each nested filter defines a list
/// of Boolean expressions using a list of resource properties. A nested filter is
/// satisfied if a single object in the list satisfies all Boolean
/// expressions.
///
/// -
///
A list of SearchExpression objects. A search expression object
/// can be nested in a list of search expression objects.
///
/// -
///
A Boolean operator: And or Or.
///
///
structure SearchExpression {
/// A list of filter objects.
Filters: FilterList
/// A list of nested filter objects.
NestedFilters: NestedFiltersList
/// A list of search expression objects.
SubExpressions: SearchExpressionList
/// A Boolean operator used to evaluate the search expression. If you want every
/// conditional statement in all lists to be satisfied for the entire search expression to
/// be true, specify And. If only a single conditional statement needs to be
/// true for the entire search expression to be true, specify Or. The default
/// value is And.
Operator: BooleanOperator
}
/// A single resource returned as part of the Search API response.
structure SearchRecord {
/// The properties of a training job.
TrainingJob: TrainingJob
/// The properties of an experiment.
Experiment: Experiment
/// The properties of a trial.
Trial: Trial
/// The properties of a trial component.
TrialComponent: TrialComponent
Endpoint: Endpoint
ModelPackage: ModelPackage
ModelPackageGroup: ModelPackageGroup
Pipeline: Pipeline
PipelineExecution: PipelineExecution
FeatureGroup: FeatureGroup
/// The properties of a project.
Project: Project
/// The feature metadata used to search through the features.
FeatureMetadata: FeatureMetadata
/// The properties of a hyperparameter tuning job.
HyperParameterTuningJob: HyperParameterTuningJobSearchEntity
Model: ModelDashboardModel
/// An Amazon SageMaker Model Card that documents details about a machine learning model.
ModelCard: ModelCard
}
@input
structure SearchRequest {
/// The name of the Amazon SageMaker resource to search for.
@required
Resource: ResourceType
/// 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.
SearchExpression: SearchExpression
/// The name of the resource property used to sort the SearchResults. The
/// default is LastModifiedTime.
SortBy: ResourcePropertyName
/// How SearchResults are ordered. Valid values are Ascending or
/// Descending. The default is Descending.
SortOrder: SearchSortOrder
/// 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.
NextToken: NextToken
/// The maximum number of results to return.
MaxResults: MaxResults
}
@output
structure SearchResponse {
/// A list of SearchRecord objects.
Results: SearchResultsList
/// 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.
NextToken: NextToken
}
/// An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides
/// additional details about a status that the training job has transitioned through. A
/// training job can be in one of several states, for example, starting, downloading,
/// training, or uploading. Within each state, there are a number of intermediate states.
/// For example, within the starting state, SageMaker could be starting the training job or
/// launching the ML instances. These transitional states are referred to as the job's
/// secondary
/// status.
///
///
structure SecondaryStatusTransition {
/// Contains a secondary status information from a training
/// job.
/// Status might be one of the following secondary statuses:
///
/// - 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.
///
/// -
///
/// 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.
///
/// -
///
/// Stopped - The training job has stopped.
///
///
///
/// - Stopping
/// -
///
/// -
///
/// Stopping - Stopping the training job.
///
///
///
///
/// We no longer support the following secondary statuses:
///
/// -
///
/// LaunchingMLInstances
///
///
/// -
///
/// PreparingTrainingStack
///
///
/// -
///
/// DownloadingTrainingImage
///
///
///
@required
Status: SecondaryStatus
/// A timestamp that shows when the training job transitioned to the current secondary
/// status state.
@required
StartTime: Timestamp
/// A timestamp that shows when the training job transitioned out of this secondary status
/// state into another secondary status state or when the training job has ended.
EndTime: Timestamp
/// A detailed description of the progress within a secondary status.
///
/// SageMaker provides secondary statuses and status messages that apply to each of
/// them:
///
/// - Starting
/// -
///
/// -
///
Starting the training job.
///
/// -
///
Launching requested ML
/// instances.
///
/// -
///
Insufficient
/// capacity error from EC2 while launching instances,
/// retrying!
///
/// -
///
Launched
/// instance was unhealthy, replacing it!
///
/// -
///
Preparing the instances for training.
///
///
///
/// - Training
/// -
///
/// -
///
Downloading the training image.
///
/// -
///
Training
/// image download completed. Training in
/// progress.
///
///
///
///
///
/// Status messages are subject to change. Therefore, we recommend not including them
/// in code that programmatically initiates actions. For examples, don't use status
/// messages in if statements.
///
/// To have an overview of your training job's progress, view
/// TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For
/// example, at the start of a training job, you might see the following:
///
/// -
///
/// TrainingJobStatus - InProgress
///
/// -
///
/// SecondaryStatus - Training
///
/// -
///
/// StatusMessage - Downloading the training image
///
///
StatusMessage: StatusMessage
}
@input
structure SendPipelineExecutionStepFailureRequest {
/// The pipeline generated token from the Amazon SQS queue.
@required
CallbackToken: CallbackToken
/// A message describing why the step failed.
FailureReason: String256
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than one time.
@idempotencyToken
ClientRequestToken: IdempotencyToken
}
@output
structure SendPipelineExecutionStepFailureResponse {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
@input
structure SendPipelineExecutionStepSuccessRequest {
/// The pipeline generated token from the Amazon SQS queue.
@required
CallbackToken: CallbackToken
/// A list of the output parameters of the callback step.
OutputParameters: OutputParameterList
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than one time.
@idempotencyToken
ClientRequestToken: IdempotencyToken
}
@output
structure SendPipelineExecutionStepSuccessResponse {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
/// Details of a provisioned service catalog product. For information about service catalog,
/// see What is Amazon Web Services Service
/// Catalog.
structure ServiceCatalogProvisionedProductDetails {
/// The ID of the provisioned product.
ProvisionedProductId: ServiceCatalogEntityId
/// The current status of the product.
///
/// -
///
/// AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.
///
/// -
///
/// UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.
///
/// -
///
/// TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.
///
/// -
///
/// ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.
///
/// -
///
/// PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
///
///
ProvisionedProductStatusMessage: ProvisionedProductStatusMessage
}
/// Details that you specify to provision a service catalog product. For information about
/// service catalog, see What is Amazon Web Services Service
/// Catalog.
structure ServiceCatalogProvisioningDetails {
/// The ID of the product to provision.
@required
ProductId: ServiceCatalogEntityId
/// The ID of the provisioning artifact.
ProvisioningArtifactId: ServiceCatalogEntityId
/// The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
PathId: ServiceCatalogEntityId
/// A list of key value pairs that you specify when you provision a product.
ProvisioningParameters: ProvisioningParameters
}
/// Details that you specify to provision a service catalog product.
/// For information about service catalog, see What is Amazon Web Services Service Catalog.
///
structure ServiceCatalogProvisioningUpdateDetails {
/// The ID of the provisioning artifact.
ProvisioningArtifactId: ServiceCatalogEntityId
/// A list of key value pairs that you specify when you provision a product.
ProvisioningParameters: ProvisioningParameters
}
///
/// The configuration of ShadowMode inference experiment type, which specifies a production variant
/// to take all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the
/// inference requests. For the shadow variant it also specifies the percentage of requests that Amazon SageMaker replicates.
///
structure ShadowModeConfig {
///
/// The name of the production variant, which takes all the inference requests.
///
@required
SourceModelVariantName: ModelVariantName
/// List of shadow variant configurations.
@required
ShadowModelVariants: ShadowModelVariantConfigList
}
/// The name and sampling percentage of a shadow variant.
structure ShadowModelVariantConfig {
/// The name of the shadow variant.
@required
ShadowModelVariantName: ModelVariantName
///
/// The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
///
@required
SamplingPercentage: Percentage = 0
}
/// Specifies options for sharing SageMaker Studio notebooks. These settings are
/// specified as part of DefaultUserSettings when the CreateDomain
/// API is called, and as part of UserSettings when the CreateUserProfile
/// API is called. When SharingSettings is not specified, notebook sharing
/// isn't allowed.
structure SharingSettings {
/// Whether to include the notebook cell output when sharing the notebook. The default
/// is Disabled.
NotebookOutputOption: NotebookOutputOption
/// When NotebookOutputOption is Allowed, the Amazon S3 bucket used
/// to store the shared notebook snapshots.
S3OutputPath: S3Uri
/// When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS)
/// encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
S3KmsKeyId: KmsKeyId
}
/// A configuration for a shuffle option for input data in a channel. If you use
/// S3Prefix for S3DataType, the results of the S3 key prefix
/// matches are shuffled. If you use ManifestFile, the order of the S3 object
/// references in the ManifestFile is shuffled. If you use
/// AugmentedManifestFile, the order of the JSON lines in the
/// AugmentedManifestFile is shuffled. The shuffling order is determined
/// using the Seed value.
/// For Pipe input mode, when ShuffleConfig is specified shuffling is done at
/// the start of every epoch. With large datasets, this ensures that the order of the
/// training data is different for each epoch, and it helps reduce bias and possible
/// overfitting. In a multi-node training job when ShuffleConfig is combined
/// with S3DataDistributionType of ShardedByS3Key, the data is
/// shuffled across nodes so that the content sent to a particular node on the first epoch
/// might be sent to a different node on the second epoch.
structure ShuffleConfig {
/// Determines the shuffling order in ShuffleConfig value.
@required
Seed: Seed = 0
}
/// Specifies an algorithm that was used to create the model package. The algorithm must
/// be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
structure SourceAlgorithm {
/// The Amazon S3 path where the model artifacts, which result from model training, are stored.
/// This path must point to a single gzip compressed tar archive
/// (.tar.gz suffix).
///
/// The model artifacts must be in an S3 bucket that is in the same region as the
/// algorithm.
///
ModelDataUrl: Url
/// The name of an algorithm that was used to create the model package. The algorithm must
/// be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
@required
AlgorithmName: ArnOrName
}
/// A list of algorithms that were used to create a model package.
structure SourceAlgorithmSpecification {
/// A list of the algorithms that were used to create a model package.
@required
SourceAlgorithms: SourceAlgorithmList
}
/// A list of IP address ranges (CIDRs). Used to create an allow
/// list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an
/// IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
structure SourceIpConfig {
/// A list of one to ten Classless Inter-Domain Routing (CIDR) values.
/// Maximum: Ten CIDR values
///
/// The following Length Constraints apply to individual CIDR values in
/// the CIDR value list.
///
@required
Cidrs: Cidrs
}
/// The space's details.
structure SpaceDetails {
/// The ID of the associated Domain.
DomainId: DomainId
/// The name of the space.
SpaceName: SpaceName
/// The status.
Status: SpaceStatus
/// The creation time.
CreationTime: CreationTime
/// The last modified time.
LastModifiedTime: LastModifiedTime
}
/// A collection of space settings.
structure SpaceSettings {
JupyterServerAppSettings: JupyterServerAppSettings
KernelGatewayAppSettings: KernelGatewayAppSettings
}
@input
structure StartEdgeDeploymentStageRequest {
/// The name of the edge deployment plan to start.
@required
EdgeDeploymentPlanName: EntityName
/// The name of the stage to start.
@required
StageName: EntityName
}
@input
structure StartInferenceExperimentRequest {
/// The name of the inference experiment to start.
@required
Name: InferenceExperimentName
}
@output
structure StartInferenceExperimentResponse {
/// The ARN of the started inference experiment to start.
@required
InferenceExperimentArn: InferenceExperimentArn
}
@input
structure StartMonitoringScheduleRequest {
/// The name of the schedule to start.
@required
MonitoringScheduleName: MonitoringScheduleName
}
@input
structure StartPipelineExecutionRequest {
/// The name of the pipeline.
@required
PipelineName: PipelineNameOrArn
/// The display name of the pipeline execution.
PipelineExecutionDisplayName: PipelineExecutionName
/// Contains a list of pipeline parameters. This list can be empty.
PipelineParameters: ParameterList
/// The description of the pipeline execution.
PipelineExecutionDescription: PipelineExecutionDescription
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than once.
@idempotencyToken
@required
ClientRequestToken: IdempotencyToken
/// This configuration, if specified, overrides the parallelism configuration
/// of the parent pipeline for this specific run.
ParallelismConfiguration: ParallelismConfiguration
}
@output
structure StartPipelineExecutionResponse {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
@input
structure StopAutoMLJobRequest {
/// The name of the object you are requesting.
@required
AutoMLJobName: AutoMLJobName
}
@input
structure StopCompilationJobRequest {
/// The name of the model compilation job to stop.
@required
CompilationJobName: EntityName
}
@input
structure StopEdgeDeploymentStageRequest {
/// The name of the edge deployment plan to stop.
@required
EdgeDeploymentPlanName: EntityName
/// The name of the stage to stop.
@required
StageName: EntityName
}
@input
structure StopEdgePackagingJobRequest {
/// The name of the edge packaging job.
@required
EdgePackagingJobName: EntityName
}
@input
structure StopHyperParameterTuningJobRequest {
/// The name of the tuning job to stop.
@required
HyperParameterTuningJobName: HyperParameterTuningJobName
}
@input
structure StopInferenceExperimentRequest {
/// The name of the inference experiment to stop.
@required
Name: InferenceExperimentName
///
/// 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
///
///
@required
ModelVariantActions: ModelVariantActionMap
///
/// 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.
///
DesiredModelVariants: ModelVariantConfigList
///
/// The desired state of the experiment after stopping. The possible states are the following:
///
///
/// -
///
/// Completed: The experiment completed successfully
///
/// -
///
/// Cancelled: The experiment was canceled
///
///
DesiredState: InferenceExperimentStopDesiredState
/// The reason for stopping the experiment.
Reason: InferenceExperimentStatusReason
}
@output
structure StopInferenceExperimentResponse {
/// The ARN of the stopped inference experiment.
@required
InferenceExperimentArn: InferenceExperimentArn
}
@input
structure StopInferenceRecommendationsJobRequest {
/// The name of the job you want to stop.
@required
JobName: RecommendationJobName
}
@input
structure StopLabelingJobRequest {
/// The name of the labeling job to stop.
@required
LabelingJobName: LabelingJobName
}
@input
structure StopMonitoringScheduleRequest {
/// The name of the schedule to stop.
@required
MonitoringScheduleName: MonitoringScheduleName
}
/// Specifies a limit to how long a model training job or model compilation 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 or compilation job. Use this API to cap
/// model training 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.
///
structure StoppingCondition {
/// The maximum length of time, in seconds, that a training or compilation job can run
/// before it is stopped.
/// For compilation jobs, if the job does not complete during this time, a
/// TimeOut error is generated. We recommend starting with 900 seconds and
/// increasing as necessary based on your model.
/// For all other jobs, if the job does not complete during this time, SageMaker ends the job.
/// When RetryStrategy is specified in the job request,
/// MaxRuntimeInSeconds specifies the maximum time for all of the attempts
/// in total, not each individual attempt. The default value is 1 day. The maximum value is
/// 28 days.
/// The maximum time that a TrainingJob can run in total, including any time
/// spent publishing metrics or archiving and uploading models after it has been stopped, is
/// 30 days.
MaxRuntimeInSeconds: MaxRuntimeInSeconds = 0
/// The maximum length of time, in seconds, that a managed Spot training job has to
/// complete. It is the amount of time spent waiting for Spot capacity plus the amount of
/// time the job can run. It must be equal to or greater than
/// MaxRuntimeInSeconds. If the job does not complete during this time,
/// SageMaker ends the job.
/// When RetryStrategy is specified in the job request,
/// MaxWaitTimeInSeconds specifies the maximum time for all of the attempts
/// in total, not each individual attempt.
MaxWaitTimeInSeconds: MaxWaitTimeInSeconds
}
@input
structure StopPipelineExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
/// A unique, case-sensitive identifier that you provide to ensure the idempotency of the
/// operation. An idempotent operation completes no more than once.
@idempotencyToken
@required
ClientRequestToken: IdempotencyToken
}
@output
structure StopPipelineExecutionResponse {
/// The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
@input
structure StopProcessingJobRequest {
/// The name of the processing job to stop.
@required
ProcessingJobName: ProcessingJobName
}
@input
structure StopTrainingJobRequest {
/// The name of the training job to stop.
@required
TrainingJobName: TrainingJobName
}
@input
structure StopTransformJobRequest {
/// The name of the batch transform job to stop.
@required
TransformJobName: TransformJobName
}
/// Details of the Studio Lifecycle Configuration.
structure StudioLifecycleConfigDetails {
/// The Amazon Resource Name (ARN) of the Lifecycle Configuration.
StudioLifecycleConfigArn: StudioLifecycleConfigArn
/// The name of the Studio Lifecycle Configuration.
StudioLifecycleConfigName: StudioLifecycleConfigName
/// The creation time of the Studio Lifecycle Configuration.
CreationTime: Timestamp
/// This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.
LastModifiedTime: Timestamp
/// The App type to which the Lifecycle Configuration is attached.
StudioLifecycleConfigAppType: StudioLifecycleConfigAppType
}
/// Describes a work team of a vendor that does the a labelling job.
structure SubscribedWorkteam {
/// The Amazon Resource Name (ARN) of the vendor that you have subscribed.
@required
WorkteamArn: WorkteamArn
/// The title of the service provided by the vendor in the Amazon Marketplace.
MarketplaceTitle: String200
/// The name of the vendor in the Amazon Marketplace.
SellerName: String
/// The description of the vendor from the Amazon Marketplace.
MarketplaceDescription: String200
/// Marketplace product listing ID.
ListingId: String
}
/// Specified in the GetSearchSuggestions request.
/// Limits the property names that are included in the response.
structure SuggestionQuery {
/// Defines a property name hint. Only property
/// names that begin with the specified hint are included in the response.
PropertyNameQuery: PropertyNameQuery
}
/// A tag object that consists of a key and an optional value, used to manage metadata
/// for SageMaker Amazon Web Services resources.
/// You can add tags to notebook instances, training jobs, hyperparameter tuning jobs,
/// batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and
/// endpoints. For more information on adding tags to SageMaker resources, see AddTags.
/// For more information on adding metadata to your Amazon Web Services resources with
/// tagging, see Tagging Amazon Web Services resources. For advice on best practices for
/// managing Amazon Web Services resources with tagging, see Tagging
/// Best Practices: Implement an Effective Amazon Web Services Resource Tagging
/// Strategy.
structure Tag {
/// The tag key. Tag keys must be unique per resource.
@required
Key: TagKey
/// The tag value.
@required
Value: TagValue
}
/// Contains information about a target platform that you want your model to run on, such
/// as OS, architecture, and accelerators. It is an alternative of
/// TargetDevice.
structure TargetPlatform {
/// Specifies a target platform OS.
///
/// -
///
/// LINUX: Linux-based operating systems.
///
/// -
///
/// ANDROID: Android operating systems. Android API level can be
/// specified using the ANDROID_PLATFORM compiler option. For example,
/// "CompilerOptions": {'ANDROID_PLATFORM': 28}
///
///
///
@required
Os: TargetPlatformOs
/// Specifies a target platform architecture.
///
/// -
///
/// X86_64: 64-bit version of the x86 instruction set.
///
/// -
///
/// X86: 32-bit version of the x86 instruction set.
///
/// -
///
/// ARM64: ARMv8 64-bit CPU.
///
/// -
///
/// ARM_EABIHF: ARMv7 32-bit, Hard Float.
///
/// -
///
/// ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM
/// platform.
///
///
@required
Arch: TargetPlatformArch
/// Specifies a target platform accelerator (optional).
///
/// -
///
/// NVIDIA: Nvidia graphics processing unit. It also requires
/// gpu-code, trt-ver, cuda-ver compiler
/// options
///
/// -
///
/// MALI: ARM Mali graphics processor
///
/// -
///
/// INTEL_GRAPHICS: Integrated Intel graphics
///
///
Accelerator: TargetPlatformAccelerator
}
/// The TensorBoard app settings.
structure TensorBoardAppSettings {
/// The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
DefaultResourceSpec: ResourceSpec
}
/// Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
structure TensorBoardOutputConfig {
/// Path to local storage location for tensorBoard output. Defaults to
/// /opt/ml/output/tensorboard.
LocalPath: DirectoryPath
/// Path to Amazon S3 storage location for TensorBoard output.
@required
S3OutputPath: S3Uri
}
/// Time series forecast settings for the SageMaker Canvas app.
structure TimeSeriesForecastingSettings {
/// Describes whether time series forecasting is enabled or disabled in the Canvas app.
Status: FeatureStatus
/// The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default,
/// Canvas uses the execution role specified in the UserProfile that launches the Canvas app.
/// If an execution role is not specified in the UserProfile, Canvas uses the execution
/// role specified in the Domain that owns the UserProfile.
/// To allow time series forecasting, this IAM role should have the
///
/// AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added
/// in the trust relationship as a service principal.
AmazonForecastRoleArn: RoleArn
}
/// Defines the traffic pattern of the load test.
structure TrafficPattern {
/// Defines the traffic patterns.
TrafficType: TrafficType
/// Defines the phases traffic specification.
Phases: Phases
}
/// Defines the traffic routing strategy during an endpoint deployment to shift traffic from the
/// old fleet to the new fleet.
structure TrafficRoutingConfig {
/// Traffic routing strategy type.
///
/// -
///
/// ALL_AT_ONCE: Endpoint traffic shifts to the new fleet
/// in a single step.
///
///
/// -
///
/// CANARY: Endpoint traffic shifts to the new fleet
/// in two steps. The first step is the canary, which is a small portion of the traffic. The
/// second step is the remainder of the traffic.
///
///
/// -
///
/// LINEAR: Endpoint traffic shifts to the new fleet in
/// n steps of a configurable size.
///
///
///
@required
Type: TrafficRoutingConfigType
/// The waiting time (in seconds) between incremental steps to turn on traffic on the
/// new endpoint fleet.
@required
WaitIntervalInSeconds: WaitIntervalInSeconds
/// Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than
/// or equal to 50% of the variant's total instance count.
CanarySize: CapacitySize
/// Batch size for each step to turn on traffic on the new endpoint fleet. Value must be
/// 10-50% of the variant's total instance count.
LinearStepSize: CapacitySize
}
/// The configuration to use an image from a private Docker registry for a training
/// job.
structure TrainingImageConfig {
/// The method that your training job will use to gain access to the images in your
/// private Docker registry. For access to an image in a private Docker registry, set to
/// Vpc.
@required
TrainingRepositoryAccessMode: TrainingRepositoryAccessMode
/// An object containing authentication information for a private Docker registry
/// containing your training images.
TrainingRepositoryAuthConfig: TrainingRepositoryAuthConfig
}
/// Contains information about a training job.
structure TrainingJob {
/// The name of the training job.
TrainingJobName: TrainingJobName
/// The Amazon Resource Name (ARN) of the training job.
TrainingJobArn: TrainingJobArn
/// The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the
/// training job was launched by a hyperparameter tuning job.
TuningJobArn: HyperParameterTuningJobArn
/// The Amazon Resource Name (ARN) of the labeling job.
LabelingJobArn: LabelingJobArn
/// The Amazon Resource Name (ARN) of the job.
AutoMLJobArn: AutoMLJobArn
/// Information about the Amazon S3 location that is configured for storing model
/// artifacts.
ModelArtifacts: ModelArtifacts
/// The status of the
/// training
/// job.
/// Training job statuses are:
///
/// -
///
/// 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.
TrainingJobStatus: TrainingJobStatus
/// Provides detailed information about the state of the training job. For detailed
/// information about 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.
///
/// -
///
/// 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.
///
/// -
///
/// 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
///
///
/// -
///
/// PreparingTrainingStack
///
///
/// -
///
/// DownloadingTrainingImage
///
///
///
SecondaryStatus: SecondaryStatus
/// If the training job failed, the reason it failed.
FailureReason: FailureReason
/// Algorithm-specific parameters.
HyperParameters: HyperParameters
/// Information about the algorithm used for training, and algorithm metadata.
AlgorithmSpecification: AlgorithmSpecification
/// The Amazon Web Services Identity and Access Management (IAM) role configured for the
/// training job.
RoleArn: RoleArn
/// An array of Channel objects that describes each data input
/// channel.
InputDataConfig: InputDataConfig
/// The S3 path where model artifacts that you configured when creating the job are
/// stored. SageMaker creates subfolders for model artifacts.
OutputDataConfig: OutputDataConfig
/// Resources, including ML compute instances and ML storage volumes, that are configured
/// for model training.
ResourceConfig: ResourceConfig
/// 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.
VpcConfig: VpcConfig
/// 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.
StoppingCondition: StoppingCondition
/// A timestamp that indicates when the training job was created.
CreationTime: Timestamp
/// 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.
TrainingStartTime: Timestamp
/// 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.
TrainingEndTime: Timestamp
/// A timestamp that indicates when the status of the training job was last
/// modified.
LastModifiedTime: Timestamp
/// A history of all of the secondary statuses that the training job has transitioned
/// through.
SecondaryStatusTransitions: SecondaryStatusTransitions
/// A list of final metric values that are set when the training job completes. Used only
/// if the training job was configured to use metrics.
FinalMetricDataList: FinalMetricDataList
/// If the TrainingJob was created with network isolation, the value is set
/// to true. If network isolation is enabled, nodes can't communicate beyond
/// the VPC they run in.
EnableNetworkIsolation: Boolean = 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.
EnableInterContainerTrafficEncryption: Boolean = false
/// When true, enables managed spot training using Amazon EC2 Spot instances to run
/// training jobs instead of on-demand instances. For more information, see Managed Spot Training.
EnableManagedSpotTraining: Boolean = false
CheckpointConfig: CheckpointConfig
/// The training time in seconds.
TrainingTimeInSeconds: TrainingTimeInSeconds
/// The billable time in seconds.
BillableTimeInSeconds: BillableTimeInSeconds
DebugHookConfig: DebugHookConfig
ExperimentConfig: ExperimentConfig
/// Information about the debug rule configuration.
DebugRuleConfigurations: DebugRuleConfigurations
TensorBoardOutputConfig: TensorBoardOutputConfig
/// Information about the evaluation status of the rules for the training job.
DebugRuleEvaluationStatuses: DebugRuleEvaluationStatuses
/// The environment variables to set in the Docker container.
Environment: TrainingEnvironmentMap
/// The number of times to retry the job when the job fails due to an
/// InternalServerError.
RetryStrategy: RetryStrategy
/// 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: TagList
}
/// Defines the input needed to run a training job using the algorithm.
structure TrainingJobDefinition {
@required
TrainingInputMode: TrainingInputMode
/// The hyperparameters used for the training job.
HyperParameters: HyperParameters
/// An array of Channel objects, each of which specifies an input
/// source.
@required
InputDataConfig: InputDataConfig
/// the path to the S3 bucket where you want to store model artifacts. SageMaker creates
/// subfolders for the artifacts.
@required
OutputDataConfig: OutputDataConfig
/// The resources, including the ML compute instances and ML storage volumes, to use for
/// model training.
@required
ResourceConfig: ResourceConfig
/// 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.
@required
StoppingCondition: StoppingCondition
}
/// The numbers of training jobs launched by a hyperparameter tuning job, categorized by
/// status.
structure TrainingJobStatusCounters {
/// The number of completed training jobs launched by the hyperparameter tuning
/// job.
Completed: TrainingJobStatusCounter = 0
/// The number of in-progress training jobs launched by a hyperparameter tuning
/// job.
InProgress: TrainingJobStatusCounter = 0
/// The number of training jobs that failed, but can be retried. A failed training job can
/// be retried only if it failed because an internal service error occurred.
RetryableError: TrainingJobStatusCounter = 0
/// The number of training jobs that failed and can't be retried. A failed training job
/// can't be retried if it failed because a client error occurred.
NonRetryableError: TrainingJobStatusCounter = 0
/// The number of training jobs launched by a hyperparameter tuning job that were
/// manually
/// stopped.
Stopped: TrainingJobStatusCounter = 0
}
/// Metadata for a training job step.
structure TrainingJobStepMetadata {
/// The Amazon Resource Name (ARN) of the training job that was run by this step execution.
Arn: TrainingJobArn
}
/// Provides summary information about a training job.
structure TrainingJobSummary {
/// The name of the training job that you want a summary for.
@required
TrainingJobName: TrainingJobName
/// The Amazon Resource Name (ARN) of the training job.
@required
TrainingJobArn: TrainingJobArn
/// A timestamp that shows when the training job was created.
@required
CreationTime: Timestamp
/// A timestamp that shows when the training job ended. This field is set only if the
/// training job has one of the terminal statuses (Completed,
/// Failed, or Stopped).
TrainingEndTime: Timestamp
/// Timestamp when the training job was last modified.
LastModifiedTime: Timestamp
/// The status of the training job.
@required
TrainingJobStatus: TrainingJobStatus
/// The status of the warm pool associated with the training job.
WarmPoolStatus: WarmPoolStatus
}
/// An object containing authentication information for a private Docker registry.
structure TrainingRepositoryAuthConfig {
/// The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access
/// credentials to your private Docker registry.
@required
TrainingRepositoryCredentialsProviderArn: TrainingRepositoryCredentialsProviderArn
}
/// Defines how the algorithm is used for a training job.
structure TrainingSpecification {
/// The Amazon ECR registry path of the Docker image that contains the training
/// algorithm.
@required
TrainingImage: ContainerImage
/// An MD5 hash of the training algorithm that identifies the Docker image used for
/// training.
TrainingImageDigest: ImageDigest
/// A list of the HyperParameterSpecification objects, that define the
/// supported hyperparameters. This is required if the algorithm supports automatic model
/// tuning.>
SupportedHyperParameters: HyperParameterSpecifications
/// A list of the instance types that this algorithm can use for training.
@required
SupportedTrainingInstanceTypes: TrainingInstanceTypes
/// Indicates whether the algorithm supports distributed training. If set to false, buyers
/// can't request more than one instance during training.
SupportsDistributedTraining: Boolean = false
/// A list of MetricDefinition objects, which are used for parsing metrics
/// generated by the algorithm.
MetricDefinitions: MetricDefinitionList
/// A list of ChannelSpecification objects, which specify the input sources
/// to be used by the algorithm.
@required
TrainingChannels: ChannelSpecifications
/// A list of the metrics that the algorithm emits that can be used as the objective
/// metric in a hyperparameter tuning job.
SupportedTuningJobObjectiveMetrics: HyperParameterTuningJobObjectives
}
/// Describes the location of the channel data.
structure TransformDataSource {
/// The S3 location of the data source that is associated with a channel.
@required
S3DataSource: TransformS3DataSource
}
/// Describes the input source of a transform job and the way the transform job consumes
/// it.
structure TransformInput {
/// Describes the location of
/// the
/// channel data, which is, the S3 location of the input data that the
/// model can consume.
@required
DataSource: TransformDataSource
/// The multipurpose internet mail extension
/// (MIME)
/// type of the data. Amazon SageMaker uses the MIME type with each http call to
/// transfer data to the transform job.
ContentType: ContentType
/// If your transform data
/// is
/// compressed, specify the compression type. Amazon SageMaker automatically
/// decompresses the data for the transform job accordingly. The default value is
/// None.
CompressionType: CompressionType
/// The method to use to split the transform job's data files into smaller batches.
/// Splitting is necessary when the total size of each object is too large to fit in a
/// single request. You can also use data splitting to improve performance by processing
/// multiple concurrent mini-batches. The default value for SplitType is
/// None, which indicates that input data files are not split, and request
/// payloads contain the entire contents of an input object. Set the value of this parameter
/// to Line to split records on a newline character boundary.
/// SplitType also supports a number of record-oriented binary data
/// formats. Currently, the supported record formats are:
///
/// -
///
RecordIO
///
/// -
///
TFRecord
///
///
/// When splitting is enabled, the size of a mini-batch depends on the values of the
/// BatchStrategy and MaxPayloadInMB parameters. When the
/// value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum
/// number of records in each request, up to the MaxPayloadInMB limit. If the
/// value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual
/// records in each request.
///
/// Some data formats represent a record as a binary payload wrapped with extra
/// padding bytes. When splitting is applied to a binary data format, padding is removed
/// if the value of BatchStrategy is set to SingleRecord.
/// Padding is not removed if the value of BatchStrategy is set to
/// MultiRecord.
/// For more information about RecordIO, see Create a Dataset Using
/// RecordIO in the MXNet documentation. For more information about
/// TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
///
SplitType: SplitType
}
/// A batch transform job. For information about SageMaker batch transform, see Use Batch
/// Transform.
structure TransformJob {
/// The name of the transform job.
TransformJobName: TransformJobName
/// The Amazon Resource Name (ARN) of the transform job.
TransformJobArn: TransformJobArn
/// The status of the transform job.
/// Transform job statuses are:
///
/// -
///
/// InProgress - The job is in progress.
///
/// -
///
/// Completed - The job has completed.
///
/// -
///
/// Failed - The transform job has failed. To see the reason for the failure,
/// see the FailureReason field in the response to a
/// DescribeTransformJob call.
///
/// -
///
/// Stopping - The transform job is stopping.
///
/// -
///
/// Stopped - The transform job has stopped.
///
///
TransformJobStatus: TransformJobStatus
/// If the transform job failed, the reason it failed.
FailureReason: FailureReason
/// The name of the model associated with the transform job.
ModelName: ModelName
/// 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, 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 built-in algorithms,
/// you don't need to set a value for MaxConcurrentTransforms.
MaxConcurrentTransforms: MaxConcurrentTransforms
ModelClientConfig: ModelClientConfig
/// The maximum allowed size of the payload, in MB. A payload is the data portion of a record
/// (without metadata). The value in MaxPayloadInMB must be greater than, or equal
/// to, the size of a single record. To estimate the size of a record in MB, divide the size of
/// your dataset by the number of records. To ensure that the records fit within the maximum
/// payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases
/// where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding,
/// set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in
/// algorithms do not support HTTP chunked encoding.
MaxPayloadInMB: MaxPayloadInMB
/// 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.
BatchStrategy: BatchStrategy
/// The environment variables to set in the Docker container. We support up to 16 key and
/// values entries in the map.
Environment: TransformEnvironmentMap
TransformInput: TransformInput
TransformOutput: TransformOutput
TransformResources: TransformResources
/// A timestamp that shows when the transform Job was created.
CreationTime: Timestamp
/// Indicates when the transform job starts on ML instances. You are billed for the time
/// interval between this time and the value of TransformEndTime.
TransformStartTime: Timestamp
/// 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.
TransformEndTime: Timestamp
/// The Amazon Resource Name (ARN) of the labeling job that created the transform job.
LabelingJobArn: LabelingJobArn
/// The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
AutoMLJobArn: AutoMLJobArn
DataProcessing: DataProcessing
ExperimentConfig: ExperimentConfig
/// A list of tags associated with the transform job.
Tags: TagList
}
/// Defines the input needed to run a transform job using the inference specification
/// specified in the algorithm.
structure TransformJobDefinition {
/// The maximum number of parallel requests that can be sent to each instance in a
/// transform job. The default value is 1.
MaxConcurrentTransforms: MaxConcurrentTransforms
/// The maximum payload size allowed, in MB. A payload is the data portion of a record
/// (without metadata).
MaxPayloadInMB: MaxPayloadInMB
/// A string that determines the number of records included in a single mini-batch.
///
/// SingleRecord means only one record is used per mini-batch.
/// MultiRecord means a mini-batch is set to contain as many records that
/// can fit within the MaxPayloadInMB limit.
BatchStrategy: BatchStrategy
/// The environment variables to set in the Docker container. We support up to 16 key and
/// values entries in the map.
Environment: TransformEnvironmentMap
/// A description of the input source and the way the transform job consumes it.
@required
TransformInput: TransformInput
/// Identifies the Amazon S3 location where you want Amazon SageMaker to save the results
/// from the transform job.
@required
TransformOutput: TransformOutput
/// Identifies the ML compute instances for the transform job.
@required
TransformResources: TransformResources
}
/// Metadata for a transform job step.
structure TransformJobStepMetadata {
/// The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Arn: TransformJobArn
}
/// Provides a
/// summary
/// of a transform job. Multiple TransformJobSummary objects are returned as a
/// list after in response to a ListTransformJobs call.
structure TransformJobSummary {
/// The name of the transform job.
@required
TransformJobName: TransformJobName
/// The Amazon Resource Name (ARN) of the transform job.
@required
TransformJobArn: TransformJobArn
/// A timestamp that shows when the transform Job was created.
@required
CreationTime: Timestamp
/// Indicates when the transform
/// job
/// ends on compute instances. For successful jobs and stopped jobs, this
/// is the exact time
/// recorded
/// after the results are uploaded. For failed jobs, this is when Amazon SageMaker
/// detected that the job failed.
TransformEndTime: Timestamp
/// Indicates when the transform job was last modified.
LastModifiedTime: Timestamp
/// The status of the transform job.
@required
TransformJobStatus: TransformJobStatus
/// If the transform job failed,
/// the
/// reason it failed.
FailureReason: FailureReason
}
/// Describes the results of a transform job.
structure TransformOutput {
/// The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For
/// example, s3://bucket-name/key-name-prefix.
/// For every S3 object used as input for the transform job, batch transform stores the
/// transformed data with an .out suffix in a corresponding subfolder in the
/// location in the output prefix. For example, for the input data stored at
/// s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform
/// stores the transformed data at
/// s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out.
/// Batch transform doesn't upload partially processed objects. For an input S3 object that
/// contains multiple records, it creates an .out file only if the transform
/// job succeeds on the entire file. When the input contains multiple S3 objects, the batch
/// transform job processes the listed S3 objects and uploads only the output for
/// successfully processed objects. If any object fails in the transform job batch transform
/// marks the job as failed to prompt investigation.
@required
S3OutputPath: S3Uri
/// The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http
/// call to transfer data from the transform job.
Accept: Accept
/// Defines how to assemble the results of the transform job as a single S3 object. Choose
/// a format that is most convenient to you. To concatenate the results in binary format,
/// specify None. To add a newline character at the end of every transformed
/// record, specify
/// Line.
AssembleWith: AssemblyType
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using
/// Amazon S3 server-side encryption. 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
///
///
///
/// If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your
/// role's account. 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
/// CreateModel
/// request. For more information, see Using
/// Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer
/// Guide.
KmsKeyId: KmsKeyId
}
/// Describes the resources, including ML instance types and ML instance count, to use for
/// transform job.
structure TransformResources {
/// The ML compute instance type for the transform job. If you are using built-in
/// algorithms to
/// transform
/// moderately sized datasets, we recommend using ml.m4.xlarge or
/// ml.m5.largeinstance types.
@required
InstanceType: TransformInstanceType
/// The number of
/// ML
/// compute instances to use in the transform job. The default value is
/// 1, and the maximum is 100. For distributed transform jobs,
/// specify a value greater than 1.
@required
InstanceCount: TransformInstanceCount
/// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume
/// attached to the ML compute instance(s) that run the batch transform job.
///
/// 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 VolumeKmsKeyId when using an instance type with
/// local storage.
/// 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.
///
///
/// The VolumeKmsKeyId 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
///
///
///
VolumeKmsKeyId: KmsKeyId
}
/// Describes the S3 data source.
structure TransformS3DataSource {
/// If you choose S3Prefix, S3Uri identifies a key name prefix.
/// Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
/// If you choose ManifestFile, S3Uri identifies an object that
/// is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch
/// transform.
/// The following values are compatible: ManifestFile,
/// S3Prefix
///
/// The following value is not compatible: AugmentedManifestFile
///
@required
S3DataType: S3DataType
/// Depending on the value specified for the S3DataType, identifies either a
/// key name prefix or a manifest. For example:
///
/// -
///
A key name prefix might look like this:
/// s3://bucketname/exampleprefix.
///
/// -
///
A manifest might look like this:
/// s3://bucketname/example.manifest
///
/// The manifest is an S3 object which is a JSON file with the following format:
///
/// [ {"prefix": "s3://customer_bucket/some/prefix/"},
///
///
/// "relative/path/to/custdata-1",
///
///
/// "relative/path/custdata-2",
///
///
/// ...
///
///
/// "relative/path/custdata-N"
///
///
/// ]
///
/// The preceding JSON matches the following S3Uris:
///
/// s3://customer_bucket/some/prefix/relative/path/to/custdata-1
///
///
/// s3://customer_bucket/some/prefix/relative/path/custdata-2
///
///
/// ...
///
///
/// s3://customer_bucket/some/prefix/relative/path/custdata-N
///
/// The complete set of S3Uris in this manifest constitutes the
/// input data for the channel for this datasource. The object that each
/// S3Uris points to must be readable by the IAM role that Amazon SageMaker
/// uses to perform tasks on your behalf.
///
///
@required
S3Uri: S3Uri
}
/// The properties of a trial as returned by the Search API.
structure Trial {
/// The name of the trial.
TrialName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
/// The name of the trial as displayed. If DisplayName isn't specified,
/// TrialName is displayed.
DisplayName: ExperimentEntityName
/// The name of the experiment the trial is part of.
ExperimentName: ExperimentEntityName
Source: TrialSource
/// When the trial was created.
CreationTime: Timestamp
/// Who created the trial.
CreatedBy: UserContext
/// Who last modified the trial.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
MetadataProperties: MetadataProperties
/// The list of tags that are associated with the trial. You can use Search
/// API to search on the tags.
Tags: TagList
/// A list of the components associated with the trial. For each component, a summary of the
/// component's properties is included.
TrialComponentSummaries: TrialComponentSimpleSummaries
}
/// The properties of a trial component as returned by the Search
/// API.
structure TrialComponent {
/// The name of the trial component.
TrialComponentName: ExperimentEntityName
/// The name of the component as displayed. If DisplayName isn't specified,
/// TrialComponentName is displayed.
DisplayName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
/// The Amazon Resource Name (ARN) and job type of the source of the component.
Source: TrialComponentSource
Status: TrialComponentStatus
/// When the component started.
StartTime: Timestamp
/// When the component ended.
EndTime: Timestamp
/// When the component was created.
CreationTime: Timestamp
/// Who created the trial component.
CreatedBy: UserContext
/// When the component was last modified.
LastModifiedTime: Timestamp
LastModifiedBy: UserContext
/// The hyperparameters of the component.
Parameters: TrialComponentParameters
/// The input artifacts of the component.
InputArtifacts: TrialComponentArtifacts
/// The output artifacts of the component.
OutputArtifacts: TrialComponentArtifacts
/// The metrics for the component.
Metrics: TrialComponentMetricSummaries
MetadataProperties: MetadataProperties
/// Details of the source of the component.
SourceDetail: TrialComponentSourceDetail
/// The Amazon Resource Name (ARN) of the lineage group resource.
LineageGroupArn: LineageGroupArn
/// The list of tags that are associated with the component. You can use Search API to search on the tags.
Tags: TagList
/// An array of the parents of the component. A parent is a trial the component is associated
/// with and the experiment the trial is part of. A component might not have any parents.
Parents: Parents
/// The name of the experiment run.
RunName: ExperimentEntityName
}
/// Represents an input or output artifact of a trial component. You specify
/// TrialComponentArtifact as part of the InputArtifacts and
/// OutputArtifacts parameters in the CreateTrialComponent
/// request.
/// Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and
/// instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
structure TrialComponentArtifact {
/// The media type of the artifact, which indicates the type of data in the artifact file. The
/// media type consists of a type and a subtype
/// concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The
/// type specifies the category of the media. The subtype specifies the kind of data.
MediaType: MediaType
/// The location of the artifact.
@required
Value: TrialComponentArtifactValue
}
/// A summary of the metrics of a trial component.
structure TrialComponentMetricSummary {
/// The name of the metric.
MetricName: MetricName
/// The Amazon Resource Name (ARN) of the source.
SourceArn: TrialComponentSourceArn
/// When the metric was last updated.
TimeStamp: Timestamp
/// The maximum value of the metric.
Max: OptionalDouble
/// The minimum value of the metric.
Min: OptionalDouble
/// The most recent value of the metric.
Last: OptionalDouble
/// The number of samples used to generate the metric.
Count: OptionalInteger
/// The average value of the metric.
Avg: OptionalDouble
/// The standard deviation of the metric.
StdDev: OptionalDouble
}
/// A short summary of a trial component.
structure TrialComponentSimpleSummary {
/// The name of the trial component.
TrialComponentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
TrialComponentSource: TrialComponentSource
/// When the component was created.
CreationTime: Timestamp
CreatedBy: UserContext
}
/// The Amazon Resource Name (ARN) and job type of the source of a trial component.
structure TrialComponentSource {
/// The source Amazon Resource Name (ARN).
@required
SourceArn: TrialComponentSourceArn
/// The source job type.
SourceType: SourceType
}
/// Detailed information about the source of a trial component. Either
/// ProcessingJob or TrainingJob is returned.
structure TrialComponentSourceDetail {
/// The Amazon Resource Name (ARN) of the source.
SourceArn: TrialComponentSourceArn
/// Information about a training job that's the source of a trial component.
TrainingJob: TrainingJob
/// Information about a processing job that's the source of a trial component.
ProcessingJob: ProcessingJob
/// Information about a transform job that's the source of a trial component.
TransformJob: TransformJob
}
/// The status of the trial component.
structure TrialComponentStatus {
/// The status of the trial component.
PrimaryStatus: TrialComponentPrimaryStatus
/// If the component failed, a message describing why.
Message: TrialComponentStatusMessage
}
/// A summary of the properties of a trial component. To get all the properties, call the
/// DescribeTrialComponent API and provide the
/// TrialComponentName.
structure TrialComponentSummary {
/// The name of the trial component.
TrialComponentName: ExperimentEntityName
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
/// The name of the component as displayed. If DisplayName isn't specified,
/// TrialComponentName is displayed.
DisplayName: ExperimentEntityName
TrialComponentSource: TrialComponentSource
/// The status of the component. States include:
///
/// -
///
InProgress
///
/// -
///
Completed
///
/// -
///
Failed
///
///
Status: TrialComponentStatus
/// When the component started.
StartTime: Timestamp
/// When the component ended.
EndTime: Timestamp
/// When the component was created.
CreationTime: Timestamp
/// Who created the trial component.
CreatedBy: UserContext
/// When the component was last modified.
LastModifiedTime: Timestamp
/// Who last modified the component.
LastModifiedBy: UserContext
}
/// The source of the trial.
structure TrialSource {
/// The Amazon Resource Name (ARN) of the source.
@required
SourceArn: TrialSourceArn
/// The source job type.
SourceType: SourceType
}
/// A summary of the properties of a trial. To get the complete set of properties, call the
/// DescribeTrial API and provide the TrialName.
structure TrialSummary {
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
/// The name of the trial.
TrialName: ExperimentEntityName
/// The name of the trial as displayed. If DisplayName isn't specified,
/// TrialName is displayed.
DisplayName: ExperimentEntityName
TrialSource: TrialSource
/// When the trial was created.
CreationTime: Timestamp
/// When the trial was last modified.
LastModifiedTime: Timestamp
}
/// The job completion criteria.
structure TuningJobCompletionCriteria {
/// The value of the objective metric.
TargetObjectiveMetricValue: TargetObjectiveMetricValue
/// A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
BestObjectiveNotImproving: BestObjectiveNotImproving
/// A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
ConvergenceDetected: ConvergenceDetected
}
/// Metadata for a tuning step.
structure TuningJobStepMetaData {
/// The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Arn: HyperParameterTuningJobArn
}
/// Provided configuration information for the worker UI for a labeling job. Provide
/// either HumanTaskUiArn or UiTemplateS3Uri.
/// For named entity recognition, 3D point cloud and video frame labeling jobs, use
/// HumanTaskUiArn.
/// For all other Ground Truth built-in task types and custom task types, use
/// UiTemplateS3Uri to specify the location of a worker task template in
/// Amazon S3.
structure UiConfig {
/// The Amazon S3 bucket location of the UI template, or worker task template. This is the
/// template used to render the worker UI and tools for labeling job tasks. For more
/// information about the contents of a UI template, see Creating Your Custom
/// Labeling Task Template.
UiTemplateS3Uri: S3Uri
/// The ARN of the worker task template used to render the worker UI and tools for
/// labeling job tasks.
/// Use this parameter when you are creating a labeling job for named entity recognition,
/// 3D point cloud and video frame labeling jobs. Use your labeling job task type to select
/// one of the following ARNs and use it with this parameter when you create a labeling job.
/// Replace aws-region with the Amazon Web Services Region you are creating your labeling job
/// in. For example, replace aws-region with us-west-1 if you
/// create a labeling job in US West (N. California).
///
/// Named Entity Recognition
///
/// Use the following HumanTaskUiArn for named entity recognition labeling
/// jobs:
///
/// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition
///
///
/// 3D Point Cloud HumanTaskUiArns
///
/// Use this HumanTaskUiArn for 3D point cloud object detection and 3D point
/// cloud object detection adjustment labeling jobs.
///
/// -
///
/// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
///
///
///
/// Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point
/// cloud object tracking adjustment labeling jobs.
///
/// -
///
/// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
///
///
///
/// Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D
/// point cloud semantic segmentation adjustment labeling jobs.
///
/// -
///
/// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
///
///
///
///
/// Video Frame HumanTaskUiArns
///
/// Use this HumanTaskUiArn for video frame object detection and video frame
/// object detection adjustment labeling jobs.
///
/// -
///
/// arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
///
///
///
/// Use this HumanTaskUiArn for video frame object tracking and video frame
/// object tracking adjustment labeling jobs.
///
/// -
///
/// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
///
///
///
HumanTaskUiArn: HumanTaskUiArn
}
/// The Liquid template for the worker user interface.
structure UiTemplate {
/// The content of the Liquid template for the worker user interface.
@required
Content: TemplateContent
}
/// Container for user interface template information.
structure UiTemplateInfo {
/// The URL for the user interface template.
Url: TemplateUrl
/// The SHA-256 digest of the contents of the template.
ContentSha256: TemplateContentSha256
}
@input
structure UpdateActionRequest {
/// The name of the action to update.
@required
ActionName: ExperimentEntityName
/// The new description for the action.
Description: ExperimentDescription
/// The new status for the action.
Status: ActionStatus
/// The new list of properties. Overwrites the current property list.
Properties: LineageEntityParameters
/// A list of properties to remove.
PropertiesToRemove: ListLineageEntityParameterKey
}
@output
structure UpdateActionResponse {
/// The Amazon Resource Name (ARN) of the action.
ActionArn: ActionArn
}
@input
structure UpdateAppImageConfigRequest {
/// The name of the AppImageConfig to update.
@required
AppImageConfigName: AppImageConfigName
/// The new KernelGateway app to run on the image.
KernelGatewayImageConfig: KernelGatewayImageConfig
}
@output
structure UpdateAppImageConfigResponse {
/// The Amazon Resource Name (ARN) for the AppImageConfig.
AppImageConfigArn: AppImageConfigArn
}
@input
structure UpdateArtifactRequest {
/// The Amazon Resource Name (ARN) of the artifact to update.
@required
ArtifactArn: ArtifactArn
/// The new name for the artifact.
ArtifactName: ExperimentEntityName
/// The new list of properties. Overwrites the current property list.
Properties: LineageEntityParameters
/// A list of properties to remove.
PropertiesToRemove: ListLineageEntityParameterKey
}
@output
structure UpdateArtifactResponse {
/// The Amazon Resource Name (ARN) of the artifact.
ArtifactArn: ArtifactArn
}
@input
structure UpdateContextRequest {
/// The name of the context to update.
@required
ContextName: ExperimentEntityName
/// The new description for the context.
Description: ExperimentDescription
/// The new list of properties. Overwrites the current property list.
Properties: LineageEntityParameters
/// A list of properties to remove.
PropertiesToRemove: ListLineageEntityParameterKey
}
@output
structure UpdateContextResponse {
/// The Amazon Resource Name (ARN) of the context.
ContextArn: ContextArn
}
@input
structure UpdateDeviceFleetRequest {
/// The name of the fleet.
@required
DeviceFleetName: EntityName
/// The Amazon Resource Name (ARN) of the device.
RoleArn: RoleArn
/// Description of the fleet.
Description: DeviceFleetDescription
/// Output configuration for storing sample data collected by the fleet.
@required
OutputConfig: EdgeOutputConfig
/// 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".
EnableIotRoleAlias: EnableIotRoleAlias
}
@input
structure UpdateDevicesRequest {
/// The name of the fleet the devices belong to.
@required
DeviceFleetName: EntityName
/// List of devices to register with Edge Manager agent.
@required
Devices: Devices
}
@input
structure UpdateDomainRequest {
/// The ID of the domain to be updated.
@required
DomainId: DomainId
/// A collection of settings.
DefaultUserSettings: UserSettings
/// A collection of DomainSettings configuration values to update.
DomainSettingsForUpdate: DomainSettingsForUpdate
/// The default settings used to create a space within the Domain.
DefaultSpaceSettings: DefaultSpaceSettings
/// 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.
AppSecurityGroupManagement: AppSecurityGroupManagement
}
@output
structure UpdateDomainResponse {
/// The Amazon Resource Name (ARN) of the domain.
DomainArn: DomainArn
}
@input
structure UpdateExperimentRequest {
/// The name of the experiment to update.
@required
ExperimentName: ExperimentEntityName
/// The name of the experiment as displayed. The name doesn't need to be unique. If
/// DisplayName isn't specified, ExperimentName is displayed.
DisplayName: ExperimentEntityName
/// The description of the experiment.
Description: ExperimentDescription
}
@output
structure UpdateExperimentResponse {
/// The Amazon Resource Name (ARN) of the experiment.
ExperimentArn: ExperimentArn
}
@input
structure UpdateFeatureGroupRequest {
/// The name of the feature group that you're updating.
@required
FeatureGroupName: FeatureGroupName
/// 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.
FeatureAdditions: FeatureAdditions
}
@output
structure UpdateFeatureGroupResponse {
/// The Amazon Resource Number (ARN) of the feature group that you're updating.
@required
FeatureGroupArn: FeatureGroupArn
}
@input
structure UpdateFeatureMetadataRequest {
/// The name of the feature group containing the feature that you're updating.
@required
FeatureGroupName: FeatureGroupName
/// The name of the feature that you're updating.
@required
FeatureName: FeatureName
/// A description that you can write to better describe the feature.
Description: FeatureDescription
/// A list of key-value pairs that you can add to better describe the feature.
ParameterAdditions: FeatureParameterAdditions
/// A list of parameter keys that you can specify to remove parameters that describe your feature.
ParameterRemovals: FeatureParameterRemovals
}
@input
structure UpdateHubRequest {
/// The name of the hub to update.
@required
HubName: HubName
/// A description of the updated hub.
HubDescription: HubDescription
/// The display name of the hub.
HubDisplayName: HubDisplayName
/// The searchable keywords for the hub.
HubSearchKeywords: HubSearchKeywordList
}
@output
structure UpdateHubResponse {
/// The Amazon Resource Name (ARN) of the updated hub.
@required
HubArn: HubArn
}
@input
structure UpdateImageRequest {
/// A list of properties to delete. Only the Description and
/// DisplayName properties can be deleted.
DeleteProperties: ImageDeletePropertyList
/// The new description for the image.
Description: ImageDescription
/// The new display name for the image.
DisplayName: ImageDisplayName
/// The name of the image to update.
@required
ImageName: ImageName
/// The new ARN for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
RoleArn: RoleArn
}
@output
structure UpdateImageResponse {
/// The ARN of the image.
ImageArn: ImageArn
}
@input
structure UpdateImageVersionRequest {
/// The name of the image.
@required
ImageName: ImageName
/// The alias of the image version.
Alias: SageMakerImageVersionAlias
/// The version of the image.
Version: ImageVersionNumber
/// A list of aliases to add.
AliasesToAdd: SageMakerImageVersionAliases
/// A list of aliases to delete.
AliasesToDelete: SageMakerImageVersionAliases
/// 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.
///
///
VendorGuidance: VendorGuidance
/// Indicates SageMaker job type compatibility.
///
/// -
///
/// TRAINING: The image version is compatible with SageMaker training jobs.
///
/// -
///
/// INFERENCE: The image version is compatible with SageMaker inference jobs.
///
/// -
///
/// NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.
///
///
JobType: JobType
/// The machine learning framework vended in the image version.
MLFramework: MLFramework
/// The supported programming language and its version.
ProgrammingLang: ProgrammingLang
/// Indicates CPU or GPU compatibility.
///
/// -
///
/// CPU: The image version is compatible with CPU.
///
/// -
///
/// GPU: The image version is compatible with GPU.
///
///
Processor: Processor
/// Indicates Horovod compatibility.
Horovod: Horovod = false
/// The maintainer description of the image version.
ReleaseNotes: ReleaseNotes
}
@output
structure UpdateImageVersionResponse {
/// The ARN of the image version.
ImageVersionArn: ImageVersionArn
}
@input
structure UpdateInferenceExperimentRequest {
/// The name of the inference experiment to be updated.
@required
Name: InferenceExperimentName
///
/// 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.
///
Schedule: InferenceExperimentSchedule
/// The description of the inference experiment.
Description: InferenceExperimentDescription
///
/// An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure
/// configuration you want to update.
///
ModelVariants: ModelVariantConfigList
/// The Amazon S3 location and configuration for storing inference request and response data.
DataStorageConfig: InferenceExperimentDataStorageConfig
///
/// 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.
///
ShadowModeConfig: ShadowModeConfig
}
@output
structure UpdateInferenceExperimentResponse {
/// The ARN of the updated inference experiment.
@required
InferenceExperimentArn: InferenceExperimentArn
}
@input
structure UpdateModelCardRequest {
/// The name of the model card to update.
@required
ModelCardName: EntityName
/// 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.
Content: ModelCardContent
/// 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.
///
///
ModelCardStatus: ModelCardStatus
}
@output
structure UpdateModelCardResponse {
/// The Amazon Resource Name (ARN) of the updated model card.
@required
ModelCardArn: ModelCardArn
}
@input
structure UpdateMonitoringAlertRequest {
/// The name of a monitoring schedule.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The name of a monitoring alert.
@required
MonitoringAlertName: MonitoringAlertName
/// Within EvaluationPeriod, how many execution failures will raise an
/// alert.
@required
DatapointsToAlert: MonitoringDatapointsToAlert
/// The number of most recent monitoring executions to consider when evaluating alert
/// status.
@required
EvaluationPeriod: MonitoringEvaluationPeriod
}
@output
structure UpdateMonitoringAlertResponse {
/// The Amazon Resource Name (ARN) of the monitoring schedule.
@required
MonitoringScheduleArn: MonitoringScheduleArn
/// The name of a monitoring alert.
MonitoringAlertName: MonitoringAlertName
}
@input
structure UpdateMonitoringScheduleRequest {
/// The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within
/// an Amazon Web Services account.
@required
MonitoringScheduleName: MonitoringScheduleName
/// The configuration object that specifies the monitoring schedule and defines the
/// monitoring job.
@required
MonitoringScheduleConfig: MonitoringScheduleConfig
}
@output
structure UpdateMonitoringScheduleResponse {
/// The Amazon Resource Name (ARN) of the monitoring schedule.
@required
MonitoringScheduleArn: MonitoringScheduleArn
}
@input
structure UpdatePipelineExecutionRequest {
/// The Amazon Resource Name (ARN) of the pipeline execution.
@required
PipelineExecutionArn: PipelineExecutionArn
/// The description of the pipeline execution.
PipelineExecutionDescription: PipelineExecutionDescription
/// The display name of the pipeline execution.
PipelineExecutionDisplayName: PipelineExecutionName
/// This configuration, if specified, overrides the parallelism configuration
/// of the parent pipeline for this specific run.
ParallelismConfiguration: ParallelismConfiguration
}
@output
structure UpdatePipelineExecutionResponse {
/// The Amazon Resource Name (ARN) of the updated pipeline execution.
PipelineExecutionArn: PipelineExecutionArn
}
@input
structure UpdatePipelineRequest {
/// The name of the pipeline to update.
@required
PipelineName: PipelineName
/// The display name of the pipeline.
PipelineDisplayName: PipelineName
/// The JSON pipeline definition.
PipelineDefinition: PipelineDefinition
/// The location of the pipeline definition stored in Amazon S3. If specified,
/// SageMaker will retrieve the pipeline definition from this location.
PipelineDefinitionS3Location: PipelineDefinitionS3Location
/// The description of the pipeline.
PipelineDescription: PipelineDescription
/// The Amazon Resource Name (ARN) that the pipeline uses to execute.
RoleArn: RoleArn
/// If specified, it applies to all executions of this pipeline by default.
ParallelismConfiguration: ParallelismConfiguration
}
@output
structure UpdatePipelineResponse {
/// The Amazon Resource Name (ARN) of the updated pipeline.
PipelineArn: PipelineArn
}
@input
structure UpdateSpaceRequest {
/// The ID of the associated Domain.
@required
DomainId: DomainId
/// The name of the space.
@required
SpaceName: SpaceName
/// A collection of space settings.
SpaceSettings: SpaceSettings
}
@output
structure UpdateSpaceResponse {
/// The space's Amazon Resource Name (ARN).
SpaceArn: SpaceArn
}
@input
structure UpdateTrainingJobRequest {
/// The name of a training job to update the Debugger profiling configuration.
@required
TrainingJobName: TrainingJobName
/// Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and
/// storage paths.
ProfilerConfig: ProfilerConfigForUpdate
/// Configuration information for Amazon SageMaker Debugger rules for profiling system and framework
/// metrics.
ProfilerRuleConfigurations: ProfilerRuleConfigurations
/// The training job ResourceConfig to update warm pool retention
/// length.
ResourceConfig: ResourceConfigForUpdate
}
@output
structure UpdateTrainingJobResponse {
/// The Amazon Resource Name (ARN) of the training job.
@required
TrainingJobArn: TrainingJobArn
}
@input
structure UpdateTrialComponentRequest {
/// The name of the component to update.
@required
TrialComponentName: ExperimentEntityName
/// The name of the component as displayed. The name doesn't need to be unique. If
/// DisplayName isn't specified, TrialComponentName is
/// displayed.
DisplayName: ExperimentEntityName
/// The new status of the component.
Status: TrialComponentStatus
/// When the component started.
StartTime: Timestamp
/// When the component ended.
EndTime: Timestamp
/// Replaces all of the component's hyperparameters with the specified hyperparameters.
Parameters: TrialComponentParameters
/// The hyperparameters to remove from the component.
ParametersToRemove: ListTrialComponentKey256
/// Replaces all of the component's input artifacts with the specified artifacts.
InputArtifacts: TrialComponentArtifacts
/// The input artifacts to remove from the component.
InputArtifactsToRemove: ListTrialComponentKey256
/// Replaces all of the component's output artifacts with the specified artifacts.
OutputArtifacts: TrialComponentArtifacts
/// The output artifacts to remove from the component.
OutputArtifactsToRemove: ListTrialComponentKey256
}
@output
structure UpdateTrialComponentResponse {
/// The Amazon Resource Name (ARN) of the trial component.
TrialComponentArn: TrialComponentArn
}
@input
structure UpdateTrialRequest {
/// The name of the trial to update.
@required
TrialName: ExperimentEntityName
/// The name of the trial as displayed. The name doesn't need to be unique. If
/// DisplayName isn't specified, TrialName is displayed.
DisplayName: ExperimentEntityName
}
@output
structure UpdateTrialResponse {
/// The Amazon Resource Name (ARN) of the trial.
TrialArn: TrialArn
}
@input
structure UpdateUserProfileRequest {
/// The domain ID.
@required
DomainId: DomainId
/// The user profile name.
@required
UserProfileName: UserProfileName
/// A collection of settings.
UserSettings: UserSettings
}
@output
structure UpdateUserProfileResponse {
/// The user profile Amazon Resource Name (ARN).
UserProfileArn: UserProfileArn
}
@input
structure UpdateWorkforceRequest {
/// The name of the private workforce that you want to update. You can find your workforce
/// name by using the operation.
@required
WorkforceName: WorkforceName
/// 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
SourceIpConfig: SourceIpConfig
/// Use this parameter to update your OIDC Identity Provider (IdP)
/// configuration for a workforce made using your own IdP.
OidcConfig: OidcConfig
/// Use this parameter to update your VPC configuration for a workforce.
WorkforceVpcConfig: WorkforceVpcConfigRequest
}
@output
structure UpdateWorkforceResponse {
/// 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.
@required
Workforce: Workforce
}
@input
structure UpdateWorkteamRequest {
/// The name of the work team to update.
@required
WorkteamName: WorkteamName
/// 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.
MemberDefinitions: MemberDefinitions
/// An updated description for the work team.
Description: String200
/// Configures SNS topic notifications for available or expiring work items
NotificationConfiguration: NotificationConfiguration
}
@output
structure UpdateWorkteamResponse {
/// A Workteam object that describes the updated work team.
@required
Workteam: Workteam
}
/// Represents an amount of money in United States dollars.
structure USD {
/// The whole number of dollars in the amount.
Dollars: Dollars = 0
/// The fractional portion, in cents, of the amount.
Cents: Cents = 0
/// Fractions of a cent, in tenths.
TenthFractionsOfACent: TenthFractionsOfACent = 0
}
/// Information about the user who created or modified an experiment, trial, trial
/// component, lineage group, project, or model card.
structure UserContext {
/// The Amazon Resource Name (ARN) of the user's profile.
UserProfileArn: String
/// The name of the user's profile.
UserProfileName: String
/// The domain associated with the user.
DomainId: String
}
/// The user profile details.
structure UserProfileDetails {
/// The domain ID.
DomainId: DomainId
/// The user profile name.
UserProfileName: UserProfileName
/// The status.
Status: UserProfileStatus
/// The creation time.
CreationTime: CreationTime
/// The last modified time.
LastModifiedTime: LastModifiedTime
}
/// A collection of settings that apply to users of Amazon SageMaker Studio. These settings are
/// specified when the CreateUserProfile API is called, and as DefaultUserSettings
/// when the CreateDomain API is called.
///
/// 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.
structure UserSettings {
/// The execution role for the user.
ExecutionRole: RoleArn
/// The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
/// Optional when the CreateDomain.AppNetworkAccessType parameter is set to
/// PublicInternetOnly.
/// Required when the CreateDomain.AppNetworkAccessType parameter is set to
/// VpcOnly.
/// Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the
/// number of security groups that you can specify is one less than the maximum number shown.
SecurityGroups: SecurityGroupIds
/// Specifies options for sharing SageMaker Studio notebooks.
SharingSettings: SharingSettings
/// The Jupyter server's app settings.
JupyterServerAppSettings: JupyterServerAppSettings
/// The kernel gateway app settings.
KernelGatewayAppSettings: KernelGatewayAppSettings
/// The TensorBoard app settings.
TensorBoardAppSettings: TensorBoardAppSettings
/// A collection of settings that configure user interaction with the
/// RStudioServerPro app.
RStudioServerProAppSettings: RStudioServerProAppSettings
/// A collection of settings that configure the RSessionGateway app.
RSessionAppSettings: RSessionAppSettings
/// The Canvas app settings.
CanvasAppSettings: CanvasAppSettings
}
/// Specifies a production variant property type for an Endpoint.
/// If you are updating an endpoint with the UpdateEndpointInput$RetainAllVariantProperties option set to
/// true, the VariantProperty objects listed in UpdateEndpointInput$ExcludeRetainedVariantProperties override the
/// existing variant properties of the endpoint.
structure VariantProperty {
/// The type of variant property. The supported values are:
///
/// -
///
/// DesiredInstanceCount: Overrides the existing variant instance
/// counts using the ProductionVariant$InitialInstanceCount values
/// in the CreateEndpointConfigInput$ProductionVariants.
///
/// -
///
/// DesiredWeight: Overrides the existing variant weights using the
/// ProductionVariant$InitialVariantWeight values in the CreateEndpointConfigInput$ProductionVariants.
///
/// -
///
/// DataCaptureConfig: (Not currently supported.)
///
///
@required
VariantPropertyType: VariantPropertyType
}
/// A lineage entity connected to the starting entity(ies).
structure Vertex {
/// The Amazon Resource Name (ARN) of the lineage entity resource.
Arn: AssociationEntityArn
/// The type of the lineage entity resource. For example: DataSet, Model, Endpoint,
/// etc...
Type: String40
/// The type of resource of the lineage entity.
LineageType: LineageType
}
/// Specifies a VPC that your training jobs and hosted models have access to. Control
/// access to and from your training and model containers by configuring the VPC. For more
/// information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs
/// by Using an Amazon Virtual Private Cloud.
structure VpcConfig {
/// The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for
/// the VPC that is specified in the Subnets field.
@required
SecurityGroupIds: VpcSecurityGroupIds
/// The ID of the subnets in the VPC to which you want to connect your training job or
/// model. For information about the availability of specific instance types, see Supported
/// Instance Types and Availability Zones.
@required
Subnets: Subnets
}
/// Status and billing information about the warm pool.
structure WarmPoolStatus {
/// The status of the warm pool.
///
/// -
///
/// InUse: The warm pool is in use for the training job.
///
/// -
///
/// Available: The warm pool is available to reuse for a matching
/// training job.
///
/// -
///
/// Reused: The warm pool moved to a matching training job for
/// reuse.
///
/// -
///
/// Terminated: The warm pool is no longer available. Warm pools are
/// unavailable if they are terminated by a user, terminated for a patch update, or
/// terminated for exceeding the specified
/// KeepAlivePeriodInSeconds.
///
///
@required
Status: WarmPoolResourceStatus
/// The billable time in seconds used by the warm pool. Billable time refers to the
/// absolute wall-clock time.
/// Multiply ResourceRetainedBillableTimeInSeconds by the number of instances
/// (InstanceCount) in your training cluster to get the total compute time
/// SageMaker bills you if you run warm pool training. The formula is as follows:
/// ResourceRetainedBillableTimeInSeconds * InstanceCount.
ResourceRetainedBillableTimeInSeconds: ResourceRetainedBillableTimeInSeconds
/// The name of the matching training job that reused the warm pool.
ReusedByJob: TrainingJobName
}
/// 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.
structure Workforce {
/// The name of the private workforce.
@required
WorkforceName: WorkforceName
/// The Amazon Resource Name (ARN) of the private workforce.
@required
WorkforceArn: WorkforceArn
/// The most recent date that was used to
/// successfully add one or more IP address ranges (CIDRs) to a private workforce's
/// allow list.
LastUpdatedDate: Timestamp
/// A list of one to ten IP address ranges (CIDRs) to be added to the
/// workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
SourceIpConfig: SourceIpConfig
/// The subdomain for your OIDC Identity Provider.
SubDomain: String
/// The configuration of an Amazon Cognito workforce.
/// A single Cognito workforce is created using and corresponds to a single
///
/// Amazon Cognito user pool.
CognitoConfig: CognitoConfig
/// The configuration of an OIDC Identity Provider (IdP) private workforce.
OidcConfig: OidcConfigForResponse
/// The date that the workforce is created.
CreateDate: Timestamp
/// The configuration of a VPC workforce.
WorkforceVpcConfig: WorkforceVpcConfigResponse
/// The status of your workforce.
Status: WorkforceStatus
/// The reason your workforce failed.
FailureReason: WorkforceFailureReason
}
/// The VPC object you use to create or update a workforce.
structure WorkforceVpcConfigRequest {
/// The ID of the VPC that the workforce uses for communication.
VpcId: WorkforceVpcId
/// The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
SecurityGroupIds: WorkforceSecurityGroupIds
/// The ID of the subnets in the VPC that you want to connect.
Subnets: WorkforceSubnets
}
/// A VpcConfig object that specifies the VPC that you want your workforce to connect to.
structure WorkforceVpcConfigResponse {
/// The ID of the VPC that the workforce uses for communication.
@required
VpcId: WorkforceVpcId
/// The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
@required
SecurityGroupIds: WorkforceSecurityGroupIds
/// The ID of the subnets in the VPC that you want to connect.
@required
Subnets: WorkforceSubnets
/// The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
VpcEndpointId: WorkforceVpcEndpointId
}
/// Provides details about a labeling work team.
structure Workteam {
/// The name of the work team.
@required
WorkteamName: WorkteamName
/// 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.
@required
MemberDefinitions: MemberDefinitions
/// The Amazon Resource Name (ARN) that identifies the work team.
@required
WorkteamArn: WorkteamArn
/// The Amazon Resource Name (ARN) of the workforce.
WorkforceArn: WorkforceArn
/// The Amazon Marketplace identifier for a vendor's work team.
ProductListingIds: ProductListings
/// A description of the work team.
@required
Description: String200
/// The URI of the labeling job's user interface. Workers open this URI to start labeling
/// your data objects.
SubDomain: String
/// The date and time that the work team was created (timestamp).
CreateDate: Timestamp
/// The date and time that the work team was last updated (timestamp).
LastUpdatedDate: Timestamp
/// Configures SNS notifications of available or expiring work items for work
/// teams.
NotificationConfiguration: NotificationConfiguration
}
/// The value of a hyperparameter. Only one of NumberValue or
/// StringValue can be specified.
/// This object is specified in the CreateTrialComponent request.
union TrialComponentParameterValue {
/// The string value of a categorical hyperparameter. If you specify a value for this
/// parameter, you can't specify the NumberValue parameter.
StringValue: StringParameterValue
/// The numeric value of a numeric hyperparameter. If you specify a value for this parameter,
/// you can't specify the StringValue parameter.
NumberValue: DoubleParameterValue
}
list ActionSummaries {
member: ActionSummary
}
@length(
min: 0
max: 3
)
list AdditionalCodeRepositoryNamesOrUrls {
member: CodeRepositoryNameOrUrl
}
@length(
min: 1
max: 15
)
list AdditionalInferenceSpecifications {
member: AdditionalInferenceSpecificationDefinition
}
list AgentVersions {
member: AgentVersion
}
@length(
min: 1
max: 10
)
list AlarmList {
member: Alarm
}
list AlgorithmStatusItemList {
member: AlgorithmStatusItem
}
list AlgorithmSummaryList {
member: AlgorithmSummary
}
@length(
min: 1
max: 1
)
list AlgorithmValidationProfiles {
member: AlgorithmValidationProfile
}
list AppImageConfigList {
member: AppImageConfigDetails
}
list AppList {
member: AppDetails
}
list ArtifactSourceTypes {
member: ArtifactSourceType
}
list ArtifactSummaries {
member: ArtifactSummary
}
list AssociationSummaries {
member: AssociationSummary
}
@length(
min: 0
max: 16
)
list AttributeNames {
member: AttributeName
}
list AutoMLCandidates {
member: AutoMLCandidate
}
@length(
min: 0
max: 5
)
list AutoMLContainerDefinitions {
member: AutoMLContainerDefinition
}
@length(
min: 1
max: 2
)
list AutoMLInputDataConfig {
member: AutoMLChannel
}
list AutoMLJobSummaries {
member: AutoMLJobSummary
}
@length(
min: 1
max: 5
)
list AutoMLPartialFailureReasons {
member: AutoMLPartialFailureReason
}
list CandidateSteps {
member: AutoMLCandidateStep
}
@length(
min: 1
max: 2
)
list CaptureOptionList {
member: CaptureOption
}
@length(
min: 0
max: 30
)
list CategoricalParameterRanges {
member: CategoricalParameterRange
}
@length(
min: 1
max: 3
)
list CategoricalParameterRangeValues {
member: String128
}
@length(
min: 1
max: 5
)
list CategoricalParameters {
member: CategoricalParameter
}
@length(
min: 1
max: 8
)
list ChannelSpecifications {
member: ChannelSpecification
}
list Cidrs {
member: Cidr
}
@length(
min: 1
max: 256
)
list ClarifyFeatureHeaders {
member: ClarifyHeader
}
@length(
min: 1
max: 256
)
list ClarifyFeatureTypes {
member: ClarifyFeatureType
}
@length(
min: 1
max: 16
)
list ClarifyLabelHeaders {
member: ClarifyHeader
}
@length(
min: 0
max: 10
)
list CodeRepositories {
member: CodeRepository
}
list CodeRepositorySummaryList {
member: CodeRepositorySummary
}
@length(
min: 0
max: 20
)
list CollectionConfigurations {
member: CollectionConfiguration
}
list CompilationJobSummaries {
member: CompilationJobSummary
}
list CompressionTypes {
member: CompressionType
}
@length(
min: 1
max: 100
)
list ContainerArguments {
member: ContainerArgument
}
@length(
min: 0
max: 15
)
list ContainerDefinitionList {
member: ContainerDefinition
}
@length(
min: 1
max: 100
)
list ContainerEntrypoint {
member: ContainerEntrypointString
}
@length(
min: 0
max: 256
)
list ContentClassifiers {
member: ContentClassifier
}
list ContentTypes {
member: ContentType
}
list ContextSummaries {
member: ContextSummary
}
@length(
min: 0
max: 30
)
list ContinuousParameterRanges {
member: ContinuousParameterRange
}
@length(
min: 1
max: 10
)
list CsvContentTypes {
member: CsvContentType
}
list CustomerMetadataKeyList {
member: CustomerMetadataKey
}
@length(
min: 0
max: 200
)
list CustomImages {
member: CustomImage
}
@length(
min: 0
max: 20
)
list DebugRuleConfigurations {
member: DebugRuleConfiguration
}
@length(
min: 0
max: 20
)
list DebugRuleEvaluationStatuses {
member: DebugRuleEvaluationStatus
}
list DeployedImages {
member: DeployedImage
}
list DeploymentStages {
member: DeploymentStage
}
list DeploymentStageStatusSummaries {
member: DeploymentStageStatusSummary
}
@length(
min: 1
)
list DesiredWeightAndCapacityList {
member: DesiredWeightAndCapacity
}
list DeviceDeploymentSummaries {
member: DeviceDeploymentSummary
}
list DeviceFleetSummaries {
member: DeviceFleetSummary
}
list DeviceNames {
member: DeviceName
}
list Devices {
member: Device
}
list DeviceSummaries {
member: DeviceSummary
}
list DomainList {
member: DomainDetails
}
@length(
min: 0
max: 3
)
list DomainSecurityGroupIds {
member: SecurityGroupId
}
list EdgeDeploymentModelConfigs {
member: EdgeDeploymentModelConfig
}
list EdgeDeploymentPlanSummaries {
member: EdgeDeploymentPlanSummary
}
list EdgeModels {
member: EdgeModel
}
list EdgeModelStats {
member: EdgeModelStat
}
list EdgeModelSummaries {
member: EdgeModelSummary
}
list EdgePackagingJobSummaries {
member: EdgePackagingJobSummary
}
list Edges {
member: Edge
}
list EndpointConfigSummaryList {
member: EndpointConfigSummary
}
@length(
min: 1
max: 10
)
list EndpointInputConfigurations {
member: EndpointInputConfiguration
}
@length(
min: 0
max: 1
)
list EndpointPerformances {
member: EndpointPerformance
}
@length(
min: 0
max: 1
)
list Endpoints {
member: EndpointInfo
}
list EndpointSummaryList {
member: EndpointSummary
}
@length(
min: 1
max: 10
)
list EnvironmentParameters {
member: EnvironmentParameter
}
list ExperimentSummaries {
member: ExperimentSummary
}
@length(
min: 1
max: 100
)
list FeatureAdditions {
member: FeatureDefinition
}
@length(
min: 1
max: 2500
)
list FeatureDefinitions {
member: FeatureDefinition
}
list FeatureGroupSummaries {
member: FeatureGroupSummary
}
@length(
min: 0
max: 25
)
list FeatureParameterAdditions {
member: FeatureParameter
}
@length(
min: 0
max: 25
)
list FeatureParameterRemovals {
member: FeatureParameterKey
}
@length(
min: 0
max: 25
)
list FeatureParameters {
member: FeatureParameter
}
@length(
min: 1
max: 20
)
list FilterList {
member: Filter
}
@length(
min: 0
max: 40
)
list FinalMetricDataList {
member: MetricData
}
list FlowDefinitionSummaries {
member: FlowDefinitionSummary
}
@length(
min: 1
max: 5
)
list FlowDefinitionTaskKeywords {
member: FlowDefinitionTaskKeyword
}
@length(
min: 1
max: 10
)
list Groups {
member: Group
}
@length(
min: 0
max: 50
)
list HubContentDependencyList {
member: HubContentDependency
}
list HubContentInfoList {
member: HubContentInfo
}
@length(
min: 0
max: 50
)
list HubContentSearchKeywordList {
member: HubSearchKeyword
}
list HubInfoList {
member: HubInfo
}
@length(
min: 0
max: 50
)
list HubSearchKeywordList {
member: HubSearchKeyword
}
list HumanTaskUiSummaries {
member: HumanTaskUiSummary
}
@length(
min: 0
max: 100
)
list HyperParameterSpecifications {
member: HyperParameterSpecification
}
@length(
min: 1
max: 10
)
list HyperParameterTrainingJobDefinitions {
member: HyperParameterTrainingJobDefinition
}
list HyperParameterTrainingJobSummaries {
member: HyperParameterTrainingJobSummary
}
@length(
min: 1
max: 6
)
list HyperParameterTuningInstanceConfigs {
member: HyperParameterTuningInstanceConfig
}
list HyperParameterTuningJobObjectives {
member: HyperParameterTuningJobObjective
}
list HyperParameterTuningJobSummaries {
member: HyperParameterTuningJobSummary
}
@length(
min: 0
max: 2
)
list ImageDeletePropertyList {
member: ImageDeleteProperty
}
list Images {
member: Image
}
list ImageVersions {
member: ImageVersion
}
list InferenceExperimentList {
member: InferenceExperimentSummary
}
@length(
min: 1
max: 10
)
list InferenceRecommendations {
member: InferenceRecommendation
}
list InferenceRecommendationsJobs {
member: InferenceRecommendationsJob
}
list InferenceRecommendationsJobSteps {
member: InferenceRecommendationsJobStep
}
@length(
min: 1
max: 20
)
list InputDataConfig {
member: Channel
}
@length(
min: 1
)
list InputModes {
member: TrainingInputMode
}
@length(
min: 0
max: 5
)
list InstanceGroupNames {
member: InstanceGroupName
}
@length(
min: 0
max: 5
)
list InstanceGroups {
member: InstanceGroup
}
@length(
min: 0
max: 30
)
list IntegerParameterRanges {
member: IntegerParameterRange
}
@length(
min: 1
max: 10
)
list JsonContentTypes {
member: JsonContentType
}
@length(
min: 1
max: 1
)
list KernelSpecs {
member: KernelSpec
}
list LabelingJobForWorkteamSummaryList {
member: LabelingJobForWorkteamSummary
}
list LabelingJobSummaryList {
member: LabelingJobSummary
}
list LifecycleConfigArns {
member: StudioLifecycleConfigArn
}
list LineageGroupSummaries {
member: LineageGroupSummary
}
list ListLineageEntityParameterKey {
member: StringParameterValue
}
list ListTrialComponentKey256 {
member: TrialComponentKey256
}
@length(
min: 1
max: 10
)
list MemberDefinitions {
member: MemberDefinition
}
@length(
min: 0
max: 40
)
list MetricDataList {
member: MetricDatum
}
@length(
min: 0
max: 40
)
list MetricDefinitionList {
member: MetricDefinition
}
list ModelCardExportJobSummaryList {
member: ModelCardExportJobSummary
}
list ModelCardSummaryList {
member: ModelCardSummary
}
list ModelCardVersionSummaryList {
member: ModelCardVersionSummary
}
list ModelDashboardEndpoints {
member: ModelDashboardEndpoint
}
list ModelDashboardMonitoringSchedules {
member: ModelDashboardMonitoringSchedule
}
@length(
min: 1
max: 1
)
list ModelLatencyThresholds {
member: ModelLatencyThreshold
}
@length(
min: 1
max: 4
)
list ModelMetadataFilters {
member: ModelMetadataFilter
}
list ModelMetadataSummaries {
member: ModelMetadataSummary
}
@length(
min: 1
max: 100
)
list ModelPackageArnList {
member: ModelPackageArn
}
@length(
min: 1
max: 15
)
list ModelPackageContainerDefinitionList {
member: ModelPackageContainerDefinition
}
list ModelPackageGroupSummaryList {
member: ModelPackageGroupSummary
}
list ModelPackageStatusItemList {
member: ModelPackageStatusItem
}
list ModelPackageSummaryList {
member: ModelPackageSummary
}
@length(
min: 1
max: 1
)
list ModelPackageValidationProfiles {
member: ModelPackageValidationProfile
}
list ModelSummaryList {
member: ModelSummary
}
@length(
min: 1
max: 2
)
list ModelVariantConfigList {
member: ModelVariantConfig
}
list ModelVariantConfigSummaryList {
member: ModelVariantConfigSummary
}
list MonitoringAlertHistoryList {
member: MonitoringAlertHistorySummary
}
@length(
min: 1
max: 100
)
list MonitoringAlertSummaryList {
member: MonitoringAlertSummary
}
@length(
min: 1
max: 50
)
list MonitoringContainerArguments {
member: ContainerArgument
}
list MonitoringExecutionSummaryList {
member: MonitoringExecutionSummary
}
@length(
min: 1
max: 1
)
list MonitoringInputs {
member: MonitoringInput
}
list MonitoringJobDefinitionSummaryList {
member: MonitoringJobDefinitionSummary
}
@length(
min: 1
max: 1
)
list MonitoringOutputs {
member: MonitoringOutput
}
list MonitoringScheduleList {
member: MonitoringSchedule
}
list MonitoringScheduleSummaryList {
member: MonitoringScheduleSummary
}
@length(
min: 1
max: 5
)
list NeoVpcSecurityGroupIds {
member: NeoVpcSecurityGroupId
}
@length(
min: 1
max: 16
)
list NeoVpcSubnets {
member: NeoVpcSubnetId
}
@length(
min: 1
max: 20
)
list NestedFiltersList {
member: NestedFilters
}
list NotebookInstanceAcceleratorTypes {
member: NotebookInstanceAcceleratorType
}
@length(
min: 0
max: 1
)
list NotebookInstanceLifecycleConfigList {
member: NotebookInstanceLifecycleHook
}
list NotebookInstanceLifecycleConfigSummaryList {
member: NotebookInstanceLifecycleConfigSummary
}
list NotebookInstanceSummaryList {
member: NotebookInstanceSummary
}
@length(
min: 0
max: 50
)
list OutputParameterList {
member: OutputParameter
}
@length(
min: 0
max: 50
)
list ParameterList {
member: Parameter
}
@length(
min: 1
max: 30
)
list ParameterValues {
member: ParameterValue
}
@length(
min: 1
max: 5
)
list ParentHyperParameterTuningJobs {
member: ParentHyperParameterTuningJob
}
list Parents {
member: Parent
}
@length(
min: 1
)
list PendingProductionVariantSummaryList {
member: PendingProductionVariantSummary
}
@length(
min: 1
)
list Phases {
member: Phase
}
@length(
min: 0
max: 100
)
list PipelineExecutionStepList {
member: PipelineExecutionStep
}
@length(
min: 0
max: 100
)
list PipelineExecutionSummaryList {
member: PipelineExecutionSummary
}
@length(
min: 0
max: 100
)
list PipelineSummaryList {
member: PipelineSummary
}
@length(
min: 0
max: 10
)
list ProcessingInputs {
member: ProcessingInput
}
list ProcessingJobSummaries {
member: ProcessingJobSummary
}
@length(
min: 0
max: 10
)
list ProcessingOutputs {
member: ProcessingOutput
}
@length(
min: 1
max: 10
)
list ProductionVariantList {
member: ProductionVariant
}
@length(
min: 0
max: 5
)
list ProductionVariantStatusList {
member: ProductionVariantStatus
}
@length(
min: 1
)
list ProductionVariantSummaryList {
member: ProductionVariantSummary
}
list ProductListings {
member: String
}
@length(
min: 0
max: 20
)
list ProfilerRuleConfigurations {
member: ProfilerRuleConfiguration
}
@length(
min: 0
max: 20
)
list ProfilerRuleEvaluationStatuses {
member: ProfilerRuleEvaluationStatus
}
list ProjectSummaryList {
member: ProjectSummary
}
list PropertyNameSuggestionList {
member: PropertyNameSuggestion
}
list ProvisioningParameters {
member: ProvisioningParameter
}
@length(
min: 0
max: 1
)
list QueryLineageStartArns {
member: AssociationEntityArn
}
@length(
min: 0
max: 4
)
list QueryLineageTypes {
member: LineageType
}
@length(
min: 0
max: 5
)
list QueryTypes {
member: String40
}
list RealtimeInferenceInstanceTypes {
member: ProductionVariantInstanceType
}
list RecommendationJobSupportedContentTypes {
member: String
}
list RecommendationJobSupportedInstanceTypes {
member: String
}
@length(
min: 1
max: 5
)
list RecommendationJobVpcSecurityGroupIds {
member: RecommendationJobVpcSecurityGroupId
}
@length(
min: 1
max: 16
)
list RecommendationJobVpcSubnets {
member: RecommendationJobVpcSubnetId
}
list RenderingErrorList {
member: RenderingError
}
list ResponseMIMETypes {
member: ResponseMIMEType
}
list SageMakerImageVersionAliases {
member: SageMakerImageVersionAlias
}
@length(
min: 1
max: 20
)
list SearchExpressionList {
member: SearchExpression
}
list SearchResultsList {
member: SearchRecord
}
list SecondaryStatusTransitions {
member: SecondaryStatusTransition
}
@length(
min: 0
max: 5
)
list SecurityGroupIds {
member: SecurityGroupId
}
@length(
min: 1
max: 1
)
list ShadowModelVariantConfigList {
member: ShadowModelVariantConfig
}
@length(
min: 1
max: 1
)
list SourceAlgorithmList {
member: SourceAlgorithm
}
list SpaceList {
member: SpaceDetails
}
list StudioLifecycleConfigsList {
member: StudioLifecycleConfigDetails
}
@length(
min: 1
max: 16
)
list Subnets {
member: SubnetId
}
list SubscribedWorkteams {
member: SubscribedWorkteam
}
@length(
min: 1
max: 50
)
list TagKeyList {
member: TagKey
}
@length(
min: 0
max: 50
)
list TagList {
member: Tag
}
@length(
min: 1
max: 5
)
list TaskKeywords {
member: TaskKeyword
}
@length(
min: 1
max: 100
)
list TrainingContainerArguments {
member: TrainingContainerArgument
}
@length(
min: 1
max: 10
)
list TrainingContainerEntrypoint {
member: TrainingContainerEntrypointString
}
list TrainingInstanceTypes {
member: TrainingInstanceType
}
list TrainingJobSummaries {
member: TrainingJobSummary
}
@length(
min: 1
)
list TransformInstanceTypes {
member: TransformInstanceType
}
list TransformJobSummaries {
member: TransformJobSummary
}
list TrialComponentMetricSummaries {
member: TrialComponentMetricSummary
}
list TrialComponentSimpleSummaries {
member: TrialComponentSimpleSummary
}
list TrialComponentSources {
member: TrialComponentSource
}
list TrialComponentSummaries {
member: TrialComponentSummary
}
list TrialSummaries {
member: TrialSummary
}
list UserProfileList {
member: UserProfileDetails
}
@length(
min: 0
max: 3
)
list VariantPropertyList {
member: VariantProperty
}
list Vertices {
member: Vertex
}
@length(
min: 1
max: 5
)
list VpcSecurityGroupIds {
member: SecurityGroupId
}
list Workforces {
member: Workforce
}
@length(
min: 1
max: 5
)
list WorkforceSecurityGroupIds {
member: WorkforceSecurityGroupId
}
@length(
min: 1
max: 16
)
list WorkforceSubnets {
member: WorkforceSubnetId
}
list Workteams {
member: Workteam
}
map BatchDescribeModelPackageErrorMap {
key: ModelPackageArn
value: BatchDescribeModelPackageError
}
@length(
min: 0
max: 20
)
map CollectionParameters {
key: ConfigKey
value: ConfigValue
}
@length(
min: 1
max: 50
)
map CustomerMetadataMap {
key: CustomerMetadataKey
value: CustomerMetadataValue
}
@length(
min: 0
max: 16
)
map EnvironmentMap {
key: EnvironmentKey
value: EnvironmentValue
}
@length(
min: 0
max: 20
)
map HookParameters {
key: ConfigKey
value: ConfigValue
}
@length(
min: 0
max: 100
)
map HyperParameters {
key: HyperParameterKey
value: HyperParameterValue
}
@length(
min: 0
max: 48
)
map HyperParameterTrainingJobEnvironmentMap {
key: HyperParameterTrainingJobEnvironmentKey
value: HyperParameterTrainingJobEnvironmentValue
}
@length(
min: 0
max: 30
)
map LineageEntityParameters {
key: StringParameterValue
value: StringParameterValue
}
map ModelPackageSummaries {
key: ModelPackageArn
value: BatchDescribeModelPackageSummary
}
@length(
min: 1
max: 2
)
map ModelVariantActionMap {
key: ModelVariantName
value: ModelVariantAction
}
@length(
min: 0
max: 50
)
map MonitoringEnvironmentMap {
key: ProcessingEnvironmentKey
value: ProcessingEnvironmentValue
}
@length(
min: 0
max: 100
)
map ProcessingEnvironmentMap {
key: ProcessingEnvironmentKey
value: ProcessingEnvironmentValue
}
@length(
min: 0
max: 20
)
map ProfilingParameters {
key: ConfigKey
value: ConfigValue
}
@length(
min: 0
max: 5
)
map QueryProperties {
key: String256
value: String256
}
@length(
min: 0
max: 100
)
map RuleParameters {
key: ConfigKey
value: ConfigValue
}
@length(
min: 0
max: 48
)
map TrainingEnvironmentMap {
key: TrainingEnvironmentKey
value: TrainingEnvironmentValue
}
@length(
min: 0
max: 16
)
map TransformEnvironmentMap {
key: TransformEnvironmentKey
value: TransformEnvironmentValue
}
@length(
min: 0
max: 30
)
map TrialComponentArtifacts {
key: TrialComponentKey64
value: TrialComponentArtifact
}
@length(
min: 0
max: 150
)
map TrialComponentParameters {
key: TrialComponentKey256
value: TrialComponentParameterValue
}
@length(
min: 0
max: 256
)
@pattern(".*")
string Accept
@pattern("^\\d+$")
string AccountId
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:action/")
string ActionArn
enum ActionStatus {
UNKNOWN = "Unknown"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 1
max: 255
)
@pattern("^(?!\\s*$).+$")
string AlarmName
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:algorithm/")
string AlgorithmArn
@length(
min: 0
max: 255
)
@pattern(".*")
string AlgorithmImage
enum AlgorithmSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum AlgorithmStatus {
PENDING = "Pending"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
DELETING = "Deleting"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:app/")
string AppArn
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:app-image-config/")
string AppImageConfigArn
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string AppImageConfigName
enum AppImageConfigSortKey {
CreationTime
LastModifiedTime
Name
}
enum AppInstanceType {
SYSTEM = "system"
ML_T3_MICRO = "ml.t3.micro"
ML_T3_SMALL = "ml.t3.small"
ML_T3_MEDIUM = "ml.t3.medium"
ML_T3_LARGE = "ml.t3.large"
ML_T3_XLARGE = "ml.t3.xlarge"
ML_T3_2XLARGE = "ml.t3.2xlarge"
ML_M5_LARGE = "ml.m5.large"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_8XLARGE = "ml.m5.8xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_16XLARGE = "ml.m5.16xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_M5D_LARGE = "ml.m5d.large"
ML_M5D_XLARGE = "ml.m5d.xlarge"
ML_M5D_2XLARGE = "ml.m5d.2xlarge"
ML_M5D_4XLARGE = "ml.m5d.4xlarge"
ML_M5D_8XLARGE = "ml.m5d.8xlarge"
ML_M5D_12XLARGE = "ml.m5d.12xlarge"
ML_M5D_16XLARGE = "ml.m5d.16xlarge"
ML_M5D_24XLARGE = "ml.m5d.24xlarge"
ML_C5_LARGE = "ml.c5.large"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_12XLARGE = "ml.c5.12xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_C5_24XLARGE = "ml.c5.24xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_P3DN_24XLARGE = "ml.p3dn.24xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
ML_R5_LARGE = "ml.r5.large"
ML_R5_XLARGE = "ml.r5.xlarge"
ML_R5_2XLARGE = "ml.r5.2xlarge"
ML_R5_4XLARGE = "ml.r5.4xlarge"
ML_R5_8XLARGE = "ml.r5.8xlarge"
ML_R5_12XLARGE = "ml.r5.12xlarge"
ML_R5_16XLARGE = "ml.r5.16xlarge"
ML_R5_24XLARGE = "ml.r5.24xlarge"
ML_G5_XLARGE = "ml.g5.xlarge"
ML_G5_2XLARGE = "ml.g5.2xlarge"
ML_G5_4XLARGE = "ml.g5.4xlarge"
ML_G5_8XLARGE = "ml.g5.8xlarge"
ML_G5_16XLARGE = "ml.g5.16xlarge"
ML_G5_12XLARGE = "ml.g5.12xlarge"
ML_G5_24XLARGE = "ml.g5.24xlarge"
ML_G5_48XLARGE = "ml.g5.48xlarge"
}
@default(false)
boolean AppManaged
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string AppName
enum AppNetworkAccessType {
PublicInternetOnly
VpcOnly
}
@length(
min: 0
max: 1024
)
@pattern(".*")
string ApprovalDescription
enum AppSecurityGroupManagement {
Service
Customer
}
enum AppSortKey {
CreationTime
}
enum AppStatus {
Deleted
Deleting
Failed
InService
Pending
}
enum AppType {
JupyterServer
KernelGateway
TensorBoard
RStudioServerPro
RSessionGateway
}
@length(
min: 1
max: 170
)
@pattern("^(arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:[a-z\\-]*\\/)?([a-zA-Z0-9]([a-zA-Z0-9-]){0,62})(?The name of the data catalog used in Athena query execution.
@length(
min: 1
max: 256
)
@pattern("^[\\u0020-\\uD7FF\\uE000-\\uFFFD\\uD800\\uDC00-\\uDBFF\\uDFFF\\t]*$")
string AthenaCatalog
/// The name of the database used in the Athena query execution.
@length(
min: 1
max: 255
)
@pattern(".*")
string AthenaDatabase
/// The SQL query statements, to be executed.
@length(
min: 1
max: 4096
)
@pattern("^[\\s\\S]+$")
string AthenaQueryString
/// The compression used for Athena query results.
enum AthenaResultCompressionType {
GZIP
SNAPPY
ZLIB
}
/// The data storage format for Athena query results.
enum AthenaResultFormat {
PARQUET
ORC
AVRO
JSON
TEXTFILE
}
/// The name of the workgroup in which the Athena query is being started.
@length(
min: 1
max: 128
)
@pattern("^[a-zA-Z0-9._-]+$")
string AthenaWorkGroup
@length(
min: 1
max: 256
)
@pattern("^.+$")
string AttributeName
enum AuthMode {
SSO
IAM
}
@default(false)
boolean AutoGenerateEndpointName
enum AutoMLChannelType {
TRAINING = "training"
VALIDATION = "validation"
}
@length(
min: 0
max: 1024
)
string AutoMLFailureReason
@length(
min: 1
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:automl-job/")
string AutoMLJobArn
@length(
min: 1
max: 32
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}$")
string AutoMLJobName
enum AutoMLJobObjectiveType {
MAXIMIZE = "Maximize"
MINIMIZE = "Minimize"
}
enum AutoMLJobSecondaryStatus {
STARTING = "Starting"
ANALYZING_DATA = "AnalyzingData"
FEATURE_ENGINEERING = "FeatureEngineering"
MODEL_TUNING = "ModelTuning"
MAX_CANDIDATES_REACHED = "MaxCandidatesReached"
FAILED = "Failed"
STOPPED = "Stopped"
MAX_AUTO_ML_JOB_RUNTIME_REACHED = "MaxAutoMLJobRuntimeReached"
STOPPING = "Stopping"
CANDIDATE_DEFINITIONS_GENERATED = "CandidateDefinitionsGenerated"
GENERATING_EXPLAINABILITY_REPORT = "GeneratingExplainabilityReport"
COMPLETED = "Completed"
EXPLAINABILITY_ERROR = "ExplainabilityError"
DEPLOYING_MODEL = "DeployingModel"
MODEL_DEPLOYMENT_ERROR = "ModelDeploymentError"
GENERATING_MODEL_INSIGHTS_REPORT = "GeneratingModelInsightsReport"
MODEL_INSIGHTS_ERROR = "ModelInsightsError"
}
enum AutoMLJobStatus {
COMPLETED = "Completed"
IN_PROGRESS = "InProgress"
FAILED = "Failed"
STOPPED = "Stopped"
STOPPING = "Stopping"
}
@default(0)
@range(
min: 1
max: 100
)
integer AutoMLMaxResults
enum AutoMLMetricEnum {
ACCURACY = "Accuracy"
MSE
F1
F1_MACRO = "F1macro"
AUC
RMSE
MAE
R2
BALANCED_ACCURACY = "BalancedAccuracy"
PRECISION = "Precision"
PRECISION_MACRO = "PrecisionMacro"
RECALL = "Recall"
RECALL_MACRO = "RecallMacro"
}
enum AutoMLMetricExtendedEnum {
ACCURACY = "Accuracy"
MSE
F1
F1_MACRO = "F1macro"
AUC
RMSE
MAE
R2
BALANCED_ACCURACY = "BalancedAccuracy"
PRECISION = "Precision"
PRECISION_MACRO = "PrecisionMacro"
RECALL = "Recall"
RECALL_MACRO = "RecallMacro"
LogLoss
INFERENCE_LATENCY = "InferenceLatency"
}
enum AutoMLMode {
AUTO
ENSEMBLING
HYPERPARAMETER_TUNING
}
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9\\-]+$")
string AutoMLNameContains
enum AutoMLS3DataType {
MANIFEST_FILE = "ManifestFile"
S3_PREFIX = "S3Prefix"
}
enum AutoMLSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum AutoMLSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum AwsManagedHumanLoopRequestSource {
REKOGNITION_DETECT_MODERATION_LABELS_IMAGE_V3 = "AWS/Rekognition/DetectModerationLabels/Image/V3"
TEXTRACT_ANALYZE_DOCUMENT_FORMS_V1 = "AWS/Textract/AnalyzeDocument/Forms/V1"
}
enum BatchStrategy {
MULTI_RECORD = "MultiRecord"
SINGLE_RECORD = "SingleRecord"
}
@range(
min: 1
)
integer BillableTimeInSeconds
@length(
min: 0
max: 1024
)
string BlockedReason
@default(false)
boolean Boolean
enum BooleanOperator {
AND = "And"
OR = "Or"
}
@length(
min: 1
max: 1024
)
@pattern("^[^ ~^:?*\\[]+$")
string Branch
@length(
min: 3
max: 63
)
@pattern("^[a-z0-9][\\.\\-a-z0-9]{1,61}[a-z0-9]$")
string BucketName
@length(
min: 10
max: 10
)
@pattern("^[a-zA-Z0-9]+$")
string CallbackToken
@length(
min: 1
)
string CandidateDefinitionNotebookLocation
@length(
min: 1
max: 64
)
string CandidateName
enum CandidateSortBy {
CreationTime
Status
FinalObjectiveMetricValue
}
enum CandidateStatus {
COMPLETED = "Completed"
IN_PROGRESS = "InProgress"
FAILED = "Failed"
STOPPED = "Stopped"
STOPPING = "Stopping"
}
@length(
min: 1
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:.*/")
string CandidateStepArn
@length(
min: 1
max: 64
)
string CandidateStepName
enum CandidateStepType {
TRAINING = "AWS::SageMaker::TrainingJob"
TRANSFORM = "AWS::SageMaker::TransformJob"
PROCESSING = "AWS::SageMaker::ProcessingJob"
}
enum CapacitySizeType {
INSTANCE_COUNT
CAPACITY_PERCENT
}
@range(
min: 1
)
integer CapacitySizeValue
enum CaptureMode {
INPUT = "Input"
OUTPUT = "Output"
}
enum CaptureStatus {
STARTED = "Started"
STOPPED = "Stopped"
}
@length(
min: 1
max: 255
)
@pattern("^[\\u0020-\\uD7FF\\uE000-\\uFFFD\\uD800\\uDC00-\\uDBFF\\uDFFF\\t]*$")
string Catalog
@default(0)
@range(
min: 0
max: 99
)
integer Cents
@default(false)
boolean CertifyForMarketplace
@length(
min: 1
max: 64
)
@pattern("^[A-Za-z0-9\\.\\-_]+$")
string ChannelName
@length(
min: 4
max: 64
)
@pattern("^(^(([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])(\\/(3[0-2]|[1-2][0-9]|[0-9]))$)|(^s*((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]d|1dd|[1-9]?d)(.(25[0-5]|2[0-4]d|1dd|[1-9]?d)){3}))|:)))(%.+)?s*(\\/(12[0-8]|1[0-1][0-9]|[1-9][0-9]|[0-9]))$)$")
string Cidr
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyContentTemplate
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyEnableExplanations
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyFeaturesAttribute
enum ClarifyFeatureType {
NUMERICAL = "numerical"
CATEGORICAL = "categorical"
TEXT = "text"
}
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyHeader
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyLabelAttribute
@range(
min: 0
)
integer ClarifyLabelIndex
@range(
min: 1
max: 25
)
integer ClarifyMaxPayloadInMB
@range(
min: 1
)
integer ClarifyMaxRecordCount
@length(
min: 0
max: 255
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*\\/[a-zA-Z0-9](-*[a-zA-Z0-9+.])*$")
string ClarifyMimeType
@length(
min: 1
max: 64
)
@pattern(".*")
string ClarifyProbabilityAttribute
@range(
min: 0
)
integer ClarifyProbabilityIndex
@length(
min: 1
max: 4096
)
@pattern("^[\\s\\S]+$")
string ClarifyShapBaseline
@range(
min: 1
)
integer ClarifyShapNumberOfSamples
integer ClarifyShapSeed
boolean ClarifyShapUseLogit
enum ClarifyTextGranularity {
TOKEN = "token"
SENTENCE = "sentence"
PARAGRAPH = "paragraph"
}
enum ClarifyTextLanguage {
AFRIKAANS = "af"
ALBANIAN = "sq"
ARABIC = "ar"
ARMENIAN = "hy"
BASQUE = "eu"
BENGALI = "bn"
BULGARIAN = "bg"
CATALAN = "ca"
CHINESE = "zh"
CROATIAN = "hr"
CZECH = "cs"
DANISH = "da"
DUTCH = "nl"
ENGLISH = "en"
ESTONIAN = "et"
FINNISH = "fi"
FRENCH = "fr"
GERMAN = "de"
GREEK = "el"
GUJARATI = "gu"
HEBREW = "he"
HINDI = "hi"
HUNGARIAN = "hu"
ICELANDIC = "is"
INDONESIAN = "id"
IRISH = "ga"
ITALIAN = "it"
KANNADA = "kn"
KYRGYZ = "ky"
LATVIAN = "lv"
LITHUANIAN = "lt"
LUXEMBOURGISH = "lb"
MACEDONIAN = "mk"
MALAYALAM = "ml"
MARATHI = "mr"
NEPALI = "ne"
NORWEGIAN_BOKMAL = "nb"
PERSIAN = "fa"
POLISH = "pl"
PORTUGUESE = "pt"
ROMANIAN = "ro"
RUSSIAN = "ru"
SANSKRIT = "sa"
SERBIAN = "sr"
SETSWANA = "tn"
SINHALA = "si"
SLOVAK = "sk"
SLOVENIAN = "sl"
SPANISH = "es"
SWEDISH = "sv"
TAGALOG = "tl"
TAMIL = "ta"
TATAR = "tt"
TELUGU = "te"
TURKISH = "tr"
UKRAINIAN = "uk"
URDU = "ur"
YORUBA = "yo"
LIGURIAN = "lij"
MULTI_LANGUAGE = "xx"
}
@length(
min: 1
max: 1024
)
@pattern("^[ -~]+$")
string ClientId
@length(
min: 1
max: 1024
)
@pattern("^[ -~]+$")
@sensitive
string ClientSecret
@length(
min: 1
max: 36
)
@pattern("^[a-zA-Z0-9-]+$")
string ClientToken
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:code-repository/")
string CodeRepositoryArn
@length(
min: 0
max: 1024
)
@pattern("^[a-zA-Z0-9-]+$")
string CodeRepositoryContains
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string CodeRepositoryNameContains
@length(
min: 1
max: 1024
)
@pattern("^https://([^/]+)/?(.*)$|^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string CodeRepositoryNameOrUrl
enum CodeRepositorySortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
LAST_MODIFIED_TIME = "LastModifiedTime"
}
enum CodeRepositorySortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
@length(
min: 1
max: 128
)
@pattern("^[\\p{L}\\p{M}\\p{S}\\p{N}\\p{P}]+$")
string CognitoUserGroup
@length(
min: 1
max: 55
)
@pattern("^[\\w-]+_[0-9a-zA-Z]+$")
string CognitoUserPool
@length(
min: 1
max: 256
)
@pattern(".*")
string CollectionName
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:compilation-job/")
string CompilationJobArn
enum CompilationJobStatus {
INPROGRESS
COMPLETED
FAILED
STARTING
STOPPING
STOPPED
}
@length(
min: 3
max: 1024
)
@pattern(".*")
string CompilerOptions
enum CompleteOnConvergence {
DISABLED = "Disabled"
ENABLED = "Enabled"
}
enum CompressionType {
NONE = "None"
GZIP = "Gzip"
}
enum ConditionOutcome {
TRUE = "True"
FALSE = "False"
}
@length(
min: 1
max: 256
)
@pattern(".*")
string ConfigKey
@length(
min: 0
max: 256
)
@pattern(".*")
string ConfigValue
@length(
min: 0
max: 256
)
@pattern(".*")
string ContainerArgument
@length(
min: 0
max: 256
)
@pattern(".*")
string ContainerEntrypointString
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string ContainerHostname
@length(
min: 0
max: 255
)
@pattern("^[\\S]+$")
string ContainerImage
enum ContainerMode {
SINGLE_MODEL = "SingleModel"
MULTI_MODEL = "MultiModel"
}
enum ContentClassifier {
FREE_OF_PERSONALLY_IDENTIFIABLE_INFORMATION = "FreeOfPersonallyIdentifiableInformation"
FREE_OF_ADULT_CONTENT = "FreeOfAdultContent"
}
@length(
min: 0
max: 72
)
@pattern("^[Ss][Hh][Aa]256:[0-9a-fA-F]{64}$")
string ContentDigest
@length(
min: 0
max: 256
)
@pattern(".*")
string ContentType
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:context/")
string ContextArn
timestamp CreationTime
@length(
min: 1
max: 256
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*\\/[a-zA-Z0-9](-*[a-zA-Z0-9.])*$")
string CsvContentType
@length(
min: 1
max: 128
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:\\/=+\\-@]*)${1,128}$")
string CustomerMetadataKey
@length(
min: 1
max: 256
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:\\/=+\\-@]*)${1,256}$")
string CustomerMetadataValue
@length(
min: 1
max: 255
)
@pattern("^[\\u0020-\\uD7FF\\uE000-\\uFFFD\\uD800\\uDC00-\\uDBFF\\uDFFF\\t]*$")
string Database
enum DataDistributionType {
FULLYREPLICATED = "FullyReplicated"
SHARDEDBYS3KEY = "ShardedByS3Key"
}
@length(
min: 1
)
string DataExplorationNotebookLocation
@length(
min: 1
max: 1024
)
@pattern("^[\\S\\s]+$")
string DataInputConfig
@default(0)
@range(
min: 0
max: 65535
)
integer DefaultGid
@default(0)
@range(
min: 0
max: 65535
)
integer DefaultUid
@length(
min: 0
max: 1023
)
@pattern(".*")
string DependencyCopyPath
@length(
min: 0
max: 1023
)
@pattern(".*")
string DependencyOriginPath
@default(0)
@range(
max: 10
)
integer DeploymentStageMaxResults
@length(
min: 0
max: 128
)
string Description
@length(
min: 0
max: 512
)
@pattern("^(https|s3)://([^/])/?(.*)$")
string DestinationS3Uri
enum DetailedAlgorithmStatus {
NOT_STARTED = "NotStarted"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
}
enum DetailedModelPackageStatus {
NOT_STARTED = "NotStarted"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
}
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:[a-z\\-]*:[a-z\\-]*:\\d{12}:[a-z\\-]*/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string DeviceArn
enum DeviceDeploymentStatus {
ReadyToDeploy = "READYTODEPLOY"
InProgress = "INPROGRESS"
Deployed = "DEPLOYED"
Failed = "FAILED"
Stopping = "STOPPING"
Stopped = "STOPPED"
}
@length(
min: 1
max: 40
)
@pattern("^[\\S\\s]+$")
string DeviceDescription
@pattern("^arn:aws[a-z\\-]*:iam::\\d{12}:device-fleet/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string DeviceFleetArn
@length(
min: 1
max: 800
)
@pattern("^[\\S\\s]+$")
string DeviceFleetDescription
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string DeviceName
enum DeviceSubsetType {
Percentage = "PERCENTAGE"
Selection = "SELECTION"
NameContains = "NAMECONTAINS"
}
enum DirectInternetAccess {
ENABLED = "Enabled"
DISABLED = "Disabled"
}
enum Direction {
BOTH = "Both"
ASCENDANTS = "Ascendants"
DESCENDANTS = "Descendants"
}
@length(
min: 0
max: 4096
)
@pattern(".*")
string DirectoryPath
@default(false)
boolean DisableProfiler
@default(false)
boolean DisassociateAdditionalCodeRepositories
@default(false)
boolean DisassociateDefaultCodeRepository
@default(false)
boolean DisassociateNotebookInstanceAcceleratorTypes
@default(false)
boolean DisassociateNotebookInstanceLifecycleConfig
@length(
min: 5
max: 14
)
@pattern("^\\d{1,4}.\\d{1,4}.\\d{1,4}$")
string DocumentSchemaVersion
@default(0)
@range(
min: 0
max: 2
)
integer Dollars
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:domain/")
string DomainArn
@length(
min: 0
max: 63
)
string DomainId
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string DomainName
enum DomainStatus {
Deleting
Failed
InService
Pending
Updating
Update_Failed
Delete_Failed
}
double DoubleParameterValue
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z\\-]*:\\d{12}:edge-deployment/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string EdgeDeploymentPlanArn
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z\\-]*:\\d{12}:edge-packaging-job/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string EdgePackagingJobArn
enum EdgePackagingJobStatus {
Starting = "STARTING"
InProgress = "INPROGRESS"
Completed = "COMPLETED"
Failed = "FAILED"
Stopping = "STOPPING"
Stopped = "STOPPED"
}
@length(
min: 20
max: 2048
)
string EdgePresetDeploymentArtifact
enum EdgePresetDeploymentStatus {
Completed = "COMPLETED"
Failed = "FAILED"
}
enum EdgePresetDeploymentType {
GreengrassV2Component
}
@length(
min: 1
max: 30
)
@pattern("^[a-zA-Z0-9\\ \\_\\.]+$")
string EdgeVersion
@length(
min: 0
max: 10
)
@pattern("^\\d+$")
string EfsUid
@default(false)
boolean EnableCapture
boolean EnableIotRoleAlias
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:endpoint/")
string EndpointArn
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:endpoint-config/")
string EndpointConfigArn
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string EndpointConfigName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string EndpointConfigNameContains
enum EndpointConfigSortKey {
Name
CreationTime
}
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string EndpointName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string EndpointNameContains
enum EndpointSortKey {
Name
CreationTime
Status
}
enum EndpointStatus {
OUT_OF_SERVICE = "OutOfService"
CREATING = "Creating"
UPDATING = "Updating"
SYSTEM_UPDATING = "SystemUpdating"
ROLLING_BACK = "RollingBack"
IN_SERVICE = "InService"
DELETING = "Deleting"
FAILED = "Failed"
}
@length(
min: 0
max: 1024
)
@pattern("^[\\p{L}\\p{M}\\p{Z}\\p{S}\\p{N}\\p{P}]*$")
string EntityDescription
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string EntityName
@length(
min: 0
max: 1024
)
@pattern("^[a-zA-Z_][a-zA-Z0-9_]*$")
string EnvironmentKey
@length(
min: 0
max: 1024
)
@pattern("^[\\S\\s]*$")
string EnvironmentValue
enum ExecutionRoleIdentityConfig {
USER_PROFILE_NAME
DISABLED
}
enum ExecutionStatus {
PENDING = "Pending"
COMPLETED = "Completed"
COMPLETED_WITH_VIOLATIONS = "CompletedWithViolations"
IN_PROGRESS = "InProgress"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 0
max: 1024
)
@pattern("^[\\S\\s]*$")
string ExitMessage
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:experiment/")
string ExperimentArn
@length(
min: 0
max: 3072
)
@pattern(".*")
string ExperimentDescription
@length(
min: 1
max: 120
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,119}$")
string ExperimentEntityName
@length(
min: 1
max: 256
)
@pattern("^(arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:(experiment|experiment-trial|experiment-trial-component|artifact|action|context)\\/)?([a-zA-Z0-9](-*[a-zA-Z0-9]){0,119})$")
string ExperimentEntityNameOrArn
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:")
string ExperimentSourceArn
@range(
min: 5
max: 300
)
integer ExpiresInSeconds
@length(
min: 1
)
string ExplainabilityLocation
enum FailureHandlingPolicy {
RollbackOnFailure = "ROLLBACK_ON_FAILURE"
DoNothing = "DO_NOTHING"
}
@length(
min: 0
max: 1024
)
string FailureReason
@length(
min: 0
max: 255
)
@pattern(".*")
string FeatureDescription
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:feature-group/")
string FeatureGroupArn
@range(
min: 1
max: 100
)
integer FeatureGroupMaxResults
@length(
min: 1
max: 64
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,63}$")
string FeatureGroupName
@length(
min: 1
max: 64
)
string FeatureGroupNameContains
enum FeatureGroupSortBy {
NAME = "Name"
FEATURE_GROUP_STATUS = "FeatureGroupStatus"
OFFLINE_STORE_STATUS = "OfflineStoreStatus"
CREATION_TIME = "CreationTime"
}
enum FeatureGroupSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum FeatureGroupStatus {
CREATING = "Creating"
CREATED = "Created"
CREATE_FAILED = "CreateFailed"
DELETING = "Deleting"
DELETE_FAILED = "DeleteFailed"
}
@length(
min: 1
max: 64
)
@pattern("^[a-zA-Z0-9]([-_]*[a-zA-Z0-9]){0,63}$")
string FeatureName
@length(
min: 1
max: 255
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:/=+\\-]*)$")
string FeatureParameterKey
@length(
min: 1
max: 255
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:/=+\\-]*)$")
string FeatureParameterValue
enum FeatureStatus {
Enabled = "ENABLED"
Disabled = "DISABLED"
}
enum FeatureType {
INTEGRAL = "Integral"
FRACTIONAL = "Fractional"
STRING = "String"
}
enum FileSystemAccessMode {
RW = "rw"
RO = "ro"
}
@length(
min: 11
)
@pattern(".*")
string FileSystemId
enum FileSystemType {
EFS
FSXLUSTRE = "FSxLustre"
}
@length(
min: 1
max: 1024
)
@pattern("^.+$")
string FilterValue
@default(0)
float Float
@length(
min: 0
max: 1024
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:flow-definition/")
string FlowDefinitionArn
@length(
min: 1
max: 63
)
@pattern("^[a-z0-9](-*[a-z0-9]){0,62}$")
string FlowDefinitionName
enum FlowDefinitionStatus {
INITIALIZING = "Initializing"
ACTIVE = "Active"
FAILED = "Failed"
DELETING = "Deleting"
}
@range(
min: 1
max: 864000
)
integer FlowDefinitionTaskAvailabilityLifetimeInSeconds
@range(
min: 1
max: 3
)
integer FlowDefinitionTaskCount
@length(
min: 1
max: 255
)
@pattern("^.+$")
string FlowDefinitionTaskDescription
@length(
min: 1
max: 30
)
@pattern("^[A-Za-z0-9]+( [A-Za-z0-9]+)*$")
string FlowDefinitionTaskKeyword
@range(
min: 30
max: 28800
)
integer FlowDefinitionTaskTimeLimitInSeconds
@length(
min: 1
max: 128
)
@pattern("^[\\t\\n\\r -\\uD7FF\\uE000-\\uFFFD]*$")
string FlowDefinitionTaskTitle
enum Framework {
TENSORFLOW
KERAS
MXNET
ONNX
PYTORCH
XGBOOST
TFLITE
DARKNET
SKLEARN
}
@length(
min: 3
max: 10
)
@pattern("^[0-9]\\.[A-Za-z0-9.]+$")
string FrameworkVersion
@default(false)
boolean GenerateCandidateDefinitionsOnly
@pattern("^https://([^/]+)/?(.*)$")
string GitConfigUrl
@length(
min: 1
max: 63
)
@pattern("^[\\p{L}\\p{M}\\p{S}\\p{N}\\p{P}]+$")
string Group
@default(false)
boolean Horovod
@length(
min: 0
max: 255
)
@pattern(".*")
string HubArn
@length(
min: 0
max: 255
)
@pattern(".*")
string HubContentArn
@length(
min: 0
max: 1023
)
@pattern(".*")
string HubContentDescription
@length(
min: 0
max: 255
)
@pattern(".*")
string HubContentDisplayName
@length(
min: 0
max: 65535
)
@pattern(".*")
string HubContentDocument
@length(
min: 0
max: 65535
)
@pattern(".*")
string HubContentMarkdown
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string HubContentName
enum HubContentSortBy {
HUB_CONTENT_NAME = "HubContentName"
CREATION_TIME = "CreationTime"
HUB_CONTENT_STATUS = "HubContentStatus"
}
enum HubContentStatus {
AVAILABLE = "Available"
IMPORTING = "Importing"
DELETING = "Deleting"
IMPORT_FAILED = "ImportFailed"
DELETE_FAILED = "DeleteFailed"
}
enum HubContentType {
MODEL = "Model"
NOTEBOOK = "Notebook"
}
@length(
min: 5
max: 14
)
@pattern("^\\d{1,4}.\\d{1,4}.\\d{1,4}$")
string HubContentVersion
@length(
min: 0
max: 1023
)
@pattern(".*")
string HubDescription
@length(
min: 0
max: 255
)
@pattern(".*")
string HubDisplayName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string HubName
@length(
min: 0
max: 255
)
@pattern("^[^A-Z]*$")
string HubSearchKeyword
enum HubSortBy {
HUB_NAME = "HubName"
CREATION_TIME = "CreationTime"
HUB_STATUS = "HubStatus"
ACCOUNT_ID_OWNER = "AccountIdOwner"
}
enum HubStatus {
IN_SERVICE = "InService"
CREATING = "Creating"
UPDATING = "Updating"
DELETING = "Deleting"
CREATE_FAILED = "CreateFailed"
UPDATE_FAILED = "UpdateFailed"
DELETE_FAILED = "DeleteFailed"
}
@length(
min: 0
max: 1024
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:human-task-ui/")
string HumanTaskUiArn
@length(
min: 1
max: 63
)
@pattern("^[a-z0-9](-*[a-z0-9])*$")
string HumanTaskUiName
enum HumanTaskUiStatus {
ACTIVE = "Active"
DELETING = "Deleting"
}
@range(
min: 1
)
integer HyperbandStrategyMaxResource
@range(
min: 1
)
integer HyperbandStrategyMinResource
@length(
min: 0
max: 256
)
@pattern(".*")
string HyperParameterKey
enum HyperParameterScalingType {
AUTO = "Auto"
LINEAR = "Linear"
LOGARITHMIC = "Logarithmic"
REVERSE_LOGARITHMIC = "ReverseLogarithmic"
}
@length(
min: 1
max: 64
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,63}$")
string HyperParameterTrainingJobDefinitionName
@length(
min: 0
max: 512
)
@pattern("^[a-zA-Z_][a-zA-Z0-9_]*$")
string HyperParameterTrainingJobEnvironmentKey
@length(
min: 0
max: 512
)
@pattern("^[\\S\\s]*$")
string HyperParameterTrainingJobEnvironmentValue
enum HyperParameterTuningAllocationStrategy {
PRIORITIZED = "Prioritized"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:hyper-parameter-tuning-job/")
string HyperParameterTuningJobArn
@length(
min: 1
max: 32
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}$")
string HyperParameterTuningJobName
enum HyperParameterTuningJobObjectiveType {
MAXIMIZE = "Maximize"
MINIMIZE = "Minimize"
}
enum HyperParameterTuningJobSortByOptions {
Name
Status
CreationTime
}
enum HyperParameterTuningJobStatus {
COMPLETED = "Completed"
IN_PROGRESS = "InProgress"
FAILED = "Failed"
STOPPED = "Stopped"
STOPPING = "Stopping"
}
/// The strategy hyperparameter tuning uses to find the best combination of
/// hyperparameters for your model.
enum HyperParameterTuningJobStrategyType {
BAYESIAN = "Bayesian"
RANDOM = "Random"
HYPERBAND = "Hyperband"
GRID = "Grid"
}
enum HyperParameterTuningJobWarmStartType {
IDENTICAL_DATA_AND_ALGORITHM = "IdenticalDataAndAlgorithm"
TRANSFER_LEARNING = "TransferLearning"
}
@range(
min: 120
max: 15768000
)
integer HyperParameterTuningMaxRuntimeInSeconds
@length(
min: 0
max: 2500
)
@pattern(".*")
string HyperParameterValue
@length(
min: 32
max: 128
)
string IdempotencyToken
@length(
min: 0
max: 256
)
@pattern("^arn:aws(-[\\w]+)*:sagemaker:.+:[0-9]{12}:image/[a-z0-9]([-.]?[a-z0-9])*$")
string ImageArn
@length(
min: 1
max: 255
)
@pattern(".*")
string ImageBaseImage
@length(
min: 1
max: 255
)
string ImageContainerImage
@length(
min: 1
max: 11
)
@pattern("^(^DisplayName$)|(^Description$)$")
string ImageDeleteProperty
@length(
min: 1
max: 512
)
@pattern(".*")
string ImageDescription
@length(
min: 0
max: 72
)
@pattern("^[Ss][Hh][Aa]256:[0-9a-fA-F]{64}$")
string ImageDigest
@length(
min: 1
max: 128
)
@pattern("^\\S(.*\\S)?$")
string ImageDisplayName
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9]([-.]?[a-zA-Z0-9]){0,62}$")
string ImageName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9\\-.]+$")
string ImageNameContains
enum ImageSortBy {
CREATION_TIME
LAST_MODIFIED_TIME
IMAGE_NAME
}
enum ImageSortOrder {
ASCENDING
DESCENDING
}
enum ImageStatus {
CREATING
CREATED
CREATE_FAILED
UPDATING
UPDATE_FAILED
DELETING
DELETE_FAILED
}
@length(
min: 0
max: 255
)
@pattern(".*")
string ImageUri
@length(
min: 0
max: 256
)
@pattern("^arn:aws(-[\\w]+)*:sagemaker:.+:[0-9]{12}:image-version/[a-z0-9]([-.]?[a-z0-9])*/[0-9]+$")
string ImageVersionArn
@range(
min: 0
)
integer ImageVersionNumber
enum ImageVersionSortBy {
CREATION_TIME
LAST_MODIFIED_TIME
VERSION
}
enum ImageVersionSortOrder {
ASCENDING
DESCENDING
}
enum ImageVersionStatus {
CREATING
CREATED
CREATE_FAILED
DELETING
DELETE_FAILED
}
enum InferenceExecutionMode {
SERIAL = "Serial"
DIRECT = "Direct"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:inference-experiment/")
string InferenceExperimentArn
@length(
min: 0
max: 1024
)
@pattern(".*")
string InferenceExperimentDescription
@length(
min: 1
max: 120
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,119}$")
string InferenceExperimentName
enum InferenceExperimentStatus {
CREATING = "Creating"
CREATED = "Created"
UPDATING = "Updating"
RUNNING = "Running"
STARTING = "Starting"
STOPPING = "Stopping"
COMPLETED = "Completed"
CANCELLED = "Cancelled"
}
@length(
min: 0
max: 1024
)
@pattern(".*")
string InferenceExperimentStatusReason
enum InferenceExperimentStopDesiredState {
COMPLETED = "Completed"
CANCELLED = "Cancelled"
}
enum InferenceExperimentType {
SHADOW_MODE = "ShadowMode"
}
@length(
min: 0
max: 256
)
string InferenceImage
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string InferenceSpecificationName
@range(
min: 1
)
integer InitialNumberOfUsers
@range(
min: 1
)
integer InitialTaskCount
enum InputMode {
PIPE = "Pipe"
FILE = "File"
}
@length(
min: 1
max: 64
)
@pattern("^.+$")
string InstanceGroupName
enum InstanceType {
ML_T2_MEDIUM = "ml.t2.medium"
ML_T2_LARGE = "ml.t2.large"
ML_T2_XLARGE = "ml.t2.xlarge"
ML_T2_2XLARGE = "ml.t2.2xlarge"
ML_T3_MEDIUM = "ml.t3.medium"
ML_T3_LARGE = "ml.t3.large"
ML_T3_XLARGE = "ml.t3.xlarge"
ML_T3_2XLARGE = "ml.t3.2xlarge"
ML_M4_XLARGE = "ml.m4.xlarge"
ML_M4_2XLARGE = "ml.m4.2xlarge"
ML_M4_4XLARGE = "ml.m4.4xlarge"
ML_M4_10XLARGE = "ml.m4.10xlarge"
ML_M4_16XLARGE = "ml.m4.16xlarge"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_M5D_LARGE = "ml.m5d.large"
ML_M5D_XLARGE = "ml.m5d.xlarge"
ML_M5D_2XLARGE = "ml.m5d.2xlarge"
ML_M5D_4XLARGE = "ml.m5d.4xlarge"
ML_M5D_8XLARGE = "ml.m5d.8xlarge"
ML_M5D_12XLARGE = "ml.m5d.12xlarge"
ML_M5D_16XLARGE = "ml.m5d.16xlarge"
ML_M5D_24XLARGE = "ml.m5d.24xlarge"
ML_C4_XLARGE = "ml.c4.xlarge"
ML_C4_2XLARGE = "ml.c4.2xlarge"
ML_C4_4XLARGE = "ml.c4.4xlarge"
ML_C4_8XLARGE = "ml.c4.8xlarge"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_C5D_XLARGE = "ml.c5d.xlarge"
ML_C5D_2XLARGE = "ml.c5d.2xlarge"
ML_C5D_4XLARGE = "ml.c5d.4xlarge"
ML_C5D_9XLARGE = "ml.c5d.9xlarge"
ML_C5D_18XLARGE = "ml.c5d.18xlarge"
ML_P2_XLARGE = "ml.p2.xlarge"
ML_P2_8XLARGE = "ml.p2.8xlarge"
ML_P2_16XLARGE = "ml.p2.16xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_P3DN_24XLARGE = "ml.p3dn.24xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
ML_R5_LARGE = "ml.r5.large"
ML_R5_XLARGE = "ml.r5.xlarge"
ML_R5_2XLARGE = "ml.r5.2xlarge"
ML_R5_4XLARGE = "ml.r5.4xlarge"
ML_R5_8XLARGE = "ml.r5.8xlarge"
ML_R5_12XLARGE = "ml.r5.12xlarge"
ML_R5_16XLARGE = "ml.r5.16xlarge"
ML_R5_24XLARGE = "ml.r5.24xlarge"
ML_G5_XLARGE = "ml.g5.xlarge"
ML_G5_2XLARGE = "ml.g5.2xlarge"
ML_G5_4XLARGE = "ml.g5.4xlarge"
ML_G5_8XLARGE = "ml.g5.8xlarge"
ML_G5_16XLARGE = "ml.g5.16xlarge"
ML_G5_12XLARGE = "ml.g5.12xlarge"
ML_G5_24XLARGE = "ml.g5.24xlarge"
ML_G5_48XLARGE = "ml.g5.48xlarge"
}
@default(0)
integer Integer
@default(0)
integer IntegerValue
@range(
min: 0
max: 3
)
integer InvocationsMaxRetries
@range(
min: 1
max: 3600
)
integer InvocationsTimeoutInSeconds
@pattern("^arn:aws[a-z\\-]*:iam::\\d{12}:rolealias/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string IotRoleAlias
@range(
min: 1
)
integer JobDurationInSeconds
@length(
min: 1
)
@pattern("^.+$")
string JobReferenceCode
@length(
min: 1
max: 255
)
@pattern("^.+$")
string JobReferenceCodeContains
enum JobType {
TRAINING
INFERENCE
NOTEBOOK_KERNEL
}
enum JoinSource {
INPUT = "Input"
NONE = "None"
}
@length(
min: 1
max: 256
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*\\/[a-zA-Z0-9](-*[a-zA-Z0-9.])*$")
string JsonContentType
@length(
min: 0
max: 63
)
string JsonPath
@range(
min: 0
max: 3600
)
integer KeepAlivePeriodInSeconds
@length(
min: 0
max: 1024
)
string KernelDisplayName
@length(
min: 0
max: 1024
)
string KernelName
@length(
min: 1
max: 1024
)
@pattern("^.+$")
string Key
@length(
min: 0
max: 2048
)
@pattern(".*")
string KmsKeyId
@length(
min: 1
max: 127
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,126}$")
string LabelAttributeName
@default(0)
@range(
min: 0
)
integer LabelCounter
@length(
min: 0
max: 2048
)
@pattern("^arn:")
string LabelingJobAlgorithmSpecificationArn
@length(
min: 0
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:labeling-job/")
string LabelingJobArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string LabelingJobName
enum LabelingJobStatus {
INITIALIZING = "Initializing"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 0
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:lambda:[a-z0-9\\-]*:[0-9]{12}:function:")
string LambdaFunctionArn
timestamp LastModifiedTime
enum LastUpdateStatusValue {
SUCCESSFUL = "Successful"
FAILED = "Failed"
IN_PROGRESS = "InProgress"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:lineage-group/")
string LineageGroupArn
@length(
min: 1
max: 256
)
@pattern("^(arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:lineage-group\\/)?([a-zA-Z0-9](-*[a-zA-Z0-9]){0,119})$")
string LineageGroupNameOrArn
enum LineageType {
TRIAL_COMPONENT = "TrialComponent"
ARTIFACT = "Artifact"
CONTEXT = "Context"
ACTION = "Action"
}
enum ListCompilationJobsSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum ListDeviceFleetsSortBy {
Name = "NAME"
CreationTime = "CREATION_TIME"
LastModifiedTime = "LAST_MODIFIED_TIME"
}
enum ListEdgeDeploymentPlansSortBy {
Name = "NAME"
DeviceFleetName = "DEVICE_FLEET_NAME"
CreationTime = "CREATION_TIME"
LastModifiedTime = "LAST_MODIFIED_TIME"
}
enum ListEdgePackagingJobsSortBy {
Name = "NAME"
ModelName = "MODEL_NAME"
CreationTime = "CREATION_TIME"
LastModifiedTime = "LAST_MODIFIED_TIME"
EdgePackagingJobStatus = "STATUS"
}
enum ListInferenceRecommendationsJobsSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum ListLabelingJobsForWorkteamSortByOptions {
CREATION_TIME = "CreationTime"
}
@default(0)
@range(
max: 100
)
integer ListMaxResults
@range(
min: 50
)
integer ListTagsMaxResults
enum ListWorkforcesSortByOptions {
Name
CreateDate
}
enum ListWorkteamsSortByOptions {
Name
CreateDate
}
@default(0)
long Long
@range(
min: 1
)
integer MaxAutoMLJobRuntimeInSeconds
@range(
min: 1
)
integer MaxCandidates
@range(
min: 1
max: 1000
)
integer MaxConcurrentInvocationsPerInstance
@range(
min: 1
max: 5000
)
integer MaxConcurrentTaskCount
@range(
min: 0
)
integer MaxConcurrentTransforms
@range(
min: 1
)
integer MaxHumanLabeledObjectCount
@range(
min: 600
max: 14400
)
integer MaximumExecutionTimeoutInSeconds
@default(0)
@range(
min: 1
max: 30
)
integer MaximumRetryAttempts
@range(
min: 1
)
integer MaxNumberOfTests
@range(
min: 1
)
integer MaxNumberOfTrainingJobs
@range(
min: 3
)
integer MaxNumberOfTrainingJobsNotImproving
@default(0)
@range(
min: 1
)
integer MaxParallelExecutionSteps
@range(
min: 1
)
integer MaxParallelOfTests
@default(0)
@range(
min: 1
)
integer MaxParallelTrainingJobs
@range(
min: 0
)
integer MaxPayloadInMB
@range(
min: 1
max: 100
)
integer MaxPercentageOfInputDatasetLabeled
@range(
min: 1
max: 100
)
integer MaxResults
@default(0)
@range(
min: 1
)
integer MaxRuntimeInSeconds
@range(
min: 1
)
integer MaxRuntimePerTrainingJobInSeconds
@range(
min: 1
)
integer MaxWaitTimeInSeconds
@length(
min: 0
max: 64
)
@pattern("^[-\\w]+\\/[-\\w+]+$")
string MediaType
@length(
min: 0
max: 1024
)
@pattern(".*")
string MetadataPropertyValue
@length(
min: 1
max: 255
)
@pattern("^.+$")
string MetricName
@length(
min: 1
max: 500
)
@pattern("^.+$")
string MetricRegex
enum MetricSetSource {
TRAIN = "Train"
VALIDATION = "Validation"
TEST = "Test"
}
@default(0)
float MetricValue
@length(
min: 0
max: 1
)
@pattern("^1|2$")
string MinimumInstanceMetadataServiceVersion
@length(
min: 1
max: 128
)
@pattern("^[a-zA-Z]+ ?\\d+\\.\\d+(\\.\\d+)?$")
string MLFramework
enum ModelApprovalStatus {
APPROVED = "Approved"
REJECTED = "Rejected"
PENDING_MANUAL_APPROVAL = "PendingManualApproval"
}
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:model/")
string ModelArn
enum ModelCacheSetting {
ENABLED = "Enabled"
DISABLED = "Disabled"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]{9,16}:[0-9]{12}:model-card/[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string ModelCardArn
@length(
min: 0
max: 100000
)
@pattern(".*")
@sensitive
string ModelCardContent
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]{9,16}:[0-9]{12}:model-card/[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}/export-job/[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string ModelCardExportJobArn
/// Attribute by which to sort returned export jobs.
enum ModelCardExportJobSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum ModelCardExportJobSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum ModelCardExportJobStatus {
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
}
enum ModelCardProcessingStatus {
DELETE_INPROGRESS = "DeleteInProgress"
DELETE_PENDING = "DeletePending"
CONTENT_DELETED = "ContentDeleted"
EXPORTJOBS_DELETED = "ExportJobsDeleted"
DELETE_COMPLETED = "DeleteCompleted"
DELETE_FAILED = "DeleteFailed"
}
enum ModelCardSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum ModelCardSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum ModelCardStatus {
DRAFT = "Draft"
PENDINGREVIEW = "PendingReview"
APPROVED = "Approved"
ARCHIVED = "Archived"
}
enum ModelCardVersionSortBy {
VERSION = "Version"
}
enum ModelInfrastructureType {
REAL_TIME_INFERENCE = "RealTimeInference"
}
@length(
min: 1
)
string ModelInsightsLocation
enum ModelMetadataFilterType {
DOMAIN = "Domain"
FRAMEWORK = "Framework"
TASK = "Task"
FRAMEWORKVERSION = "FrameworkVersion"
}
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string ModelName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string ModelNameContains
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:model-package/")
string ModelPackageArn
@length(
min: 3
max: 10
)
@pattern("^[0-9]\\.[A-Za-z0-9.-]+$")
string ModelPackageFrameworkVersion
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:model-package-group/")
string ModelPackageGroupArn
enum ModelPackageGroupSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum ModelPackageGroupStatus {
PENDING = "Pending"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
DELETING = "Deleting"
DELETE_FAILED = "DeleteFailed"
}
enum ModelPackageSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum ModelPackageStatus {
PENDING = "Pending"
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
DELETING = "Deleting"
}
enum ModelPackageType {
VERSIONED = "Versioned"
UNVERSIONED = "Unversioned"
BOTH = "Both"
}
@range(
min: 1
)
integer ModelPackageVersion
enum ModelSortKey {
Name
CreationTime
}
enum ModelVariantAction {
RETAIN = "Retain"
REMOVE = "Remove"
PROMOTE = "Promote"
}
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9]([\\-a-zA-Z0-9]*[a-zA-Z0-9])?$")
string ModelVariantName
enum ModelVariantStatus {
CREATING = "Creating"
UPDATING = "Updating"
IN_SERVICE = "InService"
DELETING = "Deleting"
DELETED = "Deleted"
}
enum MonitoringAlertHistorySortKey {
CreationTime
Status
}
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string MonitoringAlertName
enum MonitoringAlertStatus {
IN_ALERT = "InAlert"
OK
}
@range(
min: 1
max: 100
)
integer MonitoringDatapointsToAlert
@range(
min: 1
max: 100
)
integer MonitoringEvaluationPeriod
enum MonitoringExecutionSortKey {
CREATION_TIME = "CreationTime"
SCHEDULED_TIME = "ScheduledTime"
STATUS = "Status"
}
@length(
min: 0
max: 256
)
@pattern(".*")
string MonitoringJobDefinitionArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string MonitoringJobDefinitionName
enum MonitoringJobDefinitionSortKey {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
@default(0)
@range(
min: 1
max: 86400
)
integer MonitoringMaxRuntimeInSeconds
enum MonitoringProblemType {
BINARY_CLASSIFICATION = "BinaryClassification"
MULTICLASS_CLASSIFICATION = "MulticlassClassification"
REGRESSION = "Regression"
}
@length(
min: 0
max: 512
)
@pattern("^(https|s3)://([^/]+)/?(.*)$")
string MonitoringS3Uri
@length(
min: 0
max: 256
)
@pattern(".*")
string MonitoringScheduleArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string MonitoringScheduleName
enum MonitoringScheduleSortKey {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
@length(
min: 1
max: 15
)
@pattern("^.?P")
string MonitoringTimeOffsetString
enum MonitoringType {
DATA_QUALITY = "DataQuality"
MODEL_QUALITY = "ModelQuality"
MODEL_BIAS = "ModelBias"
MODEL_EXPLAINABILITY = "ModelExplainability"
}
@length(
min: 0
max: 1024
)
@pattern("^\\/")
string MountPath
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9\\-]+$")
string NameContains
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string NeoVpcSecurityGroupId
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string NeoVpcSubnetId
string NetworkInterfaceId
@length(
min: 0
max: 8192
)
@pattern(".*")
string NextToken
enum NotebookInstanceAcceleratorType {
ML_EIA1_MEDIUM = "ml.eia1.medium"
ML_EIA1_LARGE = "ml.eia1.large"
ML_EIA1_XLARGE = "ml.eia1.xlarge"
ML_EIA2_MEDIUM = "ml.eia2.medium"
ML_EIA2_LARGE = "ml.eia2.large"
ML_EIA2_XLARGE = "ml.eia2.xlarge"
}
@length(
min: 0
max: 256
)
string NotebookInstanceArn
@length(
min: 0
max: 256
)
string NotebookInstanceLifecycleConfigArn
@length(
min: 1
max: 16384
)
@pattern("^[\\S\\s]+$")
string NotebookInstanceLifecycleConfigContent
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string NotebookInstanceLifecycleConfigName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string NotebookInstanceLifecycleConfigNameContains
enum NotebookInstanceLifecycleConfigSortKey {
NAME = "Name"
CREATION_TIME = "CreationTime"
LAST_MODIFIED_TIME = "LastModifiedTime"
}
enum NotebookInstanceLifecycleConfigSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string NotebookInstanceName
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9-]+$")
string NotebookInstanceNameContains
enum NotebookInstanceSortKey {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum NotebookInstanceSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum NotebookInstanceStatus {
Pending
InService
Stopping
Stopped
Failed
Deleting
Updating
}
string NotebookInstanceUrl
@range(
min: 5
max: 16384
)
integer NotebookInstanceVolumeSizeInGB
enum NotebookOutputOption {
Allowed
Disabled
}
@pattern("^arn:aws[a-z\\-]*:sns:[a-z0-9\\-]*:[0-9]{12}:[a-zA-Z0-9_.-]*$")
string NotificationTopicArn
@range(
min: 1
max: 9
)
integer NumberOfHumanWorkersPerDataObject
enum ObjectiveStatus {
Succeeded
Pending
Failed
}
@default(0)
@range(
min: 0
)
integer ObjectiveStatusCounter
enum OfflineStoreStatusValue {
ACTIVE = "Active"
BLOCKED = "Blocked"
DISABLED = "Disabled"
}
@length(
min: 0
max: 500
)
@pattern("^https://\\S+$")
string OidcEndpoint
long OnlineStoreTotalSizeBytes
enum Operator {
EQUALS = "Equals"
NOT_EQUALS = "NotEquals"
GREATER_THAN = "GreaterThan"
GREATER_THAN_OR_EQUAL_TO = "GreaterThanOrEqualTo"
LESS_THAN = "LessThan"
LESS_THAN_OR_EQUAL_TO = "LessThanOrEqualTo"
CONTAINS = "Contains"
EXISTS = "Exists"
NOT_EXISTS = "NotExists"
IN = "In"
}
double OptionalDouble
integer OptionalInteger
@default(0)
@range(
min: 0
)
integer OptionalVolumeSizeInGB
enum OrderKey {
Ascending
Descending
}
@length(
min: 0
max: 8192
)
@pattern(".*")
string PaginationToken
@length(
min: 0
max: 256
)
@pattern(".*")
string ParameterKey
@length(
min: 0
max: 256
)
@pattern("^[\\p{L}\\p{M}\\p{Z}\\p{S}\\p{N}\\p{P}]*$")
string ParameterName
enum ParameterType {
INTEGER = "Integer"
CONTINUOUS = "Continuous"
CATEGORICAL = "Categorical"
FREE_TEXT = "FreeText"
}
@length(
min: 0
max: 256
)
@pattern(".*")
string ParameterValue
@default(0)
@range(
max: 100
)
integer Percentage
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:pipeline/")
string PipelineArn
@length(
min: 1
max: 1048576
)
@pattern("(?:[ \\r\\n\\t].*)*$")
string PipelineDefinition
@length(
min: 0
max: 3072
)
@pattern(".*")
string PipelineDescription
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:pipeline\\/.*\\/execution\\/.*$")
string PipelineExecutionArn
@length(
min: 0
max: 3072
)
@pattern(".*")
string PipelineExecutionDescription
@length(
min: 0
max: 1300
)
@pattern(".*")
string PipelineExecutionFailureReason
@length(
min: 1
max: 82
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,81}$")
string PipelineExecutionName
enum PipelineExecutionStatus {
EXECUTING = "Executing"
STOPPING = "Stopping"
STOPPED = "Stopped"
FAILED = "Failed"
SUCCEEDED = "Succeeded"
}
@length(
min: 1
max: 256
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,255}$")
string PipelineName
@length(
min: 1
max: 2048
)
@pattern("^(arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:pipeline/.*)?([a-zA-Z0-9](-*[a-zA-Z0-9]){0,255})$")
string PipelineNameOrArn
@length(
min: 1
max: 256
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,255}$")
string PipelineParameterName
enum PipelineStatus {
ACTIVE = "Active"
}
@length(
min: 0
max: 15
)
@pattern("^(notebook-al1-v1|notebook-al2-v1|notebook-al2-v2)$")
string PlatformIdentifier
@length(
min: 1
max: 20480
)
@pattern(".*")
string PolicyString
string PresignedDomainUrl
double ProbabilityThresholdAttribute
enum ProblemType {
BINARY_CLASSIFICATION = "BinaryClassification"
MULTICLASS_CLASSIFICATION = "MulticlassClassification"
REGRESSION = "Regression"
}
@length(
min: 0
max: 256
)
@pattern("^[a-zA-Z_][a-zA-Z0-9_]*$")
string ProcessingEnvironmentKey
@length(
min: 0
max: 256
)
@pattern("^[\\S\\s]*$")
string ProcessingEnvironmentValue
@range(
min: 1
max: 100
)
integer ProcessingInstanceCount
enum ProcessingInstanceType {
ML_T3_MEDIUM = "ml.t3.medium"
ML_T3_LARGE = "ml.t3.large"
ML_T3_XLARGE = "ml.t3.xlarge"
ML_T3_2XLARGE = "ml.t3.2xlarge"
ML_M4_XLARGE = "ml.m4.xlarge"
ML_M4_2XLARGE = "ml.m4.2xlarge"
ML_M4_4XLARGE = "ml.m4.4xlarge"
ML_M4_10XLARGE = "ml.m4.10xlarge"
ML_M4_16XLARGE = "ml.m4.16xlarge"
ML_C4_XLARGE = "ml.c4.xlarge"
ML_C4_2XLARGE = "ml.c4.2xlarge"
ML_C4_4XLARGE = "ml.c4.4xlarge"
ML_C4_8XLARGE = "ml.c4.8xlarge"
ML_P2_XLARGE = "ml.p2.xlarge"
ML_P2_8XLARGE = "ml.p2.8xlarge"
ML_P2_16XLARGE = "ml.p2.16xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_M5_LARGE = "ml.m5.large"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_R5_LARGE = "ml.r5.large"
ML_R5_XLARGE = "ml.r5.xlarge"
ML_R5_2XLARGE = "ml.r5.2xlarge"
ML_R5_4XLARGE = "ml.r5.4xlarge"
ML_R5_8XLARGE = "ml.r5.8xlarge"
ML_R5_12XLARGE = "ml.r5.12xlarge"
ML_R5_16XLARGE = "ml.r5.16xlarge"
ML_R5_24XLARGE = "ml.r5.24xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:processing-job/")
string ProcessingJobArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string ProcessingJobName
enum ProcessingJobStatus {
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 0
max: 256
)
@pattern(".*")
string ProcessingLocalPath
@default(0)
@range(
min: 1
max: 604800
)
integer ProcessingMaxRuntimeInSeconds
enum ProcessingS3CompressionType {
NONE = "None"
GZIP = "Gzip"
}
enum ProcessingS3DataDistributionType {
FULLYREPLICATED = "FullyReplicated"
SHARDEDBYS3KEY = "ShardedByS3Key"
}
enum ProcessingS3DataType {
MANIFEST_FILE = "ManifestFile"
S3_PREFIX = "S3Prefix"
}
enum ProcessingS3InputMode {
PIPE = "Pipe"
FILE = "File"
}
enum ProcessingS3UploadMode {
CONTINUOUS = "Continuous"
END_OF_JOB = "EndOfJob"
}
@range(
min: 1
max: 16384
)
integer ProcessingVolumeSizeInGB
enum Processor {
CPU
GPU
}
@length(
min: 0
max: 256
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string ProductId
enum ProductionVariantAcceleratorType {
ML_EIA1_MEDIUM = "ml.eia1.medium"
ML_EIA1_LARGE = "ml.eia1.large"
ML_EIA1_XLARGE = "ml.eia1.xlarge"
ML_EIA2_MEDIUM = "ml.eia2.medium"
ML_EIA2_LARGE = "ml.eia2.large"
ML_EIA2_XLARGE = "ml.eia2.xlarge"
}
@range(
min: 60
max: 3600
)
integer ProductionVariantContainerStartupHealthCheckTimeoutInSeconds
enum ProductionVariantInstanceType {
ML_T2_MEDIUM = "ml.t2.medium"
ML_T2_LARGE = "ml.t2.large"
ML_T2_XLARGE = "ml.t2.xlarge"
ML_T2_2XLARGE = "ml.t2.2xlarge"
ML_M4_XLARGE = "ml.m4.xlarge"
ML_M4_2XLARGE = "ml.m4.2xlarge"
ML_M4_4XLARGE = "ml.m4.4xlarge"
ML_M4_10XLARGE = "ml.m4.10xlarge"
ML_M4_16XLARGE = "ml.m4.16xlarge"
ML_M5_LARGE = "ml.m5.large"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_M5D_LARGE = "ml.m5d.large"
ML_M5D_XLARGE = "ml.m5d.xlarge"
ML_M5D_2XLARGE = "ml.m5d.2xlarge"
ML_M5D_4XLARGE = "ml.m5d.4xlarge"
ML_M5D_12XLARGE = "ml.m5d.12xlarge"
ML_M5D_24XLARGE = "ml.m5d.24xlarge"
ML_C4_LARGE = "ml.c4.large"
ML_C4_XLARGE = "ml.c4.xlarge"
ML_C4_2XLARGE = "ml.c4.2xlarge"
ML_C4_4XLARGE = "ml.c4.4xlarge"
ML_C4_8XLARGE = "ml.c4.8xlarge"
ML_P2_XLARGE = "ml.p2.xlarge"
ML_P2_8XLARGE = "ml.p2.8xlarge"
ML_P2_16XLARGE = "ml.p2.16xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_C5_LARGE = "ml.c5.large"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_C5D_LARGE = "ml.c5d.large"
ML_C5D_XLARGE = "ml.c5d.xlarge"
ML_C5D_2XLARGE = "ml.c5d.2xlarge"
ML_C5D_4XLARGE = "ml.c5d.4xlarge"
ML_C5D_9XLARGE = "ml.c5d.9xlarge"
ML_C5D_18XLARGE = "ml.c5d.18xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
ML_R5_LARGE = "ml.r5.large"
ML_R5_XLARGE = "ml.r5.xlarge"
ML_R5_2XLARGE = "ml.r5.2xlarge"
ML_R5_4XLARGE = "ml.r5.4xlarge"
ML_R5_12XLARGE = "ml.r5.12xlarge"
ML_R5_24XLARGE = "ml.r5.24xlarge"
ML_R5D_LARGE = "ml.r5d.large"
ML_R5D_XLARGE = "ml.r5d.xlarge"
ML_R5D_2XLARGE = "ml.r5d.2xlarge"
ML_R5D_4XLARGE = "ml.r5d.4xlarge"
ML_R5D_12XLARGE = "ml.r5d.12xlarge"
ML_R5D_24XLARGE = "ml.r5d.24xlarge"
ML_INF1_XLARGE = "ml.inf1.xlarge"
ML_INF1_2XLARGE = "ml.inf1.2xlarge"
ML_INF1_6XLARGE = "ml.inf1.6xlarge"
ML_INF1_24XLARGE = "ml.inf1.24xlarge"
ML_C6I_LARGE = "ml.c6i.large"
ML_C6I_XLARGE = "ml.c6i.xlarge"
ML_C6I_2XLARGE = "ml.c6i.2xlarge"
ML_C6I_4XLARGE = "ml.c6i.4xlarge"
ML_C6I_8XLARGE = "ml.c6i.8xlarge"
ML_C6I_12XLARGE = "ml.c6i.12xlarge"
ML_C6I_16XLARGE = "ml.c6i.16xlarge"
ML_C6I_24XLARGE = "ml.c6i.24xlarge"
ML_C6I_32XLARGE = "ml.c6i.32xlarge"
ML_G5_XLARGE = "ml.g5.xlarge"
ML_G5_2XLARGE = "ml.g5.2xlarge"
ML_G5_4XLARGE = "ml.g5.4xlarge"
ML_G5_8XLARGE = "ml.g5.8xlarge"
ML_G5_12XLARGE = "ml.g5.12xlarge"
ML_G5_16XLARGE = "ml.g5.16xlarge"
ML_G5_24XLARGE = "ml.g5.24xlarge"
ML_G5_48XLARGE = "ml.g5.48xlarge"
ML_P4D_24XLARGE = "ml.p4d.24xlarge"
ML_C7G_LARGE = "ml.c7g.large"
ML_C7G_XLARGE = "ml.c7g.xlarge"
ML_C7G_2XLARGE = "ml.c7g.2xlarge"
ML_C7G_4XLARGE = "ml.c7g.4xlarge"
ML_C7G_8XLARGE = "ml.c7g.8xlarge"
ML_C7G_12XLARGE = "ml.c7g.12xlarge"
ML_C7G_16XLARGE = "ml.c7g.16xlarge"
ML_M6G_LARGE = "ml.m6g.large"
ML_M6G_XLARGE = "ml.m6g.xlarge"
ML_M6G_2XLARGE = "ml.m6g.2xlarge"
ML_M6G_4XLARGE = "ml.m6g.4xlarge"
ML_M6G_8XLARGE = "ml.m6g.8xlarge"
ML_M6G_12XLARGE = "ml.m6g.12xlarge"
ML_M6G_16XLARGE = "ml.m6g.16xlarge"
ML_M6GD_LARGE = "ml.m6gd.large"
ML_M6GD_XLARGE = "ml.m6gd.xlarge"
ML_M6GD_2XLARGE = "ml.m6gd.2xlarge"
ML_M6GD_4XLARGE = "ml.m6gd.4xlarge"
ML_M6GD_8XLARGE = "ml.m6gd.8xlarge"
ML_M6GD_12XLARGE = "ml.m6gd.12xlarge"
ML_M6GD_16XLARGE = "ml.m6gd.16xlarge"
ML_C6G_LARGE = "ml.c6g.large"
ML_C6G_XLARGE = "ml.c6g.xlarge"
ML_C6G_2XLARGE = "ml.c6g.2xlarge"
ML_C6G_4XLARGE = "ml.c6g.4xlarge"
ML_C6G_8XLARGE = "ml.c6g.8xlarge"
ML_C6G_12XLARGE = "ml.c6g.12xlarge"
ML_C6G_16XLARGE = "ml.c6g.16xlarge"
ML_C6GD_LARGE = "ml.c6gd.large"
ML_C6GD_XLARGE = "ml.c6gd.xlarge"
ML_C6GD_2XLARGE = "ml.c6gd.2xlarge"
ML_C6GD_4XLARGE = "ml.c6gd.4xlarge"
ML_C6GD_8XLARGE = "ml.c6gd.8xlarge"
ML_C6GD_12XLARGE = "ml.c6gd.12xlarge"
ML_C6GD_16XLARGE = "ml.c6gd.16xlarge"
ML_C6GN_LARGE = "ml.c6gn.large"
ML_C6GN_XLARGE = "ml.c6gn.xlarge"
ML_C6GN_2XLARGE = "ml.c6gn.2xlarge"
ML_C6GN_4XLARGE = "ml.c6gn.4xlarge"
ML_C6GN_8XLARGE = "ml.c6gn.8xlarge"
ML_C6GN_12XLARGE = "ml.c6gn.12xlarge"
ML_C6GN_16XLARGE = "ml.c6gn.16xlarge"
ML_R6G_LARGE = "ml.r6g.large"
ML_R6G_XLARGE = "ml.r6g.xlarge"
ML_R6G_2XLARGE = "ml.r6g.2xlarge"
ML_R6G_4XLARGE = "ml.r6g.4xlarge"
ML_R6G_8XLARGE = "ml.r6g.8xlarge"
ML_R6G_12XLARGE = "ml.r6g.12xlarge"
ML_R6G_16XLARGE = "ml.r6g.16xlarge"
ML_R6GD_LARGE = "ml.r6gd.large"
ML_R6GD_XLARGE = "ml.r6gd.xlarge"
ML_R6GD_2XLARGE = "ml.r6gd.2xlarge"
ML_R6GD_4XLARGE = "ml.r6gd.4xlarge"
ML_R6GD_8XLARGE = "ml.r6gd.8xlarge"
ML_R6GD_12XLARGE = "ml.r6gd.12xlarge"
ML_R6GD_16XLARGE = "ml.r6gd.16xlarge"
ML_P4DE_24XLARGE = "ml.p4de.24xlarge"
}
@range(
min: 60
max: 3600
)
integer ProductionVariantModelDataDownloadTimeoutInSeconds
@range(
min: 1
max: 512
)
integer ProductionVariantVolumeSizeInGB
long ProfilingIntervalInMilliseconds
enum ProfilingStatus {
ENABLED = "Enabled"
DISABLED = "Disabled"
}
@length(
min: 1
max: 128
)
@pattern("^[a-zA-Z]+ ?\\d+\\.\\d+(\\.\\d+)?$")
string ProgrammingLang
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:project:")
string ProjectArn
@length(
min: 1
max: 32
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}$")
string ProjectEntityName
@length(
min: 1
max: 20
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9])*$")
string ProjectId
enum ProjectSortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum ProjectSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum ProjectStatus {
PENDING = "Pending"
CREATE_IN_PROGRESS = "CreateInProgress"
CREATE_COMPLETED = "CreateCompleted"
CREATE_FAILED = "CreateFailed"
DELETE_IN_PROGRESS = "DeleteInProgress"
DELETE_FAILED = "DeleteFailed"
DELETE_COMPLETED = "DeleteCompleted"
UPDATE_IN_PROGRESS = "UpdateInProgress"
UPDATE_COMPLETED = "UpdateCompleted"
UPDATE_FAILED = "UpdateFailed"
}
@length(
min: 0
max: 100
)
@pattern(".*")
string PropertyNameHint
@pattern(".*")
string ProvisionedProductStatusMessage
@length(
min: 1
max: 1000
)
@pattern(".*")
string ProvisioningParameterKey
@length(
min: 0
max: 4096
)
@pattern(".*")
string ProvisioningParameterValue
@range(
max: 10
)
integer QueryLineageMaxDepth
@range(
max: 50
)
integer QueryLineageMaxResults
@range(
min: 0
)
integer RandomSeed
string RecommendationFailureReason
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:inference-recommendations-job/")
string RecommendationJobArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string RecommendationJobCompilationJobName
@length(
min: 1
max: 1024
)
@pattern("^[\\S\\s]+$")
string RecommendationJobDataInputConfig
@length(
min: 0
max: 128
)
string RecommendationJobDescription
@length(
min: 1
max: 64
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,63}$")
string RecommendationJobName
enum RecommendationJobStatus {
PENDING
IN_PROGRESS
COMPLETED
FAILED
STOPPING
STOPPED
}
enum RecommendationJobType {
DEFAULT = "Default"
ADVANCED = "Advanced"
}
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string RecommendationJobVpcSecurityGroupId
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string RecommendationJobVpcSubnetId
enum RecommendationStepType {
BENCHMARK
}
enum RecordWrapper {
NONE = "None"
RECORDIO = "RecordIO"
}
/// The Redshift cluster Identifier.
@length(
min: 1
max: 63
)
@pattern(".*")
string RedshiftClusterId
/// The name of the Redshift database used in Redshift query execution.
@length(
min: 1
max: 64
)
@pattern(".*")
string RedshiftDatabase
/// The SQL query statements to be executed.
@length(
min: 1
max: 4096
)
@pattern("^[\\s\\S]+$")
string RedshiftQueryString
/// The compression used for Redshift query results.
enum RedshiftResultCompressionType {
NONE = "None"
GZIP
BZIP2
ZSTD
SNAPPY
}
/// The data storage format for Redshift query results.
enum RedshiftResultFormat {
PARQUET
CSV
}
/// The database user name used in Redshift query execution.
@length(
min: 1
max: 128
)
@pattern(".*")
string RedshiftUserName
@length(
min: 1
max: 255
)
@pattern(".*")
string ReleaseNotes
enum RepositoryAccessMode {
PLATFORM = "Platform"
VPC = "Vpc"
}
@length(
min: 1
max: 2048
)
@pattern(".*")
string RepositoryCredentialsProviderArn
@length(
min: 0
max: 1024
)
@pattern("^https://([.\\-_a-zA-Z0-9]+/?){3,1016}$")
string RepositoryUrl
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z-]*:sagemaker:[a-z0-9-]*:[0-9]{12}:.+$")
string ResourceArn
@length(
min: 0
max: 32
)
string ResourceId
@length(
min: 0
max: 20480
)
@pattern("(?:[ \\r\\n\\t].*)*$")
string ResourcePolicyString
@length(
min: 1
max: 255
)
@pattern("^.+$")
string ResourcePropertyName
@range(
min: 0
)
integer ResourceRetainedBillableTimeInSeconds
enum ResourceType {
TRAINING_JOB = "TrainingJob"
EXPERIMENT = "Experiment"
EXPERIMENT_TRIAL = "ExperimentTrial"
EXPERIMENT_TRIAL_COMPONENT = "ExperimentTrialComponent"
ENDPOINT = "Endpoint"
MODEL_PACKAGE = "ModelPackage"
MODEL_PACKAGE_GROUP = "ModelPackageGroup"
PIPELINE = "Pipeline"
PIPELINE_EXECUTION = "PipelineExecution"
FEATURE_GROUP = "FeatureGroup"
PROJECT = "Project"
FEATURE_METADATA = "FeatureMetadata"
HYPER_PARAMETER_TUNING_JOB = "HyperParameterTuningJob"
MODEL_CARD = "ModelCard"
MODEL = "Model"
}
@length(
min: 0
max: 1024
)
@pattern("^[-\\w]+\\/.+$")
string ResponseMIMEType
enum RetentionType {
Retain
Delete
}
@length(
min: 20
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:iam::\\d{12}:role/?[a-zA-Z_0-9+=,.@\\-_/]+$")
string RoleArn
enum RootAccess {
ENABLED = "Enabled"
DISABLED = "Disabled"
}
enum RStudioServerProAccessStatus {
Enabled = "ENABLED"
Disabled = "DISABLED"
}
enum RStudioServerProUserGroup {
Admin = "R_STUDIO_ADMIN"
User = "R_STUDIO_USER"
}
@length(
min: 1
max: 256
)
@pattern(".*")
string RuleConfigurationName
enum RuleEvaluationStatus {
IN_PROGRESS = "InProgress"
NO_ISSUES_FOUND = "NoIssuesFound"
ISSUES_FOUND = "IssuesFound"
ERROR = "Error"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
enum S3DataDistribution {
FULLY_REPLICATED = "FullyReplicated"
SHARDED_BY_S3_KEY = "ShardedByS3Key"
}
enum S3DataType {
MANIFEST_FILE = "ManifestFile"
S3_PREFIX = "S3Prefix"
AUGMENTED_MANIFEST_FILE = "AugmentedManifestFile"
}
@length(
min: 0
max: 1024
)
@pattern("^(https|s3)://([^/]+)/?(.*)$")
string S3OutputPath
@length(
min: 0
max: 1024
)
@pattern("^(https|s3)://([^/]+)/?(.*)$")
string S3Uri
@length(
min: 1
max: 128
)
@pattern("^(?!^[.-])^([a-zA-Z0-9-_.]+)$")
string SageMakerImageVersionAlias
enum SagemakerServicecatalogStatus {
ENABLED = "Enabled"
DISABLED = "Disabled"
}
@range(
min: 0
max: 100
)
integer SamplingPercentage
@length(
min: 1
max: 256
)
string ScheduleExpression
enum ScheduleStatus {
PENDING = "Pending"
FAILED = "Failed"
SCHEDULED = "Scheduled"
STOPPED = "Stopped"
}
enum SearchSortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum SecondaryStatus {
STARTING = "Starting"
LAUNCHING_ML_INSTANCES = "LaunchingMLInstances"
PREPARING_TRAINING_STACK = "PreparingTrainingStack"
DOWNLOADING = "Downloading"
DOWNLOADING_TRAINING_IMAGE = "DownloadingTrainingImage"
TRAINING = "Training"
UPLOADING = "Uploading"
STOPPING = "Stopping"
STOPPED = "Stopped"
MAX_RUNTIME_EXCEEDED = "MaxRuntimeExceeded"
COMPLETED = "Completed"
FAILED = "Failed"
INTERRUPTED = "Interrupted"
MAX_WAIT_TIME_EXCEEDED = "MaxWaitTimeExceeded"
UPDATING = "Updating"
RESTARTING = "Restarting"
}
@length(
min: 1
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:secretsmanager:[a-z0-9\\-]*:[0-9]{12}:secret:")
string SecretArn
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string SecurityGroupId
@default(0)
long Seed
@range(
min: 1
max: 200
)
integer ServerlessMaxConcurrency
@range(
min: 1024
max: 6144
)
integer ServerlessMemorySizeInMB
@length(
min: 1
max: 100
)
@pattern("^[a-zA-Z0-9_\\-]*$")
string ServiceCatalogEntityId
@range(
min: 1800
max: 43200
)
integer SessionExpirationDurationInSeconds
@pattern("^UserName$")
string SingleSignOnUserIdentifier
@length(
min: 0
max: 2048
)
@pattern("^arn:aws[a-z\\-]*:sns:[a-z0-9\\-]*:[0-9]{12}:[a-zA-Z0-9_.-]+$")
string SnsTopicArn
enum SortActionsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortArtifactsBy {
CREATION_TIME = "CreationTime"
}
enum SortAssociationsBy {
SOURCE_ARN = "SourceArn"
DESTINATION_ARN = "DestinationArn"
SOURCE_TYPE = "SourceType"
DESTINATION_TYPE = "DestinationType"
CREATION_TIME = "CreationTime"
}
enum SortBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum SortContextsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortExperimentsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortInferenceExperimentsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
STATUS = "Status"
}
enum SortLineageGroupsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortOrder {
ASCENDING = "Ascending"
DESCENDING = "Descending"
}
enum SortPipelineExecutionsBy {
CREATION_TIME = "CreationTime"
PIPELINE_EXECUTION_ARN = "PipelineExecutionArn"
}
enum SortPipelinesBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortTrialComponentsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
enum SortTrialsBy {
NAME = "Name"
CREATION_TIME = "CreationTime"
}
@length(
min: 0
max: 128
)
string SourceType
@length(
min: 0
max: 2048
)
@pattern(".*")
string SourceUri
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:space/")
string SpaceArn
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string SpaceName
enum SpaceSortKey {
CreationTime
LastModifiedTime
}
enum SpaceStatus {
Deleting
Failed
InService
Pending
Updating
Update_Failed
Delete_Failed
}
@range(
min: 0
)
integer SpawnRate
enum SplitType {
NONE = "None"
LINE = "Line"
RECORDIO = "RecordIO"
TFRECORD = "TFRecord"
}
enum StageStatus {
Creating = "CREATING"
ReadyToDeploy = "READYTODEPLOY"
Starting = "STARTING"
InProgress = "INPROGRESS"
Deployed = "DEPLOYED"
Failed = "FAILED"
Stopping = "STOPPING"
Stopped = "STOPPED"
}
@length(
min: 0
max: 1024
)
@pattern(".*")
string StatusDetails
string StatusMessage
@length(
min: 0
max: 3072
)
@pattern(".*")
string StepDescription
@length(
min: 0
max: 256
)
@pattern(".*")
string StepDisplayName
@length(
min: 0
max: 64
)
@pattern("^[A-Za-z0-9\\-_]*$")
string StepName
enum StepStatus {
STARTING = "Starting"
EXECUTING = "Executing"
STOPPING = "Stopping"
STOPPED = "Stopped"
FAILED = "Failed"
SUCCEEDED = "Succeeded"
}
string String
@length(
min: 0
max: 1024
)
string String1024
@length(
min: 0
max: 128
)
string String128
@length(
min: 1
max: 200
)
@pattern("^.+$")
string String200
@length(
min: 0
max: 2048
)
string String2048
@length(
min: 0
max: 256
)
string String256
@length(
min: 0
max: 3072
)
string String3072
@length(
min: 0
max: 40
)
string String40
@length(
min: 0
max: 64
)
string String64
@length(
min: 0
max: 8192
)
string String8192
@length(
min: 0
max: 256
)
@pattern(".*")
string StringParameterValue
enum StudioLifecycleConfigAppType {
JupyterServer
KernelGateway
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:studio-lifecycle-config/")
string StudioLifecycleConfigArn
@length(
min: 1
max: 16384
)
@pattern("^[\\S\\s]+$")
string StudioLifecycleConfigContent
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string StudioLifecycleConfigName
enum StudioLifecycleConfigSortKey {
CreationTime
LastModifiedTime
Name
}
@length(
min: 0
max: 32
)
@pattern("^[-0-9a-zA-Z]+$")
string SubnetId
@default(false)
boolean Success
@length(
min: 0
max: 10240
)
@mediaType("application/json")
string SynthesizedJsonHumanLoopActivationConditions
enum TableFormat {
GLUE = "Glue"
ICEBERG = "Iceberg"
}
@length(
min: 1
max: 255
)
@pattern("^[\\u0020-\\uD7FF\\uE000-\\uFFFD\\uD800\\uDC00-\\uDBFF\\uDFFF\\t]*$")
string TableName
@length(
min: 1
max: 128
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:/=+\\-@]*)$")
string TagKey
@length(
min: 0
max: 256
)
@pattern("^([\\p{L}\\p{Z}\\p{N}_.:/=+\\-@]*)$")
string TagValue
@length(
min: 1
)
string TargetAttributeName
enum TargetDevice {
LAMBDA = "lambda"
ML_M4 = "ml_m4"
ML_M5 = "ml_m5"
ML_C4 = "ml_c4"
ML_C5 = "ml_c5"
ML_P2 = "ml_p2"
ML_P3 = "ml_p3"
ML_G4DN = "ml_g4dn"
ML_INF1 = "ml_inf1"
ML_EIA2 = "ml_eia2"
JETSON_TX1 = "jetson_tx1"
JETSON_TX2 = "jetson_tx2"
JETSON_NANO = "jetson_nano"
JETSON_XAVIER = "jetson_xavier"
RASP3B = "rasp3b"
IMX8QM = "imx8qm"
DEEPLENS = "deeplens"
RK3399 = "rk3399"
RK3288 = "rk3288"
AISAGE = "aisage"
SBE_C = "sbe_c"
QCS605 = "qcs605"
QCS603 = "qcs603"
SITARA_AM57X = "sitara_am57x"
AMBA_CV2 = "amba_cv2"
AMBA_CV22 = "amba_cv22"
AMBA_CV25 = "amba_cv25"
X86_WIN32 = "x86_win32"
X86_WIN64 = "x86_win64"
COREML = "coreml"
JACINTO_TDA4VM = "jacinto_tda4vm"
IMX8MPLUS = "imx8mplus"
}
float TargetObjectiveMetricValue
enum TargetPlatformAccelerator {
INTEL_GRAPHICS
MALI
NVIDIA
NNA
}
enum TargetPlatformArch {
X86_64
X86
ARM64
ARM_EABI
ARM_EABIHF
}
enum TargetPlatformOs {
ANDROID
LINUX
}
@range(
min: 60
)
integer TaskAvailabilityLifetimeInSeconds
@range(
min: 0
)
integer TaskCount
@length(
min: 1
max: 255
)
@pattern("^.+$")
string TaskDescription
@length(
min: 2
max: 128000
)
@pattern("^[\\S\\s]+$")
string TaskInput
@length(
min: 1
max: 30
)
@pattern("^[A-Za-z0-9]+( [A-Za-z0-9]+)*$")
string TaskKeyword
@range(
min: 30
)
integer TaskTimeLimitInSeconds
@length(
min: 1
max: 128
)
@pattern("^[\\t\\n\\r -\\uD7FF\\uE000-\\uFFFD]*$")
string TaskTitle
@length(
min: 1
max: 128000
)
@pattern("^[\\S\\s]+$")
string TemplateContent
@length(
min: 1
max: 128000
)
string TemplateContentSha256
@length(
min: 1
max: 2048
)
string TemplateUrl
@default(0)
@range(
min: 0
max: 9
)
integer TenthFractionsOfACent
@range(
min: 0
max: 3600
)
integer TerminationWaitInSeconds
@length(
min: 0
max: 128
)
@pattern("^[a-zA-Z0-9:_-]+$")
string ThingName
timestamp Timestamp
@range(
min: 1
)
integer TrafficDurationInSeconds
enum TrafficRoutingConfigType {
ALL_AT_ONCE
CANARY
LINEAR
}
enum TrafficType {
PHASES
}
@length(
min: 0
max: 256
)
@pattern(".*")
string TrainingContainerArgument
@length(
min: 0
max: 256
)
@pattern(".*")
string TrainingContainerEntrypointString
@length(
min: 0
max: 512
)
@pattern("^[a-zA-Z_][a-zA-Z0-9_]*$")
string TrainingEnvironmentKey
@length(
min: 0
max: 512
)
@pattern("^[\\S\\s]*$")
string TrainingEnvironmentValue
/// The training input mode that the algorithm supports. For more information about input
/// modes, see Algorithms.
///
/// Pipe mode
///
/// If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from
/// Amazon S3 to the container.
///
/// File mode
///
/// If an algorithm supports File mode, SageMaker downloads the training data from
/// S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume
/// for the training container.
/// You must provision the ML storage volume with sufficient capacity to accommodate the
/// data downloaded from S3. In addition to the training data, the ML storage volume also
/// stores the output model. The algorithm container uses the ML storage volume to also
/// store intermediate information, if any.
/// For distributed algorithms, training data is distributed uniformly. Your training
/// duration is predictable if the input data objects sizes are approximately the same. SageMaker
/// does not split the files any further for model training. If the object sizes are skewed,
/// training won't be optimal as the data distribution is also skewed when one host in a
/// training cluster is overloaded, thus becoming a bottleneck in training.
///
/// FastFile mode
///
/// If an algorithm supports FastFile mode, SageMaker streams data directly from
/// S3 to the container with no code changes, and provides file system access to the data.
/// Users can author their training script to interact with these files as if they were
/// stored on disk.
///
/// FastFile mode works best when the data is read sequentially. Augmented
/// manifest files aren't supported. The startup time is lower when there are fewer files in
/// the S3 bucket provided.
enum TrainingInputMode {
PIPE = "Pipe"
FILE = "File"
FASTFILE = "FastFile"
}
@default(0)
@range(
min: 0
)
integer TrainingInstanceCount
enum TrainingInstanceType {
ML_M4_XLARGE = "ml.m4.xlarge"
ML_M4_2XLARGE = "ml.m4.2xlarge"
ML_M4_4XLARGE = "ml.m4.4xlarge"
ML_M4_10XLARGE = "ml.m4.10xlarge"
ML_M4_16XLARGE = "ml.m4.16xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
ML_M5_LARGE = "ml.m5.large"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_C4_XLARGE = "ml.c4.xlarge"
ML_C4_2XLARGE = "ml.c4.2xlarge"
ML_C4_4XLARGE = "ml.c4.4xlarge"
ML_C4_8XLARGE = "ml.c4.8xlarge"
ML_P2_XLARGE = "ml.p2.xlarge"
ML_P2_8XLARGE = "ml.p2.8xlarge"
ML_P2_16XLARGE = "ml.p2.16xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_P3DN_24XLARGE = "ml.p3dn.24xlarge"
ML_P4D_24XLARGE = "ml.p4d.24xlarge"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_C5N_XLARGE = "ml.c5n.xlarge"
ML_C5N_2XLARGE = "ml.c5n.2xlarge"
ML_C5N_4XLARGE = "ml.c5n.4xlarge"
ML_C5N_9XLARGE = "ml.c5n.9xlarge"
ML_C5N_18XLARGE = "ml.c5n.18xlarge"
ML_G5_XLARGE = "ml.g5.xlarge"
ML_G5_2XLARGE = "ml.g5.2xlarge"
ML_G5_4XLARGE = "ml.g5.4xlarge"
ML_G5_8XLARGE = "ml.g5.8xlarge"
ML_G5_16XLARGE = "ml.g5.16xlarge"
ML_G5_12XLARGE = "ml.g5.12xlarge"
ML_G5_24XLARGE = "ml.g5.24xlarge"
ML_G5_48XLARGE = "ml.g5.48xlarge"
ML_TRN1_2XLARGE = "ml.trn1.2xlarge"
ML_TRN1_32XLARGE = "ml.trn1.32xlarge"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:training-job/")
string TrainingJobArn
enum TrainingJobEarlyStoppingType {
OFF = "Off"
AUTO = "Auto"
}
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string TrainingJobName
enum TrainingJobSortByOptions {
Name
CreationTime
Status
FinalObjectiveMetricValue
}
enum TrainingJobStatus {
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@default(0)
@range(
min: 0
)
integer TrainingJobStatusCounter
enum TrainingRepositoryAccessMode {
PLATFORM = "Platform"
VPC = "Vpc"
}
@length(
min: 1
max: 2048
)
@pattern("^arn:[\\p{Alnum}\\-]+:lambda:[\\p{Alnum}\\-]+:[0-9]{12}:function:")
string TrainingRepositoryCredentialsProviderArn
@range(
min: 1
)
integer TrainingTimeInSeconds
@length(
min: 0
max: 1024
)
@pattern("^[a-zA-Z_][a-zA-Z0-9_]{0,1023}$")
string TransformEnvironmentKey
@length(
min: 0
max: 10240
)
@pattern("^[\\S\\s]*$")
string TransformEnvironmentValue
@range(
min: 1
)
integer TransformInstanceCount
enum TransformInstanceType {
ML_M4_XLARGE = "ml.m4.xlarge"
ML_M4_2XLARGE = "ml.m4.2xlarge"
ML_M4_4XLARGE = "ml.m4.4xlarge"
ML_M4_10XLARGE = "ml.m4.10xlarge"
ML_M4_16XLARGE = "ml.m4.16xlarge"
ML_C4_XLARGE = "ml.c4.xlarge"
ML_C4_2XLARGE = "ml.c4.2xlarge"
ML_C4_4XLARGE = "ml.c4.4xlarge"
ML_C4_8XLARGE = "ml.c4.8xlarge"
ML_P2_XLARGE = "ml.p2.xlarge"
ML_P2_8XLARGE = "ml.p2.8xlarge"
ML_P2_16XLARGE = "ml.p2.16xlarge"
ML_P3_2XLARGE = "ml.p3.2xlarge"
ML_P3_8XLARGE = "ml.p3.8xlarge"
ML_P3_16XLARGE = "ml.p3.16xlarge"
ML_C5_XLARGE = "ml.c5.xlarge"
ML_C5_2XLARGE = "ml.c5.2xlarge"
ML_C5_4XLARGE = "ml.c5.4xlarge"
ML_C5_9XLARGE = "ml.c5.9xlarge"
ML_C5_18XLARGE = "ml.c5.18xlarge"
ML_M5_LARGE = "ml.m5.large"
ML_M5_XLARGE = "ml.m5.xlarge"
ML_M5_2XLARGE = "ml.m5.2xlarge"
ML_M5_4XLARGE = "ml.m5.4xlarge"
ML_M5_12XLARGE = "ml.m5.12xlarge"
ML_M5_24XLARGE = "ml.m5.24xlarge"
ML_G4DN_XLARGE = "ml.g4dn.xlarge"
ML_G4DN_2XLARGE = "ml.g4dn.2xlarge"
ML_G4DN_4XLARGE = "ml.g4dn.4xlarge"
ML_G4DN_8XLARGE = "ml.g4dn.8xlarge"
ML_G4DN_12XLARGE = "ml.g4dn.12xlarge"
ML_G4DN_16XLARGE = "ml.g4dn.16xlarge"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:transform-job/")
string TransformJobArn
@length(
min: 1
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string TransformJobName
enum TransformJobStatus {
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:experiment-trial/")
string TrialArn
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:experiment-trial-component/")
string TrialComponentArn
@length(
min: 0
max: 2048
)
@pattern(".*")
string TrialComponentArtifactValue
@length(
min: 0
max: 256
)
@pattern(".*")
string TrialComponentKey256
@length(
min: 0
max: 64
)
@pattern(".*")
string TrialComponentKey64
enum TrialComponentPrimaryStatus {
IN_PROGRESS = "InProgress"
COMPLETED = "Completed"
FAILED = "Failed"
STOPPING = "Stopping"
STOPPED = "Stopped"
}
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:")
string TrialComponentSourceArn
@length(
min: 0
max: 1024
)
@pattern(".*")
string TrialComponentStatusMessage
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:")
string TrialSourceArn
@length(
min: 0
max: 1024
)
@pattern("^(https|s3)://([^/]+)/?(.*)$")
string Url
@length(
min: 0
max: 256
)
@pattern("^arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:user-profile/")
string UserProfileArn
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string UserProfileName
enum UserProfileSortKey {
CreationTime
LastModifiedTime
}
enum UserProfileStatus {
Deleting
Failed
InService
Pending
Updating
Update_Failed
Delete_Failed
}
@range(
min: 0.0
)
float UtilizationMetric
@range(
min: 0
max: 1
)
float ValidationFraction
@length(
min: 0
max: 63
)
@pattern("^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$")
string VariantName
enum VariantPropertyType {
DesiredInstanceCount
DesiredWeight
DataCaptureConfig
}
enum VariantStatus {
CREATING = "Creating"
UPDATING = "Updating"
DELETING = "Deleting"
ACTIVATING_TRAFFIC = "ActivatingTraffic"
BAKING = "Baking"
}
@length(
min: 0
max: 1024
)
string VariantStatusMessage
@range(
min: 0
)
float VariantWeight
enum VendorGuidance {
NOT_PROVIDED
STABLE
TO_BE_ARCHIVED
ARCHIVED
}
@length(
min: 1
max: 176
)
@pattern("^(arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:[a-z\\-]*\\/)?([a-zA-Z0-9]([a-zA-Z0-9-]){0,62})(?