libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.SageMaker.Lens

Contents

Description

 
Synopsis

Operations

ListProjects

listProjects_nameContains :: Lens' ListProjects (Maybe Text) Source #

A filter that returns the projects whose name contains a specified string.

listProjects_creationTimeAfter :: Lens' ListProjects (Maybe UTCTime) Source #

A filter that returns the projects that were created after a specified time.

listProjects_nextToken :: Lens' ListProjects (Maybe Text) Source #

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.

listProjects_sortOrder :: Lens' ListProjects (Maybe ProjectSortOrder) Source #

The sort order for results. The default is Ascending.

listProjects_creationTimeBefore :: Lens' ListProjects (Maybe UTCTime) Source #

A filter that returns the projects that were created before a specified time.

listProjects_maxResults :: Lens' ListProjects (Maybe Natural) Source #

The maximum number of projects to return in the response.

listProjects_sortBy :: Lens' ListProjects (Maybe ProjectSortBy) Source #

The field by which to sort results. The default is CreationTime.

listProjectsResponse_nextToken :: Lens' ListProjectsResponse (Maybe Text) Source #

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.

DeleteProject

deleteProject_projectName :: Lens' DeleteProject Text Source #

The name of the project to delete.

GetModelPackageGroupPolicy

getModelPackageGroupPolicy_modelPackageGroupName :: Lens' GetModelPackageGroupPolicy Text Source #

The name of the model group for which to get the resource policy.

CreateNotebookInstance

createNotebookInstance_acceleratorTypes :: Lens' CreateNotebookInstance (Maybe [NotebookInstanceAcceleratorType]) Source #

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.

createNotebookInstance_platformIdentifier :: Lens' CreateNotebookInstance (Maybe Text) Source #

The platform identifier of the notebook instance runtime environment.

createNotebookInstance_securityGroupIds :: Lens' CreateNotebookInstance (Maybe [Text]) Source #

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

createNotebookInstance_additionalCodeRepositories :: Lens' CreateNotebookInstance (Maybe [Text]) Source #

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

createNotebookInstance_lifecycleConfigName :: Lens' CreateNotebookInstance (Maybe Text) Source #

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.

createNotebookInstance_subnetId :: Lens' CreateNotebookInstance (Maybe Text) Source #

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

createNotebookInstance_defaultCodeRepository :: Lens' CreateNotebookInstance (Maybe Text) Source #

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

createNotebookInstance_volumeSizeInGB :: Lens' CreateNotebookInstance (Maybe Natural) Source #

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

createNotebookInstance_kmsKeyId :: Lens' CreateNotebookInstance (Maybe Text) Source #

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

createNotebookInstance_rootAccess :: Lens' CreateNotebookInstance (Maybe RootAccess) Source #

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.

createNotebookInstance_directInternetAccess :: Lens' CreateNotebookInstance (Maybe DirectInternetAccess) Source #

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

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

createNotebookInstance_tags :: Lens' CreateNotebookInstance (Maybe [Tag]) Source #

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.

createNotebookInstance_instanceType :: Lens' CreateNotebookInstance InstanceType Source #

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

createNotebookInstance_roleArn :: Lens' CreateNotebookInstance Text Source #

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

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

UpdateModelPackage

updateModelPackage_approvalDescription :: Lens' UpdateModelPackage (Maybe Text) Source #

A description for the approval status of the model.

updateModelPackage_modelPackageArn :: Lens' UpdateModelPackage Text Source #

The Amazon Resource Name (ARN) of the model.

DeleteModelPackage

deleteModelPackage_modelPackageName :: Lens' DeleteModelPackage Text Source #

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).

DescribeMonitoringSchedule

describeMonitoringScheduleResponse_monitoringType :: Lens' DescribeMonitoringScheduleResponse (Maybe MonitoringType) Source #

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.

describeMonitoringScheduleResponse_failureReason :: Lens' DescribeMonitoringScheduleResponse (Maybe Text) Source #

A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

describeMonitoringScheduleResponse_monitoringScheduleConfig :: Lens' DescribeMonitoringScheduleResponse MonitoringScheduleConfig Source #

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

ListTrialComponents

listTrialComponents_createdAfter :: Lens' ListTrialComponents (Maybe UTCTime) Source #

A filter that returns only components created after the specified time.

listTrialComponents_sourceArn :: Lens' ListTrialComponents (Maybe Text) Source #

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.

listTrialComponents_experimentName :: Lens' ListTrialComponents (Maybe Text) Source #

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.

listTrialComponents_nextToken :: Lens' ListTrialComponents (Maybe Text) Source #

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.

listTrialComponents_sortOrder :: Lens' ListTrialComponents (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listTrialComponents_trialName :: Lens' ListTrialComponents (Maybe Text) Source #

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.

listTrialComponents_maxResults :: Lens' ListTrialComponents (Maybe Natural) Source #

The maximum number of components to return in the response. The default value is 10.

listTrialComponents_createdBefore :: Lens' ListTrialComponents (Maybe UTCTime) Source #

A filter that returns only components created before the specified time.

listTrialComponents_sortBy :: Lens' ListTrialComponents (Maybe SortTrialComponentsBy) Source #

The property used to sort results. The default value is CreationTime.

listTrialComponentsResponse_nextToken :: Lens' ListTrialComponentsResponse (Maybe Text) Source #

A token for getting the next set of components, if there are any.

DescribeEndpointConfig

describeEndpointConfigResponse_kmsKeyId :: Lens' DescribeEndpointConfigResponse (Maybe Text) Source #

Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

describeEndpointConfigResponse_endpointConfigArn :: Lens' DescribeEndpointConfigResponse Text Source #

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

describeEndpointConfigResponse_productionVariants :: Lens' DescribeEndpointConfigResponse (NonEmpty ProductionVariant) Source #

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

describeEndpointConfigResponse_creationTime :: Lens' DescribeEndpointConfigResponse UTCTime Source #

A timestamp that shows when the endpoint configuration was created.

CreateModelExplainabilityJobDefinition

createModelExplainabilityJobDefinition_tags :: Lens' CreateModelExplainabilityJobDefinition (Maybe [Tag]) Source #

(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.

createModelExplainabilityJobDefinition_jobDefinitionName :: Lens' CreateModelExplainabilityJobDefinition Text Source #

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.

createModelExplainabilityJobDefinition_roleArn :: Lens' CreateModelExplainabilityJobDefinition Text Source #

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

DescribeApp

describeAppResponse_resourceSpec :: Lens' DescribeAppResponse (Maybe ResourceSpec) Source #

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

describeAppResponse_lastUserActivityTimestamp :: Lens' DescribeAppResponse (Maybe UTCTime) Source #

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.

describeAppResponse_appArn :: Lens' DescribeAppResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the app.

ListImageVersions

listImageVersions_lastModifiedTimeBefore :: Lens' ListImageVersions (Maybe UTCTime) Source #

A filter that returns only versions modified on or before the specified time.

listImageVersions_creationTimeAfter :: Lens' ListImageVersions (Maybe UTCTime) Source #

A filter that returns only versions created on or after the specified time.

listImageVersions_nextToken :: Lens' ListImageVersions (Maybe Text) Source #

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.

listImageVersions_sortOrder :: Lens' ListImageVersions (Maybe ImageVersionSortOrder) Source #

The sort order. The default value is DESCENDING.

listImageVersions_lastModifiedTimeAfter :: Lens' ListImageVersions (Maybe UTCTime) Source #

A filter that returns only versions modified on or after the specified time.

listImageVersions_creationTimeBefore :: Lens' ListImageVersions (Maybe UTCTime) Source #

A filter that returns only versions created on or before the specified time.

listImageVersions_maxResults :: Lens' ListImageVersions (Maybe Natural) Source #

The maximum number of versions to return in the response. The default value is 10.

listImageVersions_sortBy :: Lens' ListImageVersions (Maybe ImageVersionSortBy) Source #

The property used to sort results. The default value is CREATION_TIME.

listImageVersions_imageName :: Lens' ListImageVersions Text Source #

The name of the image to list the versions of.

listImageVersionsResponse_nextToken :: Lens' ListImageVersionsResponse (Maybe Text) Source #

A token for getting the next set of versions, if there are any.

DescribeAutoMLJob

describeAutoMLJob_autoMLJobName :: Lens' DescribeAutoMLJob Text Source #

Requests information about an AutoML job using its unique name.

describeAutoMLJobResponse_generateCandidateDefinitionsOnly :: Lens' DescribeAutoMLJobResponse (Maybe Bool) Source #

Indicates whether the output for an AutoML job generates candidate definitions only.

describeAutoMLJobResponse_failureReason :: Lens' DescribeAutoMLJobResponse (Maybe Text) Source #

Returns the failure reason for an AutoML job, when applicable.

describeAutoMLJobResponse_partialFailureReasons :: Lens' DescribeAutoMLJobResponse (Maybe (NonEmpty AutoMLPartialFailureReason)) Source #

Returns a list of reasons for partial failures within an AutoML job.

describeAutoMLJobResponse_modelDeployResult :: Lens' DescribeAutoMLJobResponse (Maybe ModelDeployResult) Source #

Provides information about endpoint for the model deployment.

describeAutoMLJobResponse_autoMLJobArtifacts :: Lens' DescribeAutoMLJobResponse (Maybe AutoMLJobArtifacts) Source #

Returns information on the job's artifacts found in AutoMLJobArtifacts.

describeAutoMLJobResponse_resolvedAttributes :: Lens' DescribeAutoMLJobResponse (Maybe ResolvedAttributes) Source #

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.

describeAutoMLJobResponse_modelDeployConfig :: Lens' DescribeAutoMLJobResponse (Maybe ModelDeployConfig) Source #

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

describeAutoMLJobResponse_inputDataConfig :: Lens' DescribeAutoMLJobResponse (NonEmpty AutoMLChannel) Source #

Returns the input data configuration for the AutoML job..

describeAutoMLJobResponse_roleArn :: Lens' DescribeAutoMLJobResponse Text Source #

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.

StopProcessingJob

stopProcessingJob_processingJobName :: Lens' StopProcessingJob Text Source #

The name of the processing job to stop.

DeleteAction

deleteAction_actionName :: Lens' DeleteAction Text Source #

The name of the action to delete.

deleteActionResponse_actionArn :: Lens' DeleteActionResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the action.

UpdateAction

updateAction_status :: Lens' UpdateAction (Maybe ActionStatus) Source #

The new status for the action.

updateAction_propertiesToRemove :: Lens' UpdateAction (Maybe [Text]) Source #

A list of properties to remove.

updateAction_description :: Lens' UpdateAction (Maybe Text) Source #

The new description for the action.

updateAction_properties :: Lens' UpdateAction (Maybe (HashMap Text Text)) Source #

The new list of properties. Overwrites the current property list.

updateAction_actionName :: Lens' UpdateAction Text Source #

The name of the action to update.

updateActionResponse_actionArn :: Lens' UpdateActionResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the action.

ListLabelingJobsForWorkteam

listLabelingJobsForWorkteam_jobReferenceCodeContains :: Lens' ListLabelingJobsForWorkteam (Maybe Text) Source #

A filter the limits jobs to only the ones whose job reference code contains the specified string.

listLabelingJobsForWorkteam_creationTimeAfter :: Lens' ListLabelingJobsForWorkteam (Maybe UTCTime) Source #

A filter that returns only labeling jobs created after the specified time (timestamp).

listLabelingJobsForWorkteam_nextToken :: Lens' ListLabelingJobsForWorkteam (Maybe Text) Source #

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.

listLabelingJobsForWorkteam_sortOrder :: Lens' ListLabelingJobsForWorkteam (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listLabelingJobsForWorkteam_creationTimeBefore :: Lens' ListLabelingJobsForWorkteam (Maybe UTCTime) Source #

A filter that returns only labeling jobs created before the specified time (timestamp).

listLabelingJobsForWorkteam_maxResults :: Lens' ListLabelingJobsForWorkteam (Maybe Natural) Source #

The maximum number of labeling jobs to return in each page of the response.

listLabelingJobsForWorkteam_workteamArn :: Lens' ListLabelingJobsForWorkteam Text Source #

The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

listLabelingJobsForWorkteamResponse_nextToken :: Lens' ListLabelingJobsForWorkteamResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

CreateTransformJob

createTransformJob_modelClientConfig :: Lens' CreateTransformJob (Maybe ModelClientConfig) Source #

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

createTransformJob_batchStrategy :: Lens' CreateTransformJob (Maybe BatchStrategy) Source #

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.

createTransformJob_maxPayloadInMB :: Lens' CreateTransformJob (Maybe Natural) Source #

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

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

createTransformJob_environment :: Lens' CreateTransformJob (Maybe (HashMap Text Text)) Source #

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

createTransformJob_maxConcurrentTransforms :: Lens' CreateTransformJob (Maybe Natural) Source #

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.

createTransformJob_dataProcessing :: Lens' CreateTransformJob (Maybe DataProcessing) Source #

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.

createTransformJob_tags :: Lens' CreateTransformJob (Maybe [Tag]) Source #

(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.

createTransformJob_transformJobName :: Lens' CreateTransformJob Text Source #

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

createTransformJob_modelName :: Lens' CreateTransformJob Text Source #

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.

createTransformJob_transformInput :: Lens' CreateTransformJob TransformInput Source #

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

createTransformJob_transformResources :: Lens' CreateTransformJob TransformResources Source #

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

createTransformJobResponse_transformJobArn :: Lens' CreateTransformJobResponse Text Source #

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

ListArtifacts

listArtifacts_createdAfter :: Lens' ListArtifacts (Maybe UTCTime) Source #

A filter that returns only artifacts created on or after the specified time.

listArtifacts_nextToken :: Lens' ListArtifacts (Maybe Text) Source #

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.

listArtifacts_sortOrder :: Lens' ListArtifacts (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listArtifacts_sourceUri :: Lens' ListArtifacts (Maybe Text) Source #

A filter that returns only artifacts with the specified source URI.

listArtifacts_artifactType :: Lens' ListArtifacts (Maybe Text) Source #

A filter that returns only artifacts of the specified type.

listArtifacts_maxResults :: Lens' ListArtifacts (Maybe Natural) Source #

The maximum number of artifacts to return in the response. The default value is 10.

listArtifacts_createdBefore :: Lens' ListArtifacts (Maybe UTCTime) Source #

A filter that returns only artifacts created on or before the specified time.

listArtifacts_sortBy :: Lens' ListArtifacts (Maybe SortArtifactsBy) Source #

The property used to sort results. The default value is CreationTime.

listArtifactsResponse_nextToken :: Lens' ListArtifactsResponse (Maybe Text) Source #

A token for getting the next set of artifacts, if there are any.

DeleteDeviceFleet

UpdateDeviceFleet

updateDeviceFleet_enableIotRoleAlias :: Lens' UpdateDeviceFleet (Maybe Bool) Source #

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".

updateDeviceFleet_roleArn :: Lens' UpdateDeviceFleet (Maybe Text) Source #

The Amazon Resource Name (ARN) of the device.

updateDeviceFleet_outputConfig :: Lens' UpdateDeviceFleet EdgeOutputConfig Source #

Output configuration for storing sample data collected by the fleet.

ListCompilationJobs

listCompilationJobs_nameContains :: Lens' ListCompilationJobs (Maybe Text) Source #

A filter that returns the model compilation jobs whose name contains a specified string.

listCompilationJobs_lastModifiedTimeBefore :: Lens' ListCompilationJobs (Maybe UTCTime) Source #

A filter that returns the model compilation jobs that were modified before a specified time.

listCompilationJobs_creationTimeAfter :: Lens' ListCompilationJobs (Maybe UTCTime) Source #

A filter that returns the model compilation jobs that were created after a specified time.

listCompilationJobs_nextToken :: Lens' ListCompilationJobs (Maybe Text) Source #

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.

listCompilationJobs_sortOrder :: Lens' ListCompilationJobs (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listCompilationJobs_lastModifiedTimeAfter :: Lens' ListCompilationJobs (Maybe UTCTime) Source #

A filter that returns the model compilation jobs that were modified after a specified time.

listCompilationJobs_creationTimeBefore :: Lens' ListCompilationJobs (Maybe UTCTime) Source #

A filter that returns the model compilation jobs that were created before a specified time.

listCompilationJobs_statusEquals :: Lens' ListCompilationJobs (Maybe CompilationJobStatus) Source #

A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

listCompilationJobs_maxResults :: Lens' ListCompilationJobs (Maybe Natural) Source #

The maximum number of model compilation jobs to return in the response.

listCompilationJobs_sortBy :: Lens' ListCompilationJobs (Maybe ListCompilationJobsSortBy) Source #

The field by which to sort results. The default is CreationTime.

listCompilationJobsResponse_nextToken :: Lens' ListCompilationJobsResponse (Maybe Text) Source #

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.

listCompilationJobsResponse_compilationJobSummaries :: Lens' ListCompilationJobsResponse [CompilationJobSummary] Source #

An array of CompilationJobSummary objects, each describing a model compilation job.

DescribePipeline

describePipeline_pipelineName :: Lens' DescribePipeline Text Source #

The name of the pipeline to describe.

describePipelineResponse_pipelineArn :: Lens' DescribePipelineResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the pipeline.

describePipelineResponse_roleArn :: Lens' DescribePipelineResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) that the pipeline uses to execute.

DisassociateTrialComponent

disassociateTrialComponent_trialComponentName :: Lens' DisassociateTrialComponent Text Source #

The name of the component to disassociate from the trial.

DescribeModelPackageGroup

CreateEdgePackagingJob

createEdgePackagingJob_resourceKey :: Lens' CreateEdgePackagingJob (Maybe Text) Source #

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

createEdgePackagingJob_tags :: Lens' CreateEdgePackagingJob (Maybe [Tag]) Source #

Creates tags for the packaging job.

createEdgePackagingJob_compilationJobName :: Lens' CreateEdgePackagingJob Text Source #

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

createEdgePackagingJob_roleArn :: Lens' CreateEdgePackagingJob Text Source #

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

createEdgePackagingJob_outputConfig :: Lens' CreateEdgePackagingJob EdgeOutputConfig Source #

Provides information about the output location for the packaged model.

StopHyperParameterTuningJob

ListHumanTaskUis

listHumanTaskUis_creationTimeAfter :: Lens' ListHumanTaskUis (Maybe UTCTime) Source #

A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.

listHumanTaskUis_sortOrder :: Lens' ListHumanTaskUis (Maybe SortOrder) Source #

An optional value that specifies whether you want the results sorted in Ascending or Descending order.

listHumanTaskUis_creationTimeBefore :: Lens' ListHumanTaskUis (Maybe UTCTime) Source #

A filter that returns only human task user interfaces that were created before the specified timestamp.

listHumanTaskUis_maxResults :: Lens' ListHumanTaskUis (Maybe Natural) Source #

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.

listHumanTaskUisResponse_humanTaskUiSummaries :: Lens' ListHumanTaskUisResponse [HumanTaskUiSummary] Source #

An array of objects describing the human task user interfaces.

CreateEndpoint

createEndpoint_tags :: Lens' CreateEndpoint (Maybe [Tag]) Source #

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.

createEndpoint_endpointName :: Lens' CreateEndpoint Text Source #

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 .

createEndpoint_endpointConfigName :: Lens' CreateEndpoint Text Source #

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

createEndpointResponse_endpointArn :: Lens' CreateEndpointResponse Text Source #

The Amazon Resource Name (ARN) of the endpoint.

GetSearchSuggestions

getSearchSuggestions_suggestionQuery :: Lens' GetSearchSuggestions (Maybe SuggestionQuery) Source #

Limits the property names that are included in the response.

getSearchSuggestions_resource :: Lens' GetSearchSuggestions ResourceType Source #

The name of the Amazon SageMaker resource to search for.

getSearchSuggestionsResponse_propertyNameSuggestions :: Lens' GetSearchSuggestionsResponse (Maybe [PropertyNameSuggestion]) Source #

A list of property names for a Resource that match a SuggestionQuery.

UpdateArtifact

updateArtifact_properties :: Lens' UpdateArtifact (Maybe (HashMap Text Text)) Source #

The new list of properties. Overwrites the current property list.

updateArtifact_artifactArn :: Lens' UpdateArtifact Text Source #

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

updateArtifactResponse_artifactArn :: Lens' UpdateArtifactResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the artifact.

DeleteArtifact

deleteArtifact_artifactArn :: Lens' DeleteArtifact (Maybe Text) Source #

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

deleteArtifactResponse_artifactArn :: Lens' DeleteArtifactResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the artifact.

DescribeTrial

describeTrial_trialName :: Lens' DescribeTrial Text Source #

The name of the trial to describe.

describeTrialResponse_trialArn :: Lens' DescribeTrialResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the trial.

describeTrialResponse_experimentName :: Lens' DescribeTrialResponse (Maybe Text) Source #

The name of the experiment the trial is part of.

describeTrialResponse_source :: Lens' DescribeTrialResponse (Maybe TrialSource) Source #

The Amazon Resource Name (ARN) of the source and, optionally, the job type.

describeTrialResponse_displayName :: Lens' DescribeTrialResponse (Maybe Text) Source #

The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.

ListActions

listActions_createdAfter :: Lens' ListActions (Maybe UTCTime) Source #

A filter that returns only actions created on or after the specified time.

listActions_nextToken :: Lens' ListActions (Maybe Text) Source #

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.

listActions_sortOrder :: Lens' ListActions (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listActions_sourceUri :: Lens' ListActions (Maybe Text) Source #

A filter that returns only actions with the specified source URI.

listActions_actionType :: Lens' ListActions (Maybe Text) Source #

A filter that returns only actions of the specified type.

listActions_maxResults :: Lens' ListActions (Maybe Natural) Source #

The maximum number of actions to return in the response. The default value is 10.

listActions_createdBefore :: Lens' ListActions (Maybe UTCTime) Source #

A filter that returns only actions created on or before the specified time.

listActions_sortBy :: Lens' ListActions (Maybe SortActionsBy) Source #

The property used to sort results. The default value is CreationTime.

listActionsResponse_nextToken :: Lens' ListActionsResponse (Maybe Text) Source #

A token for getting the next set of actions, if there are any.

CreateArtifact

createArtifact_artifactName :: Lens' CreateArtifact (Maybe Text) Source #

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

createArtifact_tags :: Lens' CreateArtifact (Maybe [Tag]) Source #

A list of tags to apply to the artifact.

createArtifact_properties :: Lens' CreateArtifact (Maybe (HashMap Text Text)) Source #

A list of properties to add to the artifact.

createArtifact_source :: Lens' CreateArtifact ArtifactSource Source #

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

createArtifactResponse_artifactArn :: Lens' CreateArtifactResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the artifact.

CreatePresignedDomainUrl

createPresignedDomainUrl_sessionExpirationDurationInSeconds :: Lens' CreatePresignedDomainUrl (Maybe Natural) Source #

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

createPresignedDomainUrl_expiresInSeconds :: Lens' CreatePresignedDomainUrl (Maybe Natural) Source #

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

ListFeatureGroups

listFeatureGroups_nameContains :: Lens' ListFeatureGroups (Maybe Text) Source #

A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name.

listFeatureGroups_creationTimeAfter :: Lens' ListFeatureGroups (Maybe UTCTime) Source #

Use this parameter to search for FeatureGroupss created after a specific date and time.

listFeatureGroups_nextToken :: Lens' ListFeatureGroups (Maybe Text) Source #

A token to resume pagination of ListFeatureGroups results.

listFeatureGroups_sortOrder :: Lens' ListFeatureGroups (Maybe FeatureGroupSortOrder) Source #

The order in which feature groups are listed.

listFeatureGroups_creationTimeBefore :: Lens' ListFeatureGroups (Maybe UTCTime) Source #

Use this parameter to search for FeatureGroupss created before a specific date and time.

listFeatureGroups_offlineStoreStatusEquals :: Lens' ListFeatureGroups (Maybe OfflineStoreStatusValue) Source #

An OfflineStore status. Filters by OfflineStore status.

listFeatureGroups_featureGroupStatusEquals :: Lens' ListFeatureGroups (Maybe FeatureGroupStatus) Source #

A FeatureGroup status. Filters by FeatureGroup status.

listFeatureGroups_maxResults :: Lens' ListFeatureGroups (Maybe Natural) Source #

The maximum number of results returned by ListFeatureGroups.

listFeatureGroups_sortBy :: Lens' ListFeatureGroups (Maybe FeatureGroupSortBy) Source #

The value on which the feature group list is sorted.

listFeatureGroupsResponse_nextToken :: Lens' ListFeatureGroupsResponse (Maybe Text) Source #

A token to resume pagination of ListFeatureGroups results.

DescribeCodeRepository

describeCodeRepositoryResponse_gitConfig :: Lens' DescribeCodeRepositoryResponse (Maybe GitConfig) Source #

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.

DescribeContext

describeContext_contextName :: Lens' DescribeContext Text Source #

The name of the context to describe.

describeContextResponse_contextArn :: Lens' DescribeContextResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the context.

DescribeImage

describeImage_imageName :: Lens' DescribeImage Text Source #

The name of the image to describe.

describeImageResponse_failureReason :: Lens' DescribeImageResponse (Maybe Text) Source #

When a create, update, or delete operation fails, the reason for the failure.

describeImageResponse_imageArn :: Lens' DescribeImageResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the image.

describeImageResponse_roleArn :: Lens' DescribeImageResponse (Maybe Text) Source #

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

DescribeTrainingJob

describeTrainingJobResponse_labelingJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

describeTrainingJobResponse_failureReason :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

If the training job failed, the reason it failed.

describeTrainingJobResponse_secondaryStatusTransitions :: Lens' DescribeTrainingJobResponse (Maybe [SecondaryStatusTransition]) Source #

A history of all of the secondary statuses that the training job has transitioned through.

describeTrainingJobResponse_trainingEndTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

describeTrainingJobResponse_environment :: Lens' DescribeTrainingJobResponse (Maybe (HashMap Text Text)) Source #

The environment variables to set in the Docker container.

describeTrainingJobResponse_billableTimeInSeconds :: Lens' DescribeTrainingJobResponse (Maybe Natural) Source #

The billable time in seconds. Billable time refers to the absolute wall-clock time.

Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

describeTrainingJobResponse_retryStrategy :: Lens' DescribeTrainingJobResponse (Maybe RetryStrategy) Source #

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

describeTrainingJobResponse_enableNetworkIsolation :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

describeTrainingJobResponse_lastModifiedTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

A timestamp that indicates when the status of the training job was last modified.

describeTrainingJobResponse_debugRuleConfigurations :: Lens' DescribeTrainingJobResponse (Maybe [DebugRuleConfiguration]) Source #

Configuration information for Debugger rules for debugging output tensors.

describeTrainingJobResponse_enableManagedSpotTraining :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

A Boolean indicating whether managed spot training is enabled (True) or not (False).

describeTrainingJobResponse_autoMLJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of an AutoML job.

describeTrainingJobResponse_inputDataConfig :: Lens' DescribeTrainingJobResponse (Maybe (NonEmpty Channel)) Source #

An array of Channel objects that describes each data input channel.

describeTrainingJobResponse_profilerRuleConfigurations :: Lens' DescribeTrainingJobResponse (Maybe [ProfilerRuleConfiguration]) Source #

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

describeTrainingJobResponse_vpcConfig :: Lens' DescribeTrainingJobResponse (Maybe VpcConfig) Source #

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.

describeTrainingJobResponse_finalMetricDataList :: Lens' DescribeTrainingJobResponse (Maybe [MetricData]) Source #

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

describeTrainingJobResponse_outputDataConfig :: Lens' DescribeTrainingJobResponse (Maybe OutputDataConfig) Source #

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

describeTrainingJobResponse_trainingStartTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

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.

describeTrainingJobResponse_tuningJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

describeTrainingJobResponse_enableInterContainerTrafficEncryption :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

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.

describeTrainingJobResponse_roleArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

describeTrainingJobResponse_trainingJobArn :: Lens' DescribeTrainingJobResponse Text Source #

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

describeTrainingJobResponse_modelArtifacts :: Lens' DescribeTrainingJobResponse ModelArtifacts Source #

Information about the Amazon S3 location that is configured for storing model artifacts.

describeTrainingJobResponse_trainingJobStatus :: Lens' DescribeTrainingJobResponse TrainingJobStatus Source #

The status of the training job.

Amazon SageMaker provides the following training job statuses:

  • InProgress - The training is in progress.
  • Completed - The training job has completed.
  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
  • Stopping - The training job is stopping.
  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

describeTrainingJobResponse_secondaryStatus :: Lens' DescribeTrainingJobResponse SecondaryStatus Source #

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress
- Starting - Starting the training job.
  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
  • Training - Training is in progress.
  • Interrupted - The job stopped because the managed spot training instances were interrupted.
  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
- Completed - The training job has completed.
Failed
- Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
- MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
  • MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
  • Stopped - The training job has stopped.
Stopping
- Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances
  • PreparingTraining
  • DownloadingTrainingImage

describeTrainingJobResponse_algorithmSpecification :: Lens' DescribeTrainingJobResponse AlgorithmSpecification Source #

Information about the algorithm used for training, and algorithm metadata.

describeTrainingJobResponse_resourceConfig :: Lens' DescribeTrainingJobResponse ResourceConfig Source #

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

describeTrainingJobResponse_stoppingCondition :: Lens' DescribeTrainingJobResponse StoppingCondition Source #

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

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

describeTrainingJobResponse_creationTime :: Lens' DescribeTrainingJobResponse UTCTime Source #

A timestamp that indicates when the training job was created.

CreateAction

createAction_description :: Lens' CreateAction (Maybe Text) Source #

The description of the action.

createAction_tags :: Lens' CreateAction (Maybe [Tag]) Source #

A list of tags to apply to the action.

createAction_properties :: Lens' CreateAction (Maybe (HashMap Text Text)) Source #

A list of properties to add to the action.

createAction_actionName :: Lens' CreateAction Text Source #

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

createAction_source :: Lens' CreateAction ActionSource Source #

The source type, ID, and URI.

createActionResponse_actionArn :: Lens' CreateActionResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the action.

DeleteEndpoint

deleteEndpoint_endpointName :: Lens' DeleteEndpoint Text Source #

The name of the endpoint that you want to delete.

UpdateEndpoint

updateEndpoint_excludeRetainedVariantProperties :: Lens' UpdateEndpoint (Maybe [VariantProperty]) Source #

When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don't specify a value for ExcludeAllVariantProperties, no variant properties are overridden.

updateEndpoint_retainAllVariantProperties :: Lens' UpdateEndpoint (Maybe Bool) Source #

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.

updateEndpoint_deploymentConfig :: Lens' UpdateEndpoint (Maybe DeploymentConfig) Source #

The deployment configuration for the endpoint to be updated.

updateEndpoint_endpointName :: Lens' UpdateEndpoint Text Source #

The name of the endpoint whose configuration you want to update.

updateEndpoint_endpointConfigName :: Lens' UpdateEndpoint Text Source #

The name of the new endpoint configuration.

updateEndpointResponse_endpointArn :: Lens' UpdateEndpointResponse Text Source #

The Amazon Resource Name (ARN) of the endpoint.

DescribeDataQualityJobDefinition

describeDataQualityJobDefinition_jobDefinitionName :: Lens' DescribeDataQualityJobDefinition Text Source #

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

describeDataQualityJobDefinitionResponse_jobDefinitionArn :: Lens' DescribeDataQualityJobDefinitionResponse Text Source #

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

describeDataQualityJobDefinitionResponse_creationTime :: Lens' DescribeDataQualityJobDefinitionResponse UTCTime Source #

The time that the data quality monitoring job definition was created.

describeDataQualityJobDefinitionResponse_dataQualityJobInput :: Lens' DescribeDataQualityJobDefinitionResponse DataQualityJobInput Source #

The list of inputs for the data quality monitoring job. Currently endpoints are supported.

describeDataQualityJobDefinitionResponse_roleArn :: Lens' DescribeDataQualityJobDefinitionResponse Text Source #

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

CreateHumanTaskUi

createHumanTaskUi_tags :: Lens' CreateHumanTaskUi (Maybe [Tag]) Source #

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.

createHumanTaskUi_humanTaskUiName :: Lens' CreateHumanTaskUi Text Source #

The name of the user interface you are creating.

createHumanTaskUiResponse_humanTaskUiArn :: Lens' CreateHumanTaskUiResponse Text Source #

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

RegisterDevices

registerDevices_tags :: Lens' RegisterDevices (Maybe [Tag]) Source #

The tags associated with devices.

registerDevices_devices :: Lens' RegisterDevices [Device] Source #

A list of devices to register with SageMaker Edge Manager.

CreateCompilationJob

createCompilationJob_vpcConfig :: Lens' CreateCompilationJob (Maybe NeoVpcConfig) Source #

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.

createCompilationJob_tags :: Lens' CreateCompilationJob (Maybe [Tag]) Source #

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.

createCompilationJob_compilationJobName :: Lens' CreateCompilationJob Text Source #

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.

createCompilationJob_roleArn :: Lens' CreateCompilationJob Text Source #

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.

createCompilationJob_inputConfig :: Lens' CreateCompilationJob InputConfig Source #

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.

createCompilationJob_outputConfig :: Lens' CreateCompilationJob OutputConfig Source #

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

createCompilationJob_stoppingCondition :: Lens' CreateCompilationJob StoppingCondition Source #

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.

createCompilationJobResponse_compilationJobArn :: Lens' CreateCompilationJobResponse Text Source #

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.

DeleteAppImageConfig

UpdateAppImageConfig

updateAppImageConfigResponse_appImageConfigArn :: Lens' UpdateAppImageConfigResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) for the AppImageConfig.

DescribePipelineExecution

describePipelineExecution_pipelineExecutionArn :: Lens' DescribePipelineExecution Text Source #

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

DeleteNotebookInstanceLifecycleConfig

UpdateNotebookInstanceLifecycleConfig

updateNotebookInstanceLifecycleConfig_onCreate :: Lens' UpdateNotebookInstanceLifecycleConfig (Maybe [NotebookInstanceLifecycleHook]) Source #

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

updateNotebookInstanceLifecycleConfig_onStart :: Lens' UpdateNotebookInstanceLifecycleConfig (Maybe [NotebookInstanceLifecycleHook]) Source #

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.

DeleteWorkforce

UpdateWorkforce

updateWorkforce_sourceIpConfig :: Lens' UpdateWorkforce (Maybe SourceIpConfig) Source #

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

updateWorkforce_oidcConfig :: Lens' UpdateWorkforce (Maybe OidcConfig) Source #

Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.

updateWorkforce_workforceName :: Lens' UpdateWorkforce Text Source #

The name of the private workforce that you want to update. You can find your workforce name by using the operation.

updateWorkforceResponse_workforce :: Lens' UpdateWorkforceResponse Workforce Source #

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.

ListProcessingJobs

listProcessingJobs_nameContains :: Lens' ListProcessingJobs (Maybe Text) Source #

A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.

listProcessingJobs_lastModifiedTimeBefore :: Lens' ListProcessingJobs (Maybe UTCTime) Source #

A filter that returns only processing jobs modified before the specified time.

listProcessingJobs_creationTimeAfter :: Lens' ListProcessingJobs (Maybe UTCTime) Source #

A filter that returns only processing jobs created after the specified time.

listProcessingJobs_nextToken :: Lens' ListProcessingJobs (Maybe Text) Source #

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.

listProcessingJobs_sortOrder :: Lens' ListProcessingJobs (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listProcessingJobs_lastModifiedTimeAfter :: Lens' ListProcessingJobs (Maybe UTCTime) Source #

A filter that returns only processing jobs modified after the specified time.

listProcessingJobs_creationTimeBefore :: Lens' ListProcessingJobs (Maybe UTCTime) Source #

A filter that returns only processing jobs created after the specified time.

listProcessingJobs_statusEquals :: Lens' ListProcessingJobs (Maybe ProcessingJobStatus) Source #

A filter that retrieves only processing jobs with a specific status.

listProcessingJobs_maxResults :: Lens' ListProcessingJobs (Maybe Natural) Source #

The maximum number of processing jobs to return in the response.

listProcessingJobs_sortBy :: Lens' ListProcessingJobs (Maybe SortBy) Source #

The field to sort results by. The default is CreationTime.

listProcessingJobsResponse_nextToken :: Lens' ListProcessingJobsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.

listProcessingJobsResponse_processingJobSummaries :: Lens' ListProcessingJobsResponse [ProcessingJobSummary] Source #

An array of ProcessingJobSummary objects, each listing a processing job.

CreateLabelingJob

createLabelingJob_labelingJobAlgorithmsConfig :: Lens' CreateLabelingJob (Maybe LabelingJobAlgorithmsConfig) Source #

Configures the information required to perform automated data labeling.

createLabelingJob_labelCategoryConfigS3Uri :: Lens' CreateLabelingJob (Maybe Text) Source #

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

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

For named entity recognition jobs, in addition to "labels", you must provide worker instructions in the label category configuration file using the "instructions" parameter: "instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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

{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}

Note the following about the label category configuration file:

  • For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
  • Each label category must be unique, you cannot specify duplicate label categories.
  • If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.

createLabelingJob_stoppingConditions :: Lens' CreateLabelingJob (Maybe LabelingJobStoppingConditions) Source #

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.

createLabelingJob_tags :: Lens' CreateLabelingJob (Maybe [Tag]) Source #

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.

createLabelingJob_labelingJobName :: Lens' CreateLabelingJob Text Source #

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.

createLabelingJob_labelAttributeName :: Lens' CreateLabelingJob Text Source #

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.

createLabelingJob_inputConfig :: Lens' CreateLabelingJob LabelingJobInputConfig Source #

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.

createLabelingJob_outputConfig :: Lens' CreateLabelingJob LabelingJobOutputConfig Source #

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.

createLabelingJob_roleArn :: Lens' CreateLabelingJob Text Source #

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.

createLabelingJob_humanTaskConfig :: Lens' CreateLabelingJob HumanTaskConfig Source #

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

createLabelingJobResponse_labelingJobArn :: Lens' CreateLabelingJobResponse Text Source #

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

EnableSagemakerServicecatalogPortfolio

DescribeNotebookInstance

describeNotebookInstance_notebookInstanceName :: Lens' DescribeNotebookInstance Text Source #

The name of the notebook instance that you want information about.

describeNotebookInstanceResponse_creationTime :: Lens' DescribeNotebookInstanceResponse (Maybe UTCTime) Source #

A timestamp. Use this parameter to return the time when the notebook instance was created

describeNotebookInstanceResponse_acceleratorTypes :: Lens' DescribeNotebookInstanceResponse (Maybe [NotebookInstanceAcceleratorType]) Source #

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.

describeNotebookInstanceResponse_platformIdentifier :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

The platform identifier of the notebook instance runtime environment.

describeNotebookInstanceResponse_additionalCodeRepositories :: Lens' DescribeNotebookInstanceResponse (Maybe [Text]) Source #

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

describeNotebookInstanceResponse_url :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

describeNotebookInstanceResponse_lastModifiedTime :: Lens' DescribeNotebookInstanceResponse (Maybe UTCTime) Source #

A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

describeNotebookInstanceResponse_networkInterfaceId :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

The network interface IDs that Amazon SageMaker created at the time of creating the instance.

describeNotebookInstanceResponse_instanceType :: Lens' DescribeNotebookInstanceResponse (Maybe InstanceType) Source #

The type of ML compute instance running on the notebook instance.

describeNotebookInstanceResponse_defaultCodeRepository :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

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

describeNotebookInstanceResponse_volumeSizeInGB :: Lens' DescribeNotebookInstanceResponse (Maybe Natural) Source #

The size, in GB, of the ML storage volume attached to the notebook instance.

describeNotebookInstanceResponse_kmsKeyId :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

The Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

describeNotebookInstanceResponse_rootAccess :: Lens' DescribeNotebookInstanceResponse (Maybe RootAccess) Source #

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.

describeNotebookInstanceResponse_directInternetAccess :: Lens' DescribeNotebookInstanceResponse (Maybe DirectInternetAccess) Source #

Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.

For more information, see Notebook Instances Are Internet-Enabled by Default.

describeNotebookInstanceResponse_notebookInstanceLifecycleConfigName :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

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

describeNotebookInstanceResponse_roleArn :: Lens' DescribeNotebookInstanceResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the IAM role associated with the instance.

CreateMonitoringSchedule

createMonitoringSchedule_tags :: Lens' CreateMonitoringSchedule (Maybe [Tag]) Source #

(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.

createMonitoringSchedule_monitoringScheduleName :: Lens' CreateMonitoringSchedule Text Source #

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

createMonitoringSchedule_monitoringScheduleConfig :: Lens' CreateMonitoringSchedule MonitoringScheduleConfig Source #

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

ListAppImageConfigs

listAppImageConfigs_nameContains :: Lens' ListAppImageConfigs (Maybe Text) Source #

A filter that returns only AppImageConfigs whose name contains the specified string.

listAppImageConfigs_creationTimeAfter :: Lens' ListAppImageConfigs (Maybe UTCTime) Source #

A filter that returns only AppImageConfigs created on or after the specified time.

listAppImageConfigs_modifiedTimeAfter :: Lens' ListAppImageConfigs (Maybe UTCTime) Source #

A filter that returns only AppImageConfigs modified on or after the specified time.

listAppImageConfigs_nextToken :: Lens' ListAppImageConfigs (Maybe Text) Source #

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.

listAppImageConfigs_sortOrder :: Lens' ListAppImageConfigs (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listAppImageConfigs_creationTimeBefore :: Lens' ListAppImageConfigs (Maybe UTCTime) Source #

A filter that returns only AppImageConfigs created on or before the specified time.

listAppImageConfigs_modifiedTimeBefore :: Lens' ListAppImageConfigs (Maybe UTCTime) Source #

A filter that returns only AppImageConfigs modified on or before the specified time.

listAppImageConfigs_maxResults :: Lens' ListAppImageConfigs (Maybe Natural) Source #

The maximum number of AppImageConfigs to return in the response. The default value is 10.

listAppImageConfigs_sortBy :: Lens' ListAppImageConfigs (Maybe AppImageConfigSortKey) Source #

The property used to sort results. The default value is CreationTime.

listAppImageConfigsResponse_nextToken :: Lens' ListAppImageConfigsResponse (Maybe Text) Source #

A token for getting the next set of AppImageConfigs, if there are any.

CreateEndpointConfig

createEndpointConfig_asyncInferenceConfig :: Lens' CreateEndpointConfig (Maybe AsyncInferenceConfig) Source #

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

createEndpointConfig_kmsKeyId :: Lens' CreateEndpointConfig (Maybe Text) Source #

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

The KmsKeyId can be any of the following formats:

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

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

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

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

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

createEndpointConfig_tags :: Lens' CreateEndpointConfig (Maybe [Tag]) Source #

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.

createEndpointConfig_endpointConfigName :: Lens' CreateEndpointConfig Text Source #

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

createEndpointConfig_productionVariants :: Lens' CreateEndpointConfig (NonEmpty ProductionVariant) Source #

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

createEndpointConfigResponse_endpointConfigArn :: Lens' CreateEndpointConfigResponse Text Source #

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

SendPipelineExecutionStepSuccess

sendPipelineExecutionStepSuccess_clientRequestToken :: Lens' SendPipelineExecutionStepSuccess (Maybe Text) Source #

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

sendPipelineExecutionStepSuccess_callbackToken :: Lens' SendPipelineExecutionStepSuccess Text Source #

The pipeline generated token from the Amazon SQS queue.

DescribeModelQualityJobDefinition

describeModelQualityJobDefinition_jobDefinitionName :: Lens' DescribeModelQualityJobDefinition Text Source #

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

describeModelQualityJobDefinitionResponse_jobDefinitionName :: Lens' DescribeModelQualityJobDefinitionResponse Text Source #

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

describeModelQualityJobDefinitionResponse_roleArn :: Lens' DescribeModelQualityJobDefinitionResponse Text Source #

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

DeleteStudioLifecycleConfig

deleteStudioLifecycleConfig_studioLifecycleConfigName :: Lens' DeleteStudioLifecycleConfig Text Source #

The name of the Studio Lifecycle Configuration to delete.

DescribeModelExplainabilityJobDefinition

describeModelExplainabilityJobDefinition_jobDefinitionName :: Lens' DescribeModelExplainabilityJobDefinition Text Source #

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.

describeModelExplainabilityJobDefinitionResponse_jobDefinitionName :: Lens' DescribeModelExplainabilityJobDefinitionResponse Text Source #

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

describeModelExplainabilityJobDefinitionResponse_roleArn :: Lens' DescribeModelExplainabilityJobDefinitionResponse Text Source #

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.

StopNotebookInstance

stopNotebookInstance_notebookInstanceName :: Lens' StopNotebookInstance Text Source #

The name of the notebook instance to terminate.

UpdateEndpointWeightsAndCapacities

CreateAppImageConfig

createAppImageConfig_tags :: Lens' CreateAppImageConfig (Maybe [Tag]) Source #

A list of tags to apply to the AppImageConfig.

createAppImageConfig_appImageConfigName :: Lens' CreateAppImageConfig Text Source #

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

createAppImageConfigResponse_appImageConfigArn :: Lens' CreateAppImageConfigResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the AppImageConfig.

DeleteTags

deleteTags_resourceArn :: Lens' DeleteTags Text Source #

The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

deleteTags_tagKeys :: Lens' DeleteTags (NonEmpty Text) Source #

An array or one or more tag keys to delete.

ListExperiments

listExperiments_createdAfter :: Lens' ListExperiments (Maybe UTCTime) Source #

A filter that returns only experiments created after the specified time.

listExperiments_nextToken :: Lens' ListExperiments (Maybe Text) Source #

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.

listExperiments_sortOrder :: Lens' ListExperiments (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listExperiments_maxResults :: Lens' ListExperiments (Maybe Natural) Source #

The maximum number of experiments to return in the response. The default value is 10.

listExperiments_createdBefore :: Lens' ListExperiments (Maybe UTCTime) Source #

A filter that returns only experiments created before the specified time.

listExperiments_sortBy :: Lens' ListExperiments (Maybe SortExperimentsBy) Source #

The property used to sort results. The default value is CreationTime.

listExperimentsResponse_nextToken :: Lens' ListExperimentsResponse (Maybe Text) Source #

A token for getting the next set of experiments, if there are any.

DescribeProject

describeProject_projectName :: Lens' DescribeProject Text Source #

The name of the project to describe.

describeProjectResponse_projectArn :: Lens' DescribeProjectResponse Text Source #

The Amazon Resource Name (ARN) of the project.

ListAutoMLJobs

listAutoMLJobs_nameContains :: Lens' ListAutoMLJobs (Maybe Text) Source #

Request a list of jobs, using a search filter for name.

listAutoMLJobs_lastModifiedTimeBefore :: Lens' ListAutoMLJobs (Maybe UTCTime) Source #

Request a list of jobs, using a filter for time.

listAutoMLJobs_creationTimeAfter :: Lens' ListAutoMLJobs (Maybe UTCTime) Source #

Request a list of jobs, using a filter for time.

listAutoMLJobs_nextToken :: Lens' ListAutoMLJobs (Maybe Text) Source #

If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

listAutoMLJobs_sortOrder :: Lens' ListAutoMLJobs (Maybe AutoMLSortOrder) Source #

The sort order for the results. The default is Descending.

listAutoMLJobs_lastModifiedTimeAfter :: Lens' ListAutoMLJobs (Maybe UTCTime) Source #

Request a list of jobs, using a filter for time.

listAutoMLJobs_creationTimeBefore :: Lens' ListAutoMLJobs (Maybe UTCTime) Source #

Request a list of jobs, using a filter for time.

listAutoMLJobs_statusEquals :: Lens' ListAutoMLJobs (Maybe AutoMLJobStatus) Source #

Request a list of jobs, using a filter for status.

listAutoMLJobs_maxResults :: Lens' ListAutoMLJobs (Maybe Natural) Source #

Request a list of jobs up to a specified limit.

listAutoMLJobs_sortBy :: Lens' ListAutoMLJobs (Maybe AutoMLSortBy) Source #

The parameter by which to sort the results. The default is Name.

listAutoMLJobsResponse_nextToken :: Lens' ListAutoMLJobsResponse (Maybe Text) Source #

If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

ListApps

listApps_domainIdEquals :: Lens' ListApps (Maybe Text) Source #

A parameter to search for the domain ID.

listApps_nextToken :: Lens' ListApps (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

listApps_sortOrder :: Lens' ListApps (Maybe SortOrder) Source #

The sort order for the results. The default is Ascending.

listApps_userProfileNameEquals :: Lens' ListApps (Maybe Text) Source #

A parameter to search by user profile name.

listApps_maxResults :: Lens' ListApps (Maybe Natural) Source #

Returns a list up to a specified limit.

listApps_sortBy :: Lens' ListApps (Maybe AppSortKey) Source #

The parameter by which to sort the results. The default is CreationTime.

listAppsResponse_nextToken :: Lens' ListAppsResponse (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

listAppsResponse_httpStatus :: Lens' ListAppsResponse Int Source #

The response's http status code.

RetryPipelineExecution

retryPipelineExecution_pipelineExecutionArn :: Lens' RetryPipelineExecution Text Source #

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

retryPipelineExecution_clientRequestToken :: Lens' RetryPipelineExecution Text Source #

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

retryPipelineExecutionResponse_pipelineExecutionArn :: Lens' RetryPipelineExecutionResponse (Maybe Text) Source #

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

CreateProcessingJob

createProcessingJob_environment :: Lens' CreateProcessingJob (Maybe (HashMap Text Text)) Source #

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

createProcessingJob_stoppingCondition :: Lens' CreateProcessingJob (Maybe ProcessingStoppingCondition) Source #

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

createProcessingJob_processingInputs :: Lens' CreateProcessingJob (Maybe [ProcessingInput]) Source #

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

createProcessingJob_networkConfig :: Lens' CreateProcessingJob (Maybe NetworkConfig) Source #

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.

createProcessingJob_tags :: Lens' CreateProcessingJob (Maybe [Tag]) Source #

(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.

createProcessingJob_processingJobName :: Lens' CreateProcessingJob Text Source #

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

createProcessingJob_processingResources :: Lens' CreateProcessingJob ProcessingResources Source #

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.

createProcessingJob_appSpecification :: Lens' CreateProcessingJob AppSpecification Source #

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

createProcessingJob_roleArn :: Lens' CreateProcessingJob Text Source #

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

createProcessingJobResponse_processingJobArn :: Lens' CreateProcessingJobResponse Text Source #

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

DeleteMonitoringSchedule

DescribeModelPackage

describeModelPackage_modelPackageName :: Lens' DescribeModelPackage Text Source #

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).

describeModelPackageResponse_validationSpecification :: Lens' DescribeModelPackageResponse (Maybe ModelPackageValidationSpecification) Source #

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

describeModelPackageResponse_inferenceSpecification :: Lens' DescribeModelPackageResponse (Maybe InferenceSpecification) Source #

Details about inference jobs that can be run with models based on this model package.

describeModelPackageResponse_certifyForMarketplace :: Lens' DescribeModelPackageResponse (Maybe Bool) Source #

Whether the model package is certified for listing on Amazon Web Services Marketplace.

describeModelPackageResponse_modelPackageGroupName :: Lens' DescribeModelPackageResponse (Maybe Text) Source #

If the model is a versioned model, the name of the model group that the versioned model belongs to.

describeModelPackageResponse_modelPackageArn :: Lens' DescribeModelPackageResponse Text Source #

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

describeModelPackageResponse_creationTime :: Lens' DescribeModelPackageResponse UTCTime Source #

A timestamp specifying when the model package was created.

DeleteEndpointConfig

deleteEndpointConfig_endpointConfigName :: Lens' DeleteEndpointConfig Text Source #

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

UpdateMonitoringSchedule

updateMonitoringSchedule_monitoringScheduleName :: Lens' UpdateMonitoringSchedule Text Source #

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

updateMonitoringSchedule_monitoringScheduleConfig :: Lens' UpdateMonitoringSchedule MonitoringScheduleConfig Source #

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

AddAssociation

addAssociation_associationType :: Lens' AddAssociation (Maybe AssociationEdgeType) Source #

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.

addAssociation_destinationArn :: Lens' AddAssociation Text Source #

The Amazon Resource Name (ARN) of the destination.

addAssociationResponse_destinationArn :: Lens' AddAssociationResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the destination.

StartPipelineExecution

startPipelineExecution_pipelineParameters :: Lens' StartPipelineExecution (Maybe [Parameter]) Source #

Contains a list of pipeline parameters. This list can be empty.

startPipelineExecution_clientRequestToken :: Lens' StartPipelineExecution Text Source #

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

startPipelineExecutionResponse_pipelineExecutionArn :: Lens' StartPipelineExecutionResponse (Maybe Text) Source #

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

DeleteApp

deleteApp_appName :: Lens' DeleteApp Text Source #

The name of the app.

CreateAlgorithm

createAlgorithm_validationSpecification :: Lens' CreateAlgorithm (Maybe AlgorithmValidationSpecification) Source #

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

createAlgorithm_inferenceSpecification :: Lens' CreateAlgorithm (Maybe InferenceSpecification) Source #

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.

createAlgorithm_certifyForMarketplace :: Lens' CreateAlgorithm (Maybe Bool) Source #

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

createAlgorithm_tags :: Lens' CreateAlgorithm (Maybe [Tag]) Source #

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.

createAlgorithm_trainingSpecification :: Lens' CreateAlgorithm TrainingSpecification Source #

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.

createAlgorithmResponse_algorithmArn :: Lens' CreateAlgorithmResponse Text Source #

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

ListPipelineExecutionSteps

listPipelineExecutionSteps_pipelineExecutionArn :: Lens' ListPipelineExecutionSteps (Maybe Text) Source #

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

listPipelineExecutionSteps_nextToken :: Lens' ListPipelineExecutionSteps (Maybe Text) Source #

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.

listPipelineExecutionSteps_sortOrder :: Lens' ListPipelineExecutionSteps (Maybe SortOrder) Source #

The field by which to sort results. The default is CreatedTime.

listPipelineExecutionSteps_maxResults :: Lens' ListPipelineExecutionSteps (Maybe Natural) Source #

The maximum number of pipeline execution steps to return in the response.

listPipelineExecutionStepsResponse_pipelineExecutionSteps :: Lens' ListPipelineExecutionStepsResponse (Maybe [PipelineExecutionStep]) Source #

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.

listPipelineExecutionStepsResponse_nextToken :: Lens' ListPipelineExecutionStepsResponse (Maybe Text) Source #

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.

UpdatePipeline

updatePipeline_roleArn :: Lens' UpdatePipeline (Maybe Text) Source #

The Amazon Resource Name (ARN) that the pipeline uses to execute.

updatePipeline_pipelineName :: Lens' UpdatePipeline Text Source #

The name of the pipeline to update.

updatePipelineResponse_pipelineArn :: Lens' UpdatePipelineResponse (Maybe Text) Source #

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

StopTransformJob

stopTransformJob_transformJobName :: Lens' StopTransformJob Text Source #

The name of the transform job to stop.

DeletePipeline

deletePipeline_pipelineName :: Lens' DeletePipeline Text Source #

The name of the pipeline to delete.

deletePipeline_clientRequestToken :: Lens' DeletePipeline Text Source #

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

deletePipelineResponse_pipelineArn :: Lens' DeletePipelineResponse (Maybe Text) Source #

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

DescribeAction

describeAction_actionName :: Lens' DescribeAction Text Source #

The name of the action to describe.

describeActionResponse_actionArn :: Lens' DescribeActionResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the action.

CreateModel

createModel_primaryContainer :: Lens' CreateModel (Maybe ContainerDefinition) Source #

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.

createModel_enableNetworkIsolation :: Lens' CreateModel (Maybe Bool) Source #

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

createModel_containers :: Lens' CreateModel (Maybe [ContainerDefinition]) Source #

Specifies the containers in the inference pipeline.

createModel_vpcConfig :: Lens' CreateModel (Maybe VpcConfig) Source #

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.

createModel_inferenceExecutionConfig :: Lens' CreateModel (Maybe InferenceExecutionConfig) Source #

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

createModel_tags :: Lens' CreateModel (Maybe [Tag]) Source #

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.

createModel_modelName :: Lens' CreateModel Text Source #

The name of the new model.

createModel_executionRoleArn :: Lens' CreateModel Text Source #

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

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

createModelResponse_modelArn :: Lens' CreateModelResponse Text Source #

The ARN of the model created in Amazon SageMaker.

ListUserProfiles

listUserProfiles_domainIdEquals :: Lens' ListUserProfiles (Maybe Text) Source #

A parameter by which to filter the results.

listUserProfiles_userProfileNameContains :: Lens' ListUserProfiles (Maybe Text) Source #

A parameter by which to filter the results.

listUserProfiles_nextToken :: Lens' ListUserProfiles (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

listUserProfiles_sortOrder :: Lens' ListUserProfiles (Maybe SortOrder) Source #

The sort order for the results. The default is Ascending.

listUserProfiles_maxResults :: Lens' ListUserProfiles (Maybe Natural) Source #

Returns a list up to a specified limit.

listUserProfiles_sortBy :: Lens' ListUserProfiles (Maybe UserProfileSortKey) Source #

The parameter by which to sort the results. The default is CreationTime.

listUserProfilesResponse_nextToken :: Lens' ListUserProfilesResponse (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

CreateDataQualityJobDefinition

createDataQualityJobDefinition_tags :: Lens' CreateDataQualityJobDefinition (Maybe [Tag]) Source #

(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.

createDataQualityJobDefinition_dataQualityJobInput :: Lens' CreateDataQualityJobDefinition DataQualityJobInput Source #

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

createDataQualityJobDefinition_roleArn :: Lens' CreateDataQualityJobDefinition Text Source #

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

DeleteModelPackageGroup

DescribeArtifact

describeArtifact_artifactArn :: Lens' DescribeArtifact Text Source #

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

describeArtifactResponse_artifactArn :: Lens' DescribeArtifactResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the artifact.

StopEdgePackagingJob

CreateCodeRepository

createCodeRepository_tags :: Lens' CreateCodeRepository (Maybe [Tag]) Source #

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.

createCodeRepository_codeRepositoryName :: Lens' CreateCodeRepository Text Source #

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).

createCodeRepository_gitConfig :: Lens' CreateCodeRepository GitConfig Source #

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

createCodeRepositoryResponse_codeRepositoryArn :: Lens' CreateCodeRepositoryResponse Text Source #

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

CreateHyperParameterTuningJob

createHyperParameterTuningJob_trainingJobDefinition :: Lens' CreateHyperParameterTuningJob (Maybe HyperParameterTrainingJobDefinition) Source #

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.

createHyperParameterTuningJob_warmStartConfig :: Lens' CreateHyperParameterTuningJob (Maybe HyperParameterTuningJobWarmStartConfig) Source #

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.

createHyperParameterTuningJob_tags :: Lens' CreateHyperParameterTuningJob (Maybe [Tag]) Source #

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.

createHyperParameterTuningJob_trainingJobDefinitions :: Lens' CreateHyperParameterTuningJob (Maybe (NonEmpty HyperParameterTrainingJobDefinition)) Source #

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

createHyperParameterTuningJob_hyperParameterTuningJobName :: Lens' CreateHyperParameterTuningJob Text Source #

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.

createHyperParameterTuningJob_hyperParameterTuningJobConfig :: Lens' CreateHyperParameterTuningJob HyperParameterTuningJobConfig Source #

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.

createHyperParameterTuningJobResponse_hyperParameterTuningJobArn :: Lens' CreateHyperParameterTuningJobResponse Text Source #

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

DeleteTrial

deleteTrial_trialName :: Lens' DeleteTrial Text Source #

The name of the trial to delete.

deleteTrialResponse_trialArn :: Lens' DeleteTrialResponse (Maybe Text) Source #

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

UpdateTrial

updateTrial_displayName :: Lens' UpdateTrial (Maybe Text) Source #

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

updateTrial_trialName :: Lens' UpdateTrial Text Source #

The name of the trial to update.

updateTrialResponse_trialArn :: Lens' UpdateTrialResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the trial.

DescribeDeviceFleet

describeDeviceFleetResponse_iotRoleAlias :: Lens' DescribeDeviceFleetResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).

describeDeviceFleetResponse_roleArn :: Lens' DescribeDeviceFleetResponse (Maybe Text) Source #

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

describeDeviceFleetResponse_lastModifiedTime :: Lens' DescribeDeviceFleetResponse UTCTime Source #

Timestamp of when the device fleet was last updated.

ListCodeRepositories

listCodeRepositories_nameContains :: Lens' ListCodeRepositories (Maybe Text) Source #

A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

listCodeRepositories_lastModifiedTimeBefore :: Lens' ListCodeRepositories (Maybe UTCTime) Source #

A filter that returns only Git repositories that were last modified before the specified time.

listCodeRepositories_creationTimeAfter :: Lens' ListCodeRepositories (Maybe UTCTime) Source #

A filter that returns only Git repositories that were created after the specified time.

listCodeRepositories_nextToken :: Lens' ListCodeRepositories (Maybe Text) Source #

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.

listCodeRepositories_sortOrder :: Lens' ListCodeRepositories (Maybe CodeRepositorySortOrder) Source #

The sort order for results. The default is Ascending.

listCodeRepositories_lastModifiedTimeAfter :: Lens' ListCodeRepositories (Maybe UTCTime) Source #

A filter that returns only Git repositories that were last modified after the specified time.

listCodeRepositories_creationTimeBefore :: Lens' ListCodeRepositories (Maybe UTCTime) Source #

A filter that returns only Git repositories that were created before the specified time.

listCodeRepositories_maxResults :: Lens' ListCodeRepositories (Maybe Natural) Source #

The maximum number of Git repositories to return in the response.

listCodeRepositories_sortBy :: Lens' ListCodeRepositories (Maybe CodeRepositorySortBy) Source #

The field to sort results by. The default is Name.

listCodeRepositoriesResponse_nextToken :: Lens' ListCodeRepositoriesResponse (Maybe Text) Source #

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.

listCodeRepositoriesResponse_codeRepositorySummaryList :: Lens' ListCodeRepositoriesResponse [CodeRepositorySummary] Source #

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.

DescribeCompilationJob

describeCompilationJob_compilationJobName :: Lens' DescribeCompilationJob Text Source #

The name of the model compilation job that you want information about.

describeCompilationJobResponse_modelDigests :: Lens' DescribeCompilationJobResponse (Maybe ModelDigests) Source #

Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.

describeCompilationJobResponse_compilationStartTime :: Lens' DescribeCompilationJobResponse (Maybe UTCTime) Source #

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.

describeCompilationJobResponse_inferenceImage :: Lens' DescribeCompilationJobResponse (Maybe Text) Source #

The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.

describeCompilationJobResponse_vpcConfig :: Lens' DescribeCompilationJobResponse (Maybe NeoVpcConfig) Source #

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.

describeCompilationJobResponse_compilationEndTime :: Lens' DescribeCompilationJobResponse (Maybe UTCTime) Source #

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.

describeCompilationJobResponse_compilationJobArn :: Lens' DescribeCompilationJobResponse Text Source #

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

describeCompilationJobResponse_stoppingCondition :: Lens' DescribeCompilationJobResponse StoppingCondition Source #

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.

describeCompilationJobResponse_lastModifiedTime :: Lens' DescribeCompilationJobResponse UTCTime Source #

The time that the status of the model compilation job was last modified.

describeCompilationJobResponse_failureReason :: Lens' DescribeCompilationJobResponse Text Source #

If a model compilation job failed, the reason it failed.

describeCompilationJobResponse_modelArtifacts :: Lens' DescribeCompilationJobResponse ModelArtifacts Source #

Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

describeCompilationJobResponse_roleArn :: Lens' DescribeCompilationJobResponse Text Source #

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

describeCompilationJobResponse_inputConfig :: Lens' DescribeCompilationJobResponse InputConfig Source #

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.

describeCompilationJobResponse_outputConfig :: Lens' DescribeCompilationJobResponse OutputConfig Source #

Information about the output location for the compiled model and the target device that the model runs on.

ListPipelines

listPipelines_createdAfter :: Lens' ListPipelines (Maybe UTCTime) Source #

A filter that returns the pipelines that were created after a specified time.

listPipelines_nextToken :: Lens' ListPipelines (Maybe Text) Source #

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.

listPipelines_maxResults :: Lens' ListPipelines (Maybe Natural) Source #

The maximum number of pipelines to return in the response.

listPipelines_createdBefore :: Lens' ListPipelines (Maybe UTCTime) Source #

A filter that returns the pipelines that were created before a specified time.

listPipelines_sortBy :: Lens' ListPipelines (Maybe SortPipelinesBy) Source #

The field by which to sort results. The default is CreatedTime.

listPipelinesResponse_pipelineSummaries :: Lens' ListPipelinesResponse (Maybe [PipelineSummary]) Source #

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.

listPipelinesResponse_nextToken :: Lens' ListPipelinesResponse (Maybe Text) Source #

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.

ListHyperParameterTuningJobs

listHyperParameterTuningJobs_nameContains :: Lens' ListHyperParameterTuningJobs (Maybe Text) Source #

A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

listHyperParameterTuningJobs_lastModifiedTimeBefore :: Lens' ListHyperParameterTuningJobs (Maybe UTCTime) Source #

A filter that returns only tuning jobs that were modified before the specified time.

listHyperParameterTuningJobs_creationTimeAfter :: Lens' ListHyperParameterTuningJobs (Maybe UTCTime) Source #

A filter that returns only tuning jobs that were created after the specified time.

listHyperParameterTuningJobs_nextToken :: Lens' ListHyperParameterTuningJobs (Maybe Text) Source #

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.

listHyperParameterTuningJobs_sortOrder :: Lens' ListHyperParameterTuningJobs (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listHyperParameterTuningJobs_lastModifiedTimeAfter :: Lens' ListHyperParameterTuningJobs (Maybe UTCTime) Source #

A filter that returns only tuning jobs that were modified after the specified time.

listHyperParameterTuningJobs_creationTimeBefore :: Lens' ListHyperParameterTuningJobs (Maybe UTCTime) Source #

A filter that returns only tuning jobs that were created before the specified time.

listHyperParameterTuningJobs_maxResults :: Lens' ListHyperParameterTuningJobs (Maybe Natural) Source #

The maximum number of tuning jobs to return. The default value is 10.

listHyperParameterTuningJobsResponse_nextToken :: Lens' ListHyperParameterTuningJobsResponse (Maybe Text) Source #

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.

listHyperParameterTuningJobsResponse_hyperParameterTuningJobSummaries :: Lens' ListHyperParameterTuningJobsResponse [HyperParameterTuningJobSummary] Source #

A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

ListAlgorithms

listAlgorithms_nameContains :: Lens' ListAlgorithms (Maybe Text) Source #

A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

listAlgorithms_creationTimeAfter :: Lens' ListAlgorithms (Maybe UTCTime) Source #

A filter that returns only algorithms created after the specified time (timestamp).

listAlgorithms_nextToken :: Lens' ListAlgorithms (Maybe Text) Source #

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.

listAlgorithms_sortOrder :: Lens' ListAlgorithms (Maybe SortOrder) Source #

The sort order for the results. The default is Ascending.

listAlgorithms_creationTimeBefore :: Lens' ListAlgorithms (Maybe UTCTime) Source #

A filter that returns only algorithms created before the specified time (timestamp).

listAlgorithms_maxResults :: Lens' ListAlgorithms (Maybe Natural) Source #

The maximum number of algorithms to return in the response.

listAlgorithms_sortBy :: Lens' ListAlgorithms (Maybe AlgorithmSortBy) Source #

The parameter by which to sort the results. The default is CreationTime.

listAlgorithmsResponse_nextToken :: Lens' ListAlgorithmsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

listAlgorithmsResponse_algorithmSummaryList :: Lens' ListAlgorithmsResponse [AlgorithmSummary] Source #

An array of @AlgorithmSummary@ objects, each of which lists an

algorithm.

CreateModelPackageGroup

createModelPackageGroup_tags :: Lens' CreateModelPackageGroup (Maybe [Tag]) Source #

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.

GetSagemakerServicecatalogPortfolioStatus

DescribeFeatureGroup

describeFeatureGroup_nextToken :: Lens' DescribeFeatureGroup (Maybe Text) Source #

A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.

describeFeatureGroup_featureGroupName :: Lens' DescribeFeatureGroup Text Source #

The name of the FeatureGroup you want described.

describeFeatureGroupResponse_offlineStoreConfig :: Lens' DescribeFeatureGroupResponse (Maybe OfflineStoreConfig) Source #

The configuration of the OfflineStore, inducing the S3 location of the OfflineStore, Amazon Web Services Glue or Amazon Web Services Hive data catalogue configurations, and the security configuration.

describeFeatureGroupResponse_failureReason :: Lens' DescribeFeatureGroupResponse (Maybe Text) Source #

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.

describeFeatureGroupResponse_offlineStoreStatus :: Lens' DescribeFeatureGroupResponse (Maybe OfflineStoreStatus) Source #

The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked

describeFeatureGroupResponse_roleArn :: Lens' DescribeFeatureGroupResponse (Maybe Text) Source #

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

describeFeatureGroupResponse_featureGroupArn :: Lens' DescribeFeatureGroupResponse Text Source #

The Amazon Resource Name (ARN) of the FeatureGroup.

describeFeatureGroupResponse_recordIdentifierFeatureName :: Lens' DescribeFeatureGroupResponse Text Source #

The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.

describeFeatureGroupResponse_eventTimeFeatureName :: Lens' DescribeFeatureGroupResponse Text Source #

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.

describeFeatureGroupResponse_featureDefinitions :: Lens' DescribeFeatureGroupResponse (NonEmpty FeatureDefinition) Source #

A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.

describeFeatureGroupResponse_creationTime :: Lens' DescribeFeatureGroupResponse UTCTime Source #

A timestamp indicating when SageMaker created the FeatureGroup.

describeFeatureGroupResponse_nextToken :: Lens' DescribeFeatureGroupResponse Text Source #

A token to resume pagination of the list of Features (FeatureDefinitions).

RenderUiTemplate

renderUiTemplate_uiTemplate :: Lens' RenderUiTemplate (Maybe UiTemplate) Source #

A Template object containing the worker UI template to render.

renderUiTemplate_humanTaskUiArn :: Lens' RenderUiTemplate (Maybe Text) Source #

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.

renderUiTemplate_task :: Lens' RenderUiTemplate RenderableTask Source #

A RenderableTask object containing a representative task to render.

renderUiTemplate_roleArn :: Lens' RenderUiTemplate Text Source #

The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

renderUiTemplateResponse_renderedContent :: Lens' RenderUiTemplateResponse Text Source #

A Liquid template that renders the HTML for the worker UI.

renderUiTemplateResponse_errors :: Lens' RenderUiTemplateResponse [RenderingError] Source #

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.

DeleteFlowDefinition

deleteFlowDefinition_flowDefinitionName :: Lens' DeleteFlowDefinition Text Source #

The name of the flow definition you are deleting.

SendPipelineExecutionStepFailure

sendPipelineExecutionStepFailure_clientRequestToken :: Lens' SendPipelineExecutionStepFailure (Maybe Text) Source #

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

sendPipelineExecutionStepFailure_callbackToken :: Lens' SendPipelineExecutionStepFailure Text Source #

The pipeline generated token from the Amazon SQS queue.

CreateTrial

createTrial_displayName :: Lens' CreateTrial (Maybe Text) Source #

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

createTrial_tags :: Lens' CreateTrial (Maybe [Tag]) Source #

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

createTrial_trialName :: Lens' CreateTrial Text Source #

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

createTrial_experimentName :: Lens' CreateTrial Text Source #

The name of the experiment to associate the trial with.

createTrialResponse_trialArn :: Lens' CreateTrialResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the trial.

DeleteModel

deleteModel_modelName :: Lens' DeleteModel Text Source #

The name of the model to delete.

ListDataQualityJobDefinitions

listDataQualityJobDefinitions_nameContains :: Lens' ListDataQualityJobDefinitions (Maybe Text) Source #

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.

listDataQualityJobDefinitions_endpointName :: Lens' ListDataQualityJobDefinitions (Maybe Text) Source #

A filter that lists the data quality job definitions associated with the specified endpoint.

listDataQualityJobDefinitions_creationTimeAfter :: Lens' ListDataQualityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only data quality monitoring job definitions created after the specified time.

listDataQualityJobDefinitions_nextToken :: Lens' ListDataQualityJobDefinitions (Maybe Text) Source #

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.>

listDataQualityJobDefinitions_sortOrder :: Lens' ListDataQualityJobDefinitions (Maybe SortOrder) Source #

The sort order for results. The default is Descending.

listDataQualityJobDefinitions_creationTimeBefore :: Lens' ListDataQualityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only data quality monitoring job definitions created before the specified time.

listDataQualityJobDefinitions_maxResults :: Lens' ListDataQualityJobDefinitions (Maybe Natural) Source #

The maximum number of data quality monitoring job definitions to return in the response.

listDataQualityJobDefinitionsResponse_nextToken :: Lens' ListDataQualityJobDefinitionsResponse (Maybe Text) Source #

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.

ListModels

listModels_nameContains :: Lens' ListModels (Maybe Text) Source #

A string in the model name. This filter returns only models whose name contains the specified string.

listModels_creationTimeAfter :: Lens' ListModels (Maybe UTCTime) Source #

A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

listModels_nextToken :: Lens' ListModels (Maybe Text) Source #

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.

listModels_sortOrder :: Lens' ListModels (Maybe OrderKey) Source #

The sort order for results. The default is Descending.

listModels_creationTimeBefore :: Lens' ListModels (Maybe UTCTime) Source #

A filter that returns only models created before the specified time (timestamp).

listModels_maxResults :: Lens' ListModels (Maybe Natural) Source #

The maximum number of models to return in the response.

listModels_sortBy :: Lens' ListModels (Maybe ModelSortKey) Source #

Sorts the list of results. The default is CreationTime.

listModelsResponse_nextToken :: Lens' ListModelsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

listModelsResponse_models :: Lens' ListModelsResponse [ModelSummary] Source #

An array of ModelSummary objects, each of which lists a model.

DeleteAlgorithm

deleteAlgorithm_algorithmName :: Lens' DeleteAlgorithm Text Source #

The name of the algorithm to delete.

AssociateTrialComponent

associateTrialComponent_trialComponentName :: Lens' AssociateTrialComponent Text Source #

The name of the component to associated with the trial.

associateTrialComponent_trialName :: Lens' AssociateTrialComponent Text Source #

The name of the trial to associate with.

UpdatePipelineExecution

updatePipelineExecution_pipelineExecutionArn :: Lens' UpdatePipelineExecution Text Source #

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

updatePipelineExecutionResponse_pipelineExecutionArn :: Lens' UpdatePipelineExecutionResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the updated pipeline execution.

DescribeNotebookInstanceLifecycleConfig

describeNotebookInstanceLifecycleConfigResponse_onStart :: Lens' DescribeNotebookInstanceLifecycleConfigResponse (Maybe [NotebookInstanceLifecycleHook]) Source #

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

DescribeWorkforce

describeWorkforce_workforceName :: Lens' DescribeWorkforce Text Source #

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.

describeWorkforceResponse_workforce :: Lens' DescribeWorkforceResponse Workforce Source #

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.

DeleteModelExplainabilityJobDefinition

CreateModelPackage

createModelPackage_modelApprovalStatus :: Lens' CreateModelPackage (Maybe ModelApprovalStatus) Source #

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.

createModelPackage_sourceAlgorithmSpecification :: Lens' CreateModelPackage (Maybe SourceAlgorithmSpecification) Source #

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

createModelPackage_modelPackageName :: Lens' CreateModelPackage (Maybe Text) Source #

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.

createModelPackage_clientToken :: Lens' CreateModelPackage (Maybe Text) Source #

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

createModelPackage_modelMetrics :: Lens' CreateModelPackage (Maybe ModelMetrics) Source #

A structure that contains model metrics reports.

createModelPackage_validationSpecification :: Lens' CreateModelPackage (Maybe ModelPackageValidationSpecification) Source #

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

createModelPackage_inferenceSpecification :: Lens' CreateModelPackage (Maybe InferenceSpecification) Source #

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.

createModelPackage_certifyForMarketplace :: Lens' CreateModelPackage (Maybe Bool) Source #

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.

createModelPackage_modelPackageGroupName :: Lens' CreateModelPackage (Maybe Text) Source #

The name of the model group that this model version belongs to.

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

createModelPackage_tags :: Lens' CreateModelPackage (Maybe [Tag]) Source #

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.

createModelPackageResponse_modelPackageArn :: Lens' CreateModelPackageResponse Text Source #

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

DeleteModelQualityJobDefinition

deleteModelQualityJobDefinition_jobDefinitionName :: Lens' DeleteModelQualityJobDefinition Text Source #

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

StopMonitoringSchedule

ListModelExplainabilityJobDefinitions

listModelExplainabilityJobDefinitions_nameContains :: Lens' ListModelExplainabilityJobDefinitions (Maybe Text) Source #

Filter for model explainability jobs whose name contains a specified string.

listModelExplainabilityJobDefinitions_creationTimeAfter :: Lens' ListModelExplainabilityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model explainability jobs created after a specified time.

listModelExplainabilityJobDefinitions_nextToken :: Lens' ListModelExplainabilityJobDefinitions (Maybe Text) Source #

The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

listModelExplainabilityJobDefinitions_sortOrder :: Lens' ListModelExplainabilityJobDefinitions (Maybe SortOrder) Source #

Whether to sort the results in Ascending or Descending order. The default is Descending.

listModelExplainabilityJobDefinitions_creationTimeBefore :: Lens' ListModelExplainabilityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model explainability jobs created before a specified time.

listModelExplainabilityJobDefinitions_maxResults :: Lens' ListModelExplainabilityJobDefinitions (Maybe Natural) Source #

The maximum number of jobs to return in the response. The default value is 10.

listModelExplainabilityJobDefinitions_sortBy :: Lens' ListModelExplainabilityJobDefinitions (Maybe MonitoringJobDefinitionSortKey) Source #

Whether to sort results by the Name or CreationTime field. The default is CreationTime.

listModelExplainabilityJobDefinitionsResponse_nextToken :: Lens' ListModelExplainabilityJobDefinitionsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

DescribeAppImageConfig

ListNotebookInstances

listNotebookInstances_nameContains :: Lens' ListNotebookInstances (Maybe Text) Source #

A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.

listNotebookInstances_defaultCodeRepositoryContains :: Lens' ListNotebookInstances (Maybe Text) Source #

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.

listNotebookInstances_lastModifiedTimeBefore :: Lens' ListNotebookInstances (Maybe UTCTime) Source #

A filter that returns only notebook instances that were modified before the specified time (timestamp).

listNotebookInstances_notebookInstanceLifecycleConfigNameContains :: Lens' ListNotebookInstances (Maybe Text) Source #

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.

listNotebookInstances_creationTimeAfter :: Lens' ListNotebookInstances (Maybe UTCTime) Source #

A filter that returns only notebook instances that were created after the specified time (timestamp).

listNotebookInstances_additionalCodeRepositoryEquals :: Lens' ListNotebookInstances (Maybe Text) Source #

A filter that returns only notebook instances with associated with the specified git repository.

listNotebookInstances_nextToken :: Lens' ListNotebookInstances (Maybe Text) Source #

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.

listNotebookInstances_lastModifiedTimeAfter :: Lens' ListNotebookInstances (Maybe UTCTime) Source #

A filter that returns only notebook instances that were modified after the specified time (timestamp).

listNotebookInstances_creationTimeBefore :: Lens' ListNotebookInstances (Maybe UTCTime) Source #

A filter that returns only notebook instances that were created before the specified time (timestamp).

listNotebookInstances_statusEquals :: Lens' ListNotebookInstances (Maybe NotebookInstanceStatus) Source #

A filter that returns only notebook instances with the specified status.

listNotebookInstances_maxResults :: Lens' ListNotebookInstances (Maybe Natural) Source #

The maximum number of notebook instances to return.

listNotebookInstances_sortBy :: Lens' ListNotebookInstances (Maybe NotebookInstanceSortKey) Source #

The field to sort results by. The default is Name.

listNotebookInstancesResponse_notebookInstances :: Lens' ListNotebookInstancesResponse (Maybe [NotebookInstanceSummary]) Source #

An array of NotebookInstanceSummary objects, one for each notebook instance.

listNotebookInstancesResponse_nextToken :: Lens' ListNotebookInstancesResponse (Maybe Text) Source #

If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.

DescribeStudioLifecycleConfig

describeStudioLifecycleConfig_studioLifecycleConfigName :: Lens' DescribeStudioLifecycleConfig Text Source #

The name of the Studio Lifecycle Configuration to describe.

describeStudioLifecycleConfigResponse_lastModifiedTime :: Lens' DescribeStudioLifecycleConfigResponse (Maybe UTCTime) Source #

This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.

StopLabelingJob

stopLabelingJob_labelingJobName :: Lens' StopLabelingJob Text Source #

The name of the labeling job to stop.

DeleteNotebookInstance

deleteNotebookInstance_notebookInstanceName :: Lens' DeleteNotebookInstance Text Source #

The name of the Amazon SageMaker notebook instance to delete.

UpdateNotebookInstance

updateNotebookInstance_acceleratorTypes :: Lens' UpdateNotebookInstance (Maybe [NotebookInstanceAcceleratorType]) Source #

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.

updateNotebookInstance_disassociateAdditionalCodeRepositories :: Lens' UpdateNotebookInstance (Maybe Bool) Source #

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.

updateNotebookInstance_additionalCodeRepositories :: Lens' UpdateNotebookInstance (Maybe [Text]) Source #

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

updateNotebookInstance_lifecycleConfigName :: Lens' UpdateNotebookInstance (Maybe Text) Source #

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.

updateNotebookInstance_disassociateLifecycleConfig :: Lens' UpdateNotebookInstance (Maybe Bool) Source #

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.

updateNotebookInstance_disassociateDefaultCodeRepository :: Lens' UpdateNotebookInstance (Maybe Bool) Source #

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.

updateNotebookInstance_defaultCodeRepository :: Lens' UpdateNotebookInstance (Maybe Text) Source #

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

updateNotebookInstance_volumeSizeInGB :: Lens' UpdateNotebookInstance (Maybe Natural) Source #

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

updateNotebookInstance_rootAccess :: Lens' UpdateNotebookInstance (Maybe RootAccess) Source #

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.

updateNotebookInstance_disassociateAcceleratorTypes :: Lens' UpdateNotebookInstance (Maybe Bool) Source #

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.

updateNotebookInstance_roleArn :: Lens' UpdateNotebookInstance (Maybe Text) Source #

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.

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

ListModelPackages

listModelPackages_nameContains :: Lens' ListModelPackages (Maybe Text) Source #

A string in the model package name. This filter returns only model packages whose name contains the specified string.

listModelPackages_modelApprovalStatus :: Lens' ListModelPackages (Maybe ModelApprovalStatus) Source #

A filter that returns only the model packages with the specified approval status.

listModelPackages_modelPackageType :: Lens' ListModelPackages (Maybe ModelPackageType) Source #

A filter that returns onlyl the model packages of the specified type. This can be one of the following values.

  • VERSIONED - List only versioned models.
  • UNVERSIONED - List only unversioined models.
  • BOTH - List both versioned and unversioned models.

listModelPackages_creationTimeAfter :: Lens' ListModelPackages (Maybe UTCTime) Source #

A filter that returns only model packages created after the specified time (timestamp).

listModelPackages_nextToken :: Lens' ListModelPackages (Maybe Text) Source #

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.

listModelPackages_sortOrder :: Lens' ListModelPackages (Maybe SortOrder) Source #

The sort order for the results. The default is Ascending.

listModelPackages_modelPackageGroupName :: Lens' ListModelPackages (Maybe Text) Source #

A filter that returns only model versions that belong to the specified model group.

listModelPackages_creationTimeBefore :: Lens' ListModelPackages (Maybe UTCTime) Source #

A filter that returns only model packages created before the specified time (timestamp).

listModelPackages_maxResults :: Lens' ListModelPackages (Maybe Natural) Source #

The maximum number of model packages to return in the response.

listModelPackages_sortBy :: Lens' ListModelPackages (Maybe ModelPackageSortBy) Source #

The parameter by which to sort the results. The default is CreationTime.

listModelPackagesResponse_nextToken :: Lens' ListModelPackagesResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.

listModelPackagesResponse_modelPackageSummaryList :: Lens' ListModelPackagesResponse [ModelPackageSummary] Source #

An array of ModelPackageSummary objects, each of which lists a model package.

CreateModelQualityJobDefinition

createModelQualityJobDefinition_tags :: Lens' CreateModelQualityJobDefinition (Maybe [Tag]) Source #

(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.

createModelQualityJobDefinition_modelQualityJobInput :: Lens' CreateModelQualityJobDefinition ModelQualityJobInput Source #

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

createModelQualityJobDefinition_roleArn :: Lens' CreateModelQualityJobDefinition Text Source #

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

createModelQualityJobDefinitionResponse_jobDefinitionArn :: Lens' CreateModelQualityJobDefinitionResponse Text Source #

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

DeleteImageVersion

DescribeExperiment

describeExperiment_experimentName :: Lens' DescribeExperiment Text Source #

The name of the experiment to describe.

describeExperimentResponse_experimentArn :: Lens' DescribeExperimentResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the experiment.

describeExperimentResponse_displayName :: Lens' DescribeExperimentResponse (Maybe Text) Source #

The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.

DeleteTrialComponent

deleteTrialComponentResponse_trialComponentArn :: Lens' DeleteTrialComponentResponse (Maybe Text) Source #

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

UpdateTrialComponent

updateTrialComponent_parametersToRemove :: Lens' UpdateTrialComponent (Maybe [Text]) Source #

The hyperparameters to remove from the component.

updateTrialComponent_outputArtifacts :: Lens' UpdateTrialComponent (Maybe (HashMap Text TrialComponentArtifact)) Source #

Replaces all of the component's output artifacts with the specified artifacts.

updateTrialComponent_outputArtifactsToRemove :: Lens' UpdateTrialComponent (Maybe [Text]) Source #

The output artifacts to remove from the component.

updateTrialComponent_parameters :: Lens' UpdateTrialComponent (Maybe (HashMap Text TrialComponentParameterValue)) Source #

Replaces all of the component's hyperparameters with the specified hyperparameters.

updateTrialComponent_displayName :: Lens' UpdateTrialComponent (Maybe Text) Source #

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

updateTrialComponent_inputArtifacts :: Lens' UpdateTrialComponent (Maybe (HashMap Text TrialComponentArtifact)) Source #

Replaces all of the component's input artifacts with the specified artifacts.

updateTrialComponent_inputArtifactsToRemove :: Lens' UpdateTrialComponent (Maybe [Text]) Source #

The input artifacts to remove from the component.

updateTrialComponentResponse_trialComponentArn :: Lens' UpdateTrialComponentResponse (Maybe Text) Source #

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

DescribeLabelingJob

describeLabelingJob_labelingJobName :: Lens' DescribeLabelingJob Text Source #

The name of the labeling job to return information for.

describeLabelingJobResponse_labelCategoryConfigS3Uri :: Lens' DescribeLabelingJobResponse (Maybe Text) Source #

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"
 }
 ]
}

describeLabelingJobResponse_stoppingConditions :: Lens' DescribeLabelingJobResponse (Maybe LabelingJobStoppingConditions) Source #

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

describeLabelingJobResponse_labelAttributeName :: Lens' DescribeLabelingJobResponse (Maybe Text) Source #

The attribute used as the label in the output manifest file.

describeLabelingJobResponse_tags :: Lens' DescribeLabelingJobResponse (Maybe [Tag]) Source #

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.

describeLabelingJobResponse_labelCounters :: Lens' DescribeLabelingJobResponse LabelCounters Source #

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.

describeLabelingJobResponse_creationTime :: Lens' DescribeLabelingJobResponse UTCTime Source #

The date and time that the labeling job was created.

describeLabelingJobResponse_lastModifiedTime :: Lens' DescribeLabelingJobResponse UTCTime Source #

The date and time that the labeling job was last updated.

describeLabelingJobResponse_jobReferenceCode :: Lens' DescribeLabelingJobResponse Text Source #

A unique identifier for work done as part of a labeling job.

describeLabelingJobResponse_labelingJobName :: Lens' DescribeLabelingJobResponse Text Source #

The name assigned to the labeling job when it was created.

describeLabelingJobResponse_labelingJobArn :: Lens' DescribeLabelingJobResponse Text Source #

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

describeLabelingJobResponse_inputConfig :: Lens' DescribeLabelingJobResponse LabelingJobInputConfig Source #

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.

describeLabelingJobResponse_outputConfig :: Lens' DescribeLabelingJobResponse LabelingJobOutputConfig Source #

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.

describeLabelingJobResponse_roleArn :: Lens' DescribeLabelingJobResponse Text Source #

The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.

describeLabelingJobResponse_humanTaskConfig :: Lens' DescribeLabelingJobResponse HumanTaskConfig Source #

Configuration information required for human workers to complete a labeling task.

CreateDomain

createDomain_homeEfsFileSystemKmsKeyId :: Lens' CreateDomain (Maybe Text) Source #

This member is deprecated and replaced with KmsKeyId.

createDomain_kmsKeyId :: Lens' CreateDomain (Maybe Text) Source #

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.

createDomain_appNetworkAccessType :: Lens' CreateDomain (Maybe AppNetworkAccessType) Source #

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

createDomain_tags :: Lens' CreateDomain (Maybe [Tag]) Source #

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.

createDomain_authMode :: Lens' CreateDomain AuthMode Source #

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

createDomain_defaultUserSettings :: Lens' CreateDomain UserSettings Source #

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.

createDomain_subnetIds :: Lens' CreateDomain (NonEmpty Text) Source #

The VPC subnets that Studio uses for communication.

createDomain_vpcId :: Lens' CreateDomain Text Source #

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

createDomainResponse_domainArn :: Lens' CreateDomainResponse (Maybe Text) Source #

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

ListDeviceFleets

listDeviceFleets_nameContains :: Lens' ListDeviceFleets (Maybe Text) Source #

Filter for fleets containing this name in their fleet device name.

listDeviceFleets_lastModifiedTimeBefore :: Lens' ListDeviceFleets (Maybe UTCTime) Source #

Select fleets where the job was updated before X

listDeviceFleets_creationTimeAfter :: Lens' ListDeviceFleets (Maybe UTCTime) Source #

Filter fleets where packaging job was created after specified time.

listDeviceFleets_nextToken :: Lens' ListDeviceFleets (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

listDeviceFleets_lastModifiedTimeAfter :: Lens' ListDeviceFleets (Maybe UTCTime) Source #

Select fleets where the job was updated after X

listDeviceFleets_creationTimeBefore :: Lens' ListDeviceFleets (Maybe UTCTime) Source #

Filter fleets where the edge packaging job was created before specified time.

listDeviceFleets_maxResults :: Lens' ListDeviceFleets (Maybe Int) Source #

The maximum number of results to select.

listDeviceFleetsResponse_nextToken :: Lens' ListDeviceFleetsResponse (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

DescribeUserProfile

describeUserProfile_userProfileName :: Lens' DescribeUserProfile Text Source #

The user profile name. This value is not case sensitive.

describeUserProfileResponse_homeEfsFileSystemUid :: Lens' DescribeUserProfileResponse (Maybe Text) Source #

The ID of the user's profile in the Amazon Elastic File System (EFS) volume.

describeUserProfileResponse_domainId :: Lens' DescribeUserProfileResponse (Maybe Text) Source #

The ID of the domain that contains the profile.

ListMonitoringExecutions

listMonitoringExecutions_endpointName :: Lens' ListMonitoringExecutions (Maybe Text) Source #

Name of a specific endpoint to fetch jobs for.

listMonitoringExecutions_lastModifiedTimeBefore :: Lens' ListMonitoringExecutions (Maybe UTCTime) Source #

A filter that returns only jobs modified after a specified time.

listMonitoringExecutions_creationTimeAfter :: Lens' ListMonitoringExecutions (Maybe UTCTime) Source #

A filter that returns only jobs created after a specified time.

listMonitoringExecutions_nextToken :: Lens' ListMonitoringExecutions (Maybe Text) Source #

The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

listMonitoringExecutions_sortOrder :: Lens' ListMonitoringExecutions (Maybe SortOrder) Source #

Whether to sort the results in Ascending or Descending order. The default is Descending.

listMonitoringExecutions_lastModifiedTimeAfter :: Lens' ListMonitoringExecutions (Maybe UTCTime) Source #

A filter that returns only jobs modified before a specified time.

listMonitoringExecutions_creationTimeBefore :: Lens' ListMonitoringExecutions (Maybe UTCTime) Source #

A filter that returns only jobs created before a specified time.

listMonitoringExecutions_statusEquals :: Lens' ListMonitoringExecutions (Maybe ExecutionStatus) Source #

A filter that retrieves only jobs with a specific status.

listMonitoringExecutions_monitoringTypeEquals :: Lens' ListMonitoringExecutions (Maybe MonitoringType) Source #

A filter that returns only the monitoring job runs of the specified monitoring type.

listMonitoringExecutions_maxResults :: Lens' ListMonitoringExecutions (Maybe Natural) Source #

The maximum number of jobs to return in the response. The default value is 10.

listMonitoringExecutions_sortBy :: Lens' ListMonitoringExecutions (Maybe MonitoringExecutionSortKey) Source #

Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.

listMonitoringExecutions_monitoringJobDefinitionName :: Lens' ListMonitoringExecutions (Maybe Text) Source #

Gets a list of the monitoring job runs of the specified monitoring job definitions.

listMonitoringExecutionsResponse_nextToken :: Lens' ListMonitoringExecutionsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent reques

DeleteHumanTaskUi

deleteHumanTaskUi_humanTaskUiName :: Lens' DeleteHumanTaskUi Text Source #

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

StopTrainingJob

stopTrainingJob_trainingJobName :: Lens' StopTrainingJob Text Source #

The name of the training job to stop.

CreateFeatureGroup

createFeatureGroup_offlineStoreConfig :: Lens' CreateFeatureGroup (Maybe OfflineStoreConfig) Source #

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

  • The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore.
  • A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
  • An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.

To learn more about this parameter, see OfflineStoreConfig.

createFeatureGroup_onlineStoreConfig :: Lens' CreateFeatureGroup (Maybe OnlineStoreConfig) Source #

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.

createFeatureGroup_description :: Lens' CreateFeatureGroup (Maybe Text) Source #

A free-form description of a FeatureGroup.

createFeatureGroup_tags :: Lens' CreateFeatureGroup (Maybe [Tag]) Source #

Tags used to identify Features in each FeatureGroup.

createFeatureGroup_roleArn :: Lens' CreateFeatureGroup (Maybe Text) Source #

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

createFeatureGroup_featureGroupName :: Lens' CreateFeatureGroup Text Source #

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.

createFeatureGroup_recordIdentifierFeatureName :: Lens' CreateFeatureGroup Text Source #

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.

createFeatureGroup_eventTimeFeatureName :: Lens' CreateFeatureGroup Text Source #

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.

createFeatureGroup_featureDefinitions :: Lens' CreateFeatureGroup (NonEmpty FeatureDefinition) Source #

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.

createFeatureGroupResponse_featureGroupArn :: Lens' CreateFeatureGroupResponse Text Source #

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

DescribeAlgorithm

describeAlgorithm_algorithmName :: Lens' DescribeAlgorithm Text Source #

The name of the algorithm to describe.

describeAlgorithmResponse_validationSpecification :: Lens' DescribeAlgorithmResponse (Maybe AlgorithmValidationSpecification) Source #

Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

describeAlgorithmResponse_certifyForMarketplace :: Lens' DescribeAlgorithmResponse (Maybe Bool) Source #

Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

describeAlgorithmResponse_algorithmArn :: Lens' DescribeAlgorithmResponse Text Source #

The Amazon Resource Name (ARN) of the algorithm.

describeAlgorithmResponse_creationTime :: Lens' DescribeAlgorithmResponse UTCTime Source #

A timestamp specifying when the algorithm was created.

UpdateDevices

updateDevices_deviceFleetName :: Lens' UpdateDevices Text Source #

The name of the fleet the devices belong to.

updateDevices_devices :: Lens' UpdateDevices [Device] Source #

List of devices to register with Edge Manager agent.

DescribeModel

describeModelResponse_primaryContainer :: Lens' DescribeModelResponse (Maybe ContainerDefinition) Source #

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

describeModelResponse_enableNetworkIsolation :: Lens' DescribeModelResponse (Maybe Bool) Source #

If True, no inbound or outbound network calls can be made to or from the model container.

describeModelResponse_vpcConfig :: Lens' DescribeModelResponse (Maybe VpcConfig) Source #

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

describeModelResponse_inferenceExecutionConfig :: Lens' DescribeModelResponse (Maybe InferenceExecutionConfig) Source #

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

describeModelResponse_executionRoleArn :: Lens' DescribeModelResponse Text Source #

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

describeModelResponse_creationTime :: Lens' DescribeModelResponse UTCTime Source #

A timestamp that shows when the model was created.

describeModelResponse_modelArn :: Lens' DescribeModelResponse Text Source #

The Amazon Resource Name (ARN) of the model.

ListTransformJobs

listTransformJobs_nameContains :: Lens' ListTransformJobs (Maybe Text) Source #

A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

listTransformJobs_lastModifiedTimeBefore :: Lens' ListTransformJobs (Maybe UTCTime) Source #

A filter that returns only transform jobs modified before the specified time.

listTransformJobs_creationTimeAfter :: Lens' ListTransformJobs (Maybe UTCTime) Source #

A filter that returns only transform jobs created after the specified time.

listTransformJobs_nextToken :: Lens' ListTransformJobs (Maybe Text) Source #

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.

listTransformJobs_sortOrder :: Lens' ListTransformJobs (Maybe SortOrder) Source #

The sort order for results. The default is Descending.

listTransformJobs_lastModifiedTimeAfter :: Lens' ListTransformJobs (Maybe UTCTime) Source #

A filter that returns only transform jobs modified after the specified time.

listTransformJobs_creationTimeBefore :: Lens' ListTransformJobs (Maybe UTCTime) Source #

A filter that returns only transform jobs created before the specified time.

listTransformJobs_statusEquals :: Lens' ListTransformJobs (Maybe TransformJobStatus) Source #

A filter that retrieves only transform jobs with a specific status.

listTransformJobs_maxResults :: Lens' ListTransformJobs (Maybe Natural) Source #

The maximum number of transform jobs to return in the response. The default value is 10.

listTransformJobs_sortBy :: Lens' ListTransformJobs (Maybe SortBy) Source #

The field to sort results by. The default is CreationTime.

listTransformJobsResponse_nextToken :: Lens' ListTransformJobsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.

DeleteFeatureGroup

deleteFeatureGroup_featureGroupName :: Lens' DeleteFeatureGroup Text Source #

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.

ListEdgePackagingJobs

listEdgePackagingJobs_nameContains :: Lens' ListEdgePackagingJobs (Maybe Text) Source #

Filter for jobs containing this name in their packaging job name.

listEdgePackagingJobs_lastModifiedTimeBefore :: Lens' ListEdgePackagingJobs (Maybe UTCTime) Source #

Select jobs where the job was updated before specified time.

listEdgePackagingJobs_creationTimeAfter :: Lens' ListEdgePackagingJobs (Maybe UTCTime) Source #

Select jobs where the job was created after specified time.

listEdgePackagingJobs_nextToken :: Lens' ListEdgePackagingJobs (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

listEdgePackagingJobs_lastModifiedTimeAfter :: Lens' ListEdgePackagingJobs (Maybe UTCTime) Source #

Select jobs where the job was updated after specified time.

listEdgePackagingJobs_creationTimeBefore :: Lens' ListEdgePackagingJobs (Maybe UTCTime) Source #

Select jobs where the job was created before specified time.

listEdgePackagingJobs_modelNameContains :: Lens' ListEdgePackagingJobs (Maybe Text) Source #

Filter for jobs where the model name contains this string.

listEdgePackagingJobsResponse_nextToken :: Lens' ListEdgePackagingJobsResponse (Maybe Text) Source #

Token to use when calling the next page of results.

DescribeHyperParameterTuningJob

describeHyperParameterTuningJobResponse_trainingJobDefinition :: Lens' DescribeHyperParameterTuningJobResponse (Maybe HyperParameterTrainingJobDefinition) Source #

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

describeHyperParameterTuningJobResponse_bestTrainingJob :: Lens' DescribeHyperParameterTuningJobResponse (Maybe HyperParameterTrainingJobSummary) Source #

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

describeHyperParameterTuningJobResponse_overallBestTrainingJob :: Lens' DescribeHyperParameterTuningJobResponse (Maybe HyperParameterTrainingJobSummary) Source #

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.

describeHyperParameterTuningJobResponse_warmStartConfig :: Lens' DescribeHyperParameterTuningJobResponse (Maybe HyperParameterTuningJobWarmStartConfig) Source #

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.

describeHyperParameterTuningJobResponse_hyperParameterTuningJobConfig :: Lens' DescribeHyperParameterTuningJobResponse HyperParameterTuningJobConfig Source #

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

describeHyperParameterTuningJobResponse_trainingJobStatusCounters :: Lens' DescribeHyperParameterTuningJobResponse TrainingJobStatusCounters Source #

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

describeHyperParameterTuningJobResponse_objectiveStatusCounters :: Lens' DescribeHyperParameterTuningJobResponse ObjectiveStatusCounters Source #

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

ListEndpoints

listEndpoints_nameContains :: Lens' ListEndpoints (Maybe Text) Source #

A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

listEndpoints_lastModifiedTimeBefore :: Lens' ListEndpoints (Maybe UTCTime) Source #

A filter that returns only endpoints that were modified before the specified timestamp.

listEndpoints_creationTimeAfter :: Lens' ListEndpoints (Maybe UTCTime) Source #

A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

listEndpoints_nextToken :: Lens' ListEndpoints (Maybe Text) Source #

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.

listEndpoints_sortOrder :: Lens' ListEndpoints (Maybe OrderKey) Source #

The sort order for results. The default is Descending.

listEndpoints_lastModifiedTimeAfter :: Lens' ListEndpoints (Maybe UTCTime) Source #

A filter that returns only endpoints that were modified after the specified timestamp.

listEndpoints_creationTimeBefore :: Lens' ListEndpoints (Maybe UTCTime) Source #

A filter that returns only endpoints that were created before the specified time (timestamp).

listEndpoints_statusEquals :: Lens' ListEndpoints (Maybe EndpointStatus) Source #

A filter that returns only endpoints with the specified status.

listEndpoints_maxResults :: Lens' ListEndpoints (Maybe Natural) Source #

The maximum number of endpoints to return in the response. This value defaults to 10.

listEndpoints_sortBy :: Lens' ListEndpoints (Maybe EndpointSortKey) Source #

Sorts the list of results. The default is CreationTime.

listEndpointsResponse_nextToken :: Lens' ListEndpointsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

DescribeFlowDefinition

describeFlowDefinitionResponse_humanLoopRequestSource :: Lens' DescribeFlowDefinitionResponse (Maybe HumanLoopRequestSource) Source #

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

describeFlowDefinitionResponse_humanLoopActivationConfig :: Lens' DescribeFlowDefinitionResponse (Maybe HumanLoopActivationConfig) Source #

An object containing information about what triggers a human review workflow.

describeFlowDefinitionResponse_humanLoopConfig :: Lens' DescribeFlowDefinitionResponse HumanLoopConfig Source #

An object containing information about who works on the task, the workforce task price, and other task details.

describeFlowDefinitionResponse_roleArn :: Lens' DescribeFlowDefinitionResponse Text Source #

The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.

CreateDeviceFleet

createDeviceFleet_enableIotRoleAlias :: Lens' CreateDeviceFleet (Maybe Bool) Source #

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".

createDeviceFleet_tags :: Lens' CreateDeviceFleet (Maybe [Tag]) Source #

Creates tags for the specified fleet.

createDeviceFleet_roleArn :: Lens' CreateDeviceFleet (Maybe Text) Source #

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

createDeviceFleet_deviceFleetName :: Lens' CreateDeviceFleet Text Source #

The name of the fleet that the device belongs to.

createDeviceFleet_outputConfig :: Lens' CreateDeviceFleet EdgeOutputConfig Source #

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

CreatePresignedNotebookInstanceUrl

ListTrainingJobsForHyperParameterTuningJob

listTrainingJobsForHyperParameterTuningJob_nextToken :: Lens' ListTrainingJobsForHyperParameterTuningJob (Maybe Text) Source #

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.

listTrainingJobsForHyperParameterTuningJob_maxResults :: Lens' ListTrainingJobsForHyperParameterTuningJob (Maybe Natural) Source #

The maximum number of training jobs to return. The default value is 10.

listTrainingJobsForHyperParameterTuningJob_sortBy :: Lens' ListTrainingJobsForHyperParameterTuningJob (Maybe TrainingJobSortByOptions) Source #

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.

listTrainingJobsForHyperParameterTuningJobResponse_nextToken :: Lens' ListTrainingJobsForHyperParameterTuningJobResponse (Maybe Text) Source #

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.

listTrainingJobsForHyperParameterTuningJobResponse_trainingJobSummaries :: Lens' ListTrainingJobsForHyperParameterTuningJobResponse [HyperParameterTrainingJobSummary] Source #

A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.

DescribeDomain

describeDomainResponse_defaultUserSettings :: Lens' DescribeDomainResponse (Maybe UserSettings) Source #

Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.

describeDomainResponse_subnetIds :: Lens' DescribeDomainResponse (Maybe (NonEmpty Text)) Source #

The VPC subnets that Studio uses for communication.

describeDomainResponse_domainArn :: Lens' DescribeDomainResponse (Maybe Text) Source #

The domain's Amazon Resource Name (ARN).

describeDomainResponse_vpcId :: Lens' DescribeDomainResponse (Maybe Text) Source #

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

describeDomainResponse_homeEfsFileSystemKmsKeyId :: Lens' DescribeDomainResponse (Maybe Text) Source #

This member is deprecated and replaced with KmsKeyId.

describeDomainResponse_homeEfsFileSystemId :: Lens' DescribeDomainResponse (Maybe Text) Source #

The ID of the Amazon Elastic File System (EFS) managed by this Domain.

describeDomainResponse_kmsKeyId :: Lens' DescribeDomainResponse (Maybe Text) Source #

The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.

describeDomainResponse_appNetworkAccessType :: Lens' DescribeDomainResponse (Maybe AppNetworkAccessType) Source #

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

DeleteModelBiasJobDefinition

deleteModelBiasJobDefinition_jobDefinitionName :: Lens' DeleteModelBiasJobDefinition Text Source #

The name of the model bias job definition to delete.

UpdateWorkteam

updateWorkteam_notificationConfiguration :: Lens' UpdateWorkteam (Maybe NotificationConfiguration) Source #

Configures SNS topic notifications for available or expiring work items

updateWorkteam_memberDefinitions :: Lens' UpdateWorkteam (Maybe (NonEmpty MemberDefinition)) Source #

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 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.

updateWorkteam_description :: Lens' UpdateWorkteam (Maybe Text) Source #

An updated description for the work team.

updateWorkteam_workteamName :: Lens' UpdateWorkteam Text Source #

The name of the work team to update.

updateWorkteamResponse_workteam :: Lens' UpdateWorkteamResponse Workteam Source #

A Workteam object that describes the updated work team.

DeleteWorkteam

deleteWorkteam_workteamName :: Lens' DeleteWorkteam Text Source #

The name of the work team to delete.

deleteWorkteamResponse_success :: Lens' DeleteWorkteamResponse Bool Source #

Returns true if the work team was successfully deleted; otherwise, returns false.

ListWorkteams

listWorkteams_nameContains :: Lens' ListWorkteams (Maybe Text) Source #

A string in the work team's name. This filter returns only work teams whose name contains the specified string.

listWorkteams_nextToken :: Lens' ListWorkteams (Maybe Text) Source #

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.

listWorkteams_sortOrder :: Lens' ListWorkteams (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listWorkteams_maxResults :: Lens' ListWorkteams (Maybe Natural) Source #

The maximum number of work teams to return in each page of the response.

listWorkteams_sortBy :: Lens' ListWorkteams (Maybe ListWorkteamsSortByOptions) Source #

The field to sort results by. The default is CreationTime.

listWorkteamsResponse_nextToken :: Lens' ListWorkteamsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

listWorkteamsResponse_workteams :: Lens' ListWorkteamsResponse [Workteam] Source #

An array of Workteam objects, each describing a work team.

DescribeDevice

describeDevice_nextToken :: Lens' DescribeDevice (Maybe Text) Source #

Next token of device description.

describeDevice_deviceFleetName :: Lens' DescribeDevice Text Source #

The name of the fleet the devices belong to.

describeDeviceResponse_deviceArn :: Lens' DescribeDeviceResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the device.

describeDeviceResponse_nextToken :: Lens' DescribeDeviceResponse (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

describeDeviceResponse_iotThingName :: Lens' DescribeDeviceResponse (Maybe Text) Source #

The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.

describeDeviceResponse_deviceFleetName :: Lens' DescribeDeviceResponse Text Source #

The name of the fleet the device belongs to.

describeDeviceResponse_registrationTime :: Lens' DescribeDeviceResponse UTCTime Source #

The timestamp of the last registration or de-reregistration.

CreateAutoMLJob

createAutoMLJob_generateCandidateDefinitionsOnly :: Lens' CreateAutoMLJob (Maybe Bool) Source #

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

createAutoMLJob_problemType :: Lens' CreateAutoMLJob (Maybe ProblemType) Source #

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

createAutoMLJob_autoMLJobConfig :: Lens' CreateAutoMLJob (Maybe AutoMLJobConfig) Source #

Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

createAutoMLJob_autoMLJobObjective :: Lens' CreateAutoMLJob (Maybe AutoMLJobObjective) Source #

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.

createAutoMLJob_modelDeployConfig :: Lens' CreateAutoMLJob (Maybe ModelDeployConfig) Source #

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

createAutoMLJob_tags :: Lens' CreateAutoMLJob (Maybe [Tag]) Source #

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

createAutoMLJob_autoMLJobName :: Lens' CreateAutoMLJob Text Source #

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

createAutoMLJob_inputDataConfig :: Lens' CreateAutoMLJob (NonEmpty AutoMLChannel) Source #

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

createAutoMLJob_outputDataConfig :: Lens' CreateAutoMLJob AutoMLOutputDataConfig Source #

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

createAutoMLJob_roleArn :: Lens' CreateAutoMLJob Text Source #

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

createAutoMLJobResponse_autoMLJobArn :: Lens' CreateAutoMLJobResponse Text Source #

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

CreateApp

createApp_resourceSpec :: Lens' CreateApp (Maybe ResourceSpec) Source #

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

createApp_tags :: Lens' CreateApp (Maybe [Tag]) Source #

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

createApp_appType :: Lens' CreateApp AppType Source #

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

createApp_appName :: Lens' CreateApp Text Source #

The name of the app.

createAppResponse_appArn :: Lens' CreateAppResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the app.

createAppResponse_httpStatus :: Lens' CreateAppResponse Int Source #

The response's http status code.

CreateExperiment

createExperiment_displayName :: Lens' CreateExperiment (Maybe Text) Source #

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.

createExperiment_description :: Lens' CreateExperiment (Maybe Text) Source #

The description of the experiment.

createExperiment_tags :: Lens' CreateExperiment (Maybe [Tag]) Source #

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

createExperiment_experimentName :: Lens' CreateExperiment Text Source #

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

createExperimentResponse_experimentArn :: Lens' CreateExperimentResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the experiment.

ListNotebookInstanceLifecycleConfigs

listNotebookInstanceLifecycleConfigs_nameContains :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe Text) Source #

A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

listNotebookInstanceLifecycleConfigs_lastModifiedTimeBefore :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

listNotebookInstanceLifecycleConfigs_creationTimeAfter :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

listNotebookInstanceLifecycleConfigs_nextToken :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe Text) Source #

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.

listNotebookInstanceLifecycleConfigs_lastModifiedTimeAfter :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

listNotebookInstanceLifecycleConfigs_creationTimeBefore :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

listNotebookInstanceLifecycleConfigs_maxResults :: Lens' ListNotebookInstanceLifecycleConfigs (Maybe Natural) Source #

The maximum number of lifecycle configurations to return in the response.

listNotebookInstanceLifecycleConfigsResponse_nextToken :: Lens' ListNotebookInstanceLifecycleConfigsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.

ListWorkforces

listWorkforces_nameContains :: Lens' ListWorkforces (Maybe Text) Source #

A filter you can use to search for workforces using part of the workforce name.

listWorkforces_nextToken :: Lens' ListWorkforces (Maybe Text) Source #

A token to resume pagination.

listWorkforces_sortOrder :: Lens' ListWorkforces (Maybe SortOrder) Source #

Sort workforces in ascending or descending order.

listWorkforces_maxResults :: Lens' ListWorkforces (Maybe Natural) Source #

The maximum number of workforces returned in the response.

listWorkforces_sortBy :: Lens' ListWorkforces (Maybe ListWorkforcesSortByOptions) Source #

Sort workforces using the workforce name or creation date.

listWorkforcesResponse_workforces :: Lens' ListWorkforcesResponse [Workforce] Source #

A list containing information about your workforce.

DescribeSubscribedWorkteam

describeSubscribedWorkteam_workteamArn :: Lens' DescribeSubscribedWorkteam Text Source #

The Amazon Resource Name (ARN) of the subscribed work team to describe.

ListStudioLifecycleConfigs

listStudioLifecycleConfigs_nameContains :: Lens' ListStudioLifecycleConfigs (Maybe Text) Source #

A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.

listStudioLifecycleConfigs_creationTimeAfter :: Lens' ListStudioLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only Lifecycle Configurations created on or after the specified time.

listStudioLifecycleConfigs_modifiedTimeAfter :: Lens' ListStudioLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only Lifecycle Configurations modified after the specified time.

listStudioLifecycleConfigs_nextToken :: Lens' ListStudioLifecycleConfigs (Maybe Text) Source #

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.

listStudioLifecycleConfigs_sortOrder :: Lens' ListStudioLifecycleConfigs (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listStudioLifecycleConfigs_creationTimeBefore :: Lens' ListStudioLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only Lifecycle Configurations created on or before the specified time.

listStudioLifecycleConfigs_modifiedTimeBefore :: Lens' ListStudioLifecycleConfigs (Maybe UTCTime) Source #

A filter that returns only Lifecycle Configurations modified before the specified time.

listStudioLifecycleConfigs_appTypeEquals :: Lens' ListStudioLifecycleConfigs (Maybe StudioLifecycleConfigAppType) Source #

A parameter to search for the App Type to which the Lifecycle Configuration is attached.

listStudioLifecycleConfigs_maxResults :: Lens' ListStudioLifecycleConfigs (Maybe Natural) Source #

The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.

listStudioLifecycleConfigs_sortBy :: Lens' ListStudioLifecycleConfigs (Maybe StudioLifecycleConfigSortKey) Source #

The property used to sort results. The default value is CreationTime.

listStudioLifecycleConfigsResponse_nextToken :: Lens' ListStudioLifecycleConfigsResponse (Maybe Text) Source #

A token for getting the next set of actions, if there are any.

ListModelBiasJobDefinitions

listModelBiasJobDefinitions_nameContains :: Lens' ListModelBiasJobDefinitions (Maybe Text) Source #

Filter for model bias jobs whose name contains a specified string.

listModelBiasJobDefinitions_creationTimeAfter :: Lens' ListModelBiasJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model bias jobs created after a specified time.

listModelBiasJobDefinitions_nextToken :: Lens' ListModelBiasJobDefinitions (Maybe Text) Source #

The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

listModelBiasJobDefinitions_sortOrder :: Lens' ListModelBiasJobDefinitions (Maybe SortOrder) Source #

Whether to sort the results in Ascending or Descending order. The default is Descending.

listModelBiasJobDefinitions_creationTimeBefore :: Lens' ListModelBiasJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model bias jobs created before a specified time.

listModelBiasJobDefinitions_maxResults :: Lens' ListModelBiasJobDefinitions (Maybe Natural) Source #

The maximum number of model bias jobs to return in the response. The default value is 10.

listModelBiasJobDefinitions_sortBy :: Lens' ListModelBiasJobDefinitions (Maybe MonitoringJobDefinitionSortKey) Source #

Whether to sort results by the Name or CreationTime field. The default is CreationTime.

listModelBiasJobDefinitionsResponse_nextToken :: Lens' ListModelBiasJobDefinitionsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

CreateStudioLifecycleConfig

createStudioLifecycleConfig_tags :: Lens' CreateStudioLifecycleConfig (Maybe [Tag]) Source #

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.

createStudioLifecycleConfig_studioLifecycleConfigName :: Lens' CreateStudioLifecycleConfig Text Source #

The name of the Studio Lifecycle Configuration to create.

createStudioLifecycleConfig_studioLifecycleConfigContent :: Lens' CreateStudioLifecycleConfig Text Source #

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

DisableSagemakerServicecatalogPortfolio

CreateWorkteam

createWorkteam_notificationConfiguration :: Lens' CreateWorkteam (Maybe NotificationConfiguration) Source #

Configures notification of workers regarding available or expiring work items.

createWorkteam_tags :: Lens' CreateWorkteam (Maybe [Tag]) Source #

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.

createWorkteam_workteamName :: Lens' CreateWorkteam Text Source #

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

createWorkteam_memberDefinitions :: Lens' CreateWorkteam (NonEmpty MemberDefinition) Source #

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 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.

createWorkteam_description :: Lens' CreateWorkteam Text Source #

A description of the work team.

createWorkteamResponse_workteamArn :: Lens' CreateWorkteamResponse (Maybe Text) Source #

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

CreateNotebookInstanceLifecycleConfig

createNotebookInstanceLifecycleConfig_onCreate :: Lens' CreateNotebookInstanceLifecycleConfig (Maybe [NotebookInstanceLifecycleHook]) Source #

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

createNotebookInstanceLifecycleConfig_onStart :: Lens' CreateNotebookInstanceLifecycleConfig (Maybe [NotebookInstanceLifecycleHook]) Source #

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.

ListMonitoringSchedules

listMonitoringSchedules_nameContains :: Lens' ListMonitoringSchedules (Maybe Text) Source #

Filter for monitoring schedules whose name contains a specified string.

listMonitoringSchedules_endpointName :: Lens' ListMonitoringSchedules (Maybe Text) Source #

Name of a specific endpoint to fetch schedules for.

listMonitoringSchedules_lastModifiedTimeBefore :: Lens' ListMonitoringSchedules (Maybe UTCTime) Source #

A filter that returns only monitoring schedules modified before a specified time.

listMonitoringSchedules_creationTimeAfter :: Lens' ListMonitoringSchedules (Maybe UTCTime) Source #

A filter that returns only monitoring schedules created after a specified time.

listMonitoringSchedules_nextToken :: Lens' ListMonitoringSchedules (Maybe Text) Source #

The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.

listMonitoringSchedules_sortOrder :: Lens' ListMonitoringSchedules (Maybe SortOrder) Source #

Whether to sort the results in Ascending or Descending order. The default is Descending.

listMonitoringSchedules_lastModifiedTimeAfter :: Lens' ListMonitoringSchedules (Maybe UTCTime) Source #

A filter that returns only monitoring schedules modified after a specified time.

listMonitoringSchedules_creationTimeBefore :: Lens' ListMonitoringSchedules (Maybe UTCTime) Source #

A filter that returns only monitoring schedules created before a specified time.

listMonitoringSchedules_statusEquals :: Lens' ListMonitoringSchedules (Maybe ScheduleStatus) Source #

A filter that returns only monitoring schedules modified before a specified time.

listMonitoringSchedules_monitoringTypeEquals :: Lens' ListMonitoringSchedules (Maybe MonitoringType) Source #

A filter that returns only the monitoring schedules for the specified monitoring type.

listMonitoringSchedules_maxResults :: Lens' ListMonitoringSchedules (Maybe Natural) Source #

The maximum number of jobs to return in the response. The default value is 10.

listMonitoringSchedules_sortBy :: Lens' ListMonitoringSchedules (Maybe MonitoringScheduleSortKey) Source #

Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.

listMonitoringSchedules_monitoringJobDefinitionName :: Lens' ListMonitoringSchedules (Maybe Text) Source #

Gets a list of the monitoring schedules for the specified monitoring job definition.

listMonitoringSchedulesResponse_nextToken :: Lens' ListMonitoringSchedulesResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.

ListLabelingJobs

listLabelingJobs_nameContains :: Lens' ListLabelingJobs (Maybe Text) Source #

A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

listLabelingJobs_lastModifiedTimeBefore :: Lens' ListLabelingJobs (Maybe UTCTime) Source #

A filter that returns only labeling jobs modified before the specified time (timestamp).

listLabelingJobs_creationTimeAfter :: Lens' ListLabelingJobs (Maybe UTCTime) Source #

A filter that returns only labeling jobs created after the specified time (timestamp).

listLabelingJobs_nextToken :: Lens' ListLabelingJobs (Maybe Text) Source #

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.

listLabelingJobs_sortOrder :: Lens' ListLabelingJobs (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listLabelingJobs_lastModifiedTimeAfter :: Lens' ListLabelingJobs (Maybe UTCTime) Source #

A filter that returns only labeling jobs modified after the specified time (timestamp).

listLabelingJobs_creationTimeBefore :: Lens' ListLabelingJobs (Maybe UTCTime) Source #

A filter that returns only labeling jobs created before the specified time (timestamp).

listLabelingJobs_statusEquals :: Lens' ListLabelingJobs (Maybe LabelingJobStatus) Source #

A filter that retrieves only labeling jobs with a specific status.

listLabelingJobs_maxResults :: Lens' ListLabelingJobs (Maybe Natural) Source #

The maximum number of labeling jobs to return in each page of the response.

listLabelingJobs_sortBy :: Lens' ListLabelingJobs (Maybe SortBy) Source #

The field to sort results by. The default is CreationTime.

listLabelingJobsResponse_labelingJobSummaryList :: Lens' ListLabelingJobsResponse (Maybe [LabelingJobSummary]) Source #

An array of LabelingJobSummary objects, each describing a labeling job.

listLabelingJobsResponse_nextToken :: Lens' ListLabelingJobsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

StartNotebookInstance

UpdateExperiment

updateExperiment_displayName :: Lens' UpdateExperiment (Maybe Text) Source #

The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't specified, ExperimentName is displayed.

updateExperiment_description :: Lens' UpdateExperiment (Maybe Text) Source #

The description of the experiment.

updateExperiment_experimentName :: Lens' UpdateExperiment Text Source #

The name of the experiment to update.

updateExperimentResponse_experimentArn :: Lens' UpdateExperimentResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the experiment.

DeleteExperiment

deleteExperiment_experimentName :: Lens' DeleteExperiment Text Source #

The name of the experiment to delete.

deleteExperimentResponse_experimentArn :: Lens' DeleteExperimentResponse (Maybe Text) Source #

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

StopPipelineExecution

stopPipelineExecution_pipelineExecutionArn :: Lens' StopPipelineExecution Text Source #

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

stopPipelineExecution_clientRequestToken :: Lens' StopPipelineExecution Text Source #

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

stopPipelineExecutionResponse_pipelineExecutionArn :: Lens' StopPipelineExecutionResponse (Maybe Text) Source #

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

AddTags

addTags_resourceArn :: Lens' AddTags Text Source #

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

addTags_tags :: Lens' AddTags [Tag] Source #

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.

addTagsResponse_tags :: Lens' AddTagsResponse (Maybe [Tag]) Source #

A list of tags associated with the Amazon SageMaker resource.

addTagsResponse_httpStatus :: Lens' AddTagsResponse Int Source #

The response's http status code.

ListAssociations

listAssociations_createdAfter :: Lens' ListAssociations (Maybe UTCTime) Source #

A filter that returns only associations created on or after the specified time.

listAssociations_sourceType :: Lens' ListAssociations (Maybe Text) Source #

A filter that returns only associations with the specified source type.

listAssociations_sourceArn :: Lens' ListAssociations (Maybe Text) Source #

A filter that returns only associations with the specified source ARN.

listAssociations_associationType :: Lens' ListAssociations (Maybe AssociationEdgeType) Source #

A filter that returns only associations of the specified type.

listAssociations_destinationArn :: Lens' ListAssociations (Maybe Text) Source #

A filter that returns only associations with the specified destination Amazon Resource Name (ARN).

listAssociations_nextToken :: Lens' ListAssociations (Maybe Text) Source #

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.

listAssociations_destinationType :: Lens' ListAssociations (Maybe Text) Source #

A filter that returns only associations with the specified destination type.

listAssociations_sortOrder :: Lens' ListAssociations (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listAssociations_maxResults :: Lens' ListAssociations (Maybe Natural) Source #

The maximum number of associations to return in the response. The default value is 10.

listAssociations_createdBefore :: Lens' ListAssociations (Maybe UTCTime) Source #

A filter that returns only associations created on or before the specified time.

listAssociations_sortBy :: Lens' ListAssociations (Maybe SortAssociationsBy) Source #

The property used to sort results. The default value is CreationTime.

listAssociationsResponse_nextToken :: Lens' ListAssociationsResponse (Maybe Text) Source #

A token for getting the next set of associations, if there are any.

CreateWorkforce

createWorkforce_cognitoConfig :: Lens' CreateWorkforce (Maybe CognitoConfig) Source #

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.

createWorkforce_oidcConfig :: Lens' CreateWorkforce (Maybe OidcConfig) Source #

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

Do not use CognitoConfig if you specify values for OidcConfig.

createWorkforce_tags :: Lens' CreateWorkforce (Maybe [Tag]) Source #

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.

createWorkforce_workforceName :: Lens' CreateWorkforce Text Source #

The name of the private workforce.

createWorkforceResponse_workforceArn :: Lens' CreateWorkforceResponse Text Source #

The Amazon Resource Name (ARN) of the workforce.

DescribeTrialComponent

describeTrialComponentResponse_status :: Lens' DescribeTrialComponentResponse (Maybe TrialComponentStatus) Source #

The status of the component. States include:

  • InProgress
  • Completed
  • Failed

describeTrialComponentResponse_source :: Lens' DescribeTrialComponentResponse (Maybe TrialComponentSource) Source #

The Amazon Resource Name (ARN) of the source and, optionally, the job type.

describeTrialComponentResponse_displayName :: Lens' DescribeTrialComponentResponse (Maybe Text) Source #

The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.

DescribeImageVersion

describeImageVersion_version :: Lens' DescribeImageVersion (Maybe Natural) Source #

The version of the image. If not specified, the latest version is described.

describeImageVersionResponse_failureReason :: Lens' DescribeImageVersionResponse (Maybe Text) Source #

When a create or delete operation fails, the reason for the failure.

describeImageVersionResponse_containerImage :: Lens' DescribeImageVersionResponse (Maybe Text) Source #

The registry path of the container image that contains this image version.

describeImageVersionResponse_baseImage :: Lens' DescribeImageVersionResponse (Maybe Text) Source #

The registry path of the container image on which this image version is based.

describeImageVersionResponse_imageArn :: Lens' DescribeImageVersionResponse (Maybe Text) Source #

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

CreateModelBiasJobDefinition

createModelBiasJobDefinition_tags :: Lens' CreateModelBiasJobDefinition (Maybe [Tag]) Source #

(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.

createModelBiasJobDefinition_jobDefinitionName :: Lens' CreateModelBiasJobDefinition Text Source #

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

createModelBiasJobDefinition_modelBiasAppSpecification :: Lens' CreateModelBiasJobDefinition ModelBiasAppSpecification Source #

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

createModelBiasJobDefinition_roleArn :: Lens' CreateModelBiasJobDefinition Text Source #

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

ListEndpointConfigs

listEndpointConfigs_nameContains :: Lens' ListEndpointConfigs (Maybe Text) Source #

A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

listEndpointConfigs_creationTimeAfter :: Lens' ListEndpointConfigs (Maybe UTCTime) Source #

A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

listEndpointConfigs_nextToken :: Lens' ListEndpointConfigs (Maybe Text) Source #

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.

listEndpointConfigs_sortOrder :: Lens' ListEndpointConfigs (Maybe OrderKey) Source #

The sort order for results. The default is Descending.

listEndpointConfigs_creationTimeBefore :: Lens' ListEndpointConfigs (Maybe UTCTime) Source #

A filter that returns only endpoint configurations created before the specified time (timestamp).

listEndpointConfigs_maxResults :: Lens' ListEndpointConfigs (Maybe Natural) Source #

The maximum number of training jobs to return in the response.

listEndpointConfigs_sortBy :: Lens' ListEndpointConfigs (Maybe EndpointConfigSortKey) Source #

The field to sort results by. The default is CreationTime.

listEndpointConfigsResponse_nextToken :: Lens' ListEndpointConfigsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

DeleteAssociation

deleteAssociation_destinationArn :: Lens' DeleteAssociation Text Source #

The Amazon Resource Name (ARN) of the destination.

deleteAssociationResponse_destinationArn :: Lens' DeleteAssociationResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the destination.

CreateFlowDefinition

createFlowDefinition_humanLoopRequestSource :: Lens' CreateFlowDefinition (Maybe HumanLoopRequestSource) Source #

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

createFlowDefinition_humanLoopActivationConfig :: Lens' CreateFlowDefinition (Maybe HumanLoopActivationConfig) Source #

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

createFlowDefinition_tags :: Lens' CreateFlowDefinition (Maybe [Tag]) Source #

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.

createFlowDefinition_humanLoopConfig :: Lens' CreateFlowDefinition HumanLoopConfig Source #

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

createFlowDefinition_outputConfig :: Lens' CreateFlowDefinition FlowDefinitionOutputConfig Source #

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

createFlowDefinition_roleArn :: Lens' CreateFlowDefinition Text Source #

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.

createFlowDefinitionResponse_flowDefinitionArn :: Lens' CreateFlowDefinitionResponse Text Source #

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

ListModelPackageGroups

listModelPackageGroups_nameContains :: Lens' ListModelPackageGroups (Maybe Text) Source #

A string in the model group name. This filter returns only model groups whose name contains the specified string.

listModelPackageGroups_creationTimeAfter :: Lens' ListModelPackageGroups (Maybe UTCTime) Source #

A filter that returns only model groups created after the specified time.

listModelPackageGroups_nextToken :: Lens' ListModelPackageGroups (Maybe Text) Source #

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.

listModelPackageGroups_sortOrder :: Lens' ListModelPackageGroups (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listModelPackageGroups_creationTimeBefore :: Lens' ListModelPackageGroups (Maybe UTCTime) Source #

A filter that returns only model groups created before the specified time.

listModelPackageGroups_maxResults :: Lens' ListModelPackageGroups (Maybe Natural) Source #

The maximum number of results to return in the response.

listModelPackageGroups_sortBy :: Lens' ListModelPackageGroups (Maybe ModelPackageGroupSortBy) Source #

The field to sort results by. The default is CreationTime.

listModelPackageGroupsResponse_nextToken :: Lens' ListModelPackageGroupsResponse (Maybe Text) Source #

If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.

listModelPackageGroupsResponse_modelPackageGroupSummaryList :: Lens' ListModelPackageGroupsResponse [ModelPackageGroupSummary] Source #

A list of summaries of the model groups in your Amazon Web Services account.

ListTags

listTags_nextToken :: Lens' ListTags (Maybe Text) Source #

If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

listTags_maxResults :: Lens' ListTags (Maybe Natural) Source #

Maximum number of tags to return.

listTags_resourceArn :: Lens' ListTags Text Source #

The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

listTagsResponse_nextToken :: Lens' ListTagsResponse (Maybe Text) Source #

If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.

listTagsResponse_tags :: Lens' ListTagsResponse (Maybe [Tag]) Source #

An array of Tag objects, each with a tag key and a value.

listTagsResponse_httpStatus :: Lens' ListTagsResponse Int Source #

The response's http status code.

DeregisterDevices

deregisterDevices_deviceFleetName :: Lens' DeregisterDevices Text Source #

The name of the fleet the devices belong to.

DescribeHumanTaskUi

describeHumanTaskUi_humanTaskUiName :: Lens' DescribeHumanTaskUi Text Source #

The name of the human task user interface (worker task template) you want information about.

describeHumanTaskUiResponse_humanTaskUiStatus :: Lens' DescribeHumanTaskUiResponse (Maybe HumanTaskUiStatus) Source #

The status of the human task user interface (worker task template). Valid values are listed below.

describeHumanTaskUiResponse_humanTaskUiArn :: Lens' DescribeHumanTaskUiResponse Text Source #

The Amazon Resource Name (ARN) of the human task user interface (worker task template).

describeHumanTaskUiResponse_humanTaskUiName :: Lens' DescribeHumanTaskUiResponse Text Source #

The name of the human task user interface (worker task template).

describeHumanTaskUiResponse_creationTime :: Lens' DescribeHumanTaskUiResponse UTCTime Source #

The timestamp when the human task user interface was created.

CreateTrainingJob

createTrainingJob_environment :: Lens' CreateTrainingJob (Maybe (HashMap Text Text)) Source #

The environment variables to set in the Docker container.

createTrainingJob_checkpointConfig :: Lens' CreateTrainingJob (Maybe CheckpointConfig) Source #

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

createTrainingJob_retryStrategy :: Lens' CreateTrainingJob (Maybe RetryStrategy) Source #

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

createTrainingJob_enableNetworkIsolation :: Lens' CreateTrainingJob (Maybe Bool) Source #

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

createTrainingJob_debugRuleConfigurations :: Lens' CreateTrainingJob (Maybe [DebugRuleConfiguration]) Source #

Configuration information for Debugger rules for debugging output tensors.

createTrainingJob_enableManagedSpotTraining :: Lens' CreateTrainingJob (Maybe Bool) Source #

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.

createTrainingJob_hyperParameters :: Lens' CreateTrainingJob (Maybe (HashMap Text Text)) Source #

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

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

createTrainingJob_inputDataConfig :: Lens' CreateTrainingJob (Maybe (NonEmpty Channel)) Source #

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

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

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

createTrainingJob_profilerRuleConfigurations :: Lens' CreateTrainingJob (Maybe [ProfilerRuleConfiguration]) Source #

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

createTrainingJob_vpcConfig :: Lens' CreateTrainingJob (Maybe VpcConfig) Source #

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.

createTrainingJob_enableInterContainerTrafficEncryption :: Lens' CreateTrainingJob (Maybe Bool) Source #

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.

createTrainingJob_tags :: Lens' CreateTrainingJob (Maybe [Tag]) Source #

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.

createTrainingJob_trainingJobName :: Lens' CreateTrainingJob Text Source #

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

createTrainingJob_algorithmSpecification :: Lens' CreateTrainingJob AlgorithmSpecification Source #

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

createTrainingJob_roleArn :: Lens' CreateTrainingJob Text Source #

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

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

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

createTrainingJob_outputDataConfig :: Lens' CreateTrainingJob OutputDataConfig Source #

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

createTrainingJob_resourceConfig :: Lens' CreateTrainingJob ResourceConfig Source #

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

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

createTrainingJob_stoppingCondition :: Lens' CreateTrainingJob StoppingCondition Source #

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

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

createTrainingJobResponse_trainingJobArn :: Lens' CreateTrainingJobResponse Text Source #

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

DeleteModelPackageGroupPolicy

deleteModelPackageGroupPolicy_modelPackageGroupName :: Lens' DeleteModelPackageGroupPolicy Text Source #

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

DeleteUserProfile

UpdateUserProfile

CreateImage

createImage_displayName :: Lens' CreateImage (Maybe Text) Source #

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

createImage_description :: Lens' CreateImage (Maybe Text) Source #

The description of the image.

createImage_tags :: Lens' CreateImage (Maybe [Tag]) Source #

A list of tags to apply to the image.

createImage_imageName :: Lens' CreateImage Text Source #

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

createImage_roleArn :: Lens' CreateImage Text Source #

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

createImageResponse_imageArn :: Lens' CreateImageResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the image.

PutModelPackageGroupPolicy

putModelPackageGroupPolicy_modelPackageGroupName :: Lens' PutModelPackageGroupPolicy Text Source #

The name of the model group to add a resource policy to.

ListPipelineParametersForExecution

listPipelineParametersForExecution_nextToken :: Lens' ListPipelineParametersForExecution (Maybe Text) Source #

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.

listPipelineParametersForExecution_maxResults :: Lens' ListPipelineParametersForExecution (Maybe Natural) Source #

The maximum number of parameters to return in the response.

listPipelineParametersForExecutionResponse_nextToken :: Lens' ListPipelineParametersForExecutionResponse (Maybe Text) Source #

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.

CreateContext

createContext_description :: Lens' CreateContext (Maybe Text) Source #

The description of the context.

createContext_tags :: Lens' CreateContext (Maybe [Tag]) Source #

A list of tags to apply to the context.

createContext_properties :: Lens' CreateContext (Maybe (HashMap Text Text)) Source #

A list of properties to add to the context.

createContext_contextName :: Lens' CreateContext Text Source #

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

createContextResponse_contextArn :: Lens' CreateContextResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the context.

DescribePipelineDefinitionForExecution

ListTrials

listTrials_createdAfter :: Lens' ListTrials (Maybe UTCTime) Source #

A filter that returns only trials created after the specified time.

listTrials_experimentName :: Lens' ListTrials (Maybe Text) Source #

A filter that returns only trials that are part of the specified experiment.

listTrials_nextToken :: Lens' ListTrials (Maybe Text) Source #

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.

listTrials_sortOrder :: Lens' ListTrials (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listTrials_trialComponentName :: Lens' ListTrials (Maybe Text) Source #

A filter that returns only trials that are associated with the specified trial component.

listTrials_maxResults :: Lens' ListTrials (Maybe Natural) Source #

The maximum number of trials to return in the response. The default value is 10.

listTrials_createdBefore :: Lens' ListTrials (Maybe UTCTime) Source #

A filter that returns only trials created before the specified time.

listTrials_sortBy :: Lens' ListTrials (Maybe SortTrialsBy) Source #

The property used to sort results. The default value is CreationTime.

listTrialsResponse_nextToken :: Lens' ListTrialsResponse (Maybe Text) Source #

A token for getting the next set of trials, if there are any.

StopCompilationJob

stopCompilationJob_compilationJobName :: Lens' StopCompilationJob Text Source #

The name of the model compilation job to stop.

ListImages

listImages_nameContains :: Lens' ListImages (Maybe Text) Source #

A filter that returns only images whose name contains the specified string.

listImages_lastModifiedTimeBefore :: Lens' ListImages (Maybe UTCTime) Source #

A filter that returns only images modified on or before the specified time.

listImages_creationTimeAfter :: Lens' ListImages (Maybe UTCTime) Source #

A filter that returns only images created on or after the specified time.

listImages_nextToken :: Lens' ListImages (Maybe Text) Source #

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.

listImages_sortOrder :: Lens' ListImages (Maybe ImageSortOrder) Source #

The sort order. The default value is DESCENDING.

listImages_lastModifiedTimeAfter :: Lens' ListImages (Maybe UTCTime) Source #

A filter that returns only images modified on or after the specified time.

listImages_creationTimeBefore :: Lens' ListImages (Maybe UTCTime) Source #

A filter that returns only images created on or before the specified time.

listImages_maxResults :: Lens' ListImages (Maybe Natural) Source #

The maximum number of images to return in the response. The default value is 10.

listImages_sortBy :: Lens' ListImages (Maybe ImageSortBy) Source #

The property used to sort results. The default value is CREATION_TIME.

listImagesResponse_images :: Lens' ListImagesResponse (Maybe [Image]) Source #

A list of images and their properties.

listImagesResponse_nextToken :: Lens' ListImagesResponse (Maybe Text) Source #

A token for getting the next set of images, if there are any.

CreateUserProfile

createUserProfile_singleSignOnUserValue :: Lens' CreateUserProfile (Maybe Text) Source #

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

createUserProfile_singleSignOnUserIdentifier :: Lens' CreateUserProfile (Maybe Text) Source #

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

createUserProfile_tags :: Lens' CreateUserProfile (Maybe [Tag]) Source #

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.

createUserProfile_domainId :: Lens' CreateUserProfile Text Source #

The ID of the associated Domain.

createUserProfile_userProfileName :: Lens' CreateUserProfile Text Source #

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

Search

search_nextToken :: Lens' Search (Maybe Text) Source #

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.

search_searchExpression :: Lens' Search (Maybe SearchExpression) Source #

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.

search_sortOrder :: Lens' Search (Maybe SearchSortOrder) Source #

How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

search_maxResults :: Lens' Search (Maybe Natural) Source #

The maximum number of results to return.

search_sortBy :: Lens' Search (Maybe Text) Source #

The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

search_resource :: Lens' Search ResourceType Source #

The name of the Amazon SageMaker resource to search for.

searchResponse_results :: Lens' SearchResponse (Maybe [SearchRecord]) Source #

A list of SearchRecord objects.

searchResponse_nextToken :: Lens' SearchResponse (Maybe Text) Source #

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.

searchResponse_httpStatus :: Lens' SearchResponse Int Source #

The response's http status code.

UpdateCodeRepository

updateCodeRepository_gitConfig :: Lens' UpdateCodeRepository (Maybe GitConfigForUpdate) Source #

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}

DeleteCodeRepository

ListContexts

listContexts_createdAfter :: Lens' ListContexts (Maybe UTCTime) Source #

A filter that returns only contexts created on or after the specified time.

listContexts_nextToken :: Lens' ListContexts (Maybe Text) Source #

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.

listContexts_sortOrder :: Lens' ListContexts (Maybe SortOrder) Source #

The sort order. The default value is Descending.

listContexts_contextType :: Lens' ListContexts (Maybe Text) Source #

A filter that returns only contexts of the specified type.

listContexts_sourceUri :: Lens' ListContexts (Maybe Text) Source #

A filter that returns only contexts with the specified source URI.

listContexts_maxResults :: Lens' ListContexts (Maybe Natural) Source #

The maximum number of contexts to return in the response. The default value is 10.

listContexts_createdBefore :: Lens' ListContexts (Maybe UTCTime) Source #

A filter that returns only contexts created on or before the specified time.

listContexts_sortBy :: Lens' ListContexts (Maybe SortContextsBy) Source #

The property used to sort results. The default value is CreationTime.

listContextsResponse_nextToken :: Lens' ListContextsResponse (Maybe Text) Source #

A token for getting the next set of contexts, if there are any.

DescribeTransformJob

describeTransformJob_transformJobName :: Lens' DescribeTransformJob Text Source #

The name of the transform job that you want to view details of.

describeTransformJobResponse_labelingJobArn :: Lens' DescribeTransformJobResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

describeTransformJobResponse_failureReason :: Lens' DescribeTransformJobResponse (Maybe Text) Source #

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.

describeTransformJobResponse_modelClientConfig :: Lens' DescribeTransformJobResponse (Maybe ModelClientConfig) Source #

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

describeTransformJobResponse_batchStrategy :: Lens' DescribeTransformJobResponse (Maybe BatchStrategy) Source #

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.

describeTransformJobResponse_maxPayloadInMB :: Lens' DescribeTransformJobResponse (Maybe Natural) Source #

The maximum payload size, in MB, used in the transform job.

describeTransformJobResponse_environment :: Lens' DescribeTransformJobResponse (Maybe (HashMap Text Text)) Source #

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

describeTransformJobResponse_transformEndTime :: Lens' DescribeTransformJobResponse (Maybe UTCTime) Source #

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.

describeTransformJobResponse_transformStartTime :: Lens' DescribeTransformJobResponse (Maybe UTCTime) Source #

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

describeTransformJobResponse_autoMLJobArn :: Lens' DescribeTransformJobResponse (Maybe Text) Source #

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

describeTransformJobResponse_maxConcurrentTransforms :: Lens' DescribeTransformJobResponse (Maybe Natural) Source #

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

describeTransformJobResponse_transformOutput :: Lens' DescribeTransformJobResponse (Maybe TransformOutput) Source #

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

describeTransformJobResponse_transformJobArn :: Lens' DescribeTransformJobResponse Text Source #

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

describeTransformJobResponse_transformJobStatus :: Lens' DescribeTransformJobResponse TransformJobStatus Source #

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

describeTransformJobResponse_modelName :: Lens' DescribeTransformJobResponse Text Source #

The name of the model used in the transform job.

describeTransformJobResponse_transformInput :: Lens' DescribeTransformJobResponse TransformInput Source #

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

describeTransformJobResponse_transformResources :: Lens' DescribeTransformJobResponse TransformResources Source #

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

describeTransformJobResponse_creationTime :: Lens' DescribeTransformJobResponse UTCTime Source #

A timestamp that shows when the transform Job was created.

DescribeEdgePackagingJob

describeEdgePackagingJobResponse_resourceKey :: Lens' DescribeEdgePackagingJobResponse (Maybe Text) Source #

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

describeEdgePackagingJobResponse_modelArtifact :: Lens' DescribeEdgePackagingJobResponse (Maybe Text) Source #

The Amazon Simple Storage (S3) URI where model artifacts ares stored.

describeEdgePackagingJobResponse_compilationJobName :: Lens' DescribeEdgePackagingJobResponse (Maybe Text) Source #

The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.

describeEdgePackagingJobResponse_roleArn :: Lens' DescribeEdgePackagingJobResponse (Maybe Text) Source #

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

CreatePipeline

createPipeline_tags :: Lens' CreatePipeline (Maybe [Tag]) Source #

A list of tags to apply to the created pipeline.

createPipeline_pipelineDefinition :: Lens' CreatePipeline Text Source #

The JSON pipeline definition of the pipeline.

createPipeline_clientRequestToken :: Lens' CreatePipeline Text Source #

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

createPipeline_roleArn :: Lens' CreatePipeline Text Source #

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

createPipelineResponse_pipelineArn :: Lens' CreatePipelineResponse (Maybe Text) Source #

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

ListCandidatesForAutoMLJob

listCandidatesForAutoMLJob_candidateNameEquals :: Lens' ListCandidatesForAutoMLJob (Maybe Text) Source #

List the candidates for the job and filter by candidate name.

listCandidatesForAutoMLJob_nextToken :: Lens' ListCandidatesForAutoMLJob (Maybe Text) Source #

If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

listCandidatesForAutoMLJob_sortOrder :: Lens' ListCandidatesForAutoMLJob (Maybe AutoMLSortOrder) Source #

The sort order for the results. The default is Ascending.

listCandidatesForAutoMLJob_maxResults :: Lens' ListCandidatesForAutoMLJob (Maybe Natural) Source #

List the job's candidates up to a specified limit.

listCandidatesForAutoMLJob_sortBy :: Lens' ListCandidatesForAutoMLJob (Maybe CandidateSortBy) Source #

The parameter by which to sort the results. The default is Descending.

listCandidatesForAutoMLJob_autoMLJobName :: Lens' ListCandidatesForAutoMLJob Text Source #

List the candidates created for the job by providing the job's name.

listCandidatesForAutoMLJobResponse_nextToken :: Lens' ListCandidatesForAutoMLJobResponse (Maybe Text) Source #

If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.

DeleteImage

deleteImage_imageName :: Lens' DeleteImage Text Source #

The name of the image to delete.

UpdateImage

updateImage_deleteProperties :: Lens' UpdateImage (Maybe [Text]) Source #

A list of properties to delete. Only the Description and DisplayName properties can be deleted.

updateImage_displayName :: Lens' UpdateImage (Maybe Text) Source #

The new display name for the image.

updateImage_description :: Lens' UpdateImage (Maybe Text) Source #

The new description for the image.

updateImage_roleArn :: Lens' UpdateImage (Maybe Text) Source #

The new Amazon Resource Name (ARN) for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.

updateImage_imageName :: Lens' UpdateImage Text Source #

The name of the image to update.

updateImageResponse_imageArn :: Lens' UpdateImageResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the image.

ListFlowDefinitions

listFlowDefinitions_creationTimeAfter :: Lens' ListFlowDefinitions (Maybe UTCTime) Source #

A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.

listFlowDefinitions_sortOrder :: Lens' ListFlowDefinitions (Maybe SortOrder) Source #

An optional value that specifies whether you want the results sorted in Ascending or Descending order.

listFlowDefinitions_creationTimeBefore :: Lens' ListFlowDefinitions (Maybe UTCTime) Source #

A filter that returns only flow definitions that were created before the specified timestamp.

listFlowDefinitions_maxResults :: Lens' ListFlowDefinitions (Maybe Natural) Source #

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.

DeleteContext

deleteContext_contextName :: Lens' DeleteContext Text Source #

The name of the context to delete.

deleteContextResponse_contextArn :: Lens' DeleteContextResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the context.

UpdateContext

updateContext_propertiesToRemove :: Lens' UpdateContext (Maybe [Text]) Source #

A list of properties to remove.

updateContext_description :: Lens' UpdateContext (Maybe Text) Source #

The new description for the context.

updateContext_properties :: Lens' UpdateContext (Maybe (HashMap Text Text)) Source #

The new list of properties. Overwrites the current property list.

updateContext_contextName :: Lens' UpdateContext Text Source #

The name of the context to update.

updateContextResponse_contextArn :: Lens' UpdateContextResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the context.

DescribeEndpoint

describeEndpointResponse_failureReason :: Lens' DescribeEndpointResponse (Maybe Text) Source #

If the status of the endpoint is Failed, the reason why it failed.

describeEndpointResponse_asyncInferenceConfig :: Lens' DescribeEndpointResponse (Maybe AsyncInferenceConfig) Source #

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

describeEndpointResponse_productionVariants :: Lens' DescribeEndpointResponse (Maybe (NonEmpty ProductionVariantSummary)) Source #

An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

describeEndpointResponse_lastDeploymentConfig :: Lens' DescribeEndpointResponse (Maybe DeploymentConfig) Source #

The most recent deployment configuration for the endpoint.

describeEndpointResponse_endpointArn :: Lens' DescribeEndpointResponse Text Source #

The Amazon Resource Name (ARN) of the endpoint.

describeEndpointResponse_endpointConfigName :: Lens' DescribeEndpointResponse Text Source #

The name of the endpoint configuration associated with this endpoint.

describeEndpointResponse_endpointStatus :: Lens' DescribeEndpointResponse EndpointStatus Source #

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.

describeEndpointResponse_creationTime :: Lens' DescribeEndpointResponse UTCTime Source #

A timestamp that shows when the endpoint was created.

describeEndpointResponse_lastModifiedTime :: Lens' DescribeEndpointResponse UTCTime Source #

A timestamp that shows when the endpoint was last modified.

UpdateTrainingJob

updateTrainingJob_profilerConfig :: Lens' UpdateTrainingJob (Maybe ProfilerConfigForUpdate) Source #

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

updateTrainingJob_profilerRuleConfigurations :: Lens' UpdateTrainingJob (Maybe [ProfilerRuleConfiguration]) Source #

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

updateTrainingJob_trainingJobName :: Lens' UpdateTrainingJob Text Source #

The name of a training job to update the Debugger profiling configuration.

updateTrainingJobResponse_trainingJobArn :: Lens' UpdateTrainingJobResponse Text Source #

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

ListTrainingJobs

listTrainingJobs_nameContains :: Lens' ListTrainingJobs (Maybe Text) Source #

A string in the training job name. This filter returns only training jobs whose name contains the specified string.

listTrainingJobs_lastModifiedTimeBefore :: Lens' ListTrainingJobs (Maybe UTCTime) Source #

A filter that returns only training jobs modified before the specified time (timestamp).

listTrainingJobs_creationTimeAfter :: Lens' ListTrainingJobs (Maybe UTCTime) Source #

A filter that returns only training jobs created after the specified time (timestamp).

listTrainingJobs_nextToken :: Lens' ListTrainingJobs (Maybe Text) Source #

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.

listTrainingJobs_sortOrder :: Lens' ListTrainingJobs (Maybe SortOrder) Source #

The sort order for results. The default is Ascending.

listTrainingJobs_lastModifiedTimeAfter :: Lens' ListTrainingJobs (Maybe UTCTime) Source #

A filter that returns only training jobs modified after the specified time (timestamp).

listTrainingJobs_creationTimeBefore :: Lens' ListTrainingJobs (Maybe UTCTime) Source #

A filter that returns only training jobs created before the specified time (timestamp).

listTrainingJobs_statusEquals :: Lens' ListTrainingJobs (Maybe TrainingJobStatus) Source #

A filter that retrieves only training jobs with a specific status.

listTrainingJobs_maxResults :: Lens' ListTrainingJobs (Maybe Natural) Source #

The maximum number of training jobs to return in the response.

listTrainingJobs_sortBy :: Lens' ListTrainingJobs (Maybe SortBy) Source #

The field to sort results by. The default is CreationTime.

listTrainingJobsResponse_nextToken :: Lens' ListTrainingJobsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

listTrainingJobsResponse_trainingJobSummaries :: Lens' ListTrainingJobsResponse [TrainingJobSummary] Source #

An array of TrainingJobSummary objects, each listing a training job.

GetDeviceFleetReport

getDeviceFleetReportResponse_agentVersions :: Lens' GetDeviceFleetReportResponse (Maybe [AgentVersion]) Source #

The versions of Edge Manager agent deployed on the fleet.

getDeviceFleetReportResponse_outputConfig :: Lens' GetDeviceFleetReportResponse (Maybe EdgeOutputConfig) Source #

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

DeleteDataQualityJobDefinition

deleteDataQualityJobDefinition_jobDefinitionName :: Lens' DeleteDataQualityJobDefinition Text Source #

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

DescribeWorkteam

describeWorkteam_workteamName :: Lens' DescribeWorkteam Text Source #

The name of the work team to return a description of.

describeWorkteamResponse_workteam :: Lens' DescribeWorkteamResponse Workteam Source #

A Workteam instance that contains information about the work team.

ListSubscribedWorkteams

listSubscribedWorkteams_nameContains :: Lens' ListSubscribedWorkteams (Maybe Text) Source #

A string in the work team name. This filter returns only work teams whose name contains the specified string.

listSubscribedWorkteams_nextToken :: Lens' ListSubscribedWorkteams (Maybe Text) Source #

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.

listSubscribedWorkteams_maxResults :: Lens' ListSubscribedWorkteams (Maybe Natural) Source #

The maximum number of work teams to return in each page of the response.

listSubscribedWorkteamsResponse_nextToken :: Lens' ListSubscribedWorkteamsResponse (Maybe Text) Source #

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

DeleteDomain

deleteDomain_retentionPolicy :: Lens' DeleteDomain (Maybe RetentionPolicy) Source #

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).

UpdateDomain

updateDomain_domainId :: Lens' UpdateDomain Text Source #

The ID of the domain to be updated.

updateDomainResponse_domainArn :: Lens' UpdateDomainResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the domain.

ListDomains

listDomains_nextToken :: Lens' ListDomains (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

listDomains_maxResults :: Lens' ListDomains (Maybe Natural) Source #

Returns a list up to a specified limit.

listDomainsResponse_nextToken :: Lens' ListDomainsResponse (Maybe Text) Source #

If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.

ListModelQualityJobDefinitions

listModelQualityJobDefinitions_nameContains :: Lens' ListModelQualityJobDefinitions (Maybe Text) Source #

A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.

listModelQualityJobDefinitions_endpointName :: Lens' ListModelQualityJobDefinitions (Maybe Text) Source #

A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.

listModelQualityJobDefinitions_creationTimeAfter :: Lens' ListModelQualityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model quality monitoring job definitions created after the specified time.

listModelQualityJobDefinitions_nextToken :: Lens' ListModelQualityJobDefinitions (Maybe Text) Source #

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.

listModelQualityJobDefinitions_sortOrder :: Lens' ListModelQualityJobDefinitions (Maybe SortOrder) Source #

The sort order for results. The default is Descending.

listModelQualityJobDefinitions_creationTimeBefore :: Lens' ListModelQualityJobDefinitions (Maybe UTCTime) Source #

A filter that returns only model quality monitoring job definitions created before the specified time.

listModelQualityJobDefinitions_maxResults :: Lens' ListModelQualityJobDefinitions (Maybe Natural) Source #

The maximum number of results to return in a call to ListModelQualityJobDefinitions.

listModelQualityJobDefinitionsResponse_nextToken :: Lens' ListModelQualityJobDefinitionsResponse (Maybe Text) Source #

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.

CreateImageVersion

createImageVersion_baseImage :: Lens' CreateImageVersion Text Source #

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

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

createImageVersion_clientToken :: Lens' CreateImageVersion Text Source #

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.

createImageVersion_imageName :: Lens' CreateImageVersion Text Source #

The ImageName of the Image to create a version of.

createImageVersionResponse_imageVersionArn :: Lens' CreateImageVersionResponse (Maybe Text) Source #

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

ListDevices

listDevices_latestHeartbeatAfter :: Lens' ListDevices (Maybe UTCTime) Source #

Select fleets where the job was updated after X

listDevices_modelName :: Lens' ListDevices (Maybe Text) Source #

A filter that searches devices that contains this name in any of their models.

listDevices_nextToken :: Lens' ListDevices (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

listDevices_maxResults :: Lens' ListDevices (Maybe Int) Source #

Maximum number of results to select.

listDevices_deviceFleetName :: Lens' ListDevices (Maybe Text) Source #

Filter for fleets containing this name in their device fleet name.

listDevicesResponse_nextToken :: Lens' ListDevicesResponse (Maybe Text) Source #

The response from the last list when returning a list large enough to need tokening.

ListPipelineExecutions

listPipelineExecutions_createdAfter :: Lens' ListPipelineExecutions (Maybe UTCTime) Source #

A filter that returns the pipeline executions that were created after a specified time.

listPipelineExecutions_nextToken :: Lens' ListPipelineExecutions (Maybe Text) Source #

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.

listPipelineExecutions_maxResults :: Lens' ListPipelineExecutions (Maybe Natural) Source #

The maximum number of pipeline executions to return in the response.

listPipelineExecutions_createdBefore :: Lens' ListPipelineExecutions (Maybe UTCTime) Source #

A filter that returns the pipeline executions that were created before a specified time.

listPipelineExecutions_sortBy :: Lens' ListPipelineExecutions (Maybe SortPipelineExecutionsBy) Source #

The field by which to sort results. The default is CreatedTime.

listPipelineExecutionsResponse_nextToken :: Lens' ListPipelineExecutionsResponse (Maybe Text) Source #

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.

listPipelineExecutionsResponse_pipelineExecutionSummaries :: Lens' ListPipelineExecutionsResponse (Maybe [PipelineExecutionSummary]) Source #

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.

CreateProject

createProject_tags :: Lens' CreateProject (Maybe [Tag]) Source #

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.

createProject_serviceCatalogProvisioningDetails :: Lens' CreateProject ServiceCatalogProvisioningDetails Source #

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.

createProjectResponse_projectArn :: Lens' CreateProjectResponse Text Source #

The Amazon Resource Name (ARN) of the project.

DescribeModelBiasJobDefinition

describeModelBiasJobDefinition_jobDefinitionName :: Lens' DescribeModelBiasJobDefinition Text Source #

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.

describeModelBiasJobDefinitionResponse_jobDefinitionName :: Lens' DescribeModelBiasJobDefinitionResponse Text Source #

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

describeModelBiasJobDefinitionResponse_roleArn :: Lens' DescribeModelBiasJobDefinitionResponse Text Source #

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.

StartMonitoringSchedule

StopAutoMLJob

stopAutoMLJob_autoMLJobName :: Lens' StopAutoMLJob Text Source #

The name of the object you are requesting.

CreateTrialComponent

createTrialComponent_status :: Lens' CreateTrialComponent (Maybe TrialComponentStatus) Source #

The status of the component. States include:

  • InProgress
  • Completed
  • Failed

createTrialComponent_outputArtifacts :: Lens' CreateTrialComponent (Maybe (HashMap Text TrialComponentArtifact)) Source #

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

createTrialComponent_displayName :: Lens' CreateTrialComponent (Maybe Text) Source #

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

createTrialComponent_inputArtifacts :: Lens' CreateTrialComponent (Maybe (HashMap Text TrialComponentArtifact)) Source #

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

createTrialComponent_tags :: Lens' CreateTrialComponent (Maybe [Tag]) Source #

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

createTrialComponent_trialComponentName :: Lens' CreateTrialComponent Text Source #

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

createTrialComponentResponse_trialComponentArn :: Lens' CreateTrialComponentResponse (Maybe Text) Source #

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

DescribeProcessingJob

describeProcessingJob_processingJobName :: Lens' DescribeProcessingJob Text Source #

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

describeProcessingJobResponse_failureReason :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

describeProcessingJobResponse_monitoringScheduleArn :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

The ARN of a monitoring schedule for an endpoint associated with this processing job.

describeProcessingJobResponse_environment :: Lens' DescribeProcessingJobResponse (Maybe (HashMap Text Text)) Source #

The environment variables set in the Docker container.

describeProcessingJobResponse_lastModifiedTime :: Lens' DescribeProcessingJobResponse (Maybe UTCTime) Source #

The time at which the processing job was last modified.

describeProcessingJobResponse_autoMLJobArn :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

The ARN of an AutoML job associated with this processing job.

describeProcessingJobResponse_trainingJobArn :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

The ARN of a training job associated with this processing job.

describeProcessingJobResponse_exitMessage :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

describeProcessingJobResponse_roleArn :: Lens' DescribeProcessingJobResponse (Maybe Text) Source #

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

describeProcessingJobResponse_processingJobName :: Lens' DescribeProcessingJobResponse Text Source #

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

describeProcessingJobResponse_processingResources :: Lens' DescribeProcessingJobResponse ProcessingResources Source #

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.

describeProcessingJobResponse_appSpecification :: Lens' DescribeProcessingJobResponse AppSpecification Source #

Configures the processing job to run a specified container image.

describeProcessingJobResponse_processingJobArn :: Lens' DescribeProcessingJobResponse Text Source #

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

Types

ActionSource

ActionSummary

actionSummary_lastModifiedTime :: Lens' ActionSummary (Maybe UTCTime) Source #

When the action was last modified.

actionSummary_actionArn :: Lens' ActionSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the action.

AgentVersion

agentVersion_agentCount :: Lens' AgentVersion Integer Source #

The number of Edge Manager agents.

Alarm

AlgorithmSpecification

algorithmSpecification_enableSageMakerMetricsTimeSeries :: Lens' AlgorithmSpecification (Maybe Bool) Source #

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 Amazon SageMaker built-in algorithms
  • You use one of the following Prebuilt Amazon SageMaker Docker Images:

    • Tensorflow (version >= 1.15)
    • MXNet (version >= 1.6)
    • PyTorch (version >= 1.3)
  • You specify at least one MetricDefinition

algorithmSpecification_algorithmName :: Lens' AlgorithmSpecification (Maybe Text) Source #

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. If you specify a value for this parameter, you can't specify a value for TrainingImage.

algorithmSpecification_trainingImage :: Lens' AlgorithmSpecification (Maybe Text) Source #

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. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

algorithmSpecification_metricDefinitions :: Lens' AlgorithmSpecification (Maybe [MetricDefinition]) Source #

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

AlgorithmStatusDetails

algorithmStatusDetails_imageScanStatuses :: Lens' AlgorithmStatusDetails (Maybe [AlgorithmStatusItem]) Source #

The status of the scan of the algorithm's Docker image container.

AlgorithmStatusItem

algorithmStatusItem_failureReason :: Lens' AlgorithmStatusItem (Maybe Text) Source #

if the overall status is Failed, the reason for the failure.

algorithmStatusItem_name :: Lens' AlgorithmStatusItem Text Source #

The name of the algorithm for which the overall status is being reported.

AlgorithmSummary

algorithmSummary_algorithmName :: Lens' AlgorithmSummary Text Source #

The name of the algorithm that is described by the summary.

algorithmSummary_algorithmArn :: Lens' AlgorithmSummary Text Source #

The Amazon Resource Name (ARN) of the algorithm.

algorithmSummary_creationTime :: Lens' AlgorithmSummary UTCTime Source #

A timestamp that shows when the algorithm was created.

AlgorithmValidationProfile

algorithmValidationProfile_transformJobDefinition :: Lens' AlgorithmValidationProfile (Maybe TransformJobDefinition) Source #

The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.

algorithmValidationProfile_profileName :: Lens' AlgorithmValidationProfile Text Source #

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).

algorithmValidationProfile_trainingJobDefinition :: Lens' AlgorithmValidationProfile TrainingJobDefinition Source #

The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.

AlgorithmValidationSpecification

algorithmValidationSpecification_validationRole :: Lens' AlgorithmValidationSpecification Text Source #

The IAM roles that Amazon SageMaker uses to run the training jobs.

algorithmValidationSpecification_validationProfiles :: Lens' AlgorithmValidationSpecification (NonEmpty AlgorithmValidationProfile) Source #

An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.

AnnotationConsolidationConfig

annotationConsolidationConfig_annotationConsolidationLambdaArn :: Lens' AnnotationConsolidationConfig Text Source #

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

AppDetails

AppImageConfigDetails

appImageConfigDetails_appImageConfigName :: Lens' AppImageConfigDetails (Maybe Text) Source #

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

appImageConfigDetails_kernelGatewayImageConfig :: Lens' AppImageConfigDetails (Maybe KernelGatewayImageConfig) Source #

The configuration for the file system and kernels in the SageMaker image.

appImageConfigDetails_appImageConfigArn :: Lens' AppImageConfigDetails (Maybe Text) Source #

The Amazon Resource Name (ARN) of the AppImageConfig.

AppSpecification

appSpecification_containerArguments :: Lens' AppSpecification (Maybe (NonEmpty Text)) Source #

The arguments for a container used to run a processing job.

appSpecification_containerEntrypoint :: Lens' AppSpecification (Maybe (NonEmpty Text)) Source #

The entrypoint for a container used to run a processing job.

appSpecification_imageUri :: Lens' AppSpecification Text Source #

The container image to be run by the processing job.

ArtifactSource

ArtifactSourceType

ArtifactSummary

artifactSummary_artifactArn :: Lens' ArtifactSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the artifact.

AssociationSummary

associationSummary_destinationArn :: Lens' AssociationSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the destination.

AsyncInferenceClientConfig

asyncInferenceClientConfig_maxConcurrentInvocationsPerInstance :: Lens' AsyncInferenceClientConfig (Maybe Natural) Source #

The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, Amazon SageMaker will choose an optimal value for you.

AsyncInferenceConfig

asyncInferenceConfig_clientConfig :: Lens' AsyncInferenceConfig (Maybe AsyncInferenceClientConfig) Source #

Configures the behavior of the client used by Amazon SageMaker to interact with the model container during asynchronous inference.

asyncInferenceConfig_outputConfig :: Lens' AsyncInferenceConfig AsyncInferenceOutputConfig Source #

Specifies the configuration for asynchronous inference invocation outputs.

AsyncInferenceNotificationConfig

asyncInferenceNotificationConfig_errorTopic :: Lens' AsyncInferenceNotificationConfig (Maybe Text) Source #

Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

asyncInferenceNotificationConfig_successTopic :: Lens' AsyncInferenceNotificationConfig (Maybe Text) Source #

Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

AsyncInferenceOutputConfig

asyncInferenceOutputConfig_notificationConfig :: Lens' AsyncInferenceOutputConfig (Maybe AsyncInferenceNotificationConfig) Source #

Specifies the configuration for notifications of inference results for asynchronous inference.

asyncInferenceOutputConfig_kmsKeyId :: Lens' AsyncInferenceOutputConfig (Maybe Text) Source #

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

asyncInferenceOutputConfig_s3OutputPath :: Lens' AsyncInferenceOutputConfig Text Source #

The Amazon S3 location to upload inference responses to.

AthenaDatasetDefinition

athenaDatasetDefinition_kmsKeyId :: Lens' AthenaDatasetDefinition (Maybe Text) Source #

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.

athenaDatasetDefinition_outputS3Uri :: Lens' AthenaDatasetDefinition Text Source #

The location in Amazon S3 where Athena query results are stored.

AutoMLCandidate

autoMLCandidate_inferenceContainers :: Lens' AutoMLCandidate (Maybe [AutoMLContainerDefinition]) Source #

Information about the inference container definitions.

AutoMLCandidateStep

autoMLCandidateStep_candidateStepType :: Lens' AutoMLCandidateStep CandidateStepType Source #

Whether the candidate is at the transform, training, or processing step.

AutoMLChannel

autoMLChannel_compressionType :: Lens' AutoMLChannel (Maybe CompressionType) Source #

You can use Gzip or None. The default value is None.

autoMLChannel_dataSource :: Lens' AutoMLChannel AutoMLDataSource Source #

The data source for an AutoML channel.

autoMLChannel_targetAttributeName :: Lens' AutoMLChannel Text Source #

The name of the target variable in supervised learning, usually represented by 'y'.

AutoMLContainerDefinition

autoMLContainerDefinition_environment :: Lens' AutoMLContainerDefinition (Maybe (HashMap Text Text)) Source #

The environment variables to set in the container. For more information, see .

autoMLContainerDefinition_image :: Lens' AutoMLContainerDefinition Text Source #

The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .

autoMLContainerDefinition_modelDataUrl :: Lens' AutoMLContainerDefinition Text Source #

The location of the model artifacts. For more information, see .

AutoMLDataSource

autoMLDataSource_s3DataSource :: Lens' AutoMLDataSource AutoMLS3DataSource Source #

The Amazon S3 location of the input data.

The input data must be in CSV format and contain at least 500 rows.

AutoMLJobArtifacts

AutoMLJobCompletionCriteria

autoMLJobCompletionCriteria_maxCandidates :: Lens' AutoMLJobCompletionCriteria (Maybe Natural) Source #

The maximum number of times a training job is allowed to run.

autoMLJobCompletionCriteria_maxRuntimePerTrainingJobInSeconds :: Lens' AutoMLJobCompletionCriteria (Maybe Natural) Source #

The maximum time, in seconds, that each training job is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.

autoMLJobCompletionCriteria_maxAutoMLJobRuntimeInSeconds :: Lens' AutoMLJobCompletionCriteria (Maybe Natural) Source #

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.

AutoMLJobConfig

autoMLJobConfig_securityConfig :: Lens' AutoMLJobConfig (Maybe AutoMLSecurityConfig) Source #

The security configuration for traffic encryption or Amazon VPC settings.

autoMLJobConfig_completionCriteria :: Lens' AutoMLJobConfig (Maybe AutoMLJobCompletionCriteria) Source #

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

AutoMLJobObjective

autoMLJobObjective_metricName :: Lens' AutoMLJobObjective AutoMLMetricEnum Source #

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:

  • 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. When the data contains outliers, they tend to dominate the MSE, which might cause subpar prediction performance.
  • Accuracy: The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect accuracy and zero indicates perfect inaccuracy.
  • F1: The F1 score is the harmonic mean of the precision and 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 (including the false positives) in a data set and 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 and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates the best possible performance and zero the worst.
  • AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that 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 receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random classifier. Values under one half predict less accurately than a random predictor. But such consistently bad predictors can simply be inverted to obtain better than random predictors.
  • F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then, use these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best possible performance and zero the worst.

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.

AutoMLJobSummary

autoMLJobSummary_failureReason :: Lens' AutoMLJobSummary (Maybe Text) Source #

The failure reason of an AutoML job.

autoMLJobSummary_partialFailureReasons :: Lens' AutoMLJobSummary (Maybe (NonEmpty AutoMLPartialFailureReason)) Source #

The list of reasons for partial failures within an AutoML job.

autoMLJobSummary_autoMLJobName :: Lens' AutoMLJobSummary Text Source #

The name of the AutoML job you are requesting.

autoMLJobSummary_lastModifiedTime :: Lens' AutoMLJobSummary UTCTime Source #

When the AutoML job was last modified.

AutoMLOutputDataConfig

autoMLOutputDataConfig_kmsKeyId :: Lens' AutoMLOutputDataConfig (Maybe Text) Source #

The Amazon Web Services KMS encryption key ID.

autoMLOutputDataConfig_s3OutputPath :: Lens' AutoMLOutputDataConfig Text Source #

The Amazon S3 output path. Must be 128 characters or less.

AutoMLPartialFailureReason

autoMLPartialFailureReason_partialFailureMessage :: Lens' AutoMLPartialFailureReason (Maybe Text) Source #

The message containing the reason for a partial failure of an AutoML job.

AutoMLS3DataSource

autoMLS3DataSource_s3Uri :: Lens' AutoMLS3DataSource Text Source #

The URL to the Amazon S3 data source.

AutoMLSecurityConfig

autoMLSecurityConfig_enableInterContainerTrafficEncryption :: Lens' AutoMLSecurityConfig (Maybe Bool) Source #

Whether to use traffic encryption between the container layers.

AutoRollbackConfig

Bias

bias_report :: Lens' Bias (Maybe MetricsSource) Source #

The bias report for a model

BlueGreenUpdatePolicy

CacheHitResult

cacheHitResult_sourcePipelineExecutionArn :: Lens' CacheHitResult (Maybe Text) Source #

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

CallbackStepMetadata

callbackStepMetadata_callbackToken :: Lens' CallbackStepMetadata (Maybe Text) Source #

The pipeline generated token from the Amazon SQS queue.

callbackStepMetadata_outputParameters :: Lens' CallbackStepMetadata (Maybe [OutputParameter]) Source #

A list of the output parameters of the callback step.

callbackStepMetadata_sqsQueueUrl :: Lens' CallbackStepMetadata (Maybe Text) Source #

The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.

CandidateArtifactLocations

candidateArtifactLocations_explainability :: Lens' CandidateArtifactLocations Text Source #

The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

CandidateProperties

candidateProperties_candidateArtifactLocations :: Lens' CandidateProperties (Maybe CandidateArtifactLocations) Source #

The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

candidateProperties_candidateMetrics :: Lens' CandidateProperties (Maybe [MetricDatum]) Source #

Information about the candidate metrics for an AutoML job.

CapacitySize

CaptureContentTypeHeader

CaptureOption

CategoricalParameterRange

categoricalParameterRange_name :: Lens' CategoricalParameterRange Text Source #

The name of the categorical hyperparameter to tune.

categoricalParameterRange_values :: Lens' CategoricalParameterRange (NonEmpty Text) Source #

A list of the categories for the hyperparameter.

CategoricalParameterRangeSpecification

Channel

channel_shuffleConfig :: Lens' Channel (Maybe ShuffleConfig) Source #

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.

channel_recordWrapperType :: Lens' Channel (Maybe RecordWrapper) Source #

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon 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.

channel_inputMode :: Lens' Channel (Maybe TrainingInputMode) Source #

(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, Amazon 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.

channel_compressionType :: Lens' Channel (Maybe CompressionType) Source #

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.

channel_contentType :: Lens' Channel (Maybe Text) Source #

The MIME type of the data.

channel_channelName :: Lens' Channel Text Source #

The name of the channel.

channel_dataSource :: Lens' Channel DataSource Source #

The location of the channel data.

ChannelSpecification

channelSpecification_supportedCompressionTypes :: Lens' ChannelSpecification (Maybe [CompressionType]) Source #

The allowed compression types, if data compression is used.

channelSpecification_isRequired :: Lens' ChannelSpecification (Maybe Bool) Source #

Indicates whether the channel is required by the algorithm.

channelSpecification_supportedInputModes :: Lens' ChannelSpecification (NonEmpty TrainingInputMode) Source #

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.

CheckpointConfig

checkpointConfig_localPath :: Lens' CheckpointConfig (Maybe Text) Source #

(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.

checkpointConfig_s3Uri :: Lens' CheckpointConfig Text Source #

Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.

CodeRepositorySummary

codeRepositorySummary_gitConfig :: Lens' CodeRepositorySummary (Maybe GitConfig) Source #

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.

codeRepositorySummary_codeRepositoryArn :: Lens' CodeRepositorySummary Text Source #

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

codeRepositorySummary_creationTime :: Lens' CodeRepositorySummary UTCTime Source #

The date and time that the Git repository was created.

codeRepositorySummary_lastModifiedTime :: Lens' CodeRepositorySummary UTCTime Source #

The date and time that the Git repository was last modified.

CognitoConfig

cognitoConfig_userPool :: Lens' CognitoConfig Text Source #

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.

cognitoConfig_clientId :: Lens' CognitoConfig Text Source #

The client ID for your Amazon Cognito user pool.

CognitoMemberDefinition

cognitoMemberDefinition_userPool :: Lens' CognitoMemberDefinition Text Source #

An identifier for a user pool. The user pool must be in the same region as the service that you are calling.

cognitoMemberDefinition_clientId :: Lens' CognitoMemberDefinition Text Source #

An identifier for an application client. You must create the app client ID using Amazon Cognito.

CollectionConfiguration

collectionConfiguration_collectionParameters :: Lens' CollectionConfiguration (Maybe (HashMap Text Text)) Source #

Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".

collectionConfiguration_collectionName :: Lens' CollectionConfiguration (Maybe Text) Source #

The name of the tensor collection. The name must be unique relative to other rule configuration names.

CompilationJobSummary

compilationJobSummary_compilationStartTime :: Lens' CompilationJobSummary (Maybe UTCTime) Source #

The time when the model compilation job started.

compilationJobSummary_compilationTargetPlatformAccelerator :: Lens' CompilationJobSummary (Maybe TargetPlatformAccelerator) Source #

The type of accelerator that the model will run on after the compilation job has completed.

compilationJobSummary_compilationTargetDevice :: Lens' CompilationJobSummary (Maybe TargetDevice) Source #

The type of device that the model will run on after the compilation job has completed.

compilationJobSummary_lastModifiedTime :: Lens' CompilationJobSummary (Maybe UTCTime) Source #

The time when the model compilation job was last modified.

compilationJobSummary_compilationTargetPlatformArch :: Lens' CompilationJobSummary (Maybe TargetPlatformArch) Source #

The type of architecture that the model will run on after the compilation job has completed.

compilationJobSummary_compilationEndTime :: Lens' CompilationJobSummary (Maybe UTCTime) Source #

The time when the model compilation job completed.

compilationJobSummary_compilationTargetPlatformOs :: Lens' CompilationJobSummary (Maybe TargetPlatformOs) Source #

The type of OS that the model will run on after the compilation job has completed.

compilationJobSummary_compilationJobName :: Lens' CompilationJobSummary Text Source #

The name of the model compilation job that you want a summary for.

compilationJobSummary_compilationJobArn :: Lens' CompilationJobSummary Text Source #

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

compilationJobSummary_creationTime :: Lens' CompilationJobSummary UTCTime Source #

The time when the model compilation job was created.

ConditionStepMetadata

conditionStepMetadata_outcome :: Lens' ConditionStepMetadata (Maybe ConditionOutcome) Source #

The outcome of the Condition step evaluation.

ContainerDefinition

containerDefinition_multiModelConfig :: Lens' ContainerDefinition (Maybe MultiModelConfig) Source #

Specifies additional configuration for multi-model endpoints.

containerDefinition_modelDataUrl :: Lens' ContainerDefinition (Maybe Text) Source #

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 Amazon 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, Amazon 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, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

containerDefinition_image :: Lens' ContainerDefinition (Maybe Text) Source #

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 Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

containerDefinition_modelPackageName :: Lens' ContainerDefinition (Maybe Text) Source #

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

containerDefinition_environment :: Lens' ContainerDefinition (Maybe (HashMap Text Text)) Source #

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.

containerDefinition_imageConfig :: Lens' ContainerDefinition (Maybe ImageConfig) Source #

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

containerDefinition_mode :: Lens' ContainerDefinition (Maybe ContainerMode) Source #

Whether the container hosts a single model or multiple models.

containerDefinition_containerHostname :: Lens' ContainerDefinition (Maybe Text) Source #

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.

ContextSource

ContextSummary

contextSummary_lastModifiedTime :: Lens' ContextSummary (Maybe UTCTime) Source #

When the context was last modified.

contextSummary_contextArn :: Lens' ContextSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the context.

ContinuousParameterRange

continuousParameterRange_scalingType :: Lens' ContinuousParameterRange (Maybe HyperParameterScalingType) Source #

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
Amazon 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.

continuousParameterRange_name :: Lens' ContinuousParameterRange Text Source #

The name of the continuous hyperparameter to tune.

continuousParameterRange_minValue :: Lens' ContinuousParameterRange Text Source #

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

continuousParameterRange_maxValue :: Lens' ContinuousParameterRange Text Source #

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

ContinuousParameterRangeSpecification

CustomImage

customImage_imageVersionNumber :: Lens' CustomImage (Maybe Natural) Source #

The version number of the CustomImage.

customImage_imageName :: Lens' CustomImage Text Source #

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

customImage_appImageConfigName :: Lens' CustomImage Text Source #

The name of the AppImageConfig.

DataCaptureConfig

DataCaptureConfigSummary

DataCatalogConfig

dataCatalogConfig_catalog :: Lens' DataCatalogConfig Text Source #

The name of the Glue table catalog.

dataCatalogConfig_database :: Lens' DataCatalogConfig Text Source #

The name of the Glue table database.

DataProcessing

dataProcessing_outputFilter :: Lens' DataProcessing (Maybe Text) Source #

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 Amazon 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']"

dataProcessing_joinSource :: Lens' DataProcessing (Maybe JoinSource) Source #

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.

dataProcessing_inputFilter :: Lens' DataProcessing (Maybe Text) Source #

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 Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $.

Examples: "$", "$[1:]", "$.features"

DataQualityAppSpecification

dataQualityAppSpecification_containerArguments :: Lens' DataQualityAppSpecification (Maybe (NonEmpty Text)) Source #

The arguments to send to the container that the monitoring job runs.

dataQualityAppSpecification_recordPreprocessorSourceUri :: Lens' DataQualityAppSpecification (Maybe Text) Source #

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.

dataQualityAppSpecification_environment :: Lens' DataQualityAppSpecification (Maybe (HashMap Text Text)) Source #

Sets the environment variables in the container that the monitoring job runs.

dataQualityAppSpecification_containerEntrypoint :: Lens' DataQualityAppSpecification (Maybe (NonEmpty Text)) Source #

The entrypoint for a container used to run a monitoring job.

dataQualityAppSpecification_postAnalyticsProcessorSourceUri :: Lens' DataQualityAppSpecification (Maybe Text) Source #

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

dataQualityAppSpecification_imageUri :: Lens' DataQualityAppSpecification Text Source #

The container image that the data quality monitoring job runs.

DataQualityBaselineConfig

dataQualityBaselineConfig_baseliningJobName :: Lens' DataQualityBaselineConfig (Maybe Text) Source #

The name of the job that performs baselining for the data quality monitoring job.

DataQualityJobInput

DataSource

dataSource_s3DataSource :: Lens' DataSource (Maybe S3DataSource) Source #

The S3 location of the data source that is associated with a channel.

dataSource_fileSystemDataSource :: Lens' DataSource (Maybe FileSystemDataSource) Source #

The file system that is associated with a channel.

DatasetDefinition

datasetDefinition_localPath :: Lens' DatasetDefinition (Maybe Text) Source #

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).

datasetDefinition_dataDistributionType :: Lens' DatasetDefinition (Maybe DataDistributionType) Source #

Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).

datasetDefinition_inputMode :: Lens' DatasetDefinition (Maybe InputMode) Source #

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.

DebugHookConfig

debugHookConfig_localPath :: Lens' DebugHookConfig (Maybe Text) Source #

Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.

debugHookConfig_collectionConfigurations :: Lens' DebugHookConfig (Maybe [CollectionConfiguration]) Source #

Configuration information for 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.

debugHookConfig_hookParameters :: Lens' DebugHookConfig (Maybe (HashMap Text Text)) Source #

Configuration information for the Debugger hook parameters.

debugHookConfig_s3OutputPath :: Lens' DebugHookConfig Text Source #

Path to Amazon S3 storage location for metrics and tensors.

DebugRuleConfiguration

debugRuleConfiguration_s3OutputPath :: Lens' DebugRuleConfiguration (Maybe Text) Source #

Path to Amazon S3 storage location for rules.

debugRuleConfiguration_localPath :: Lens' DebugRuleConfiguration (Maybe Text) Source #

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.

debugRuleConfiguration_instanceType :: Lens' DebugRuleConfiguration (Maybe ProcessingInstanceType) Source #

The instance type to deploy a Debugger custom rule for debugging a training job.

debugRuleConfiguration_volumeSizeInGB :: Lens' DebugRuleConfiguration (Maybe Natural) Source #

The size, in GB, of the ML storage volume attached to the processing instance.

debugRuleConfiguration_ruleConfigurationName :: Lens' DebugRuleConfiguration Text Source #

The name of the rule configuration. It must be unique relative to other rule configuration names.

debugRuleConfiguration_ruleEvaluatorImage :: Lens' DebugRuleConfiguration Text Source #

The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

DebugRuleEvaluationStatus

debugRuleEvaluationStatus_lastModifiedTime :: Lens' DebugRuleEvaluationStatus (Maybe UTCTime) Source #

Timestamp when the rule evaluation status was last modified.

debugRuleEvaluationStatus_ruleEvaluationJobArn :: Lens' DebugRuleEvaluationStatus (Maybe Text) Source #

The Amazon Resource Name (ARN) of the rule evaluation job.

DeployedImage

deployedImage_resolvedImage :: Lens' DeployedImage (Maybe Text) Source #

The specific digest path of the image hosted in this ProductionVariant.

deployedImage_specifiedImage :: Lens' DeployedImage (Maybe Text) Source #

The image path you specified when you created the model.

deployedImage_resolutionTime :: Lens' DeployedImage (Maybe UTCTime) Source #

The date and time when the image path for the model resolved to the ResolvedImage

DeploymentConfig

DesiredWeightAndCapacity

Device

device_description :: Lens' Device (Maybe Text) Source #

Description of the device.

device_iotThingName :: Lens' Device (Maybe Text) Source #

Amazon Web Services Internet of Things (IoT) object name.

device_deviceName :: Lens' Device Text Source #

The name of the device.

DeviceFleetSummary

deviceFleetSummary_creationTime :: Lens' DeviceFleetSummary (Maybe UTCTime) Source #

Timestamp of when the device fleet was created.

deviceFleetSummary_lastModifiedTime :: Lens' DeviceFleetSummary (Maybe UTCTime) Source #

Timestamp of when the device fleet was last updated.

deviceFleetSummary_deviceFleetArn :: Lens' DeviceFleetSummary Text Source #

Amazon Resource Name (ARN) of the device fleet.

DeviceStats

deviceStats_connectedDeviceCount :: Lens' DeviceStats Integer Source #

The number of devices connected with a heartbeat.

DeviceSummary

deviceSummary_registrationTime :: Lens' DeviceSummary (Maybe UTCTime) Source #

The timestamp of the last registration or de-reregistration.

deviceSummary_latestHeartbeat :: Lens' DeviceSummary (Maybe UTCTime) Source #

The last heartbeat received from the device.

deviceSummary_deviceFleetName :: Lens' DeviceSummary (Maybe Text) Source #

The name of the fleet the device belongs to.

deviceSummary_iotThingName :: Lens' DeviceSummary (Maybe Text) Source #

The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..

deviceSummary_deviceName :: Lens' DeviceSummary Text Source #

The unique identifier of the device.

deviceSummary_deviceArn :: Lens' DeviceSummary Text Source #

Amazon Resource Name (ARN) of the device.

DomainDetails

domainDetails_domainArn :: Lens' DomainDetails (Maybe Text) Source #

The domain's Amazon Resource Name (ARN).

EdgeModel

edgeModel_latestInference :: Lens' EdgeModel (Maybe UTCTime) Source #

The timestamp of the last inference that was made.

edgeModel_latestSampleTime :: Lens' EdgeModel (Maybe UTCTime) Source #

The timestamp of the last data sample taken.

edgeModel_modelName :: Lens' EdgeModel Text Source #

The name of the model.

EdgeModelStat

edgeModelStat_offlineDeviceCount :: Lens' EdgeModelStat Integer Source #

The number of devices that have this model version and do not have a heart beat.

edgeModelStat_connectedDeviceCount :: Lens' EdgeModelStat Integer Source #

The number of devices that have this model version and have a heart beat.

edgeModelStat_activeDeviceCount :: Lens' EdgeModelStat Integer Source #

The number of devices that have this model version, a heart beat, and are currently running.

edgeModelStat_samplingDeviceCount :: Lens' EdgeModelStat Integer Source #

The number of devices with this model version and are producing sample data.

EdgeModelSummary

EdgeOutputConfig

edgeOutputConfig_presetDeploymentType :: Lens' EdgeOutputConfig (Maybe EdgePresetDeploymentType) Source #

The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

edgeOutputConfig_kmsKeyId :: Lens' EdgeOutputConfig (Maybe Text) Source #

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.

edgeOutputConfig_presetDeploymentConfig :: Lens' EdgeOutputConfig (Maybe Text) Source #

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.

edgeOutputConfig_s3OutputLocation :: Lens' EdgeOutputConfig Text Source #

The Amazon Simple Storage (S3) bucker URI.

EdgePackagingJobSummary

edgePackagingJobSummary_lastModifiedTime :: Lens' EdgePackagingJobSummary (Maybe UTCTime) Source #

The timestamp of when the edge packaging job was last updated.

edgePackagingJobSummary_edgePackagingJobArn :: Lens' EdgePackagingJobSummary Text Source #

The Amazon Resource Name (ARN) of the edge packaging job.

EdgePresetDeploymentOutput

edgePresetDeploymentOutput_artifact :: Lens' EdgePresetDeploymentOutput (Maybe Text) Source #

The Amazon Resource Name (ARN) of the generated deployable resource.

edgePresetDeploymentOutput_statusMessage :: Lens' EdgePresetDeploymentOutput (Maybe Text) Source #

Returns a message describing the status of the deployed resource.

edgePresetDeploymentOutput_type :: Lens' EdgePresetDeploymentOutput EdgePresetDeploymentType Source #

The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

Endpoint

endpoint_failureReason :: Lens' Endpoint (Maybe Text) Source #

If the endpoint failed, the reason it failed.

endpoint_productionVariants :: Lens' Endpoint (Maybe (NonEmpty ProductionVariantSummary)) Source #

A list of the production variants hosted on the endpoint. Each production variant is a model.

endpoint_monitoringSchedules :: Lens' Endpoint (Maybe [MonitoringSchedule]) Source #

A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.

endpoint_tags :: Lens' Endpoint (Maybe [Tag]) Source #

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.

endpoint_endpointName :: Lens' Endpoint Text Source #

The name of the endpoint.

endpoint_endpointArn :: Lens' Endpoint Text Source #

The Amazon Resource Name (ARN) of the endpoint.

endpoint_endpointConfigName :: Lens' Endpoint Text Source #

The endpoint configuration associated with the endpoint.

endpoint_creationTime :: Lens' Endpoint UTCTime Source #

The time that the endpoint was created.

endpoint_lastModifiedTime :: Lens' Endpoint UTCTime Source #

The last time the endpoint was modified.

EndpointConfigSummary

endpointConfigSummary_endpointConfigArn :: Lens' EndpointConfigSummary Text Source #

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

endpointConfigSummary_creationTime :: Lens' EndpointConfigSummary UTCTime Source #

A timestamp that shows when the endpoint configuration was created.

EndpointInput

endpointInput_inferenceAttribute :: Lens' EndpointInput (Maybe Text) Source #

The attribute of the input data that represents the ground truth label.

endpointInput_s3DataDistributionType :: Lens' EndpointInput (Maybe ProcessingS3DataDistributionType) Source #

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

endpointInput_s3InputMode :: Lens' EndpointInput (Maybe ProcessingS3InputMode) Source #

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.

endpointInput_startTimeOffset :: Lens' EndpointInput (Maybe Text) Source #

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.

endpointInput_featuresAttribute :: Lens' EndpointInput (Maybe Text) Source #

The attributes of the input data that are the input features.

endpointInput_endTimeOffset :: Lens' EndpointInput (Maybe Text) Source #

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.

endpointInput_probabilityThresholdAttribute :: Lens' EndpointInput (Maybe Double) Source #

The threshold for the class probability to be evaluated as a positive result.

endpointInput_probabilityAttribute :: Lens' EndpointInput (Maybe Text) Source #

In a classification problem, the attribute that represents the class probability.

endpointInput_endpointName :: Lens' EndpointInput Text Source #

An endpoint in customer's account which has enabled DataCaptureConfig enabled.

endpointInput_localPath :: Lens' EndpointInput Text Source #

Path to the filesystem where the endpoint data is available to the container.

EndpointSummary

endpointSummary_endpointArn :: Lens' EndpointSummary Text Source #

The Amazon Resource Name (ARN) of the endpoint.

endpointSummary_creationTime :: Lens' EndpointSummary UTCTime Source #

A timestamp that shows when the endpoint was created.

endpointSummary_lastModifiedTime :: Lens' EndpointSummary UTCTime Source #

A timestamp that shows when the endpoint was last modified.

endpointSummary_endpointStatus :: Lens' EndpointSummary EndpointStatus Source #

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.

Experiment

experiment_creationTime :: Lens' Experiment (Maybe UTCTime) Source #

When the experiment was created.

experiment_lastModifiedTime :: Lens' Experiment (Maybe UTCTime) Source #

When the experiment was last modified.

experiment_experimentName :: Lens' Experiment (Maybe Text) Source #

The name of the experiment.

experiment_experimentArn :: Lens' Experiment (Maybe Text) Source #

The Amazon Resource Name (ARN) of the experiment.

experiment_displayName :: Lens' Experiment (Maybe Text) Source #

The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.

experiment_description :: Lens' Experiment (Maybe Text) Source #

The description of the experiment.

experiment_tags :: Lens' Experiment (Maybe [Tag]) Source #

The list of tags that are associated with the experiment. You can use Search API to search on the tags.

ExperimentConfig

experimentConfig_trialComponentDisplayName :: Lens' ExperimentConfig (Maybe Text) Source #

The display name for the trial component. If this key isn't specified, the display name is the trial component name.

experimentConfig_experimentName :: Lens' ExperimentConfig (Maybe Text) Source #

The name of an existing experiment to associate the trial component with.

experimentConfig_trialName :: Lens' ExperimentConfig (Maybe Text) Source #

The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

ExperimentSource

experimentSource_sourceArn :: Lens' ExperimentSource Text Source #

The Amazon Resource Name (ARN) of the source.

ExperimentSummary

experimentSummary_experimentArn :: Lens' ExperimentSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the experiment.

experimentSummary_displayName :: Lens' ExperimentSummary (Maybe Text) Source #

The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.

Explainability

explainability_report :: Lens' Explainability (Maybe MetricsSource) Source #

The explainability report for a model.

FeatureDefinition

featureDefinition_featureType :: Lens' FeatureDefinition (Maybe FeatureType) Source #

The value type of a feature. Valid values are Integral, Fractional, or String.

featureDefinition_featureName :: Lens' FeatureDefinition (Maybe Text) Source #

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.

FeatureGroup

featureGroup_creationTime :: Lens' FeatureGroup (Maybe UTCTime) Source #

The time a FeatureGroup was created.

featureGroup_failureReason :: Lens' FeatureGroup (Maybe Text) Source #

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.

featureGroup_featureDefinitions :: Lens' FeatureGroup (Maybe (NonEmpty FeatureDefinition)) Source #

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.

featureGroup_eventTimeFeatureName :: Lens' FeatureGroup (Maybe Text) Source #

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.

featureGroup_recordIdentifierFeatureName :: Lens' FeatureGroup (Maybe Text) Source #

The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions.

featureGroup_featureGroupArn :: Lens' FeatureGroup (Maybe Text) Source #

The Amazon Resource Name (ARN) of a FeatureGroup.

featureGroup_description :: Lens' FeatureGroup (Maybe Text) Source #

A free form description of a FeatureGroup.

featureGroup_tags :: Lens' FeatureGroup (Maybe [Tag]) Source #

Tags used to define a FeatureGroup.

featureGroup_roleArn :: Lens' FeatureGroup (Maybe Text) Source #

The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.

FeatureGroupSummary

featureGroupSummary_featureGroupStatus :: Lens' FeatureGroupSummary (Maybe FeatureGroupStatus) Source #

The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail.

featureGroupSummary_offlineStoreStatus :: Lens' FeatureGroupSummary (Maybe OfflineStoreStatus) Source #

Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked.

featureGroupSummary_featureGroupArn :: Lens' FeatureGroupSummary Text Source #

Unique identifier for the FeatureGroup.

featureGroupSummary_creationTime :: Lens' FeatureGroupSummary UTCTime Source #

A timestamp indicating the time of creation time of the FeatureGroup.

FileSystemConfig

fileSystemConfig_defaultGid :: Lens' FileSystemConfig (Maybe Natural) Source #

The default POSIX group ID (GID). If not specified, defaults to 100.

fileSystemConfig_mountPath :: Lens' FileSystemConfig (Maybe Text) Source #

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.

fileSystemConfig_defaultUid :: Lens' FileSystemConfig (Maybe Natural) Source #

The default POSIX user ID (UID). If not specified, defaults to 1000.

FileSystemDataSource

fileSystemDataSource_fileSystemAccessMode :: Lens' FileSystemDataSource FileSystemAccessMode Source #

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.

fileSystemDataSource_directoryPath :: Lens' FileSystemDataSource Text Source #

The full path to the directory to associate with the channel.

Filter

filter_operator :: Lens' Filter (Maybe Operator) Source #

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."

filter_value :: Lens' Filter (Maybe Text) Source #

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.

filter_name :: Lens' Filter Text Source #

A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.

FinalAutoMLJobObjectiveMetric

finalAutoMLJobObjectiveMetric_metricName :: Lens' FinalAutoMLJobObjectiveMetric AutoMLMetricEnum Source #

The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.

FinalHyperParameterTuningJobObjectiveMetric

finalHyperParameterTuningJobObjectiveMetric_type :: Lens' FinalHyperParameterTuningJobObjectiveMetric (Maybe HyperParameterTuningJobObjectiveType) Source #

Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.

FlowDefinitionOutputConfig

flowDefinitionOutputConfig_kmsKeyId :: Lens' FlowDefinitionOutputConfig (Maybe Text) Source #

The Amazon Key Management Service (KMS) key ID for server-side encryption.

flowDefinitionOutputConfig_s3OutputPath :: Lens' FlowDefinitionOutputConfig Text Source #

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.

FlowDefinitionSummary

flowDefinitionSummary_failureReason :: Lens' FlowDefinitionSummary (Maybe Text) Source #

The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.

flowDefinitionSummary_flowDefinitionArn :: Lens' FlowDefinitionSummary Text Source #

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

flowDefinitionSummary_creationTime :: Lens' FlowDefinitionSummary UTCTime Source #

The timestamp when SageMaker created the flow definition.

GitConfig

gitConfig_branch :: Lens' GitConfig (Maybe Text) Source #

The default branch for the Git repository.

gitConfig_secretArn :: Lens' GitConfig (Maybe Text) Source #

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}

gitConfig_repositoryUrl :: Lens' GitConfig Text Source #

The URL where the Git repository is located.

GitConfigForUpdate

gitConfigForUpdate_secretArn :: Lens' GitConfigForUpdate (Maybe Text) Source #

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}

HumanLoopActivationConditionsConfig

humanLoopActivationConditionsConfig_humanLoopActivationConditions :: Lens' HumanLoopActivationConditionsConfig Text Source #

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.

HumanLoopActivationConfig

humanLoopActivationConfig_humanLoopActivationConditionsConfig :: Lens' HumanLoopActivationConfig HumanLoopActivationConditionsConfig Source #

Container structure for defining under what conditions SageMaker creates a human loop.

HumanLoopConfig

humanLoopConfig_taskKeywords :: Lens' HumanLoopConfig (Maybe (NonEmpty Text)) Source #

Keywords used to describe the task so that workers can discover the task.

humanLoopConfig_taskTimeLimitInSeconds :: Lens' HumanLoopConfig (Maybe Natural) Source #

The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).

humanLoopConfig_taskAvailabilityLifetimeInSeconds :: Lens' HumanLoopConfig (Maybe Natural) Source #

The length of time that a task remains available for review by human workers.

humanLoopConfig_workteamArn :: Lens' HumanLoopConfig Text Source #

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.

humanLoopConfig_humanTaskUiArn :: Lens' HumanLoopConfig Text Source #

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.

humanLoopConfig_taskTitle :: Lens' HumanLoopConfig Text Source #

A title for the human worker task.

humanLoopConfig_taskDescription :: Lens' HumanLoopConfig Text Source #

A description for the human worker task.

humanLoopConfig_taskCount :: Lens' HumanLoopConfig Natural Source #

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.

HumanLoopRequestSource

humanLoopRequestSource_awsManagedHumanLoopRequestSource :: Lens' HumanLoopRequestSource AwsManagedHumanLoopRequestSource Source #

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:

HumanTaskConfig

humanTaskConfig_taskKeywords :: Lens' HumanTaskConfig (Maybe (NonEmpty Text)) Source #

Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

humanTaskConfig_publicWorkforceTaskPrice :: Lens' HumanTaskConfig (Maybe PublicWorkforceTaskPrice) Source #

The price that you pay for each task performed by an Amazon Mechanical Turk worker.

humanTaskConfig_taskAvailabilityLifetimeInSeconds :: Lens' HumanTaskConfig (Maybe Natural) Source #

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 10 days (864,000 seconds). For most users, the maximum is also 10 days. If you want to change this limit, contact Amazon Web Services Support.

humanTaskConfig_maxConcurrentTaskCount :: Lens' HumanTaskConfig (Maybe Natural) Source #

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.

humanTaskConfig_workteamArn :: Lens' HumanTaskConfig Text Source #

The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

humanTaskConfig_uiConfig :: Lens' HumanTaskConfig UiConfig Source #

Information about the user interface that workers use to complete the labeling task.

humanTaskConfig_preHumanTaskLambdaArn :: Lens' HumanTaskConfig Text Source #

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

humanTaskConfig_taskTitle :: Lens' HumanTaskConfig Text Source #

A title for the task for your human workers.

humanTaskConfig_taskDescription :: Lens' HumanTaskConfig Text Source #

A description of the task for your human workers.

humanTaskConfig_numberOfHumanWorkersPerDataObject :: Lens' HumanTaskConfig Natural Source #

The number of human workers that will label an object.

humanTaskConfig_taskTimeLimitInSeconds :: Lens' HumanTaskConfig Natural Source #

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 7 days (604,800 seconds). If you want to change these limits, contact Amazon Web Services Support.

humanTaskConfig_annotationConsolidationConfig :: Lens' HumanTaskConfig AnnotationConsolidationConfig Source #

Configures how labels are consolidated across human workers.

HumanTaskUiSummary

humanTaskUiSummary_humanTaskUiName :: Lens' HumanTaskUiSummary Text Source #

The name of the human task user interface.

humanTaskUiSummary_humanTaskUiArn :: Lens' HumanTaskUiSummary Text Source #

The Amazon Resource Name (ARN) of the human task user interface.

humanTaskUiSummary_creationTime :: Lens' HumanTaskUiSummary UTCTime Source #

A timestamp when SageMaker created the human task user interface.

HyperParameterAlgorithmSpecification

hyperParameterAlgorithmSpecification_algorithmName :: Lens' HyperParameterAlgorithmSpecification (Maybe Text) Source #

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.

hyperParameterAlgorithmSpecification_trainingImage :: Lens' HyperParameterAlgorithmSpecification (Maybe Text) Source #

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. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

hyperParameterAlgorithmSpecification_metricDefinitions :: Lens' HyperParameterAlgorithmSpecification (Maybe [MetricDefinition]) Source #

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

HyperParameterSpecification

hyperParameterSpecification_isTunable :: Lens' HyperParameterSpecification (Maybe Bool) Source #

Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

hyperParameterSpecification_defaultValue :: Lens' HyperParameterSpecification (Maybe Text) Source #

The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

hyperParameterSpecification_isRequired :: Lens' HyperParameterSpecification (Maybe Bool) Source #

Indicates whether this hyperparameter is required.

hyperParameterSpecification_name :: Lens' HyperParameterSpecification Text Source #

The name of this hyperparameter. The name must be unique.

hyperParameterSpecification_type :: Lens' HyperParameterSpecification ParameterType Source #

The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.

HyperParameterTrainingJobDefinition

hyperParameterTrainingJobDefinition_retryStrategy :: Lens' HyperParameterTrainingJobDefinition (Maybe RetryStrategy) Source #

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

hyperParameterTrainingJobDefinition_enableNetworkIsolation :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

hyperParameterTrainingJobDefinition_staticHyperParameters :: Lens' HyperParameterTrainingJobDefinition (Maybe (HashMap Text Text)) Source #

Specifies the values of hyperparameters that do not change for the tuning job.

hyperParameterTrainingJobDefinition_enableManagedSpotTraining :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #

A Boolean indicating whether managed spot training is enabled (True) or not (False).

hyperParameterTrainingJobDefinition_inputDataConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe (NonEmpty Channel)) Source #

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

hyperParameterTrainingJobDefinition_vpcConfig :: Lens' HyperParameterTrainingJobDefinition (Maybe VpcConfig) Source #

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.

hyperParameterTrainingJobDefinition_enableInterContainerTrafficEncryption :: Lens' HyperParameterTrainingJobDefinition (Maybe Bool) Source #

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.

hyperParameterTrainingJobDefinition_algorithmSpecification :: Lens' HyperParameterTrainingJobDefinition HyperParameterAlgorithmSpecification Source #

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

hyperParameterTrainingJobDefinition_roleArn :: Lens' HyperParameterTrainingJobDefinition Text Source #

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

hyperParameterTrainingJobDefinition_outputDataConfig :: Lens' HyperParameterTrainingJobDefinition OutputDataConfig Source #

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

hyperParameterTrainingJobDefinition_resourceConfig :: Lens' HyperParameterTrainingJobDefinition ResourceConfig Source #

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 Amazon 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.

hyperParameterTrainingJobDefinition_stoppingCondition :: Lens' HyperParameterTrainingJobDefinition StoppingCondition Source #

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

HyperParameterTrainingJobSummary

hyperParameterTrainingJobSummary_tuningJobName :: Lens' HyperParameterTrainingJobSummary (Maybe Text) Source #

The HyperParameter tuning job that launched the training job.

hyperParameterTrainingJobSummary_trainingEndTime :: Lens' HyperParameterTrainingJobSummary (Maybe UTCTime) Source #

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 Amazon SageMaker detects a job failure.

hyperParameterTrainingJobSummary_objectiveStatus :: Lens' HyperParameterTrainingJobSummary (Maybe ObjectiveStatus) Source #

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.

hyperParameterTrainingJobSummary_finalHyperParameterTuningJobObjectiveMetric :: Lens' HyperParameterTrainingJobSummary (Maybe FinalHyperParameterTuningJobObjectiveMetric) Source #

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

hyperParameterTrainingJobSummary_tunedHyperParameters :: Lens' HyperParameterTrainingJobSummary (HashMap Text Text) Source #

A list of the hyperparameters for which you specified ranges to search.

HyperParameterTuningJobConfig

hyperParameterTuningJobConfig_parameterRanges :: Lens' HyperParameterTuningJobConfig (Maybe ParameterRanges) Source #

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

hyperParameterTuningJobConfig_hyperParameterTuningJobObjective :: Lens' HyperParameterTuningJobConfig (Maybe HyperParameterTuningJobObjective) Source #

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

hyperParameterTuningJobConfig_trainingJobEarlyStoppingType :: Lens' HyperParameterTuningJobConfig (Maybe TrainingJobEarlyStoppingType) Source #

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be 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
Amazon 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.

hyperParameterTuningJobConfig_strategy :: Lens' HyperParameterTuningJobConfig HyperParameterTuningJobStrategyType Source #

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to Bayesian. To randomly search, set it to Random. For information about search strategies, see How Hyperparameter Tuning Works.

hyperParameterTuningJobConfig_resourceLimits :: Lens' HyperParameterTuningJobConfig ResourceLimits Source #

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

HyperParameterTuningJobObjective

hyperParameterTuningJobObjective_metricName :: Lens' HyperParameterTuningJobObjective Text Source #

The name of the metric to use for the objective metric.

HyperParameterTuningJobSummary

hyperParameterTuningJobSummary_resourceLimits :: Lens' HyperParameterTuningJobSummary (Maybe ResourceLimits) Source #

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

hyperParameterTuningJobSummary_strategy :: Lens' HyperParameterTuningJobSummary HyperParameterTuningJobStrategyType Source #

Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.

hyperParameterTuningJobSummary_trainingJobStatusCounters :: Lens' HyperParameterTuningJobSummary TrainingJobStatusCounters Source #

The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

hyperParameterTuningJobSummary_objectiveStatusCounters :: Lens' HyperParameterTuningJobSummary ObjectiveStatusCounters Source #

The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

HyperParameterTuningJobWarmStartConfig

hyperParameterTuningJobWarmStartConfig_parentHyperParameterTuningJobs :: Lens' HyperParameterTuningJobWarmStartConfig (NonEmpty ParentHyperParameterTuningJob) Source #

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.

hyperParameterTuningJobWarmStartConfig_warmStartType :: Lens' HyperParameterTuningJobWarmStartConfig HyperParameterTuningJobWarmStartType Source #

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.

Image

image_failureReason :: Lens' Image (Maybe Text) Source #

When a create, update, or delete operation fails, the reason for the failure.

image_displayName :: Lens' Image (Maybe Text) Source #

The name of the image as displayed.

image_description :: Lens' Image (Maybe Text) Source #

The description of the image.

image_creationTime :: Lens' Image UTCTime Source #

When the image was created.

image_imageArn :: Lens' Image Text Source #

The Amazon Resource Name (ARN) of the image.

image_imageName :: Lens' Image Text Source #

The name of the image.

image_imageStatus :: Lens' Image ImageStatus Source #

The status of the image.

image_lastModifiedTime :: Lens' Image UTCTime Source #

When the image was last modified.

ImageConfig

imageConfig_repositoryAuthConfig :: Lens' ImageConfig (Maybe RepositoryAuthConfig) Source #

(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.

imageConfig_repositoryAccessMode :: Lens' ImageConfig RepositoryAccessMode Source #

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.

ImageVersion

imageVersion_failureReason :: Lens' ImageVersion (Maybe Text) Source #

When a create or delete operation fails, the reason for the failure.

imageVersion_imageArn :: Lens' ImageVersion Text Source #

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

imageVersion_lastModifiedTime :: Lens' ImageVersion UTCTime Source #

When the version was last modified.

InferenceExecutionConfig

inferenceExecutionConfig_mode :: Lens' InferenceExecutionConfig InferenceExecutionMode Source #

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.

InferenceSpecification

inferenceSpecification_supportedRealtimeInferenceInstanceTypes :: Lens' InferenceSpecification (Maybe [ProductionVariantInstanceType]) Source #

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.

inferenceSpecification_supportedTransformInstanceTypes :: Lens' InferenceSpecification (Maybe (NonEmpty TransformInstanceType)) Source #

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.

inferenceSpecification_containers :: Lens' InferenceSpecification (NonEmpty ModelPackageContainerDefinition) Source #

The Amazon ECR registry path of the Docker image that contains the inference code.

InputConfig

inputConfig_frameworkVersion :: Lens' InputConfig (Maybe Text) Source #

Specifies the framework version to use.

This API field is only supported for PyTorch framework versions 1.4, 1.5, and 1.6 for cloud instance target devices: ml_c4, ml_c5, ml_m4, ml_m5, ml_p2, ml_p3, and ml_g4dn.

inputConfig_s3Uri :: Lens' InputConfig Text Source #

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).

inputConfig_dataInputConfig :: Lens' InputConfig Text Source #

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"]}

inputConfig_framework :: Lens' InputConfig Framework Source #

Identifies the framework in which the model was trained. For example: TENSORFLOW.

IntegerParameterRange

integerParameterRange_scalingType :: Lens' IntegerParameterRange (Maybe HyperParameterScalingType) Source #

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
Amazon 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.

integerParameterRange_name :: Lens' IntegerParameterRange Text Source #

The name of the hyperparameter to search.

integerParameterRange_minValue :: Lens' IntegerParameterRange Text Source #

The minimum value of the hyperparameter to search.

integerParameterRange_maxValue :: Lens' IntegerParameterRange Text Source #

The maximum value of the hyperparameter to search.

IntegerParameterRangeSpecification

JupyterServerAppSettings

jupyterServerAppSettings_defaultResourceSpec :: Lens' JupyterServerAppSettings (Maybe ResourceSpec) Source #

The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.

jupyterServerAppSettings_lifecycleConfigArns :: Lens' JupyterServerAppSettings (Maybe [Text]) Source #

The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp.

KernelGatewayAppSettings

kernelGatewayAppSettings_defaultResourceSpec :: Lens' KernelGatewayAppSettings (Maybe ResourceSpec) Source #

The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

kernelGatewayAppSettings_customImages :: Lens' KernelGatewayAppSettings (Maybe [CustomImage]) Source #

A list of custom SageMaker images that are configured to run as a KernelGateway app.

kernelGatewayAppSettings_lifecycleConfigArns :: Lens' KernelGatewayAppSettings (Maybe [Text]) Source #

The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

KernelGatewayImageConfig

kernelGatewayImageConfig_fileSystemConfig :: Lens' KernelGatewayImageConfig (Maybe FileSystemConfig) Source #

The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

kernelGatewayImageConfig_kernelSpecs :: Lens' KernelGatewayImageConfig (NonEmpty KernelSpec) Source #

The specification of the Jupyter kernels in the image.

KernelSpec

kernelSpec_displayName :: Lens' KernelSpec (Maybe Text) Source #

The display name of the kernel.

kernelSpec_name :: Lens' KernelSpec Text Source #

The name of the Jupyter kernel in the image. This value is case sensitive.

LabelCounters

labelCounters_machineLabeled :: Lens' LabelCounters (Maybe Natural) Source #

The total number of objects labeled by automated data labeling.

labelCounters_totalLabeled :: Lens' LabelCounters (Maybe Natural) Source #

The total number of objects labeled.

labelCounters_failedNonRetryableError :: Lens' LabelCounters (Maybe Natural) Source #

The total number of objects that could not be labeled due to an error.

labelCounters_unlabeled :: Lens' LabelCounters (Maybe Natural) Source #

The total number of objects not yet labeled.

labelCounters_humanLabeled :: Lens' LabelCounters (Maybe Natural) Source #

The total number of objects labeled by a human worker.

LabelCountersForWorkteam

labelCountersForWorkteam_pendingHuman :: Lens' LabelCountersForWorkteam (Maybe Natural) Source #

The total number of data objects that need to be labeled by a human worker.

labelCountersForWorkteam_total :: Lens' LabelCountersForWorkteam (Maybe Natural) Source #

The total number of tasks in the labeling job.

labelCountersForWorkteam_humanLabeled :: Lens' LabelCountersForWorkteam (Maybe Natural) Source #

The total number of data objects labeled by a human worker.

LabelingJobAlgorithmsConfig

labelingJobAlgorithmsConfig_initialActiveLearningModelArn :: Lens' LabelingJobAlgorithmsConfig (Maybe Text) Source #

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.

labelingJobAlgorithmsConfig_labelingJobAlgorithmSpecificationArn :: Lens' LabelingJobAlgorithmsConfig Text Source #

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

LabelingJobDataAttributes

labelingJobDataAttributes_contentClassifiers :: Lens' LabelingJobDataAttributes (Maybe [ContentClassifier]) Source #

Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

LabelingJobDataSource

labelingJobDataSource_snsDataSource :: Lens' LabelingJobDataSource (Maybe LabelingJobSnsDataSource) Source #

An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.

LabelingJobForWorkteamSummary

labelingJobForWorkteamSummary_labelingJobName :: Lens' LabelingJobForWorkteamSummary (Maybe Text) Source #

The name of the labeling job that the work team is assigned to.

labelingJobForWorkteamSummary_jobReferenceCode :: Lens' LabelingJobForWorkteamSummary Text Source #

A unique identifier for a labeling job. You can use this to refer to a specific labeling job.

labelingJobForWorkteamSummary_workRequesterAccountId :: Lens' LabelingJobForWorkteamSummary Text Source #

The Amazon Web Services account ID of the account used to start the labeling job.

labelingJobForWorkteamSummary_creationTime :: Lens' LabelingJobForWorkteamSummary UTCTime Source #

The date and time that the labeling job was created.

LabelingJobInputConfig

LabelingJobOutput

labelingJobOutput_finalActiveLearningModelArn :: Lens' LabelingJobOutput (Maybe Text) Source #

The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.

labelingJobOutput_outputDatasetS3Uri :: Lens' LabelingJobOutput Text Source #

The Amazon S3 bucket location of the manifest file for labeled data.

LabelingJobOutputConfig

labelingJobOutputConfig_snsTopicArn :: Lens' LabelingJobOutputConfig (Maybe Text) Source #

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.

labelingJobOutputConfig_kmsKeyId :: Lens' LabelingJobOutputConfig (Maybe Text) Source #

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.

labelingJobOutputConfig_s3OutputPath :: Lens' LabelingJobOutputConfig Text Source #

The Amazon S3 location to write output data.

LabelingJobResourceConfig

labelingJobResourceConfig_volumeKmsKeyId :: Lens' LabelingJobResourceConfig (Maybe Text) Source #

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"

LabelingJobS3DataSource

labelingJobS3DataSource_manifestS3Uri :: Lens' LabelingJobS3DataSource Text Source #

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.

LabelingJobSnsDataSource

labelingJobSnsDataSource_snsTopicArn :: Lens' LabelingJobSnsDataSource Text Source #

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.

LabelingJobStoppingConditions

labelingJobStoppingConditions_maxHumanLabeledObjectCount :: Lens' LabelingJobStoppingConditions (Maybe Natural) Source #

The maximum number of objects that can be labeled by human workers.

LabelingJobSummary

labelingJobSummary_failureReason :: Lens' LabelingJobSummary (Maybe Text) Source #

If the LabelingJobStatus field is Failed, this field contains a description of the error.

labelingJobSummary_annotationConsolidationLambdaArn :: Lens' LabelingJobSummary (Maybe Text) Source #

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.

labelingJobSummary_labelingJobOutput :: Lens' LabelingJobSummary (Maybe LabelingJobOutput) Source #

The location of the output produced by the labeling job.

labelingJobSummary_labelingJobArn :: Lens' LabelingJobSummary Text Source #

The Amazon Resource Name (ARN) assigned to the labeling job when it was created.

labelingJobSummary_creationTime :: Lens' LabelingJobSummary UTCTime Source #

The date and time that the job was created (timestamp).

labelingJobSummary_lastModifiedTime :: Lens' LabelingJobSummary UTCTime Source #

The date and time that the job was last modified (timestamp).

labelingJobSummary_labelCounters :: Lens' LabelingJobSummary LabelCounters Source #

Counts showing the progress of the labeling job.

labelingJobSummary_workteamArn :: Lens' LabelingJobSummary Text Source #

The Amazon Resource Name (ARN) of the work team assigned to the job.

labelingJobSummary_preHumanTaskLambdaArn :: Lens' LabelingJobSummary Text Source #

The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.

LambdaStepMetadata

lambdaStepMetadata_arn :: Lens' LambdaStepMetadata (Maybe Text) Source #

The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.

lambdaStepMetadata_outputParameters :: Lens' LambdaStepMetadata (Maybe [OutputParameter]) Source #

A list of the output parameters of the Lambda step.

MemberDefinition

memberDefinition_oidcMemberDefinition :: Lens' MemberDefinition (Maybe OidcMemberDefinition) Source #

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.

memberDefinition_cognitoMemberDefinition :: Lens' MemberDefinition (Maybe CognitoMemberDefinition) Source #

The Amazon Cognito user group that is part of the work team.

MetadataProperties

metadataProperties_generatedBy :: Lens' MetadataProperties (Maybe Text) Source #

The entity this entity was generated by.

MetricData

metricData_value :: Lens' MetricData (Maybe Double) Source #

The value of the metric.

metricData_timestamp :: Lens' MetricData (Maybe UTCTime) Source #

The date and time that the algorithm emitted the metric.

MetricDatum

metricDatum_set :: Lens' MetricDatum (Maybe MetricSetSource) Source #

The dataset split from which the AutoML job produced the metric.

metricDatum_value :: Lens' MetricDatum (Maybe Double) Source #

The value of the metric.

MetricDefinition

metricDefinition_regex :: Lens' MetricDefinition Text Source #

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.

MetricsSource

ModelArtifacts

modelArtifacts_s3ModelArtifacts :: Lens' ModelArtifacts Text Source #

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

ModelBiasAppSpecification

modelBiasAppSpecification_environment :: Lens' ModelBiasAppSpecification (Maybe (HashMap Text Text)) Source #

Sets the environment variables in the Docker container.

modelBiasAppSpecification_imageUri :: Lens' ModelBiasAppSpecification Text Source #

The container image to be run by the model bias job.

modelBiasAppSpecification_configUri :: Lens' ModelBiasAppSpecification Text Source #

JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.

ModelBiasBaselineConfig

ModelBiasJobInput

modelBiasJobInput_groundTruthS3Input :: Lens' ModelBiasJobInput MonitoringGroundTruthS3Input Source #

Location of ground truth labels to use in model bias job.

ModelClientConfig

modelClientConfig_invocationsTimeoutInSeconds :: Lens' ModelClientConfig (Maybe Natural) Source #

The timeout value in seconds for an invocation request.

modelClientConfig_invocationsMaxRetries :: Lens' ModelClientConfig (Maybe Natural) Source #

The maximum number of retries when invocation requests are failing.

ModelDataQuality

ModelDeployConfig

modelDeployConfig_endpointName :: Lens' ModelDeployConfig (Maybe Text) Source #

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.

modelDeployConfig_autoGenerateEndpointName :: Lens' ModelDeployConfig (Maybe Bool) Source #

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.

ModelDeployResult

modelDeployResult_endpointName :: Lens' ModelDeployResult (Maybe Text) Source #

The name of the endpoint to which the model has been deployed.

If model deployment fails, this field is omitted from the response.

ModelDigests

modelDigests_artifactDigest :: Lens' ModelDigests (Maybe Text) Source #

Provides a hash value that uniquely identifies the stored model artifacts.

ModelExplainabilityAppSpecification

modelExplainabilityAppSpecification_imageUri :: Lens' ModelExplainabilityAppSpecification Text Source #

The container image to be run by the model explainability job.

modelExplainabilityAppSpecification_configUri :: Lens' ModelExplainabilityAppSpecification Text Source #

JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.

ModelExplainabilityBaselineConfig

ModelExplainabilityJobInput

ModelMetrics

modelMetrics_bias :: Lens' ModelMetrics (Maybe Bias) Source #

Metrics that measure bais in a model.

modelMetrics_modelDataQuality :: Lens' ModelMetrics (Maybe ModelDataQuality) Source #

Metrics that measure the quality of the input data for a model.

modelMetrics_modelQuality :: Lens' ModelMetrics (Maybe ModelQuality) Source #

Metrics that measure the quality of a model.

ModelPackage

modelPackage_creationTime :: Lens' ModelPackage (Maybe UTCTime) Source #

The time that the model package was created.

modelPackage_modelApprovalStatus :: Lens' ModelPackage (Maybe ModelApprovalStatus) Source #

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.

modelPackage_modelPackageArn :: Lens' ModelPackage (Maybe Text) Source #

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

modelPackage_modelPackageDescription :: Lens' ModelPackage (Maybe Text) Source #

The description of the model package.

modelPackage_lastModifiedTime :: Lens' ModelPackage (Maybe UTCTime) Source #

The last time the model package was modified.

modelPackage_approvalDescription :: Lens' ModelPackage (Maybe Text) Source #

A description provided when the model approval is set.

modelPackage_modelPackageVersion :: Lens' ModelPackage (Maybe Natural) Source #

The version number of a versioned model.

modelPackage_certifyForMarketplace :: Lens' ModelPackage (Maybe Bool) Source #

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.

modelPackage_modelPackageGroupName :: Lens' ModelPackage (Maybe Text) Source #

The model group to which the model belongs.

modelPackage_tags :: Lens' ModelPackage (Maybe [Tag]) Source #

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.

modelPackage_modelPackageStatus :: Lens' ModelPackage (Maybe ModelPackageStatus) Source #

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.

ModelPackageContainerDefinition

modelPackageContainerDefinition_modelDataUrl :: Lens' ModelPackageContainerDefinition (Maybe Text) Source #

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.

modelPackageContainerDefinition_environment :: Lens' ModelPackageContainerDefinition (Maybe (HashMap Text Text)) Source #

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.

modelPackageContainerDefinition_imageDigest :: Lens' ModelPackageContainerDefinition (Maybe Text) Source #

An MD5 hash of the training algorithm that identifies the Docker image used for training.

modelPackageContainerDefinition_productId :: Lens' ModelPackageContainerDefinition (Maybe Text) Source #

The Amazon Web Services Marketplace product ID of the model package.

modelPackageContainerDefinition_image :: Lens' ModelPackageContainerDefinition Text Source #

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 Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

ModelPackageGroup

modelPackageGroup_creationTime :: Lens' ModelPackageGroup (Maybe UTCTime) Source #

The time that the model group was created.

modelPackageGroup_modelPackageGroupArn :: Lens' ModelPackageGroup (Maybe Text) Source #

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

modelPackageGroup_modelPackageGroupStatus :: Lens' ModelPackageGroup (Maybe ModelPackageGroupStatus) Source #

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.

modelPackageGroup_tags :: Lens' ModelPackageGroup (Maybe [Tag]) Source #

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.

ModelPackageGroupSummary

modelPackageGroupSummary_modelPackageGroupArn :: Lens' ModelPackageGroupSummary Text Source #

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

ModelPackageStatusDetails

modelPackageStatusDetails_imageScanStatuses :: Lens' ModelPackageStatusDetails (Maybe [ModelPackageStatusItem]) Source #

The status of the scan of the Docker image container for the model package.

ModelPackageStatusItem

modelPackageStatusItem_failureReason :: Lens' ModelPackageStatusItem (Maybe Text) Source #

if the overall status is Failed, the reason for the failure.

modelPackageStatusItem_name :: Lens' ModelPackageStatusItem Text Source #

The name of the model package for which the overall status is being reported.

ModelPackageSummary

modelPackageSummary_modelApprovalStatus :: Lens' ModelPackageSummary (Maybe ModelApprovalStatus) Source #

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.

modelPackageSummary_modelPackageVersion :: Lens' ModelPackageSummary (Maybe Natural) Source #

If the model package is a versioned model, the version of the model.

modelPackageSummary_modelPackageGroupName :: Lens' ModelPackageSummary (Maybe Text) Source #

If the model package is a versioned model, the model group that the versioned model belongs to.

modelPackageSummary_modelPackageArn :: Lens' ModelPackageSummary Text Source #

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

modelPackageSummary_creationTime :: Lens' ModelPackageSummary UTCTime Source #

A timestamp that shows when the model package was created.

ModelPackageValidationProfile

modelPackageValidationProfile_transformJobDefinition :: Lens' ModelPackageValidationProfile TransformJobDefinition Source #

The TransformJobDefinition object that describes the transform job used for the validation of the model package.

ModelPackageValidationSpecification

modelPackageValidationSpecification_validationRole :: Lens' ModelPackageValidationSpecification Text Source #

The IAM roles to be used for the validation of the model package.

modelPackageValidationSpecification_validationProfiles :: Lens' ModelPackageValidationSpecification (NonEmpty ModelPackageValidationProfile) Source #

An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.

ModelQuality

ModelQualityAppSpecification

modelQualityAppSpecification_containerArguments :: Lens' ModelQualityAppSpecification (Maybe (NonEmpty Text)) Source #

An array of arguments for the container used to run the monitoring job.

modelQualityAppSpecification_recordPreprocessorSourceUri :: Lens' ModelQualityAppSpecification (Maybe Text) Source #

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.

modelQualityAppSpecification_environment :: Lens' ModelQualityAppSpecification (Maybe (HashMap Text Text)) Source #

Sets the environment variables in the container that the monitoring job runs.

modelQualityAppSpecification_problemType :: Lens' ModelQualityAppSpecification (Maybe MonitoringProblemType) Source #

The machine learning problem type of the model that the monitoring job monitors.

modelQualityAppSpecification_containerEntrypoint :: Lens' ModelQualityAppSpecification (Maybe (NonEmpty Text)) Source #

Specifies the entrypoint for a container that the monitoring job runs.

modelQualityAppSpecification_postAnalyticsProcessorSourceUri :: Lens' ModelQualityAppSpecification (Maybe Text) Source #

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

modelQualityAppSpecification_imageUri :: Lens' ModelQualityAppSpecification Text Source #

The address of the container image that the monitoring job runs.

ModelQualityBaselineConfig

modelQualityBaselineConfig_baseliningJobName :: Lens' ModelQualityBaselineConfig (Maybe Text) Source #

The name of the job that performs baselining for the monitoring job.

ModelQualityJobInput

ModelStepMetadata

modelStepMetadata_arn :: Lens' ModelStepMetadata (Maybe Text) Source #

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

ModelSummary

modelSummary_modelName :: Lens' ModelSummary Text Source #

The name of the model that you want a summary for.

modelSummary_modelArn :: Lens' ModelSummary Text Source #

The Amazon Resource Name (ARN) of the model.

modelSummary_creationTime :: Lens' ModelSummary UTCTime Source #

A timestamp that indicates when the model was created.

MonitoringAppSpecification

monitoringAppSpecification_containerArguments :: Lens' MonitoringAppSpecification (Maybe (NonEmpty Text)) Source #

An array of arguments for the container used to run the monitoring job.

monitoringAppSpecification_recordPreprocessorSourceUri :: Lens' MonitoringAppSpecification (Maybe Text) Source #

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.

monitoringAppSpecification_containerEntrypoint :: Lens' MonitoringAppSpecification (Maybe (NonEmpty Text)) Source #

Specifies the entrypoint for a container used to run the monitoring job.

monitoringAppSpecification_postAnalyticsProcessorSourceUri :: Lens' MonitoringAppSpecification (Maybe Text) Source #

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

monitoringAppSpecification_imageUri :: Lens' MonitoringAppSpecification Text Source #

The container image to be run by the monitoring job.

MonitoringBaselineConfig

monitoringBaselineConfig_constraintsResource :: Lens' MonitoringBaselineConfig (Maybe MonitoringConstraintsResource) Source #

The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

monitoringBaselineConfig_statisticsResource :: Lens' MonitoringBaselineConfig (Maybe MonitoringStatisticsResource) Source #

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

monitoringBaselineConfig_baseliningJobName :: Lens' MonitoringBaselineConfig (Maybe Text) Source #

The name of the job that performs baselining for the monitoring job.

MonitoringClusterConfig

monitoringClusterConfig_volumeKmsKeyId :: Lens' MonitoringClusterConfig (Maybe Text) Source #

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.

monitoringClusterConfig_instanceCount :: Lens' MonitoringClusterConfig Natural Source #

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.

monitoringClusterConfig_volumeSizeInGB :: Lens' MonitoringClusterConfig Natural Source #

The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

MonitoringConstraintsResource

monitoringConstraintsResource_s3Uri :: Lens' MonitoringConstraintsResource (Maybe Text) Source #

The Amazon S3 URI for the constraints resource.

MonitoringExecutionSummary

monitoringExecutionSummary_failureReason :: Lens' MonitoringExecutionSummary (Maybe Text) Source #

Contains the reason a monitoring job failed, if it failed.

monitoringExecutionSummary_endpointName :: Lens' MonitoringExecutionSummary (Maybe Text) Source #

The name of the endpoint used to run the monitoring job.

monitoringExecutionSummary_processingJobArn :: Lens' MonitoringExecutionSummary (Maybe Text) Source #

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

monitoringExecutionSummary_creationTime :: Lens' MonitoringExecutionSummary UTCTime Source #

The time at which the monitoring job was created.

monitoringExecutionSummary_lastModifiedTime :: Lens' MonitoringExecutionSummary UTCTime Source #

A timestamp that indicates the last time the monitoring job was modified.

MonitoringGroundTruthS3Input

monitoringGroundTruthS3Input_s3Uri :: Lens' MonitoringGroundTruthS3Input (Maybe Text) Source #

The address of the Amazon S3 location of the ground truth labels.

MonitoringInput

MonitoringJobDefinition

monitoringJobDefinition_environment :: Lens' MonitoringJobDefinition (Maybe (HashMap Text Text)) Source #

Sets the environment variables in the Docker container.

monitoringJobDefinition_stoppingCondition :: Lens' MonitoringJobDefinition (Maybe MonitoringStoppingCondition) Source #

Specifies a time limit for how long the monitoring job is allowed to run.

monitoringJobDefinition_networkConfig :: Lens' MonitoringJobDefinition (Maybe NetworkConfig) Source #

Specifies networking options for an monitoring job.

monitoringJobDefinition_baselineConfig :: Lens' MonitoringJobDefinition (Maybe MonitoringBaselineConfig) Source #

Baseline configuration used to validate that the data conforms to the specified constraints and statistics

monitoringJobDefinition_monitoringInputs :: Lens' MonitoringJobDefinition (NonEmpty MonitoringInput) Source #

The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.

monitoringJobDefinition_monitoringOutputConfig :: Lens' MonitoringJobDefinition MonitoringOutputConfig Source #

The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).

monitoringJobDefinition_monitoringResources :: Lens' MonitoringJobDefinition MonitoringResources Source #

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.

monitoringJobDefinition_monitoringAppSpecification :: Lens' MonitoringJobDefinition MonitoringAppSpecification Source #

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

monitoringJobDefinition_roleArn :: Lens' MonitoringJobDefinition Text Source #

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

MonitoringJobDefinitionSummary

MonitoringNetworkConfig

monitoringNetworkConfig_enableNetworkIsolation :: Lens' MonitoringNetworkConfig (Maybe Bool) Source #

Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

monitoringNetworkConfig_enableInterContainerTrafficEncryption :: Lens' MonitoringNetworkConfig (Maybe Bool) Source #

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.

MonitoringOutput

monitoringOutput_s3Output :: Lens' MonitoringOutput MonitoringS3Output Source #

The Amazon S3 storage location where the results of a monitoring job are saved.

MonitoringOutputConfig

monitoringOutputConfig_kmsKeyId :: Lens' MonitoringOutputConfig (Maybe Text) Source #

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.

monitoringOutputConfig_monitoringOutputs :: Lens' MonitoringOutputConfig (NonEmpty MonitoringOutput) Source #

Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

MonitoringResources

monitoringResources_clusterConfig :: Lens' MonitoringResources MonitoringClusterConfig Source #

The configuration for the cluster resources used to run the processing job.

MonitoringS3Output

monitoringS3Output_s3UploadMode :: Lens' MonitoringS3Output (Maybe ProcessingS3UploadMode) Source #

Whether to upload the results of the monitoring job continuously or after the job completes.

monitoringS3Output_s3Uri :: Lens' MonitoringS3Output Text Source #

A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

monitoringS3Output_localPath :: Lens' MonitoringS3Output Text Source #

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.

MonitoringSchedule

monitoringSchedule_creationTime :: Lens' MonitoringSchedule (Maybe UTCTime) Source #

The time that the monitoring schedule was created.

monitoringSchedule_monitoringType :: Lens' MonitoringSchedule (Maybe MonitoringType) Source #

The type of the monitoring job definition to schedule.

monitoringSchedule_failureReason :: Lens' MonitoringSchedule (Maybe Text) Source #

If the monitoring schedule failed, the reason it failed.

monitoringSchedule_monitoringScheduleArn :: Lens' MonitoringSchedule (Maybe Text) Source #

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

monitoringSchedule_endpointName :: Lens' MonitoringSchedule (Maybe Text) Source #

The endpoint that hosts the model being monitored.

monitoringSchedule_lastModifiedTime :: Lens' MonitoringSchedule (Maybe UTCTime) Source #

The last time the monitoring schedule was changed.

monitoringSchedule_monitoringScheduleStatus :: Lens' MonitoringSchedule (Maybe ScheduleStatus) Source #

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.

monitoringSchedule_tags :: Lens' MonitoringSchedule (Maybe [Tag]) Source #

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.

MonitoringScheduleConfig

monitoringScheduleConfig_monitoringType :: Lens' MonitoringScheduleConfig (Maybe MonitoringType) Source #

The type of the monitoring job definition to schedule.

monitoringScheduleConfig_monitoringJobDefinitionName :: Lens' MonitoringScheduleConfig (Maybe Text) Source #

The name of the monitoring job definition to schedule.

MonitoringScheduleSummary

monitoringScheduleSummary_monitoringType :: Lens' MonitoringScheduleSummary (Maybe MonitoringType) Source #

The type of the monitoring job definition that the schedule is for.

monitoringScheduleSummary_endpointName :: Lens' MonitoringScheduleSummary (Maybe Text) Source #

The name of the endpoint using the monitoring schedule.

monitoringScheduleSummary_monitoringJobDefinitionName :: Lens' MonitoringScheduleSummary (Maybe Text) Source #

The name of the monitoring job definition that the schedule is for.

monitoringScheduleSummary_monitoringScheduleArn :: Lens' MonitoringScheduleSummary Text Source #

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

monitoringScheduleSummary_lastModifiedTime :: Lens' MonitoringScheduleSummary UTCTime Source #

The last time the monitoring schedule was modified.

MonitoringStatisticsResource

monitoringStatisticsResource_s3Uri :: Lens' MonitoringStatisticsResource (Maybe Text) Source #

The Amazon S3 URI for the statistics resource.

MonitoringStoppingCondition

monitoringStoppingCondition_maxRuntimeInSeconds :: Lens' MonitoringStoppingCondition Natural Source #

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.

MultiModelConfig

multiModelConfig_modelCacheSetting :: Lens' MultiModelConfig (Maybe ModelCacheSetting) Source #

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.

NeoVpcConfig

neoVpcConfig_securityGroupIds :: Lens' NeoVpcConfig (NonEmpty Text) Source #

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.

neoVpcConfig_subnets :: Lens' NeoVpcConfig (NonEmpty Text) Source #

The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.

NestedFilters

nestedFilters_nestedPropertyName :: Lens' NestedFilters Text Source #

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.

nestedFilters_filters :: Lens' NestedFilters (NonEmpty Filter) Source #

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.

NetworkConfig

networkConfig_enableNetworkIsolation :: Lens' NetworkConfig (Maybe Bool) Source #

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

networkConfig_enableInterContainerTrafficEncryption :: Lens' NetworkConfig (Maybe Bool) Source #

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.

NotebookInstanceLifecycleConfigSummary

notebookInstanceLifecycleConfigSummary_creationTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime) Source #

A timestamp that tells when the lifecycle configuration was created.

notebookInstanceLifecycleConfigSummary_lastModifiedTime :: Lens' NotebookInstanceLifecycleConfigSummary (Maybe UTCTime) Source #

A timestamp that tells when the lifecycle configuration was last modified.

NotebookInstanceLifecycleHook

notebookInstanceLifecycleHook_content :: Lens' NotebookInstanceLifecycleHook (Maybe Text) Source #

A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.

NotebookInstanceSummary

notebookInstanceSummary_creationTime :: Lens' NotebookInstanceSummary (Maybe UTCTime) Source #

A timestamp that shows when the notebook instance was created.

notebookInstanceSummary_additionalCodeRepositories :: Lens' NotebookInstanceSummary (Maybe [Text]) Source #

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

notebookInstanceSummary_url :: Lens' NotebookInstanceSummary (Maybe Text) Source #

The URL that you use to connect to the Jupyter instance running in your notebook instance.

notebookInstanceSummary_lastModifiedTime :: Lens' NotebookInstanceSummary (Maybe UTCTime) Source #

A timestamp that shows when the notebook instance was last modified.

notebookInstanceSummary_instanceType :: Lens' NotebookInstanceSummary (Maybe InstanceType) Source #

The type of ML compute instance that the notebook instance is running on.

notebookInstanceSummary_defaultCodeRepository :: Lens' NotebookInstanceSummary (Maybe Text) Source #

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

notebookInstanceSummary_notebookInstanceLifecycleConfigName :: Lens' NotebookInstanceSummary (Maybe Text) Source #

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.

notebookInstanceSummary_notebookInstanceName :: Lens' NotebookInstanceSummary Text Source #

The name of the notebook instance that you want a summary for.

notebookInstanceSummary_notebookInstanceArn :: Lens' NotebookInstanceSummary Text Source #

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

NotificationConfiguration

notificationConfiguration_notificationTopicArn :: Lens' NotificationConfiguration (Maybe Text) Source #

The ARN for the Amazon SNS topic to which notifications should be published.

ObjectiveStatusCounters

objectiveStatusCounters_pending :: Lens' ObjectiveStatusCounters (Maybe Natural) Source #

The number of training jobs that are in progress and pending evaluation of their final objective metric.

objectiveStatusCounters_succeeded :: Lens' ObjectiveStatusCounters (Maybe Natural) Source #

The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

objectiveStatusCounters_failed :: Lens' ObjectiveStatusCounters (Maybe Natural) Source #

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.

OfflineStoreConfig

offlineStoreConfig_disableGlueTableCreation :: Lens' OfflineStoreConfig (Maybe Bool) Source #

Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore.

offlineStoreConfig_dataCatalogConfig :: Lens' OfflineStoreConfig (Maybe DataCatalogConfig) Source #

The meta data of the Glue table that is autogenerated when an OfflineStore is created.

offlineStoreConfig_s3StorageConfig :: Lens' OfflineStoreConfig S3StorageConfig Source #

The Amazon Simple Storage (Amazon S3) location of OfflineStore.

OfflineStoreStatus

offlineStoreStatus_blockedReason :: Lens' OfflineStoreStatus (Maybe Text) Source #

The justification for why the OfflineStoreStatus is Blocked (if applicable).

OidcConfig

oidcConfig_clientId :: Lens' OidcConfig Text Source #

The OIDC IdP client ID used to configure your private workforce.

oidcConfig_clientSecret :: Lens' OidcConfig Text Source #

The OIDC IdP client secret used to configure your private workforce.

oidcConfig_issuer :: Lens' OidcConfig Text Source #

The OIDC IdP issuer used to configure your private workforce.

oidcConfig_authorizationEndpoint :: Lens' OidcConfig Text Source #

The OIDC IdP authorization endpoint used to configure your private workforce.

oidcConfig_tokenEndpoint :: Lens' OidcConfig Text Source #

The OIDC IdP token endpoint used to configure your private workforce.

oidcConfig_userInfoEndpoint :: Lens' OidcConfig Text Source #

The OIDC IdP user information endpoint used to configure your private workforce.

oidcConfig_logoutEndpoint :: Lens' OidcConfig Text Source #

The OIDC IdP logout endpoint used to configure your private workforce.

oidcConfig_jwksUri :: Lens' OidcConfig Text Source #

The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.

OidcConfigForResponse

oidcConfigForResponse_clientId :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP client ID used to configure your private workforce.

oidcConfigForResponse_jwksUri :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.

oidcConfigForResponse_userInfoEndpoint :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP user information endpoint used to configure your private workforce.

oidcConfigForResponse_authorizationEndpoint :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP authorization endpoint used to configure your private workforce.

oidcConfigForResponse_tokenEndpoint :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP token endpoint used to configure your private workforce.

oidcConfigForResponse_issuer :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP issuer used to configure your private workforce.

oidcConfigForResponse_logoutEndpoint :: Lens' OidcConfigForResponse (Maybe Text) Source #

The OIDC IdP logout endpoint used to configure your private workforce.

OidcMemberDefinition

oidcMemberDefinition_groups :: Lens' OidcMemberDefinition (NonEmpty Text) Source #

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.

OnlineStoreConfig

onlineStoreConfig_securityConfig :: Lens' OnlineStoreConfig (Maybe OnlineStoreSecurityConfig) Source #

Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your OnlineStore.

onlineStoreConfig_enableOnlineStore :: Lens' OnlineStoreConfig (Maybe Bool) Source #

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.

OnlineStoreSecurityConfig

onlineStoreSecurityConfig_kmsKeyId :: Lens' OnlineStoreSecurityConfig (Maybe Text) Source #

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"

OutputConfig

outputConfig_targetPlatform :: Lens' OutputConfig (Maybe TargetPlatform) Source #

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}

outputConfig_kmsKeyId :: Lens' OutputConfig (Maybe Text) Source #

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

outputConfig_compilerOptions :: Lens' OutputConfig (Maybe Text) Source #

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"]}

outputConfig_targetDevice :: Lens' OutputConfig (Maybe TargetDevice) Source #

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.

outputConfig_s3OutputLocation :: Lens' OutputConfig Text Source #

Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

OutputDataConfig

outputDataConfig_kmsKeyId :: Lens' OutputDataConfig (Maybe Text) Source #

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:

  • // 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 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.

outputDataConfig_s3OutputPath :: Lens' OutputDataConfig Text Source #

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

OutputParameter

outputParameter_name :: Lens' OutputParameter Text Source #

The name of the output parameter.

outputParameter_value :: Lens' OutputParameter Text Source #

The value of the output parameter.

Parameter

parameter_name :: Lens' Parameter Text Source #

The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.

parameter_value :: Lens' Parameter Text Source #

The literal value for the parameter.

ParameterRange

parameterRange_categoricalParameterRangeSpecification :: Lens' ParameterRange (Maybe CategoricalParameterRangeSpecification) Source #

A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

parameterRange_integerParameterRangeSpecification :: Lens' ParameterRange (Maybe IntegerParameterRangeSpecification) Source #

A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

parameterRange_continuousParameterRangeSpecification :: Lens' ParameterRange (Maybe ContinuousParameterRangeSpecification) Source #

A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

ParameterRanges

parameterRanges_categoricalParameterRanges :: Lens' ParameterRanges (Maybe [CategoricalParameterRange]) Source #

The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

parameterRanges_integerParameterRanges :: Lens' ParameterRanges (Maybe [IntegerParameterRange]) Source #

The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

parameterRanges_continuousParameterRanges :: Lens' ParameterRanges (Maybe [ContinuousParameterRange]) Source #

The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

Parent

parent_experimentName :: Lens' Parent (Maybe Text) Source #

The name of the experiment.

parent_trialName :: Lens' Parent (Maybe Text) Source #

The name of the trial.

ParentHyperParameterTuningJob

parentHyperParameterTuningJob_hyperParameterTuningJobName :: Lens' ParentHyperParameterTuningJob (Maybe Text) Source #

The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

Pipeline

pipeline_creationTime :: Lens' Pipeline (Maybe UTCTime) Source #

The creation time of the pipeline.

pipeline_pipelineDisplayName :: Lens' Pipeline (Maybe Text) Source #

The display name of the pipeline.

pipeline_pipelineName :: Lens' Pipeline (Maybe Text) Source #

The name of the pipeline.

pipeline_lastRunTime :: Lens' Pipeline (Maybe UTCTime) Source #

The time when the pipeline was last run.

pipeline_lastModifiedTime :: Lens' Pipeline (Maybe UTCTime) Source #

The time that the pipeline was last modified.

pipeline_pipelineDescription :: Lens' Pipeline (Maybe Text) Source #

The description of the pipeline.

pipeline_pipelineArn :: Lens' Pipeline (Maybe Text) Source #

The Amazon Resource Name (ARN) of the pipeline.

pipeline_tags :: Lens' Pipeline (Maybe [Tag]) Source #

A list of tags that apply to the pipeline.

pipeline_roleArn :: Lens' Pipeline (Maybe Text) Source #

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

PipelineExecution

pipelineExecution_creationTime :: Lens' PipelineExecution (Maybe UTCTime) Source #

The creation time of the pipeline execution.

pipelineExecution_failureReason :: Lens' PipelineExecution (Maybe Text) Source #

If the execution failed, a message describing why.

pipelineExecution_pipelineExecutionArn :: Lens' PipelineExecution (Maybe Text) Source #

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

pipelineExecution_pipelineParameters :: Lens' PipelineExecution (Maybe [Parameter]) Source #

Contains a list of pipeline parameters. This list can be empty.

pipelineExecution_lastModifiedTime :: Lens' PipelineExecution (Maybe UTCTime) Source #

The time that the pipeline execution was last modified.

pipelineExecution_pipelineArn :: Lens' PipelineExecution (Maybe Text) Source #

The Amazon Resource Name (ARN) of the pipeline that was executed.

PipelineExecutionStep

pipelineExecutionStep_failureReason :: Lens' PipelineExecutionStep (Maybe Text) Source #

The reason why the step failed execution. This is only returned if the step failed its execution.

pipelineExecutionStep_startTime :: Lens' PipelineExecutionStep (Maybe UTCTime) Source #

The time that the step started executing.

pipelineExecutionStep_stepName :: Lens' PipelineExecutionStep (Maybe Text) Source #

The name of the step that is executed.

pipelineExecutionStep_endTime :: Lens' PipelineExecutionStep (Maybe UTCTime) Source #

The time that the step stopped executing.

pipelineExecutionStep_cacheHitResult :: Lens' PipelineExecutionStep (Maybe CacheHitResult) Source #

If this pipeline execution step was cached, details on the cache hit.

PipelineExecutionStepMetadata

pipelineExecutionStepMetadata_trainingJob :: Lens' PipelineExecutionStepMetadata (Maybe TrainingJobStepMetadata) Source #

The Amazon Resource Name (ARN) of the training job that was run by this step execution.

pipelineExecutionStepMetadata_processingJob :: Lens' PipelineExecutionStepMetadata (Maybe ProcessingJobStepMetadata) Source #

The Amazon Resource Name (ARN) of the processing job that was run by this step execution.

pipelineExecutionStepMetadata_model :: Lens' PipelineExecutionStepMetadata (Maybe ModelStepMetadata) Source #

The Amazon Resource Name (ARN) of the model that was created by this step execution.

pipelineExecutionStepMetadata_lambda :: Lens' PipelineExecutionStepMetadata (Maybe LambdaStepMetadata) Source #

The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.

pipelineExecutionStepMetadata_tuningJob :: Lens' PipelineExecutionStepMetadata (Maybe TuningJobStepMetaData) Source #

The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.

pipelineExecutionStepMetadata_condition :: Lens' PipelineExecutionStepMetadata (Maybe ConditionStepMetadata) Source #

The outcome of the condition evaluation that was run by this step execution.

pipelineExecutionStepMetadata_transformJob :: Lens' PipelineExecutionStepMetadata (Maybe TransformJobStepMetadata) Source #

The Amazon Resource Name (ARN) of the transform job that was run by this step execution.

pipelineExecutionStepMetadata_registerModel :: Lens' PipelineExecutionStepMetadata (Maybe RegisterModelStepMetadata) Source #

The Amazon Resource Name (ARN) of the model package the model was registered to by this step execution.

pipelineExecutionStepMetadata_callback :: Lens' PipelineExecutionStepMetadata (Maybe CallbackStepMetadata) Source #

The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.

PipelineExecutionSummary

pipelineExecutionSummary_pipelineExecutionArn :: Lens' PipelineExecutionSummary (Maybe Text) Source #

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

PipelineExperimentConfig

PipelineSummary

pipelineSummary_creationTime :: Lens' PipelineSummary (Maybe UTCTime) Source #

The creation time of the pipeline.

pipelineSummary_lastExecutionTime :: Lens' PipelineSummary (Maybe UTCTime) Source #

The last time that a pipeline execution began.

pipelineSummary_lastModifiedTime :: Lens' PipelineSummary (Maybe UTCTime) Source #

The time that the pipeline was last modified.

pipelineSummary_pipelineArn :: Lens' PipelineSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the pipeline.

pipelineSummary_roleArn :: Lens' PipelineSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) that the pipeline used to execute.

ProcessingClusterConfig

processingClusterConfig_volumeKmsKeyId :: Lens' ProcessingClusterConfig (Maybe Text) Source #

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.

processingClusterConfig_instanceCount :: Lens' ProcessingClusterConfig Natural Source #

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.

processingClusterConfig_volumeSizeInGB :: Lens' ProcessingClusterConfig Natural Source #

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.

ProcessingFeatureStoreOutput

processingFeatureStoreOutput_featureGroupName :: Lens' ProcessingFeatureStoreOutput Text Source #

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.

ProcessingInput

processingInput_datasetDefinition :: Lens' ProcessingInput (Maybe DatasetDefinition) Source #

Configuration for a Dataset Definition input.

processingInput_appManaged :: Lens' ProcessingInput (Maybe Bool) Source #

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.

processingInput_s3Input :: Lens' ProcessingInput (Maybe ProcessingS3Input) Source #

Configuration for downloading input data from Amazon S3 into the processing container.

processingInput_inputName :: Lens' ProcessingInput Text Source #

The name for the processing job input.

ProcessingJob

processingJob_creationTime :: Lens' ProcessingJob (Maybe UTCTime) Source #

The time the processing job was created.

processingJob_failureReason :: Lens' ProcessingJob (Maybe Text) Source #

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

processingJob_monitoringScheduleArn :: Lens' ProcessingJob (Maybe Text) Source #

The ARN of a monitoring schedule for an endpoint associated with this processing job.

processingJob_environment :: Lens' ProcessingJob (Maybe (HashMap Text Text)) Source #

Sets the environment variables in the Docker container.

processingJob_lastModifiedTime :: Lens' ProcessingJob (Maybe UTCTime) Source #

The time the processing job was last modified.

processingJob_processingInputs :: Lens' ProcessingJob (Maybe [ProcessingInput]) Source #

List of input configurations for the processing job.

processingJob_autoMLJobArn :: Lens' ProcessingJob (Maybe Text) Source #

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

processingJob_trainingJobArn :: Lens' ProcessingJob (Maybe Text) Source #

The ARN of the training job associated with this processing job.

processingJob_exitMessage :: Lens' ProcessingJob (Maybe Text) Source #

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

processingJob_processingStartTime :: Lens' ProcessingJob (Maybe UTCTime) Source #

The time that the processing job started.

processingJob_processingEndTime :: Lens' ProcessingJob (Maybe UTCTime) Source #

The time that the processing job ended.

processingJob_tags :: Lens' ProcessingJob (Maybe [Tag]) Source #

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.

processingJob_roleArn :: Lens' ProcessingJob (Maybe Text) Source #

The ARN of the role used to create the processing job.

ProcessingJobStepMetadata

processingJobStepMetadata_arn :: Lens' ProcessingJobStepMetadata (Maybe Text) Source #

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

ProcessingJobSummary

processingJobSummary_failureReason :: Lens' ProcessingJobSummary (Maybe Text) Source #

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

processingJobSummary_lastModifiedTime :: Lens' ProcessingJobSummary (Maybe UTCTime) Source #

A timestamp that indicates the last time the processing job was modified.

processingJobSummary_exitMessage :: Lens' ProcessingJobSummary (Maybe Text) Source #

An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

processingJobSummary_processingEndTime :: Lens' ProcessingJobSummary (Maybe UTCTime) Source #

The time at which the processing job completed.

processingJobSummary_processingJobArn :: Lens' ProcessingJobSummary Text Source #

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

processingJobSummary_creationTime :: Lens' ProcessingJobSummary UTCTime Source #

The time at which the processing job was created.

ProcessingOutput

processingOutput_featureStoreOutput :: Lens' ProcessingOutput (Maybe ProcessingFeatureStoreOutput) Source #

Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.

processingOutput_s3Output :: Lens' ProcessingOutput (Maybe ProcessingS3Output) Source #

Configuration for processing job outputs in Amazon S3.

processingOutput_appManaged :: Lens' ProcessingOutput (Maybe Bool) Source #

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.

processingOutput_outputName :: Lens' ProcessingOutput Text Source #

The name for the processing job output.

ProcessingOutputConfig

processingOutputConfig_kmsKeyId :: Lens' ProcessingOutputConfig (Maybe Text) Source #

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.

processingOutputConfig_outputs :: Lens' ProcessingOutputConfig [ProcessingOutput] Source #

An array of outputs configuring the data to upload from the processing container.

ProcessingResources

processingResources_clusterConfig :: Lens' ProcessingResources ProcessingClusterConfig Source #

The configuration for the resources in a cluster used to run the processing job.

ProcessingS3Input

processingS3Input_s3DataDistributionType :: Lens' ProcessingS3Input (Maybe ProcessingS3DataDistributionType) Source #

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.

processingS3Input_s3InputMode :: Lens' ProcessingS3Input (Maybe ProcessingS3InputMode) Source #

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.

processingS3Input_localPath :: Lens' ProcessingS3Input (Maybe Text) Source #

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).

processingS3Input_s3CompressionType :: Lens' ProcessingS3Input (Maybe ProcessingS3CompressionType) Source #

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.

processingS3Input_s3Uri :: Lens' ProcessingS3Input Text Source #

The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.

processingS3Input_s3DataType :: Lens' ProcessingS3Input ProcessingS3DataType Source #

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.

ProcessingS3Output

processingS3Output_s3Uri :: Lens' ProcessingS3Output Text Source #

A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.

processingS3Output_localPath :: Lens' ProcessingS3Output Text Source #

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.

processingS3Output_s3UploadMode :: Lens' ProcessingS3Output ProcessingS3UploadMode Source #

Whether to upload the results of the processing job continuously or after the job completes.

ProcessingStoppingCondition

ProductionVariant

productionVariant_acceleratorType :: Lens' ProductionVariant (Maybe ProductionVariantAcceleratorType) Source #

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.

productionVariant_coreDumpConfig :: Lens' ProductionVariant (Maybe ProductionVariantCoreDumpConfig) Source #

Specifies configuration for a core dump from the model container when the process crashes.

productionVariant_initialVariantWeight :: Lens' ProductionVariant (Maybe Double) Source #

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.

productionVariant_variantName :: Lens' ProductionVariant Text Source #

The name of the production variant.

productionVariant_modelName :: Lens' ProductionVariant Text Source #

The name of the model that you want to host. This is the name that you specified when creating the model.

ProductionVariantCoreDumpConfig

productionVariantCoreDumpConfig_kmsKeyId :: Lens' ProductionVariantCoreDumpConfig (Maybe Text) Source #

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon 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 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.

ProductionVariantSummary

productionVariantSummary_desiredInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural) Source #

The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

productionVariantSummary_desiredWeight :: Lens' ProductionVariantSummary (Maybe Double) Source #

The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

productionVariantSummary_currentInstanceCount :: Lens' ProductionVariantSummary (Maybe Natural) Source #

The number of instances associated with the variant.

productionVariantSummary_deployedImages :: Lens' ProductionVariantSummary (Maybe [DeployedImage]) Source #

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

ProfilerConfig

profilerConfig_profilingParameters :: Lens' ProfilerConfig (Maybe (HashMap Text Text)) Source #

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.

profilerConfig_profilingIntervalInMilliseconds :: Lens' ProfilerConfig (Maybe Integer) Source #

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.

profilerConfig_s3OutputPath :: Lens' ProfilerConfig Text Source #

Path to Amazon S3 storage location for system and framework metrics.

ProfilerConfigForUpdate

profilerConfigForUpdate_profilingParameters :: Lens' ProfilerConfigForUpdate (Maybe (HashMap Text Text)) Source #

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.

profilerConfigForUpdate_s3OutputPath :: Lens' ProfilerConfigForUpdate (Maybe Text) Source #

Path to Amazon S3 storage location for system and framework metrics.

profilerConfigForUpdate_profilingIntervalInMilliseconds :: Lens' ProfilerConfigForUpdate (Maybe Integer) Source #

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.

profilerConfigForUpdate_disableProfiler :: Lens' ProfilerConfigForUpdate (Maybe Bool) Source #

To disable Debugger monitoring and profiling, set to True.

ProfilerRuleConfiguration

profilerRuleConfiguration_s3OutputPath :: Lens' ProfilerRuleConfiguration (Maybe Text) Source #

Path to Amazon S3 storage location for rules.

profilerRuleConfiguration_localPath :: Lens' ProfilerRuleConfiguration (Maybe Text) Source #

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.

profilerRuleConfiguration_instanceType :: Lens' ProfilerRuleConfiguration (Maybe ProcessingInstanceType) Source #

The instance type to deploy a Debugger custom rule for profiling a training job.

profilerRuleConfiguration_volumeSizeInGB :: Lens' ProfilerRuleConfiguration (Maybe Natural) Source #

The size, in GB, of the ML storage volume attached to the processing instance.

profilerRuleConfiguration_ruleConfigurationName :: Lens' ProfilerRuleConfiguration Text Source #

The name of the rule configuration. It must be unique relative to other rule configuration names.

profilerRuleConfiguration_ruleEvaluatorImage :: Lens' ProfilerRuleConfiguration Text Source #

The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

ProfilerRuleEvaluationStatus

profilerRuleEvaluationStatus_lastModifiedTime :: Lens' ProfilerRuleEvaluationStatus (Maybe UTCTime) Source #

Timestamp when the rule evaluation status was last modified.

profilerRuleEvaluationStatus_ruleEvaluationJobArn :: Lens' ProfilerRuleEvaluationStatus (Maybe Text) Source #

The Amazon Resource Name (ARN) of the rule evaluation job.

Project

project_creationTime :: Lens' Project (Maybe UTCTime) Source #

A timestamp specifying when the project was created.

project_createdBy :: Lens' Project (Maybe UserContext) Source #

Who created the project.

project_projectName :: Lens' Project (Maybe Text) Source #

The name of the project.

project_projectId :: Lens' Project (Maybe Text) Source #

The ID of the project.

project_projectArn :: Lens' Project (Maybe Text) Source #

The Amazon Resource Name (ARN) of the project.

project_projectDescription :: Lens' Project (Maybe Text) Source #

The description of the project.

project_tags :: Lens' Project (Maybe [Tag]) Source #

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.

ProjectSummary

projectSummary_projectArn :: Lens' ProjectSummary Text Source #

The Amazon Resource Name (ARN) of the project.

projectSummary_creationTime :: Lens' ProjectSummary UTCTime Source #

The time that the project was created.

PropertyNameQuery

PropertyNameSuggestion

propertyNameSuggestion_propertyName :: Lens' PropertyNameSuggestion (Maybe Text) Source #

A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.

ProvisioningParameter

provisioningParameter_value :: Lens' ProvisioningParameter (Maybe Text) Source #

The value of the provisioning parameter.

provisioningParameter_key :: Lens' ProvisioningParameter (Maybe Text) Source #

The key that identifies a provisioning parameter.

PublicWorkforceTaskPrice

publicWorkforceTaskPrice_amountInUsd :: Lens' PublicWorkforceTaskPrice (Maybe USD) Source #

Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

RedshiftDatasetDefinition

redshiftDatasetDefinition_kmsKeyId :: Lens' RedshiftDatasetDefinition (Maybe Text) Source #

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.

redshiftDatasetDefinition_clusterRoleArn :: Lens' RedshiftDatasetDefinition Text Source #

The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.

redshiftDatasetDefinition_outputS3Uri :: Lens' RedshiftDatasetDefinition Text Source #

The location in Amazon S3 where the Redshift query results are stored.

RegisterModelStepMetadata

registerModelStepMetadata_arn :: Lens' RegisterModelStepMetadata (Maybe Text) Source #

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

RenderableTask

renderableTask_input :: Lens' RenderableTask Text Source #

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".

RenderingError

renderingError_code :: Lens' RenderingError Text Source #

A unique identifier for a specific class of errors.

renderingError_message :: Lens' RenderingError Text Source #

A human-readable message describing the error.

RepositoryAuthConfig

repositoryAuthConfig_repositoryCredentialsProviderArn :: Lens' RepositoryAuthConfig Text Source #

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.

ResolvedAttributes

ResourceConfig

resourceConfig_volumeKmsKeyId :: Lens' ResourceConfig (Maybe Text) Source #

The 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 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"

resourceConfig_instanceCount :: Lens' ResourceConfig Natural Source #

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

resourceConfig_volumeSizeInGB :: Lens' ResourceConfig Natural Source #

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.

You must specify sufficient ML storage for your scenario.

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, 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.

ResourceLimits

resourceLimits_maxNumberOfTrainingJobs :: Lens' ResourceLimits Natural Source #

The maximum number of training jobs that a hyperparameter tuning job can launch.

resourceLimits_maxParallelTrainingJobs :: Lens' ResourceLimits Natural Source #

The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

ResourceSpec

resourceSpec_instanceType :: Lens' ResourceSpec (Maybe AppInstanceType) Source #

The instance type that the image version runs on.

resourceSpec_sageMakerImageArn :: Lens' ResourceSpec (Maybe Text) Source #

The ARN of the SageMaker image that the image version belongs to.

resourceSpec_sageMakerImageVersionArn :: Lens' ResourceSpec (Maybe Text) Source #

The ARN of the image version created on the instance.

resourceSpec_lifecycleConfigArn :: Lens' ResourceSpec (Maybe Text) Source #

The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

RetentionPolicy

retentionPolicy_homeEfsFileSystem :: Lens' RetentionPolicy (Maybe RetentionType) Source #

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.

RetryStrategy

retryStrategy_maximumRetryAttempts :: Lens' RetryStrategy Natural Source #

The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING.

S3DataSource

s3DataSource_s3DataDistributionType :: Lens' S3DataSource (Maybe S3DataDistribution) Source #

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

If you want Amazon 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.

s3DataSource_attributeNames :: Lens' S3DataSource (Maybe [Text]) Source #

A list of one or more attribute names to use that are found in a specified augmented manifest file.

s3DataSource_s3DataType :: Lens' S3DataSource S3DataType Source #

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon 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 Amazon 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.

s3DataSource_s3Uri :: Lens' S3DataSource Text Source #

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 Amazon SageMaker uses to perform tasks on your behalf.

S3StorageConfig

s3StorageConfig_resolvedOutputS3Uri :: Lens' S3StorageConfig (Maybe Text) Source #

The S3 path where offline records are written.

s3StorageConfig_kmsKeyId :: Lens' S3StorageConfig (Maybe Text) Source #

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"

s3StorageConfig_s3Uri :: Lens' S3StorageConfig Text Source #

The S3 URI, or location in Amazon S3, of OfflineStore.

S3 URIs have a format similar to the following: s3://example-bucket/prefix/.

ScheduleConfig

scheduleConfig_scheduleExpression :: Lens' ScheduleConfig Text Source #

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.

SearchExpression

searchExpression_operator :: Lens' SearchExpression (Maybe BooleanOperator) Source #

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.

SearchRecord

searchRecord_trainingJob :: Lens' SearchRecord (Maybe TrainingJob) Source #

The properties of a training job.

searchRecord_trial :: Lens' SearchRecord (Maybe Trial) Source #

The properties of a trial.

searchRecord_trialComponent :: Lens' SearchRecord (Maybe TrialComponent) Source #

The properties of a trial component.

searchRecord_project :: Lens' SearchRecord (Maybe Project) Source #

The properties of a project.

searchRecord_experiment :: Lens' SearchRecord (Maybe Experiment) Source #

The properties of an experiment.

SecondaryStatusTransition

secondaryStatusTransition_statusMessage :: Lens' SecondaryStatusTransition (Maybe Text) Source #

A detailed description of the progress within a secondary status.

Amazon 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

secondaryStatusTransition_endTime :: Lens' SecondaryStatusTransition (Maybe UTCTime) Source #

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.

secondaryStatusTransition_status :: Lens' SecondaryStatusTransition SecondaryStatus Source #

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

secondaryStatusTransition_startTime :: Lens' SecondaryStatusTransition UTCTime Source #

A timestamp that shows when the training job transitioned to the current secondary status state.

ServiceCatalogProvisionedProductDetails

serviceCatalogProvisionedProductDetails_provisionedProductStatusMessage :: Lens' ServiceCatalogProvisionedProductDetails (Maybe Text) Source #

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.

ServiceCatalogProvisioningDetails

serviceCatalogProvisioningDetails_pathId :: Lens' ServiceCatalogProvisioningDetails (Maybe Text) Source #

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.

serviceCatalogProvisioningDetails_provisioningParameters :: Lens' ServiceCatalogProvisioningDetails (Maybe [ProvisioningParameter]) Source #

A list of key value pairs that you specify when you provision a product.

SharingSettings

sharingSettings_s3KmsKeyId :: Lens' SharingSettings (Maybe Text) Source #

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.

sharingSettings_s3OutputPath :: Lens' SharingSettings (Maybe Text) Source #

When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.

sharingSettings_notebookOutputOption :: Lens' SharingSettings (Maybe NotebookOutputOption) Source #

Whether to include the notebook cell output when sharing the notebook. The default is Disabled.

ShuffleConfig

shuffleConfig_seed :: Lens' ShuffleConfig Integer Source #

Determines the shuffling order in ShuffleConfig value.

SourceAlgorithm

sourceAlgorithm_modelDataUrl :: Lens' SourceAlgorithm (Maybe Text) Source #

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.

sourceAlgorithm_algorithmName :: Lens' SourceAlgorithm Text Source #

The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

SourceAlgorithmSpecification

sourceAlgorithmSpecification_sourceAlgorithms :: Lens' SourceAlgorithmSpecification (NonEmpty SourceAlgorithm) Source #

A list of the algorithms that were used to create a model package.

SourceIpConfig

sourceIpConfig_cidrs :: Lens' SourceIpConfig [Text] Source #

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.

StoppingCondition

stoppingCondition_maxWaitTimeInSeconds :: Lens' StoppingCondition (Maybe Natural) Source #

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, Amazon 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.

stoppingCondition_maxRuntimeInSeconds :: Lens' StoppingCondition (Maybe Natural) Source #

The maximum length of time, in seconds, that a training or compilation job can run.

For compilation jobs, if the job does not complete during this time, you will receive a TimeOut error. We recommend starting with 900 seconds and increase as necessary based on your model.

For all other jobs, if the job does not complete during this time, Amazon 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.

StudioLifecycleConfigDetails

studioLifecycleConfigDetails_creationTime :: Lens' StudioLifecycleConfigDetails (Maybe UTCTime) Source #

The creation time of the Studio Lifecycle Configuration.

studioLifecycleConfigDetails_lastModifiedTime :: Lens' StudioLifecycleConfigDetails (Maybe UTCTime) Source #

This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.

studioLifecycleConfigDetails_studioLifecycleConfigArn :: Lens' StudioLifecycleConfigDetails (Maybe Text) Source #

The Amazon Resource Name (ARN) of the Lifecycle Configuration.

SubscribedWorkteam

subscribedWorkteam_marketplaceTitle :: Lens' SubscribedWorkteam (Maybe Text) Source #

The title of the service provided by the vendor in the Amazon Marketplace.

subscribedWorkteam_sellerName :: Lens' SubscribedWorkteam (Maybe Text) Source #

The name of the vendor in the Amazon Marketplace.

subscribedWorkteam_marketplaceDescription :: Lens' SubscribedWorkteam (Maybe Text) Source #

The description of the vendor from the Amazon Marketplace.

subscribedWorkteam_workteamArn :: Lens' SubscribedWorkteam Text Source #

The Amazon Resource Name (ARN) of the vendor that you have subscribed.

SuggestionQuery

suggestionQuery_propertyNameQuery :: Lens' SuggestionQuery (Maybe PropertyNameQuery) Source #

Defines a property name hint. Only property names that begin with the specified hint are included in the response.

Tag

tag_key :: Lens' Tag Text Source #

The tag key. Tag keys must be unique per resource.

tag_value :: Lens' Tag Text Source #

The tag value.

TargetPlatform

targetPlatform_accelerator :: Lens' TargetPlatform (Maybe TargetPlatformAccelerator) Source #

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

targetPlatform_os :: Lens' TargetPlatform TargetPlatformOs Source #

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}

targetPlatform_arch :: Lens' TargetPlatform TargetPlatformArch Source #

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.

TensorBoardAppSettings

tensorBoardAppSettings_defaultResourceSpec :: Lens' TensorBoardAppSettings (Maybe ResourceSpec) Source #

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

TensorBoardOutputConfig

tensorBoardOutputConfig_localPath :: Lens' TensorBoardOutputConfig (Maybe Text) Source #

Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.

tensorBoardOutputConfig_s3OutputPath :: Lens' TensorBoardOutputConfig Text Source #

Path to Amazon S3 storage location for TensorBoard output.

TrafficRoutingConfig

TrainingJob

trainingJob_creationTime :: Lens' TrainingJob (Maybe UTCTime) Source #

A timestamp that indicates when the training job was created.

trainingJob_labelingJobArn :: Lens' TrainingJob (Maybe Text) Source #

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

trainingJob_failureReason :: Lens' TrainingJob (Maybe Text) Source #

If the training job failed, the reason it failed.

trainingJob_secondaryStatusTransitions :: Lens' TrainingJob (Maybe [SecondaryStatusTransition]) Source #

A history of all of the secondary statuses that the training job has transitioned through.

trainingJob_modelArtifacts :: Lens' TrainingJob (Maybe ModelArtifacts) Source #

Information about the Amazon S3 location that is configured for storing model artifacts.

trainingJob_trainingEndTime :: Lens' TrainingJob (Maybe UTCTime) Source #

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

trainingJob_environment :: Lens' TrainingJob (Maybe (HashMap Text Text)) Source #

The environment variables to set in the Docker container.

trainingJob_retryStrategy :: Lens' TrainingJob (Maybe RetryStrategy) Source #

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

trainingJob_stoppingCondition :: Lens' TrainingJob (Maybe StoppingCondition) Source #

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

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

trainingJob_debugRuleEvaluationStatuses :: Lens' TrainingJob (Maybe [DebugRuleEvaluationStatus]) Source #

Information about the evaluation status of the rules for the training job.

trainingJob_trainingJobStatus :: Lens' TrainingJob (Maybe TrainingJobStatus) Source #

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.

trainingJob_enableNetworkIsolation :: Lens' TrainingJob (Maybe Bool) Source #

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.

trainingJob_lastModifiedTime :: Lens' TrainingJob (Maybe UTCTime) Source #

A timestamp that indicates when the status of the training job was last modified.

trainingJob_debugRuleConfigurations :: Lens' TrainingJob (Maybe [DebugRuleConfiguration]) Source #

Information about the debug rule configuration.

trainingJob_enableManagedSpotTraining :: Lens' TrainingJob (Maybe Bool) Source #

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.

trainingJob_autoMLJobArn :: Lens' TrainingJob (Maybe Text) Source #

The Amazon Resource Name (ARN) of the job.

trainingJob_inputDataConfig :: Lens' TrainingJob (Maybe (NonEmpty Channel)) Source #

An array of Channel objects that describes each data input channel.

trainingJob_vpcConfig :: Lens' TrainingJob (Maybe VpcConfig) Source #

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.

trainingJob_trainingJobArn :: Lens' TrainingJob (Maybe Text) Source #

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

trainingJob_algorithmSpecification :: Lens' TrainingJob (Maybe AlgorithmSpecification) Source #

Information about the algorithm used for training, and algorithm metadata.

trainingJob_finalMetricDataList :: Lens' TrainingJob (Maybe [MetricData]) Source #

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.

trainingJob_outputDataConfig :: Lens' TrainingJob (Maybe OutputDataConfig) Source #

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

trainingJob_trainingStartTime :: Lens' TrainingJob (Maybe UTCTime) Source #

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.

trainingJob_tuningJobArn :: Lens' TrainingJob (Maybe Text) Source #

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

trainingJob_trainingJobName :: Lens' TrainingJob (Maybe Text) Source #

The name of the training job.

trainingJob_resourceConfig :: Lens' TrainingJob (Maybe ResourceConfig) Source #

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

trainingJob_enableInterContainerTrafficEncryption :: Lens' TrainingJob (Maybe Bool) Source #

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.

trainingJob_secondaryStatus :: Lens' TrainingJob (Maybe SecondaryStatus) Source #

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.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress
- Starting - Starting the training job.
  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
  • Training - Training is in progress.
  • 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

trainingJob_tags :: Lens' TrainingJob (Maybe [Tag]) Source #

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.

trainingJob_roleArn :: Lens' TrainingJob (Maybe Text) Source #

The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

TrainingJobDefinition

trainingJobDefinition_hyperParameters :: Lens' TrainingJobDefinition (Maybe (HashMap Text Text)) Source #

The hyperparameters used for the training job.

trainingJobDefinition_inputDataConfig :: Lens' TrainingJobDefinition (NonEmpty Channel) Source #

An array of Channel objects, each of which specifies an input source.

trainingJobDefinition_outputDataConfig :: Lens' TrainingJobDefinition OutputDataConfig Source #

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

trainingJobDefinition_resourceConfig :: Lens' TrainingJobDefinition ResourceConfig Source #

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

trainingJobDefinition_stoppingCondition :: Lens' TrainingJobDefinition StoppingCondition Source #

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

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

TrainingJobStatusCounters

trainingJobStatusCounters_stopped :: Lens' TrainingJobStatusCounters (Maybe Natural) Source #

The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

trainingJobStatusCounters_retryableError :: Lens' TrainingJobStatusCounters (Maybe Natural) Source #

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.

trainingJobStatusCounters_inProgress :: Lens' TrainingJobStatusCounters (Maybe Natural) Source #

The number of in-progress training jobs launched by a hyperparameter tuning job.

trainingJobStatusCounters_nonRetryableError :: Lens' TrainingJobStatusCounters (Maybe Natural) Source #

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.

trainingJobStatusCounters_completed :: Lens' TrainingJobStatusCounters (Maybe Natural) Source #

The number of completed training jobs launched by the hyperparameter tuning job.

TrainingJobStepMetadata

trainingJobStepMetadata_arn :: Lens' TrainingJobStepMetadata (Maybe Text) Source #

The Amazon Resource Name (ARN) of the training job that was run by this step execution.

TrainingJobSummary

trainingJobSummary_trainingEndTime :: Lens' TrainingJobSummary (Maybe UTCTime) Source #

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).

trainingJobSummary_lastModifiedTime :: Lens' TrainingJobSummary (Maybe UTCTime) Source #

Timestamp when the training job was last modified.

trainingJobSummary_trainingJobName :: Lens' TrainingJobSummary Text Source #

The name of the training job that you want a summary for.

trainingJobSummary_trainingJobArn :: Lens' TrainingJobSummary Text Source #

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

trainingJobSummary_creationTime :: Lens' TrainingJobSummary UTCTime Source #

A timestamp that shows when the training job was created.

TrainingSpecification

trainingSpecification_trainingImageDigest :: Lens' TrainingSpecification (Maybe Text) Source #

An MD5 hash of the training algorithm that identifies the Docker image used for training.

trainingSpecification_supportsDistributedTraining :: Lens' TrainingSpecification (Maybe Bool) Source #

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

trainingSpecification_supportedHyperParameters :: Lens' TrainingSpecification (Maybe [HyperParameterSpecification]) Source #

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

trainingSpecification_supportedTuningJobObjectiveMetrics :: Lens' TrainingSpecification (Maybe [HyperParameterTuningJobObjective]) Source #

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

trainingSpecification_metricDefinitions :: Lens' TrainingSpecification (Maybe [MetricDefinition]) Source #

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

trainingSpecification_trainingImage :: Lens' TrainingSpecification Text Source #

The Amazon ECR registry path of the Docker image that contains the training algorithm.

trainingSpecification_supportedTrainingInstanceTypes :: Lens' TrainingSpecification [TrainingInstanceType] Source #

A list of the instance types that this algorithm can use for training.

trainingSpecification_trainingChannels :: Lens' TrainingSpecification (NonEmpty ChannelSpecification) Source #

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

TransformDataSource

transformDataSource_s3DataSource :: Lens' TransformDataSource TransformS3DataSource Source #

The S3 location of the data source that is associated with a channel.

TransformInput

transformInput_splitType :: Lens' TransformInput (Maybe SplitType) Source #

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.

transformInput_compressionType :: Lens' TransformInput (Maybe CompressionType) Source #

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.

transformInput_contentType :: Lens' TransformInput (Maybe Text) Source #

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.

transformInput_dataSource :: Lens' TransformInput TransformDataSource Source #

Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

TransformJob

transformJob_creationTime :: Lens' TransformJob (Maybe UTCTime) Source #

A timestamp that shows when the transform Job was created.

transformJob_labelingJobArn :: Lens' TransformJob (Maybe Text) Source #

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

transformJob_failureReason :: Lens' TransformJob (Maybe Text) Source #

If the transform job failed, the reason it failed.

transformJob_batchStrategy :: Lens' TransformJob (Maybe BatchStrategy) Source #

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.

transformJob_maxPayloadInMB :: Lens' TransformJob (Maybe Natural) Source #

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.

transformJob_environment :: Lens' TransformJob (Maybe (HashMap Text Text)) Source #

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

transformJob_modelName :: Lens' TransformJob (Maybe Text) Source #

The name of the model associated with the transform job.

transformJob_transformEndTime :: Lens' TransformJob (Maybe UTCTime) Source #

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.

transformJob_transformStartTime :: Lens' TransformJob (Maybe UTCTime) Source #

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

transformJob_autoMLJobArn :: Lens' TransformJob (Maybe Text) Source #

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

transformJob_transformJobStatus :: Lens' TransformJob (Maybe TransformJobStatus) Source #

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.

transformJob_maxConcurrentTransforms :: Lens' TransformJob (Maybe Natural) Source #

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.

transformJob_transformJobArn :: Lens' TransformJob (Maybe Text) Source #

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

transformJob_tags :: Lens' TransformJob (Maybe [Tag]) Source #

A list of tags associated with the transform job.

TransformJobDefinition

transformJobDefinition_batchStrategy :: Lens' TransformJobDefinition (Maybe BatchStrategy) Source #

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.

transformJobDefinition_maxPayloadInMB :: Lens' TransformJobDefinition (Maybe Natural) Source #

The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

transformJobDefinition_environment :: Lens' TransformJobDefinition (Maybe (HashMap Text Text)) Source #

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

transformJobDefinition_maxConcurrentTransforms :: Lens' TransformJobDefinition (Maybe Natural) Source #

The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

transformJobDefinition_transformInput :: Lens' TransformJobDefinition TransformInput Source #

A description of the input source and the way the transform job consumes it.

transformJobDefinition_transformOutput :: Lens' TransformJobDefinition TransformOutput Source #

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

transformJobDefinition_transformResources :: Lens' TransformJobDefinition TransformResources Source #

Identifies the ML compute instances for the transform job.

TransformJobStepMetadata

transformJobStepMetadata_arn :: Lens' TransformJobStepMetadata (Maybe Text) Source #

The Amazon Resource Name (ARN) of the transform job that was run by this step execution.

TransformJobSummary

transformJobSummary_failureReason :: Lens' TransformJobSummary (Maybe Text) Source #

If the transform job failed, the reason it failed.

transformJobSummary_lastModifiedTime :: Lens' TransformJobSummary (Maybe UTCTime) Source #

Indicates when the transform job was last modified.

transformJobSummary_transformEndTime :: Lens' TransformJobSummary (Maybe UTCTime) Source #

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.

transformJobSummary_transformJobArn :: Lens' TransformJobSummary Text Source #

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

transformJobSummary_creationTime :: Lens' TransformJobSummary UTCTime Source #

A timestamp that shows when the transform Job was created.

TransformOutput

transformOutput_assembleWith :: Lens' TransformOutput (Maybe AssemblyType) Source #

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.

transformOutput_accept :: Lens' TransformOutput (Maybe Text) Source #

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.

transformOutput_kmsKeyId :: Lens' TransformOutput (Maybe Text) Source #

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.

transformOutput_s3OutputPath :: Lens' TransformOutput Text Source #

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.

TransformResources

transformResources_volumeKmsKeyId :: Lens' TransformResources (Maybe Text) Source #

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

transformResources_instanceType :: Lens' TransformResources TransformInstanceType Source #

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.

transformResources_instanceCount :: Lens' TransformResources Natural Source #

The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1.

TransformS3DataSource

transformS3DataSource_s3DataType :: Lens' TransformS3DataSource S3DataType Source #

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

transformS3DataSource_s3Uri :: Lens' TransformS3DataSource Text Source #

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.

Trial

trial_creationTime :: Lens' Trial (Maybe UTCTime) Source #

When the trial was created.

trial_trialComponentSummaries :: Lens' Trial (Maybe [TrialComponentSimpleSummary]) Source #

A list of the components associated with the trial. For each component, a summary of the component's properties is included.

trial_trialArn :: Lens' Trial (Maybe Text) Source #

The Amazon Resource Name (ARN) of the trial.

trial_createdBy :: Lens' Trial (Maybe UserContext) Source #

Who created the trial.

trial_lastModifiedTime :: Lens' Trial (Maybe UTCTime) Source #

Who last modified the trial.

trial_experimentName :: Lens' Trial (Maybe Text) Source #

The name of the experiment the trial is part of.

trial_source :: Lens' Trial (Maybe TrialSource) Source #

Undocumented member.

trial_displayName :: Lens' Trial (Maybe Text) Source #

The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.

trial_trialName :: Lens' Trial (Maybe Text) Source #

The name of the trial.

trial_tags :: Lens' Trial (Maybe [Tag]) Source #

The list of tags that are associated with the trial. You can use Search API to search on the tags.

TrialComponent

trialComponent_lastModifiedTime :: Lens' TrialComponent (Maybe UTCTime) Source #

When the component was last modified.

trialComponent_parents :: Lens' TrialComponent (Maybe [Parent]) Source #

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.

trialComponent_source :: Lens' TrialComponent (Maybe TrialComponentSource) Source #

The Amazon Resource Name (ARN) and job type of the source of the component.

trialComponent_displayName :: Lens' TrialComponent (Maybe Text) Source #

The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.

trialComponent_trialComponentArn :: Lens' TrialComponent (Maybe Text) Source #

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

trialComponent_tags :: Lens' TrialComponent (Maybe [Tag]) Source #

The list of tags that are associated with the component. You can use Search API to search on the tags.

TrialComponentArtifact

trialComponentArtifact_mediaType :: Lens' TrialComponentArtifact (Maybe Text) Source #

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.

TrialComponentMetricSummary

trialComponentMetricSummary_count :: Lens' TrialComponentMetricSummary (Maybe Int) Source #

The number of samples used to generate the metric.

TrialComponentParameterValue

trialComponentParameterValue_numberValue :: Lens' TrialComponentParameterValue (Maybe Double) Source #

The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.

trialComponentParameterValue_stringValue :: Lens' TrialComponentParameterValue (Maybe Text) Source #

The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.

TrialComponentSimpleSummary

trialComponentSimpleSummary_trialComponentArn :: Lens' TrialComponentSimpleSummary (Maybe Text) Source #

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

TrialComponentSource

TrialComponentSourceDetail

trialComponentSourceDetail_trainingJob :: Lens' TrialComponentSourceDetail (Maybe TrainingJob) Source #

Information about a training job that's the source of a trial component.

trialComponentSourceDetail_sourceArn :: Lens' TrialComponentSourceDetail (Maybe Text) Source #

The Amazon Resource Name (ARN) of the source.

trialComponentSourceDetail_processingJob :: Lens' TrialComponentSourceDetail (Maybe ProcessingJob) Source #

Information about a processing job that's the source of a trial component.

trialComponentSourceDetail_transformJob :: Lens' TrialComponentSourceDetail (Maybe TransformJob) Source #

Information about a transform job that's the source of a trial component.

TrialComponentStatus

trialComponentStatus_message :: Lens' TrialComponentStatus (Maybe Text) Source #

If the component failed, a message describing why.

TrialComponentSummary

trialComponentSummary_status :: Lens' TrialComponentSummary (Maybe TrialComponentStatus) Source #

The status of the component. States include:

  • InProgress
  • Completed
  • Failed

trialComponentSummary_displayName :: Lens' TrialComponentSummary (Maybe Text) Source #

The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.

TrialSource

trialSource_sourceArn :: Lens' TrialSource Text Source #

The Amazon Resource Name (ARN) of the source.

TrialSummary

trialSummary_trialArn :: Lens' TrialSummary (Maybe Text) Source #

The Amazon Resource Name (ARN) of the trial.

trialSummary_lastModifiedTime :: Lens' TrialSummary (Maybe UTCTime) Source #

When the trial was last modified.

trialSummary_displayName :: Lens' TrialSummary (Maybe Text) Source #

The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.

TuningJobCompletionCriteria

TuningJobStepMetaData

tuningJobStepMetaData_arn :: Lens' TuningJobStepMetaData (Maybe Text) Source #

The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.

USD

usd_cents :: Lens' USD (Maybe Natural) Source #

The fractional portion, in cents, of the amount.

usd_dollars :: Lens' USD (Maybe Natural) Source #

The whole number of dollars in the amount.

usd_tenthFractionsOfACent :: Lens' USD (Maybe Natural) Source #

Fractions of a cent, in tenths.

UiConfig

uiConfig_uiTemplateS3Uri :: Lens' UiConfig (Maybe Text) Source #

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.

uiConfig_humanTaskUiArn :: Lens' UiConfig (Maybe Text) Source #

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

UiTemplate

uiTemplate_content :: Lens' UiTemplate Text Source #

The content of the Liquid template for the worker user interface.

UiTemplateInfo

uiTemplateInfo_url :: Lens' UiTemplateInfo (Maybe Text) Source #

The URL for the user interface template.

uiTemplateInfo_contentSha256 :: Lens' UiTemplateInfo (Maybe Text) Source #

The SHA-256 digest of the contents of the template.

UserContext

userContext_userProfileName :: Lens' UserContext (Maybe Text) Source #

The name of the user's profile.

userContext_userProfileArn :: Lens' UserContext (Maybe Text) Source #

The Amazon Resource Name (ARN) of the user's profile.

userContext_domainId :: Lens' UserContext (Maybe Text) Source #

The domain associated with the user.

UserProfileDetails

UserSettings

userSettings_securityGroups :: Lens' UserSettings (Maybe [Text]) Source #

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.

userSettings_sharingSettings :: Lens' UserSettings (Maybe SharingSettings) Source #

Specifies options for sharing SageMaker Studio notebooks.

userSettings_executionRole :: Lens' UserSettings (Maybe Text) Source #

The execution role for the user.

VariantProperty

variantProperty_variantPropertyType :: Lens' VariantProperty VariantPropertyType Source #

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.)

VpcConfig

vpcConfig_securityGroupIds :: Lens' VpcConfig (NonEmpty Text) Source #

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

vpcConfig_subnets :: Lens' VpcConfig (NonEmpty Text) Source #

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.

Workforce

workforce_subDomain :: Lens' Workforce (Maybe Text) Source #

The subdomain for your OIDC Identity Provider.

workforce_createDate :: Lens' Workforce (Maybe UTCTime) Source #

The date that the workforce is created.

workforce_sourceIpConfig :: Lens' Workforce (Maybe SourceIpConfig) Source #

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.

workforce_cognitoConfig :: Lens' Workforce (Maybe CognitoConfig) Source #

The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.

workforce_lastUpdatedDate :: Lens' Workforce (Maybe UTCTime) Source #

The most recent date that was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.

workforce_oidcConfig :: Lens' Workforce (Maybe OidcConfigForResponse) Source #

The configuration of an OIDC Identity Provider (IdP) private workforce.

workforce_workforceName :: Lens' Workforce Text Source #

The name of the private workforce.

workforce_workforceArn :: Lens' Workforce Text Source #

The Amazon Resource Name (ARN) of the private workforce.

Workteam

workteam_subDomain :: Lens' Workteam (Maybe Text) Source #

The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.

workteam_productListingIds :: Lens' Workteam (Maybe [Text]) Source #

The Amazon Marketplace identifier for a vendor's work team.

workteam_notificationConfiguration :: Lens' Workteam (Maybe NotificationConfiguration) Source #

Configures SNS notifications of available or expiring work items for work teams.

workteam_createDate :: Lens' Workteam (Maybe UTCTime) Source #

The date and time that the work team was created (timestamp).

workteam_workforceArn :: Lens' Workteam (Maybe Text) Source #

The Amazon Resource Name (ARN) of the workforce.

workteam_lastUpdatedDate :: Lens' Workteam (Maybe UTCTime) Source #

The date and time that the work team was last updated (timestamp).

workteam_workteamName :: Lens' Workteam Text Source #

The name of the work team.

workteam_memberDefinitions :: Lens' Workteam (NonEmpty MemberDefinition) Source #

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.

workteam_workteamArn :: Lens' Workteam Text Source #

The Amazon Resource Name (ARN) that identifies the work team.

workteam_description :: Lens' Workteam Text Source #

A description of the work team.