Awso_sagemaker.Values_2SourceReturns information about a training job. Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
Retrieves the extension history for a specified training plan. The response includes details about each extension, such as the offering ID, start and end dates, status, payment status, and cost information.
Details about an extension to a training plan, including the offering ID, dates, status, and cost information.
Retrieves the extension history for a specified training plan. The response includes details about each extension, such as the offering ID, start and end dates, status, payment status, and cost information.
Retrieves detailed information about a specific training plan.
Details of a reserved capacity for the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .
Retrieves detailed information about a specific training plan.
Returns information about a transform job.
Returns information about a transform job.
Provides a list of a trials component's properties.
The Amazon Resource Name (ARN) and job type of the source of a trial component.
A summary of the metrics of a trial component.
Provides a list of a trials component's properties.
Provides a list of a trial's properties.
The source of the trial.
Provides a list of a trial's properties.
Describes a user profile. For more information, see CreateUserProfile.
Describes a user profile. For more information, see CreateUserProfile.
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks. This operation applies only to private workforces.
A VpcConfig object that specifies the VPC that you want your workforce to connect to.
Your OIDC IdP workforce configuration.
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.
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks. This operation applies only to private workforces.
Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
Specifies the serverless update concurrency configuration for an endpoint variant.
Specifies weight and capacity values for a production variant.
Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.
Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.
Contains information summarizing device details and deployment status.
Summary of the device fleet.
Status of devices.
Summary of model on edge device.
Summary of the device.
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
The domain's details.
A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.
A collection of Domain configuration settings to update.
A specification for a predefined metric.
An object containing information about a metric.
A target tracking scaling policy. Includes support for predefined or customized metrics. When using the PutScalingPolicy API, this parameter is required when you are creating a policy with the policy type TargetTrackingScaling.
An object containing a recommended scaling policy.
An object with the recommended values for you to specify when creating an autoscaling policy.
The configurations and outcomes of an Amazon EMR step execution.
Contains information summarizing an edge deployment plan.
Status of edge devices with this model.
Summary of edge packaging job.
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor.
Metadata for an endpoint configuration step.
Provides summary information for an endpoint configuration.
Metadata for an endpoint step.
Provides summary information for an endpoint.
The properties of an experiment as returned by the Search API. For information about experiments, see the CreateExperiment API.
A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName.
Extends an existing training plan by purchasing an extension offering. This allows you to add additional compute capacity time to your training plan without creating a new plan or reconfiguring your workloads. To find available extension offerings, use the SearchTrainingPlanOfferings API with the TrainingPlanArn parameter. To view the history of extensions for a training plan, use the DescribeTrainingPlanExtensionHistory API.
Extends an existing training plan by purchasing an extension offering. This allows you to add additional compute capacity time to your training plan without creating a new plan or reconfiguring your workloads. To find available extension offerings, use the SearchTrainingPlanOfferings API with the TrainingPlanArn parameter. To view the history of extensions for a training plan, use the DescribeTrainingPlanExtensionHistory API.
The container for the metadata for Fail step.
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.
The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a Value, but not an Operator, SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9": { "Name": "Metrics.accuracy", "Operator": "GreaterThan", "Value": "0.9" } HyperParameters To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5": { "Name": "HyperParameters.learning_rate", "Operator": "LessThan", "Value": "0.5" } Tags To define a tag filter, enter a value with the form Tags.<key>.
Contains summary information about the flow definition.
Describes a fleet.
Describes a fleet.
The resource policy for the lineage group.
The resource policy for the lineage group.
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
An object where you specify the anticipated traffic pattern for an endpoint.
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
The metric for a scaling policy.
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
Part of the SuggestionQuery type. Specifies a hint for retrieving property names that begin with the specified text.
Specified in the GetSearchSuggestions request. Limits the property names that are included in the response.
An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.
An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
Specifies configuration details for a Git repository when the repository is updated.
Information about hub content.
Container for human task user interface information.
An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.
Provides summary information about a hyperparameter tuning job.
A SageMaker AI image. A SageMaker AI image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker AI ImageVersion.
A version of a SageMaker AI Image. A version represents an existing container image.
Import hub content.
Import hub content.
The metadata of the inference component.
A summary of the properties of an inference component.
Lists a summary of properties of an inference experiment.
A structure that contains a list of recommendation jobs.
The details for a specific benchmark from an Inference Recommender job.
A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.
Provides counts for human-labeled tasks in the labeling job.
Provides summary information for a work team.
Provides summary information about a labeling job.
Metadata for a Lambda step.
Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity resources.
The metadata that tracks relationships between ML artifacts, actions, and contexts.
Returns a list of AI benchmark jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.
Returns a list of AI benchmark jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.
Returns a list of AI recommendation jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.
Returns a list of AI recommendation jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.
Returns a list of AI workload configurations in your account. You can filter the results by name and creation time, and sort the results. The response is paginated.
Returns a list of AI workload configurations in your account. You can filter the results by name and creation time, and sort the results. The response is paginated.
Lists the actions in your account and their properties.
Lists the actions in your account and their properties.
Lists the machine learning algorithms that have been created.
Lists the machine learning algorithms that have been created.
Lists the aliases of a specified image or image version.
Lists the aliases of a specified image or image version.
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
Lists apps.
Lists apps.
Lists the artifacts in your account and their properties.
Lists the artifacts in your account and their properties.
Lists the associations in your account and their properties.
Lists the associations in your account and their properties.
Request a list of jobs.
Request a list of jobs.
List the candidates created for the job.
List the candidates created for the job.
Retrieves a list of event summaries for a specified HyperPod cluster. The operation supports filtering, sorting, and pagination of results. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
Retrieves a list of event summaries for a specified HyperPod cluster. The operation supports filtering, sorting, and pagination of results. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
List the cluster policy configurations.
List the cluster policy configurations.
Retrieves the list of SageMaker HyperPod clusters.
Retrieves the list of SageMaker HyperPod clusters.
Gets a list of the Git repositories in your account.
Gets a list of the Git repositories in your account.
Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
List the resource allocation definitions.
List the resource allocation definitions.
Lists the contexts in your account and their properties.
Lists the contexts in your account and their properties.
Lists the data quality job definitions in your account.
Summary information about a monitoring job.
Lists the data quality job definitions in your account.
Returns a list of devices in the fleet.
Returns a list of devices in the fleet.
A list of devices.
A list of devices.
Lists the domains.
Lists the domains.
Lists all edge deployment plans.
Lists all edge deployment plans.
Returns a list of edge packaging jobs.
Returns a list of edge packaging jobs.
Lists endpoint configurations.
Lists endpoint configurations.
Lists endpoints.
Lists endpoints.
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
List FeatureGroups based on given filter and order.
List FeatureGroups based on given filter and order.
Returns information about the flow definitions in your account.
Returns information about the flow definitions in your account.
List hub content versions.
List hub content versions.
List the contents of a hub.
List the contents of a hub.
List all existing hubs.
List all existing hubs.
Returns information about the human task user interfaces in your account.
Returns information about the human task user interfaces in your account.
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
Lists the inference components in your account and their properties.
Lists the inference components in your account and their properties.
Returns the list of all inference experiments.
Returns the list of all inference experiments.
Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
Lists recommendation jobs that satisfy various filters.
Lists recommendation jobs that satisfy various filters.
Gets a list of labeling jobs assigned to a specified work team.
Gets a list of labeling jobs assigned to a specified work team.
Gets a list of labeling jobs.
Gets a list of labeling jobs.
A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Lists all MLflow Apps
The summary of the Mlflow App to list.
Lists all MLflow Apps
Lists all MLflow Tracking Servers.
The summary of the tracking server to list.
Lists all MLflow Tracking Servers.
Lists model bias jobs definitions that satisfy various filters.
Lists model bias jobs definitions that satisfy various filters.
List the export jobs for the Amazon SageMaker Model Card.
The summary of the Amazon SageMaker Model Card export job.
List the export jobs for the Amazon SageMaker Model Card.
List existing versions of an Amazon SageMaker Model Card.
A summary of a specific version of the model card.
List existing versions of an Amazon SageMaker Model Card.
List existing model cards.
A summary of the model card.
List existing model cards.
Lists model explainability job definitions that satisfy various filters.
Lists model explainability job definitions that satisfy various filters.
Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model).
One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
A summary of the model metadata.
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
Gets a list of the model groups in your Amazon Web Services account.
Summary information about a model group.
Gets a list of the model groups in your Amazon Web Services account.
Lists the model packages that have been created.
Provides summary information about a model package.
Lists the model packages that have been created.
Gets a list of model quality monitoring job definitions in your account.
Gets a list of model quality monitoring job definitions in your account.
Lists models created with the CreateModel API.
Provides summary information about a model.
Lists models created with the CreateModel API.
Gets a list of past alerts in a model monitoring schedule.
Provides summary information of an alert's history.
Gets a list of past alerts in a model monitoring schedule.
Gets the alerts for a single monitoring schedule.
An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into InAlert status.
A list of alert actions taken in response to an alert going into InAlert status.
Provides summary information about a monitor alert.
Gets the alerts for a single monitoring schedule.
Returns list of all monitoring job executions.
Returns list of all monitoring job executions.
Returns list of all monitoring schedules.
Summarizes the monitoring schedule.
Returns list of all monitoring schedules.
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
Provides a summary of a notebook instance lifecycle configuration.
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.
Provides summary information for an SageMaker AI notebook instance.
Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.
Lists the optimization jobs in your account and their properties.
Summarizes an optimization job by providing some of its key properties.
Lists the optimization jobs in your account and their properties.
Lists all of the SageMaker Partner AI Apps in an account.
A subset of information related to a SageMaker Partner AI App. This information is used as part of the ListPartnerApps API response.
Lists all of the SageMaker Partner AI Apps in an account.
Gets a list of PipeLineExecutionStep objects.
The ARN from an execution of the current pipeline.
Metadata for a tuning step.
Metadata for a transform job step.
Metadata for a training job step.
Metadata for a register model job step.
Container for the metadata for a Quality check step. For more information, see the topic on QualityCheck step in the Amazon SageMaker Developer Guide.
Metadata for a processing job step.
Metadata for Model steps.
Metadata for a step execution.
An execution of a step in a pipeline.
Gets a list of PipeLineExecutionStep objects.
Gets a list of the pipeline executions.
A pipeline execution summary.
Gets a list of the pipeline executions.
Gets a list of parameters for a pipeline execution.
Gets a list of parameters for a pipeline execution.
Gets a list of all versions of the pipeline.
The summary of the pipeline version.
Gets a list of all versions of the pipeline.
Gets a list of pipelines.
A summary of a pipeline.
Gets a list of pipelines.
Lists processing jobs that satisfy various filters.
Summary of information about a processing job.
Lists processing jobs that satisfy various filters.
Gets a list of the projects in an Amazon Web Services account.
Information about a project.
Gets a list of the projects in an Amazon Web Services account.
Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.
A resource catalog containing all of the resources of a specific resource type within a resource owner account. For an example on sharing the Amazon SageMaker Feature Store DefaultFeatureGroupCatalog, see Share Amazon SageMaker Catalog resource type in the Amazon SageMaker Developer Guide.
Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.
Lists spaces.
Specifies summary information about the space sharing settings.
Specifies summary information about the space settings.
Specifies summary information about the ownership settings.
The space's details.
Lists spaces.
Lists devices allocated to the stage, containing detailed device information and deployment status.
Lists devices allocated to the stage, containing detailed device information and deployment status.
Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.
Details of the Amazon SageMaker AI Studio Lifecycle Configuration.
Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
Returns the tags for the specified SageMaker resource.
Returns the tags for the specified SageMaker resource.
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
Lists training jobs. When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters: { ... MaxResults: 100, StatusEquals: InProgress ... } First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code. aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
Provides summary information about a training job.
Lists training jobs. When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters: { ... MaxResults: 100, StatusEquals: InProgress ... } First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code. aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
A filter to apply when listing or searching for training plans. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .
Retrieves a list of training plans for the current account.
Details of the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .
Retrieves a list of training plans for the current account.
Lists transform jobs.
Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.
Lists transform jobs.
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following: ExperimentName SourceArn TrialName
A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the TrialComponentName.
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following: ExperimentName SourceArn TrialName
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the TrialName.
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
Lists all UltraServers that are part of a specified reserved capacity.
Represents a high-performance compute server used for distributed training in SageMaker AI. An UltraServer consists of multiple instances within a shared NVLink interconnect domain.
Lists all UltraServers that are part of a specified reserved capacity.
Lists user profiles.
The user profile details.
Lists user profiles.
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
The model card for a model displayed in the Amazon SageMaker Model Dashboard.
A model displayed in the Amazon SageMaker Model Dashboard.
A container for your trained model that can be deployed for SageMaker inference. This can include inference code, artifacts, and metadata. The model package type can be one of the following. Versioned model: A part of a model package group in Model Registry. Unversioned model: Not part of a model package group and used in Amazon Web Services Marketplace. For more information, see CreateModelPackage .
A group of versioned models in the Model Registry.
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API. For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters: '{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}', '{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
Updates the feature group online store configuration.
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
An execution of a pipeline.
The version of the pipeline.
An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.
Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the QueryLineage API action.
Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
Register devices.
Configuration for remote debugging for the UpdateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
Contains input values for a task.
Renders the UI template so that you can preview the worker's experience.
A description of an error that occurred while rendering the template.
Renders the UI template so that you can preview the worker's experience.
Details about a reserved capacity offering for a training plan offering. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .
The ResourceConfig to update KeepAlivePeriodInSeconds. Other fields in the ResourceConfig cannot be updated.
Retry the execution of the pipeline.
Retry the execution of the pipeline.
module SearchExpression : sig ... endA multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements. A SearchExpression contains the following components: A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions. A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects. A Boolean operator: And or Or.
module SearchExpressionList : sig ... endContains information about a training job.
Detailed information about the source of a trial component. Either ProcessingJob or TrainingJob is returned.
The properties of a trial component as returned by the Search API.
A short summary of a trial component.
A single resource returned as part of the Search API response.
The list of key-value pairs used to filter your search results. If a search result contains a key from your list, it is included in the final search response if the value associated with the key in the result matches the value you specified. If the value doesn't match, the result is excluded from the search response. Any resources that don't have a key from the list that you've provided will also be included in the search response.
Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp. The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
Represents the total number of matching results and indicates how accurate that count is. The Value field provides the count, which may be exact or estimated. The Relation field indicates whether it's an exact figure or a lower bound. This helps understand the full scope of search results, especially when dealing with large result sets.
Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp. The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
Searches for available training plan offerings based on specified criteria. Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration). And then, they create a plan that best matches their needs using the ID of the plan offering they want to use. For more information about how to reserve GPU capacity for your SageMaker training jobs or SageMaker HyperPod clusters using Amazon SageMaker Training Plan , see CreateTrainingPlan .
Details about a training plan offering. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .
Details about an available extension offering for a training plan. Use the offering ID with the ExtendTrainingPlan API to extend a training plan.
Searches for available training plan offerings based on specified criteria. Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration). And then, they create a plan that best matches their needs using the ID of the plan offering they want to use. For more information about how to reserve GPU capacity for your SageMaker training jobs or SageMaker HyperPod clusters using Amazon SageMaker Training Plan , see CreateTrainingPlan .
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Start deep health checks for a SageMaker HyperPod cluster. You can use DescribeClusterNode API to track progress of the deep health checks. The unhealthy nodes will be automatically rebooted or replaced. Please see Resilience-related Kubernetes labels by SageMaker HyperPod for details.
Start deep health checks for a SageMaker HyperPod cluster. You can use DescribeClusterNode API to track progress of the deep health checks. The unhealthy nodes will be automatically rebooted or replaced. Please see Resilience-related Kubernetes labels by SageMaker HyperPod for details.
Starts a stage in an edge deployment plan.
Starts an inference experiment.
Starts an inference experiment.
Programmatically start an MLflow Tracking Server.
Programmatically start an MLflow Tracking Server.
Starts a previously stopped monitoring schedule. By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker AI sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.
Starts a pipeline execution.
Starts a pipeline execution.
Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space.
Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space.
Stops a running AI benchmark job.
Stops a running AI benchmark job.
Stops a running AI recommendation job.
Stops a running AI recommendation job.
A method for forcing a running job to shut down.
Stops a model compilation job. To stop a job, Amazon SageMaker AI sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal. When it receives a StopCompilationJob request, Amazon SageMaker AI changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped.
Stops a stage in an edge deployment plan.
Request to stop an edge packaging job.
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
Stops an inference experiment.
Stops an inference experiment.
Stops an Inference Recommender job.
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
Programmatically stop an MLflow Tracking Server.
Programmatically stop an MLflow Tracking Server.
Stops a previously started monitoring schedule.
Terminates the ML compute instance. Before terminating the instance, SageMaker AI disconnects the ML storage volume from it. SageMaker AI preserves the ML storage volume. SageMaker AI stops charging you for the ML compute instance when you call StopNotebookInstance. To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.
Ends a running inference optimization job.
Stops a pipeline execution. Callback Step A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. Lambda Step A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
Stops a pipeline execution. Callback Step A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. Lambda Step A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
Stops a processing job.
Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost. When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.
Stops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.
Updates an action.
Updates an action.
Updates the properties of an AppImageConfig.
Updates the properties of an AppImageConfig.
Updates an artifact.
Updates an artifact.
Updates a SageMaker HyperPod cluster.
Updates a SageMaker HyperPod cluster.
Update the cluster policy configuration.
Update the cluster policy configuration.
The configuration that describes specifications of the instance groups to update.
Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster. The UpgradeClusterSoftware API call may impact your SageMaker HyperPod cluster uptime and availability. Plan accordingly to mitigate potential disruptions to your workloads.
Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster. The UpgradeClusterSoftware API call may impact your SageMaker HyperPod cluster uptime and availability. Plan accordingly to mitigate potential disruptions to your workloads.
Updates the specified Git repository with the specified values.
Updates the specified Git repository with the specified values.
Update the compute allocation definition.
Update the compute allocation definition.
Updates a context.
Updates a context.
Updates a fleet of devices.
Updates one or more devices in a fleet.
Updates the default settings for new user profiles in the domain.
Updates the default settings for new user profiles in the domain.
Specifies a production variant property type for an Endpoint. If you are updating an endpoint with the RetainAllVariantProperties option of UpdateEndpointInput set to true, the VariantProperty objects listed in the ExcludeRetainedVariantProperties parameter of UpdateEndpointInput override the existing variant properties of the endpoint.
Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.
Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.
Updates the description and parameters of the feature group.
Updates the contents of a SageMaker hub for a ModelReference resource. A ModelReference allows you to access public SageMaker JumpStart models from within your private hub. When using this API, you can update the MinVersion field for additional flexibility in the model version. You shouldn't update any additional fields when using this API, because the metadata in your private hub should match the public JumpStart model's metadata. If you want to update a Model or Notebook resource in your hub, use the UpdateHubContent API instead. For more information about adding model references to your hub, see Add models to a private hub.
Updates the contents of a SageMaker hub for a ModelReference resource. A ModelReference allows you to access public SageMaker JumpStart models from within your private hub. When using this API, you can update the MinVersion field for additional flexibility in the model version. You shouldn't update any additional fields when using this API, because the metadata in your private hub should match the public JumpStart model's metadata. If you want to update a Model or Notebook resource in your hub, use the UpdateHubContent API instead. For more information about adding model references to your hub, see Add models to a private hub.
Updates SageMaker hub content (either a Model or Notebook resource). You can update the metadata that describes the resource. In addition to the required request fields, specify at least one of the following fields to update: HubContentDescription HubContentDisplayName HubContentMarkdown HubContentSearchKeywords SupportStatus For more information about hubs, see Private curated hubs for foundation model access control in JumpStart. If you want to update a ModelReference resource in your hub, use the UpdateHubContentResource API instead.
Updates SageMaker hub content (either a Model or Notebook resource). You can update the metadata that describes the resource. In addition to the required request fields, specify at least one of the following fields to update: HubContentDescription HubContentDisplayName HubContentMarkdown HubContentSearchKeywords SupportStatus For more information about hubs, see Private curated hubs for foundation model access control in JumpStart. If you want to update a ModelReference resource in your hub, use the UpdateHubContentResource API instead.
Update a hub.
Update a hub.
Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.
Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.
Updates the properties of a SageMaker AI image version.
Updates the properties of a SageMaker AI image version.
Updates an inference component.
Updates an inference component.
Runtime settings for a model that is deployed with an inference component.
Runtime settings for a model that is deployed with an inference component.
Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.
Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.
Updates an MLflow App.
Updates an MLflow App.
Updates properties of an existing MLflow Tracking Server.
Updates properties of an existing MLflow Tracking Server.
Update an Amazon SageMaker Model Card. You cannot update both model card content and model card status in a single call.
Update an Amazon SageMaker Model Card. You cannot update both model card content and model card status in a single call.
Updates a versioned model.
Updates a versioned model.
Update the parameters of a model monitor alert.
Update the parameters of a model monitor alert.
Updates a previously created schedule.
Updates a previously created schedule.
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. This API can attach lifecycle configurations to notebook instances. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Principals with this permission and access to lifecycle configurations can execute code with the execution role's credentials. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API. Updates to lifecycle configurations affect all notebook instances using that configuration upon their next start. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API. Updates to lifecycle configurations affect all notebook instances using that configuration upon their next start. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. This API can attach lifecycle configurations to notebook instances. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Principals with this permission and access to lifecycle configurations can execute code with the execution role's credentials. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.
Updates all of the SageMaker Partner AI Apps in an account.
Updates all of the SageMaker Partner AI Apps in an account.
Updates a pipeline execution.
Updates a pipeline execution.
Updates a pipeline.
Updates a pipeline.
Updates a pipeline version.
Updates a pipeline version.
Contains configuration details for updating an existing template provider in the project.
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model. You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model. You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.
Updates the settings of a space. You can't edit the app type of a space in the SpaceSettings.
Updates the settings of a space. You can't edit the app type of a space in the SpaceSettings.
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
Updates one or more properties of a trial component.
Updates one or more properties of a trial component.
Updates the display name of a trial.
Updates the display name of a trial.
Updates a user profile.
Updates a user profile.
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet. Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict public internet access for all workers, configure the SourceIpConfig CIDR value. For example, when using SourceIpConfig with an IpAddressType of IPv4, you can restrict access to the IPv4 CIDR block "10.0.0.0/16". When using an IpAddressType of dualstack, you can specify both the IPv4 and IPv6 CIDR blocks, such as "10.0.0.0/16" for IPv4 only, "2001:db8:1234:1a00::/56" for IPv6 only, or "10.0.0.0/16" and "2001:db8:1234:1a00::/56" for dual stack. Amazon SageMaker does not support Source Ip restriction for worker portals in VPC. Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP. You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation. After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation. This operation only applies to private workforces.
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet. Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict public internet access for all workers, configure the SourceIpConfig CIDR value. For example, when using SourceIpConfig with an IpAddressType of IPv4, you can restrict access to the IPv4 CIDR block "10.0.0.0/16". When using an IpAddressType of dualstack, you can specify both the IPv4 and IPv6 CIDR blocks, such as "10.0.0.0/16" for IPv4 only, "2001:db8:1234:1a00::/56" for IPv6 only, or "10.0.0.0/16" and "2001:db8:1234:1a00::/56" for dual stack. Amazon SageMaker does not support Source Ip restriction for worker portals in VPC. Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP. You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation. After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation. This operation only applies to private workforces.
Updates an existing work team with new member definitions or description.
Updates an existing work team with new member definitions or description.