Amazon SageMaker Service 2017-07-24
- Client: Aws\SageMaker\SageMakerClient
- Service ID: sagemaker
- Version: 2017-07-24
This page describes the parameters and results for the operations of the Amazon SageMaker Service (2017-07-24), and shows how to use the Aws\SageMaker\SageMakerClient object to call the described operations. This documentation is specific to the 2017-07-24 API version of the service.
Operation Summary
Each of the following operations can be created from a client using
$client->getCommand('CommandName'), where "CommandName" is the
name of one of the following operations. Note: a command is a value that
encapsulates an operation and the parameters used to create an HTTP request.
You can also create and send a command immediately using the magic methods
available on a client object: $client->commandName(/* parameters */).
You can send the command asynchronously (returning a promise) by appending the
word "Async" to the operation name: $client->commandNameAsync(/* parameters */).
- AddAssociation ( array $params = [] )
Creates an association between the source and the destination.
- AddTags ( array $params = [] )
Adds or overwrites one or more tags for the specified SageMaker resource.
- AssociateTrialComponent ( array $params = [] )
Associates a trial component with a trial.
- BatchDescribeModelPackage ( array $params = [] )
This action batch describes a list of versioned model packages
- CreateAction ( array $params = [] )
Creates an action.
- CreateAlgorithm ( array $params = [] )
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
- CreateApp ( array $params = [] )
Creates a running app for the specified UserProfile.
- CreateAppImageConfig ( array $params = [] )
Creates a configuration for running a SageMaker image as a KernelGateway app.
- CreateArtifact ( array $params = [] )
Creates an artifact.
- CreateAutoMLJob ( array $params = [] )
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
- CreateAutoMLJobV2 ( array $params = [] )
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
- CreateCluster ( array $params = [] )
Creates a SageMaker HyperPod cluster.
- CreateCodeRepository ( array $params = [] )
Creates a Git repository as a resource in your SageMaker account.
- CreateCompilationJob ( array $params = [] )
Starts a model compilation job.
- CreateContext ( array $params = [] )
Creates a context.
- CreateDataQualityJobDefinition ( array $params = [] )
Creates a definition for a job that monitors data quality and drift.
- CreateDeviceFleet ( array $params = [] )
Creates a device fleet.
- CreateDomain ( array $params = [] )
Creates a Domain.
- CreateEdgeDeploymentPlan ( array $params = [] )
Creates an edge deployment plan, consisting of multiple stages.
- CreateEdgeDeploymentStage ( array $params = [] )
Creates a new stage in an existing edge deployment plan.
- CreateEdgePackagingJob ( array $params = [] )
Starts a SageMaker Edge Manager model packaging job.
- CreateEndpoint ( array $params = [] )
Creates an endpoint using the endpoint configuration specified in the request.
- CreateEndpointConfig ( array $params = [] )
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
- CreateExperiment ( array $params = [] )
Creates a SageMaker experiment.
- CreateFeatureGroup ( array $params = [] )
Create a new FeatureGroup.
- CreateFlowDefinition ( array $params = [] )
Creates a flow definition.
- CreateHub ( array $params = [] )
Create a hub.
- CreateHumanTaskUi ( array $params = [] )
Defines the settings you will use for the human review workflow user interface.
- CreateHyperParameterTuningJob ( array $params = [] )
Starts a hyperparameter tuning job.
- CreateImage ( array $params = [] )
Creates a custom SageMaker image.
- CreateImageVersion ( array $params = [] )
Creates a version of the SageMaker image specified by ImageName.
- CreateInferenceComponent ( array $params = [] )
Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint.
- CreateInferenceExperiment ( array $params = [] )
Creates an inference experiment using the configurations specified in the request.
- CreateInferenceRecommendationsJob ( array $params = [] )
Starts a recommendation job.
- CreateLabelingJob ( array $params = [] )
Creates a job that uses workers to label the data objects in your input dataset.
- CreateModel ( array $params = [] )
Creates a model in SageMaker.
- CreateModelBiasJobDefinition ( array $params = [] )
Creates the definition for a model bias job.
- CreateModelCard ( array $params = [] )
Creates an Amazon SageMaker Model Card.
- CreateModelCardExportJob ( array $params = [] )
Creates an Amazon SageMaker Model Card export job.
- CreateModelExplainabilityJobDefinition ( array $params = [] )
Creates the definition for a model explainability job.
- CreateModelPackage ( array $params = [] )
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.
- CreateModelPackageGroup ( array $params = [] )
Creates a model group.
- CreateModelQualityJobDefinition ( array $params = [] )
Creates a definition for a job that monitors model quality and drift.
- CreateMonitoringSchedule ( array $params = [] )
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
- CreateNotebookInstance ( array $params = [] )
Creates an SageMaker notebook instance.
- CreateNotebookInstanceLifecycleConfig ( array $params = [] )
Creates a lifecycle configuration that you can associate with a notebook instance.
- CreatePipeline ( array $params = [] )
Creates a pipeline using a JSON pipeline definition.
- CreatePresignedDomainUrl ( array $params = [] )
Creates a URL for a specified UserProfile in a Domain.
- CreatePresignedNotebookInstanceUrl ( array $params = [] )
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
- CreateProcessingJob ( array $params = [] )
Creates a processing job.
- CreateProject ( array $params = [] )
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
- CreateSpace ( array $params = [] )
Creates a space used for real time collaboration in a Domain.
- CreateStudioLifecycleConfig ( array $params = [] )
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
- CreateTrainingJob ( array $params = [] )
Starts a model training job.
- CreateTransformJob ( array $params = [] )
Starts a transform job.
- CreateTrial ( array $params = [] )
Creates an SageMaker trial.
- CreateTrialComponent ( array $params = [] )
Creates a trial component, which is a stage of a machine learning trial.
- CreateUserProfile ( array $params = [] )
Creates a user profile.
- CreateWorkforce ( array $params = [] )
Use this operation to create a workforce.
- CreateWorkteam ( array $params = [] )
Creates a new work team for labeling your data.
- DeleteAction ( array $params = [] )
Deletes an action.
- DeleteAlgorithm ( array $params = [] )
Removes the specified algorithm from your account.
- DeleteApp ( array $params = [] )
Used to stop and delete an app.
- DeleteAppImageConfig ( array $params = [] )
Deletes an AppImageConfig.
- DeleteArtifact ( array $params = [] )
Deletes an artifact.
- DeleteAssociation ( array $params = [] )
Deletes an association.
- DeleteCluster ( array $params = [] )
Delete a SageMaker HyperPod cluster.
- DeleteCodeRepository ( array $params = [] )
Deletes the specified Git repository from your account.
- DeleteContext ( array $params = [] )
Deletes an context.
- DeleteDataQualityJobDefinition ( array $params = [] )
Deletes a data quality monitoring job definition.
- DeleteDeviceFleet ( array $params = [] )
Deletes a fleet.
- DeleteDomain ( array $params = [] )
Used to delete a domain.
- DeleteEdgeDeploymentPlan ( array $params = [] )
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
- DeleteEdgeDeploymentStage ( array $params = [] )
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
- DeleteEndpoint ( array $params = [] )
Deletes an endpoint.
- DeleteEndpointConfig ( array $params = [] )
Deletes an endpoint configuration.
- DeleteExperiment ( array $params = [] )
Deletes an SageMaker experiment.
- DeleteFeatureGroup ( array $params = [] )
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup.
- DeleteFlowDefinition ( array $params = [] )
Deletes the specified flow definition.
- DeleteHub ( array $params = [] )
Delete a hub.
- DeleteHubContent ( array $params = [] )
Delete the contents of a hub.
- DeleteHumanTaskUi ( array $params = [] )
Use this operation to delete a human task user interface (worker task template).
- DeleteImage ( array $params = [] )
Deletes a SageMaker image and all versions of the image.
- DeleteImageVersion ( array $params = [] )
Deletes a version of a SageMaker image.
- DeleteInferenceComponent ( array $params = [] )
Deletes an inference component.
- DeleteInferenceExperiment ( array $params = [] )
Deletes an inference experiment.
- DeleteModel ( array $params = [] )
Deletes a model.
- DeleteModelBiasJobDefinition ( array $params = [] )
Deletes an Amazon SageMaker model bias job definition.
- DeleteModelCard ( array $params = [] )
Deletes an Amazon SageMaker Model Card.
- DeleteModelExplainabilityJobDefinition ( array $params = [] )
Deletes an Amazon SageMaker model explainability job definition.
- DeleteModelPackage ( array $params = [] )
Deletes a model package.
- DeleteModelPackageGroup ( array $params = [] )
Deletes the specified model group.
- DeleteModelPackageGroupPolicy ( array $params = [] )
Deletes a model group resource policy.
- DeleteModelQualityJobDefinition ( array $params = [] )
Deletes the secified model quality monitoring job definition.
- DeleteMonitoringSchedule ( array $params = [] )
Deletes a monitoring schedule.
- DeleteNotebookInstance ( array $params = [] )
Deletes an SageMaker notebook instance.
- DeleteNotebookInstanceLifecycleConfig ( array $params = [] )
Deletes a notebook instance lifecycle configuration.
- DeletePipeline ( array $params = [] )
Deletes a pipeline if there are no running instances of the pipeline.
- DeleteProject ( array $params = [] )
Delete the specified project.
- DeleteSpace ( array $params = [] )
Used to delete a space.
- DeleteStudioLifecycleConfig ( array $params = [] )
Deletes the Amazon SageMaker Studio Lifecycle Configuration.
- DeleteTags ( array $params = [] )
Deletes the specified tags from an SageMaker resource.
- DeleteTrial ( array $params = [] )
Deletes the specified trial.
- DeleteTrialComponent ( array $params = [] )
Deletes the specified trial component.
- DeleteUserProfile ( array $params = [] )
Deletes a user profile.
- DeleteWorkforce ( array $params = [] )
Use this operation to delete a workforce.
- DeleteWorkteam ( array $params = [] )
Deletes an existing work team.
- DeregisterDevices ( array $params = [] )
Deregisters the specified devices.
- DescribeAction ( array $params = [] )
Describes an action.
- DescribeAlgorithm ( array $params = [] )
Returns a description of the specified algorithm that is in your account.
- DescribeApp ( array $params = [] )
Describes the app.
- DescribeAppImageConfig ( array $params = [] )
Describes an AppImageConfig.
- DescribeArtifact ( array $params = [] )
Describes an artifact.
- DescribeAutoMLJob ( array $params = [] )
Returns information about an AutoML job created by calling CreateAutoMLJob.
- DescribeAutoMLJobV2 ( array $params = [] )
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
- DescribeCluster ( array $params = [] )
Retrieves information of a SageMaker HyperPod cluster.
- DescribeClusterNode ( array $params = [] )
Retrieves information of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.
- DescribeCodeRepository ( array $params = [] )
Gets details about the specified Git repository.
- DescribeCompilationJob ( array $params = [] )
Returns information about a model compilation job.
- DescribeContext ( array $params = [] )
Describes a context.
- DescribeDataQualityJobDefinition ( array $params = [] )
Gets the details of a data quality monitoring job definition.
- DescribeDevice ( array $params = [] )
Describes the device.
- DescribeDeviceFleet ( array $params = [] )
A description of the fleet the device belongs to.
- DescribeDomain ( array $params = [] )
The description of the domain.
- DescribeEdgeDeploymentPlan ( array $params = [] )
Describes an edge deployment plan with deployment status per stage.
- DescribeEdgePackagingJob ( array $params = [] )
A description of edge packaging jobs.
- DescribeEndpoint ( array $params = [] )
Returns the description of an endpoint.
- DescribeEndpointConfig ( array $params = [] )
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
- DescribeExperiment ( array $params = [] )
Provides a list of an experiment's properties.
- DescribeFeatureGroup ( array $params = [] )
Use this operation to describe a FeatureGroup.
- DescribeFeatureMetadata ( array $params = [] )
Shows the metadata for a feature within a feature group.
- DescribeFlowDefinition ( array $params = [] )
Returns information about the specified flow definition.
- DescribeHub ( array $params = [] )
Describe a hub.
- DescribeHubContent ( array $params = [] )
Describe the content of a hub.
- DescribeHumanTaskUi ( array $params = [] )
Returns information about the requested human task user interface (worker task template).
- DescribeHyperParameterTuningJob ( array $params = [] )
Returns a description of a hyperparameter tuning job, depending on the fields selected.
- DescribeImage ( array $params = [] )
Describes a SageMaker image.
- DescribeImageVersion ( array $params = [] )
Describes a version of a SageMaker image.
- DescribeInferenceComponent ( array $params = [] )
Returns information about an inference component.
- DescribeInferenceExperiment ( array $params = [] )
Returns details about an inference experiment.
- DescribeInferenceRecommendationsJob ( array $params = [] )
Provides the results of the Inference Recommender job.
- DescribeLabelingJob ( array $params = [] )
Gets information about a labeling job.
- DescribeLineageGroup ( array $params = [] )
Provides a list of properties for the requested lineage group.
- DescribeModel ( array $params = [] )
Describes a model that you created using the CreateModel API.
- DescribeModelBiasJobDefinition ( array $params = [] )
Returns a description of a model bias job definition.
- DescribeModelCard ( array $params = [] )
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
- DescribeModelCardExportJob ( array $params = [] )
Describes an Amazon SageMaker Model Card export job.
- DescribeModelExplainabilityJobDefinition ( array $params = [] )
Returns a description of a model explainability job definition.
- DescribeModelPackage ( array $params = [] )
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
- DescribeModelPackageGroup ( array $params = [] )
Gets a description for the specified model group.
- DescribeModelQualityJobDefinition ( array $params = [] )
Returns a description of a model quality job definition.
- DescribeMonitoringSchedule ( array $params = [] )
Describes the schedule for a monitoring job.
- DescribeNotebookInstance ( array $params = [] )
Returns information about a notebook instance.
- DescribeNotebookInstanceLifecycleConfig ( array $params = [] )
Returns a description of a notebook instance lifecycle configuration.
- DescribePipeline ( array $params = [] )
Describes the details of a pipeline.
- DescribePipelineDefinitionForExecution ( array $params = [] )
Describes the details of an execution's pipeline definition.
- DescribePipelineExecution ( array $params = [] )
Describes the details of a pipeline execution.
- DescribeProcessingJob ( array $params = [] )
Returns a description of a processing job.
- DescribeProject ( array $params = [] )
Describes the details of a project.
- DescribeSpace ( array $params = [] )
Describes the space.
- DescribeStudioLifecycleConfig ( array $params = [] )
Describes the Amazon SageMaker Studio Lifecycle Configuration.
- DescribeSubscribedWorkteam ( array $params = [] )
Gets information about a work team provided by a vendor.
- DescribeTrainingJob ( array $params = [] )
Returns information about a training job.
- DescribeTransformJob ( array $params = [] )
Returns information about a transform job.
- DescribeTrial ( array $params = [] )
Provides a list of a trial's properties.
- DescribeTrialComponent ( array $params = [] )
Provides a list of a trials component's properties.
- DescribeUserProfile ( array $params = [] )
Describes a user profile.
- DescribeWorkforce ( array $params = [] )
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
- DescribeWorkteam ( array $params = [] )
Gets information about a specific work team.
- DisableSagemakerServicecatalogPortfolio ( array $params = [] )
Disables using Service Catalog in SageMaker.
- DisassociateTrialComponent ( array $params = [] )
Disassociates a trial component from a trial.
- EnableSagemakerServicecatalogPortfolio ( array $params = [] )
Enables using Service Catalog in SageMaker.
- GetDeviceFleetReport ( array $params = [] )
Describes a fleet.
- GetLineageGroupPolicy ( array $params = [] )
The resource policy for the lineage group.
- GetModelPackageGroupPolicy ( array $params = [] )
Gets a resource policy that manages access for a model group.
- GetSagemakerServicecatalogPortfolioStatus ( array $params = [] )
Gets the status of Service Catalog in SageMaker.
- GetScalingConfigurationRecommendation ( array $params = [] )
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.
- GetSearchSuggestions ( array $params = [] )
An auto-complete API for the search functionality in the SageMaker console.
- ImportHubContent ( array $params = [] )
Import hub content.
- ListActions ( array $params = [] )
Lists the actions in your account and their properties.
- ListAlgorithms ( array $params = [] )
Lists the machine learning algorithms that have been created.
- ListAliases ( array $params = [] )
Lists the aliases of a specified image or image version.
- ListAppImageConfigs ( array $params = [] )
Lists the AppImageConfigs in your account and their properties.
- ListApps ( array $params = [] )
Lists apps.
- ListArtifacts ( array $params = [] )
Lists the artifacts in your account and their properties.
- ListAssociations ( array $params = [] )
Lists the associations in your account and their properties.
- ListAutoMLJobs ( array $params = [] )
Request a list of jobs.
- ListCandidatesForAutoMLJob ( array $params = [] )
List the candidates created for the job.
- ListClusterNodes ( array $params = [] )
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
- ListClusters ( array $params = [] )
Retrieves the list of SageMaker HyperPod clusters.
- ListCodeRepositories ( array $params = [] )
Gets a list of the Git repositories in your account.
- ListCompilationJobs ( array $params = [] )
Lists model compilation jobs that satisfy various filters.
- ListContexts ( array $params = [] )
Lists the contexts in your account and their properties.
- ListDataQualityJobDefinitions ( array $params = [] )
Lists the data quality job definitions in your account.
- ListDeviceFleets ( array $params = [] )
Returns a list of devices in the fleet.
- ListDevices ( array $params = [] )
A list of devices.
- ListDomains ( array $params = [] )
Lists the domains.
- ListEdgeDeploymentPlans ( array $params = [] )
Lists all edge deployment plans.
- ListEdgePackagingJobs ( array $params = [] )
Returns a list of edge packaging jobs.
- ListEndpointConfigs ( array $params = [] )
Lists endpoint configurations.
- ListEndpoints ( array $params = [] )
Lists endpoints.
- ListExperiments ( array $params = [] )
Lists all the experiments in your account.
- ListFeatureGroups ( array $params = [] )
List FeatureGroups based on given filter and order.
- ListFlowDefinitions ( array $params = [] )
Returns information about the flow definitions in your account.
- ListHubContentVersions ( array $params = [] )
List hub content versions.
- ListHubContents ( array $params = [] )
List the contents of a hub.
- ListHubs ( array $params = [] )
List all existing hubs.
- ListHumanTaskUis ( array $params = [] )
Returns information about the human task user interfaces in your account.
- ListHyperParameterTuningJobs ( array $params = [] )
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
- ListImageVersions ( array $params = [] )
Lists the versions of a specified image and their properties.
- ListImages ( array $params = [] )
Lists the images in your account and their properties.
- ListInferenceComponents ( array $params = [] )
Lists the inference components in your account and their properties.
- ListInferenceExperiments ( array $params = [] )
Returns the list of all inference experiments.
- ListInferenceRecommendationsJobSteps ( array $params = [] )
Returns a list of the subtasks for an Inference Recommender job.
- ListInferenceRecommendationsJobs ( array $params = [] )
Lists recommendation jobs that satisfy various filters.
- ListLabelingJobs ( array $params = [] )
Gets a list of labeling jobs.
- ListLabelingJobsForWorkteam ( array $params = [] )
Gets a list of labeling jobs assigned to a specified work team.
- ListLineageGroups ( array $params = [] )
A list of lineage groups shared with your Amazon Web Services account.
- ListModelBiasJobDefinitions ( array $params = [] )
Lists model bias jobs definitions that satisfy various filters.
- ListModelCardExportJobs ( array $params = [] )
List the export jobs for the Amazon SageMaker Model Card.
- ListModelCardVersions ( array $params = [] )
List existing versions of an Amazon SageMaker Model Card.
- ListModelCards ( array $params = [] )
List existing model cards.
- ListModelExplainabilityJobDefinitions ( array $params = [] )
Lists model explainability job definitions that satisfy various filters.
- ListModelMetadata ( array $params = [] )
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
- ListModelPackageGroups ( array $params = [] )
Gets a list of the model groups in your Amazon Web Services account.
- ListModelPackages ( array $params = [] )
Lists the model packages that have been created.
- ListModelQualityJobDefinitions ( array $params = [] )
Gets a list of model quality monitoring job definitions in your account.
- ListModels ( array $params = [] )
Lists models created with the CreateModel API.
- ListMonitoringAlertHistory ( array $params = [] )
Gets a list of past alerts in a model monitoring schedule.
- ListMonitoringAlerts ( array $params = [] )
Gets the alerts for a single monitoring schedule.
- ListMonitoringExecutions ( array $params = [] )
Returns list of all monitoring job executions.
- ListMonitoringSchedules ( array $params = [] )
Returns list of all monitoring schedules.
- ListNotebookInstanceLifecycleConfigs ( array $params = [] )
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
- ListNotebookInstances ( array $params = [] )
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
- ListPipelineExecutionSteps ( array $params = [] )
Gets a list of PipeLineExecutionStep objects.
- ListPipelineExecutions ( array $params = [] )
Gets a list of the pipeline executions.
- ListPipelineParametersForExecution ( array $params = [] )
Gets a list of parameters for a pipeline execution.
- ListPipelines ( array $params = [] )
Gets a list of pipelines.
- ListProcessingJobs ( array $params = [] )
Lists processing jobs that satisfy various filters.
- ListProjects ( array $params = [] )
Gets a list of the projects in an Amazon Web Services account.
- ListResourceCatalogs ( array $params = [] )
Lists Amazon SageMaker Catalogs based on given filters and orders.
- ListSpaces ( array $params = [] )
Lists spaces.
- ListStageDevices ( array $params = [] )
Lists devices allocated to the stage, containing detailed device information and deployment status.
- ListStudioLifecycleConfigs ( array $params = [] )
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
- ListSubscribedWorkteams ( array $params = [] )
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
- ListTags ( array $params = [] )
Returns the tags for the specified SageMaker resource.
- ListTrainingJobs ( array $params = [] )
Lists training jobs.
- ListTrainingJobsForHyperParameterTuningJob ( array $params = [] )
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
- ListTransformJobs ( array $params = [] )
Lists transform jobs.
- ListTrialComponents ( array $params = [] )
Lists the trial components in your account.
- ListTrials ( array $params = [] )
Lists the trials in your account.
- ListUserProfiles ( array $params = [] )
Lists user profiles.
- ListWorkforces ( array $params = [] )
Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
- ListWorkteams ( array $params = [] )
Gets a list of private work teams that you have defined in a region.
- PutModelPackageGroupPolicy ( array $params = [] )
Adds a resouce policy to control access to a model group.
- QueryLineage ( array $params = [] )
Use this action to inspect your lineage and discover relationships between entities.
- RegisterDevices ( array $params = [] )
Register devices.
- RenderUiTemplate ( array $params = [] )
Renders the UI template so that you can preview the worker's experience.
- RetryPipelineExecution ( array $params = [] )
Retry the execution of the pipeline.
- Search ( array $params = [] )
Finds SageMaker resources that match a search query.
- SendPipelineExecutionStepFailure ( array $params = [] )
Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
- SendPipelineExecutionStepSuccess ( array $params = [] )
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
- StartEdgeDeploymentStage ( array $params = [] )
Starts a stage in an edge deployment plan.
- StartInferenceExperiment ( array $params = [] )
Starts an inference experiment.
- StartMonitoringSchedule ( array $params = [] )
Starts a previously stopped monitoring schedule.
- StartNotebookInstance ( array $params = [] )
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
- StartPipelineExecution ( array $params = [] )
Starts a pipeline execution.
- StopAutoMLJob ( array $params = [] )
A method for forcing a running job to shut down.
- StopCompilationJob ( array $params = [] )
Stops a model compilation job.
- StopEdgeDeploymentStage ( array $params = [] )
Stops a stage in an edge deployment plan.
- StopEdgePackagingJob ( array $params = [] )
Request to stop an edge packaging job.
- StopHyperParameterTuningJob ( array $params = [] )
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
- StopInferenceExperiment ( array $params = [] )
Stops an inference experiment.
- StopInferenceRecommendationsJob ( array $params = [] )
Stops an Inference Recommender job.
- StopLabelingJob ( array $params = [] )
Stops a running labeling job.
- StopMonitoringSchedule ( array $params = [] )
Stops a previously started monitoring schedule.
- StopNotebookInstance ( array $params = [] )
Terminates the ML compute instance.
- StopPipelineExecution ( array $params = [] )
Stops a pipeline execution.
- StopProcessingJob ( array $params = [] )
Stops a processing job.
- StopTrainingJob ( array $params = [] )
Stops a training job.
- StopTransformJob ( array $params = [] )
Stops a batch transform job.
- UpdateAction ( array $params = [] )
Updates an action.
- UpdateAppImageConfig ( array $params = [] )
Updates the properties of an AppImageConfig.
- UpdateArtifact ( array $params = [] )
Updates an artifact.
- UpdateCluster ( array $params = [] )
Update a SageMaker HyperPod cluster.
- UpdateCodeRepository ( array $params = [] )
Updates the specified Git repository with the specified values.
- UpdateContext ( array $params = [] )
Updates a context.
- UpdateDeviceFleet ( array $params = [] )
Updates a fleet of devices.
- UpdateDevices ( array $params = [] )
Updates one or more devices in a fleet.
- UpdateDomain ( array $params = [] )
Updates the default settings for new user profiles in the domain.
- UpdateEndpoint ( array $params = [] )
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
- UpdateEndpointWeightsAndCapacities ( array $params = [] )
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
- UpdateExperiment ( array $params = [] )
Adds, updates, or removes the description of an experiment.
- UpdateFeatureGroup ( array $params = [] )
Updates the feature group by either adding features or updating the online store configuration.
- UpdateFeatureMetadata ( array $params = [] )
Updates the description and parameters of the feature group.
- UpdateHub ( array $params = [] )
Update a hub.
- UpdateImage ( array $params = [] )
Updates the properties of a SageMaker image.
- UpdateImageVersion ( array $params = [] )
Updates the properties of a SageMaker image version.
- UpdateInferenceComponent ( array $params = [] )
Updates an inference component.
- UpdateInferenceComponentRuntimeConfig ( array $params = [] )
Runtime settings for a model that is deployed with an inference component.
- UpdateInferenceExperiment ( array $params = [] )
Updates an inference experiment that you created.
- UpdateModelCard ( array $params = [] )
Update an Amazon SageMaker Model Card.
- UpdateModelPackage ( array $params = [] )
Updates a versioned model.
- UpdateMonitoringAlert ( array $params = [] )
Update the parameters of a model monitor alert.
- UpdateMonitoringSchedule ( array $params = [] )
Updates a previously created schedule.
- UpdateNotebookInstance ( array $params = [] )
Updates a notebook instance.
- UpdateNotebookInstanceLifecycleConfig ( array $params = [] )
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
- UpdatePipeline ( array $params = [] )
Updates a pipeline.
- UpdatePipelineExecution ( array $params = [] )
Updates a pipeline execution.
- UpdateProject ( array $params = [] )
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.
- UpdateSpace ( array $params = [] )
Updates the settings of a space.
- UpdateTrainingJob ( array $params = [] )
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
- UpdateTrial ( array $params = [] )
Updates the display name of a trial.
- UpdateTrialComponent ( array $params = [] )
Updates one or more properties of a trial component.
- UpdateUserProfile ( array $params = [] )
Updates a user profile.
- UpdateWorkforce ( array $params = [] )
Use this operation to update your workforce.
- UpdateWorkteam ( array $params = [] )
Updates an existing work team with new member definitions or description.
Paginators
Paginators handle automatically iterating over paginated API results. Paginators are associated with specific API operations, and they accept the parameters that the corresponding API operation accepts. You can get a paginator from a client class using getPaginator($paginatorName, $operationParameters). This client supports the following paginators:
- ListActions
- ListAlgorithms
- ListAliases
- ListAppImageConfigs
- ListApps
- ListArtifacts
- ListAssociations
- ListAutoMLJobs
- ListCandidatesForAutoMLJob
- ListClusterNodes
- ListClusters
- ListCodeRepositories
- ListCompilationJobs
- ListContexts
- ListDataQualityJobDefinitions
- ListDeviceFleets
- ListDevices
- ListDomains
- ListEdgeDeploymentPlans
- ListEdgePackagingJobs
- ListEndpointConfigs
- ListEndpoints
- ListExperiments
- ListFeatureGroups
- ListFlowDefinitions
- ListHumanTaskUis
- ListHyperParameterTuningJobs
- ListImageVersions
- ListImages
- ListInferenceComponents
- ListInferenceExperiments
- ListInferenceRecommendationsJobSteps
- ListInferenceRecommendationsJobs
- ListLabelingJobs
- ListLabelingJobsForWorkteam
- ListLineageGroups
- ListModelBiasJobDefinitions
- ListModelCardExportJobs
- ListModelCardVersions
- ListModelCards
- ListModelExplainabilityJobDefinitions
- ListModelMetadata
- ListModelPackageGroups
- ListModelPackages
- ListModelQualityJobDefinitions
- ListModels
- ListMonitoringAlertHistory
- ListMonitoringAlerts
- ListMonitoringExecutions
- ListMonitoringSchedules
- ListNotebookInstanceLifecycleConfigs
- ListNotebookInstances
- ListPipelineExecutionSteps
- ListPipelineExecutions
- ListPipelineParametersForExecution
- ListPipelines
- ListProcessingJobs
- ListProjects
- ListResourceCatalogs
- ListSpaces
- ListStageDevices
- ListStudioLifecycleConfigs
- ListSubscribedWorkteams
- ListTags
- ListTrainingJobs
- ListTrainingJobsForHyperParameterTuningJob
- ListTransformJobs
- ListTrialComponents
- ListTrials
- ListUserProfiles
- ListWorkforces
- ListWorkteams
- QueryLineage
- Search
Waiters
Waiters allow you to poll a resource until it enters into a desired state. A waiter has a name used to describe what it does, and is associated with an API operation. When creating a waiter, you can provide the API operation parameters associated with the corresponding operation. Waiters can be accessed using the getWaiter($waiterName, $operationParameters) method of a client object. This client supports the following waiters:
| Waiter name | API Operation | Delay | Max Attempts |
|---|---|---|---|
| NotebookInstanceInService | DescribeNotebookInstance | 30 | 60 |
| NotebookInstanceStopped | DescribeNotebookInstance | 30 | 60 |
| NotebookInstanceDeleted | DescribeNotebookInstance | 30 | 60 |
| TrainingJobCompletedOrStopped | DescribeTrainingJob | 120 | 180 |
| EndpointInService | DescribeEndpoint | 30 | 120 |
| EndpointDeleted | DescribeEndpoint | 30 | 60 |
| TransformJobCompletedOrStopped | DescribeTransformJob | 60 | 60 |
| ProcessingJobCompletedOrStopped | DescribeProcessingJob | 60 | 60 |
| ImageCreated | DescribeImage | 60 | 60 |
| ImageUpdated | DescribeImage | 60 | 60 |
| ImageDeleted | DescribeImage | 60 | 60 |
| ImageVersionCreated | DescribeImageVersion | 60 | 60 |
| ImageVersionDeleted | DescribeImageVersion | 60 | 60 |
Operations
AddAssociation
$result = $client->addAssociation([/* ... */]); $promise = $client->addAssociationAsync([/* ... */]);
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
Parameter Syntax
$result = $client->addAssociation([
'AssociationType' => 'ContributedTo|AssociatedWith|DerivedFrom|Produced',
'DestinationArn' => '<string>', // REQUIRED
'SourceArn' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AssociationType
-
- Type: string
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.
- DestinationArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the destination.
- SourceArn
-
- Required: Yes
- Type: string
The ARN of the source.
Result Syntax
[
'DestinationArn' => '<string>',
'SourceArn' => '<string>',
]
Result Details
Members
- DestinationArn
-
- Type: string
The Amazon Resource Name (ARN) of the destination.
- SourceArn
-
- Type: string
The ARN of the source.
Errors
-
Resource being access is not found.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
AddTags
$result = $client->addTags([/* ... */]); $promise = $client->addTagsAsync([/* ... */]);
Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.
Parameter Syntax
$result = $client->addTags([
'ResourceArn' => '<string>', // REQUIRED
'Tags' => [ // REQUIRED
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ResourceArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the resource that you want to tag.
- Tags
-
- Required: Yes
- Type: Array of Tag structures
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.
Result Syntax
[
'Tags' => [
[
'Key' => '<string>',
'Value' => '<string>',
],
// ...
],
]
Result Details
Members
- Tags
-
- Type: Array of Tag structures
A list of tags associated with the SageMaker resource.
Errors
There are no errors described for this operation.
AssociateTrialComponent
$result = $client->associateTrialComponent([/* ... */]); $promise = $client->associateTrialComponentAsync([/* ... */]);
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Parameter Syntax
$result = $client->associateTrialComponent([
'TrialComponentName' => '<string>', // REQUIRED
'TrialName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- TrialComponentName
-
- Required: Yes
- Type: string
The name of the component to associated with the trial.
- TrialName
-
- Required: Yes
- Type: string
The name of the trial to associate with.
Result Syntax
[
'TrialArn' => '<string>',
'TrialComponentArn' => '<string>',
]
Result Details
Members
- TrialArn
-
- Type: string
The Amazon Resource Name (ARN) of the trial.
- TrialComponentArn
-
- Type: string
The Amazon Resource Name (ARN) of the trial component.
Errors
-
Resource being access is not found.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
BatchDescribeModelPackage
$result = $client->batchDescribeModelPackage([/* ... */]); $promise = $client->batchDescribeModelPackageAsync([/* ... */]);
This action batch describes a list of versioned model packages
Parameter Syntax
$result = $client->batchDescribeModelPackage([
'ModelPackageArnList' => ['<string>', ...], // REQUIRED
]);
Parameter Details
Members
- ModelPackageArnList
-
- Required: Yes
- Type: Array of strings
The list of Amazon Resource Name (ARN) of the model package groups.
Result Syntax
[
'BatchDescribeModelPackageErrorMap' => [
'<ModelPackageArn>' => [
'ErrorCode' => '<string>',
'ErrorResponse' => '<string>',
],
// ...
],
'ModelPackageSummaries' => [
'<ModelPackageArn>' => [
'CreationTime' => <DateTime>,
'InferenceSpecification' => [
'Containers' => [
[
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object',
'S3Uri' => '<string>',
],
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'Image' => '<string>',
'ImageDigest' => '<string>',
'ModelDataUrl' => '<string>',
'ModelInput' => [
'DataInputConfig' => '<string>',
],
'NearestModelName' => '<string>',
'ProductId' => '<string>',
],
// ...
],
'SupportedContentTypes' => ['<string>', ...],
'SupportedRealtimeInferenceInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'SupportedTransformInstanceTypes' => ['<string>', ...],
],
'ModelApprovalStatus' => 'Approved|Rejected|PendingManualApproval',
'ModelPackageArn' => '<string>',
'ModelPackageDescription' => '<string>',
'ModelPackageGroupName' => '<string>',
'ModelPackageStatus' => 'Pending|InProgress|Completed|Failed|Deleting',
'ModelPackageVersion' => <integer>,
],
// ...
],
]
Result Details
Members
- BatchDescribeModelPackageErrorMap
-
- Type: Associative array of custom strings keys (ModelPackageArn) to BatchDescribeModelPackageError structures
A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
- ModelPackageSummaries
-
- Type: Associative array of custom strings keys (ModelPackageArn) to BatchDescribeModelPackageSummary structures
The summaries for the model package versions
Errors
There are no errors described for this operation.
CreateAction
$result = $client->createAction([/* ... */]); $promise = $client->createActionAsync([/* ... */]);
Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
Parameter Syntax
$result = $client->createAction([
'ActionName' => '<string>', // REQUIRED
'ActionType' => '<string>', // REQUIRED
'Description' => '<string>',
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'Properties' => ['<string>', ...],
'Source' => [ // REQUIRED
'SourceId' => '<string>',
'SourceType' => '<string>',
'SourceUri' => '<string>', // REQUIRED
],
'Status' => 'Unknown|InProgress|Completed|Failed|Stopping|Stopped',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ActionName
-
- Required: Yes
- Type: string
The name of the action. Must be unique to your account in an Amazon Web Services Region.
- ActionType
-
- Required: Yes
- Type: string
The action type.
- Description
-
- Type: string
The description of the action.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- Properties
-
- Type: Associative array of custom strings keys (StringParameterValue) to strings
A list of properties to add to the action.
- Source
-
- Required: Yes
- Type: ActionSource structure
The source type, ID, and URI.
- Status
-
- Type: string
The status of the action.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the action.
Result Syntax
[
'ActionArn' => '<string>',
]
Result Details
Members
- ActionArn
-
- Type: string
The Amazon Resource Name (ARN) of the action.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateAlgorithm
$result = $client->createAlgorithm([/* ... */]); $promise = $client->createAlgorithmAsync([/* ... */]);
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
Parameter Syntax
$result = $client->createAlgorithm([
'AlgorithmDescription' => '<string>',
'AlgorithmName' => '<string>', // REQUIRED
'CertifyForMarketplace' => true || false,
'InferenceSpecification' => [
'Containers' => [ // REQUIRED
[
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'Image' => '<string>', // REQUIRED
'ImageDigest' => '<string>',
'ModelDataUrl' => '<string>',
'ModelInput' => [
'DataInputConfig' => '<string>', // REQUIRED
],
'NearestModelName' => '<string>',
'ProductId' => '<string>',
],
// ...
],
'SupportedContentTypes' => ['<string>', ...],
'SupportedRealtimeInferenceInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'SupportedTransformInstanceTypes' => ['<string>', ...],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TrainingSpecification' => [ // REQUIRED
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'MetricDefinitions' => [
[
'Name' => '<string>', // REQUIRED
'Regex' => '<string>', // REQUIRED
],
// ...
],
'SupportedHyperParameters' => [
[
'DefaultValue' => '<string>',
'Description' => '<string>',
'IsRequired' => true || false,
'IsTunable' => true || false,
'Name' => '<string>', // REQUIRED
'Range' => [
'CategoricalParameterRangeSpecification' => [
'Values' => ['<string>', ...], // REQUIRED
],
'ContinuousParameterRangeSpecification' => [
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
],
'IntegerParameterRangeSpecification' => [
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
],
],
'Type' => 'Integer|Continuous|Categorical|FreeText', // REQUIRED
],
// ...
],
'SupportedTrainingInstanceTypes' => ['<string>', ...], // REQUIRED
'SupportedTuningJobObjectiveMetrics' => [
[
'MetricName' => '<string>', // REQUIRED
'Type' => 'Maximize|Minimize', // REQUIRED
],
// ...
],
'SupportsDistributedTraining' => true || false,
'TrainingChannels' => [ // REQUIRED
[
'Description' => '<string>',
'IsRequired' => true || false,
'Name' => '<string>', // REQUIRED
'SupportedCompressionTypes' => ['<string>', ...],
'SupportedContentTypes' => ['<string>', ...], // REQUIRED
'SupportedInputModes' => ['<string>', ...], // REQUIRED
],
// ...
],
'TrainingImage' => '<string>', // REQUIRED
'TrainingImageDigest' => '<string>',
],
'ValidationSpecification' => [
'ValidationProfiles' => [ // REQUIRED
[
'ProfileName' => '<string>', // REQUIRED
'TrainingJobDefinition' => [ // REQUIRED
'HyperParameters' => ['<string>', ...],
'InputDataConfig' => [ // REQUIRED
[
'ChannelName' => '<string>', // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'FileSystemDataSource' => [
'DirectoryPath' => '<string>', // REQUIRED
'FileSystemAccessMode' => 'rw|ro', // REQUIRED
'FileSystemId' => '<string>', // REQUIRED
'FileSystemType' => 'EFS|FSxLustre', // REQUIRED
],
'S3DataSource' => [
'AttributeNames' => ['<string>', ...],
'InstanceGroupNames' => ['<string>', ...],
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'InputMode' => 'Pipe|File|FastFile',
'RecordWrapperType' => 'None|RecordIO',
'ShuffleConfig' => [
'Seed' => <integer>, // REQUIRED
],
],
// ...
],
'OutputDataConfig' => [ // REQUIRED
'CompressionType' => 'GZIP|NONE',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'ResourceConfig' => [ // REQUIRED
'InstanceCount' => <integer>,
'InstanceGroups' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceGroupName' => '<string>', // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
],
// ...
],
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'KeepAlivePeriodInSeconds' => <integer>,
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
'StoppingCondition' => [ // REQUIRED
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'TrainingInputMode' => 'Pipe|File|FastFile', // REQUIRED
],
'TransformJobDefinition' => [
'BatchStrategy' => 'MultiRecord|SingleRecord',
'Environment' => ['<string>', ...],
'MaxConcurrentTransforms' => <integer>,
'MaxPayloadInMB' => <integer>,
'TransformInput' => [ // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'S3DataSource' => [ // REQUIRED
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'SplitType' => 'None|Line|RecordIO|TFRecord',
],
'TransformOutput' => [ // REQUIRED
'Accept' => '<string>',
'AssembleWith' => 'None|Line',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'TransformResources' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
],
],
],
// ...
],
'ValidationRole' => '<string>', // REQUIRED
],
]);
Parameter Details
Members
- AlgorithmDescription
-
- Type: string
A description of the algorithm.
- AlgorithmName
-
- Required: Yes
- Type: string
The name of the algorithm.
- CertifyForMarketplace
-
- Type: boolean
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
- InferenceSpecification
-
- Type: InferenceSpecification structure
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.
- Tags
-
- Type: Array of Tag structures
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.
- TrainingSpecification
-
- Required: Yes
- Type: TrainingSpecification structure
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, andtestchannels.
- ValidationSpecification
-
- Type: AlgorithmValidationSpecification structure
Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
Result Syntax
[
'AlgorithmArn' => '<string>',
]
Result Details
Members
- AlgorithmArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the new algorithm.
Errors
There are no errors described for this operation.
CreateApp
$result = $client->createApp([/* ... */]); $promise = $client->createAppAsync([/* ... */]);
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
Parameter Syntax
$result = $client->createApp([
'AppName' => '<string>', // REQUIRED
'AppType' => 'JupyterServer|KernelGateway|TensorBoard|RStudioServerPro|RSessionGateway', // REQUIRED
'DomainId' => '<string>', // REQUIRED
'ResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'SpaceName' => '<string>',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'UserProfileName' => '<string>',
]);
Parameter Details
Members
- AppName
-
- Required: Yes
- Type: string
The name of the app.
- AppType
-
- Required: Yes
- Type: string
The type of app.
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- ResourceSpec
-
- Type: ResourceSpec structure
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The value of
InstanceTypepassed as part of theResourceSpecin theCreateAppcall overrides the value passed as part of theResourceSpecconfigured for the user profile or the domain. IfInstanceTypeis not specified in any of those threeResourceSpecvalues for aKernelGatewayapp, theCreateAppcall fails with a request validation error. - SpaceName
-
- Type: string
The name of the space. If this value is not set, then
UserProfileNamemust be set. - Tags
-
- Type: Array of Tag structures
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
- UserProfileName
-
- Type: string
The user profile name. If this value is not set, then
SpaceNamemust be set.
Result Syntax
[
'AppArn' => '<string>',
]
Result Details
Members
- AppArn
-
- Type: string
The Amazon Resource Name (ARN) of the app.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateAppImageConfig
$result = $client->createAppImageConfig([/* ... */]); $promise = $client->createAppImageConfigAsync([/* ... */]);
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
Parameter Syntax
$result = $client->createAppImageConfig([
'AppImageConfigName' => '<string>', // REQUIRED
'KernelGatewayImageConfig' => [
'FileSystemConfig' => [
'DefaultGid' => <integer>,
'DefaultUid' => <integer>,
'MountPath' => '<string>',
],
'KernelSpecs' => [ // REQUIRED
[
'DisplayName' => '<string>',
'Name' => '<string>', // REQUIRED
],
// ...
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- AppImageConfigName
-
- Required: Yes
- Type: string
The name of the AppImageConfig. Must be unique to your account.
- KernelGatewayImageConfig
-
- Type: KernelGatewayImageConfig structure
The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the AppImageConfig.
Result Syntax
[
'AppImageConfigArn' => '<string>',
]
Result Details
Members
- AppImageConfigArn
-
- Type: string
The Amazon Resource Name (ARN) of the AppImageConfig.
Errors
-
Resource being accessed is in use.
CreateArtifact
$result = $client->createArtifact([/* ... */]); $promise = $client->createArtifactAsync([/* ... */]);
Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
Parameter Syntax
$result = $client->createArtifact([
'ArtifactName' => '<string>',
'ArtifactType' => '<string>', // REQUIRED
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'Properties' => ['<string>', ...],
'Source' => [ // REQUIRED
'SourceTypes' => [
[
'SourceIdType' => 'MD5Hash|S3ETag|S3Version|Custom', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'SourceUri' => '<string>', // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ArtifactName
-
- Type: string
The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
- ArtifactType
-
- Required: Yes
- Type: string
The artifact type.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- Properties
-
- Type: Associative array of custom strings keys (StringParameterValue) to strings
A list of properties to add to the artifact.
- Source
-
- Required: Yes
- Type: ArtifactSource structure
The ID, ID type, and URI of the source.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the artifact.
Result Syntax
[
'ArtifactArn' => '<string>',
]
Result Details
Members
- ArtifactArn
-
- Type: string
The Amazon Resource Name (ARN) of the artifact.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateAutoMLJob
$result = $client->createAutoMLJob([/* ... */]); $promise = $client->createAutoMLJobAsync([/* ... */]);
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
Parameter Syntax
$result = $client->createAutoMLJob([
'AutoMLJobConfig' => [
'CandidateGenerationConfig' => [
'AlgorithmsConfig' => [
[
'AutoMLAlgorithms' => ['<string>', ...], // REQUIRED
],
// ...
],
'FeatureSpecificationS3Uri' => '<string>',
],
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'DataSplitConfig' => [
'ValidationFraction' => <float>,
],
'Mode' => 'AUTO|ENSEMBLING|HYPERPARAMETER_TUNING',
'SecurityConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'VolumeKmsKeyId' => '<string>',
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
],
'AutoMLJobName' => '<string>', // REQUIRED
'AutoMLJobObjective' => [
'MetricName' => 'Accuracy|MSE|F1|F1macro|AUC|RMSE|MAE|R2|BalancedAccuracy|Precision|PrecisionMacro|Recall|RecallMacro|MAPE|MASE|WAPE|AverageWeightedQuantileLoss', // REQUIRED
],
'GenerateCandidateDefinitionsOnly' => true || false,
'InputDataConfig' => [ // REQUIRED
[
'ChannelType' => 'training|validation',
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'S3DataSource' => [ // REQUIRED
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'SampleWeightAttributeName' => '<string>',
'TargetAttributeName' => '<string>', // REQUIRED
],
// ...
],
'ModelDeployConfig' => [
'AutoGenerateEndpointName' => true || false,
'EndpointName' => '<string>',
],
'OutputDataConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'ProblemType' => 'BinaryClassification|MulticlassClassification|Regression',
'RoleArn' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- AutoMLJobConfig
-
- Type: AutoMLJobConfig structure
A collection of settings used to configure an AutoML job.
- AutoMLJobName
-
- Required: Yes
- Type: string
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
- AutoMLJobObjective
-
- Type: AutoMLJobObjective structure
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
- GenerateCandidateDefinitionsOnly
-
- Type: boolean
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
- InputDataConfig
-
- Required: Yes
- Type: Array of AutoMLChannel structures
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
InputDataConfigsupported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset. - ModelDeployConfig
-
- Type: ModelDeployConfig structure
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
- OutputDataConfig
-
- Required: Yes
- Type: AutoMLOutputDataConfig structure
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
- ProblemType
-
- Type: string
Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.
- RoleArn
-
- Required: Yes
- Type: string
The ARN of the role that is used to access the data.
- Tags
-
- Type: Array of Tag structures
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
Result Syntax
[
'AutoMLJobArn' => '<string>',
]
Result Details
Members
- AutoMLJobArn
-
- Required: Yes
- Type: string
The unique ARN assigned to the AutoML job when it is created.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateAutoMLJobV2
$result = $client->createAutoMLJobV2([/* ... */]); $promise = $client->createAutoMLJobV2Async([/* ... */]);
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
Parameter Syntax
$result = $client->createAutoMLJobV2([
'AutoMLJobInputDataConfig' => [ // REQUIRED
[
'ChannelType' => 'training|validation',
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [
'S3DataSource' => [ // REQUIRED
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
],
// ...
],
'AutoMLJobName' => '<string>', // REQUIRED
'AutoMLJobObjective' => [
'MetricName' => 'Accuracy|MSE|F1|F1macro|AUC|RMSE|MAE|R2|BalancedAccuracy|Precision|PrecisionMacro|Recall|RecallMacro|MAPE|MASE|WAPE|AverageWeightedQuantileLoss', // REQUIRED
],
'AutoMLProblemTypeConfig' => [ // REQUIRED
'ImageClassificationJobConfig' => [
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
],
'TabularJobConfig' => [
'CandidateGenerationConfig' => [
'AlgorithmsConfig' => [
[
'AutoMLAlgorithms' => ['<string>', ...], // REQUIRED
],
// ...
],
],
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'FeatureSpecificationS3Uri' => '<string>',
'GenerateCandidateDefinitionsOnly' => true || false,
'Mode' => 'AUTO|ENSEMBLING|HYPERPARAMETER_TUNING',
'ProblemType' => 'BinaryClassification|MulticlassClassification|Regression',
'SampleWeightAttributeName' => '<string>',
'TargetAttributeName' => '<string>', // REQUIRED
],
'TextClassificationJobConfig' => [
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'ContentColumn' => '<string>', // REQUIRED
'TargetLabelColumn' => '<string>', // REQUIRED
],
'TextGenerationJobConfig' => [
'BaseModelName' => '<string>',
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'TextGenerationHyperParameters' => ['<string>', ...],
],
'TimeSeriesForecastingJobConfig' => [
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'FeatureSpecificationS3Uri' => '<string>',
'ForecastFrequency' => '<string>', // REQUIRED
'ForecastHorizon' => <integer>, // REQUIRED
'ForecastQuantiles' => ['<string>', ...],
'HolidayConfig' => [
[
'CountryCode' => '<string>',
],
// ...
],
'TimeSeriesConfig' => [ // REQUIRED
'GroupingAttributeNames' => ['<string>', ...],
'ItemIdentifierAttributeName' => '<string>', // REQUIRED
'TargetAttributeName' => '<string>', // REQUIRED
'TimestampAttributeName' => '<string>', // REQUIRED
],
'Transformations' => [
'Aggregation' => ['<string>', ...],
'Filling' => [
'<TransformationAttributeName>' => ['<string>', ...],
// ...
],
],
],
],
'DataSplitConfig' => [
'ValidationFraction' => <float>,
],
'ModelDeployConfig' => [
'AutoGenerateEndpointName' => true || false,
'EndpointName' => '<string>',
],
'OutputDataConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'RoleArn' => '<string>', // REQUIRED
'SecurityConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'VolumeKmsKeyId' => '<string>',
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- AutoMLJobInputDataConfig
-
- Required: Yes
- Type: Array of AutoMLJobChannel structures
An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the
CreateAutoMLJobinput parameters. The supported formats depend on the problem type:-
For tabular problem types:
S3Prefix,ManifestFile. -
For image classification:
S3Prefix,ManifestFile,AugmentedManifestFile. -
For text classification:
S3Prefix. -
For time-series forecasting:
S3Prefix. -
For text generation (LLMs fine-tuning):
S3Prefix.
- AutoMLJobName
-
- Required: Yes
- Type: string
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
- AutoMLJobObjective
-
- Type: AutoMLJobObjective structure
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.
-
For tabular problem types: You must either provide both the
AutoMLJobObjectiveand indicate the type of supervised learning problem inAutoMLProblemTypeConfig(TabularJobConfig.ProblemType), or none at all. -
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjectivefield. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
- AutoMLProblemTypeConfig
-
- Required: Yes
- Type: AutoMLProblemTypeConfig structure
Defines the configuration settings of one of the supported problem types.
- DataSplitConfig
-
- Type: AutoMLDataSplitConfig structure
This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling
CreateAutoMLJob, the validation dataset must be less than 2 GB in size.This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.
- ModelDeployConfig
-
- Type: ModelDeployConfig structure
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
- OutputDataConfig
-
- Required: Yes
- Type: AutoMLOutputDataConfig structure
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
- RoleArn
-
- Required: Yes
- Type: string
The ARN of the role that is used to access the data.
- SecurityConfig
-
- Type: AutoMLSecurityConfig structure
The security configuration for traffic encryption or Amazon VPC settings.
- Tags
-
- Type: Array of Tag structures
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
Result Syntax
[
'AutoMLJobArn' => '<string>',
]
Result Details
Members
- AutoMLJobArn
-
- Required: Yes
- Type: string
The unique ARN assigned to the AutoMLJob when it is created.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateCluster
$result = $client->createCluster([/* ... */]); $promise = $client->createClusterAsync([/* ... */]);
Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
Parameter Syntax
$result = $client->createCluster([
'ClusterName' => '<string>', // REQUIRED
'InstanceGroups' => [ // REQUIRED
[
'ExecutionRole' => '<string>', // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceGroupName' => '<string>', // REQUIRED
'InstanceType' => 'ml.p4d.24xlarge|ml.p4de.24xlarge|ml.p5.48xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.12xlarge|ml.g5.16xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.c5n.large|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge', // REQUIRED
'LifeCycleConfig' => [ // REQUIRED
'OnCreate' => '<string>', // REQUIRED
'SourceS3Uri' => '<string>', // REQUIRED
],
'ThreadsPerCore' => <integer>,
],
// ...
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
]);
Parameter Details
Members
- ClusterName
-
- Required: Yes
- Type: string
The name for the new SageMaker HyperPod cluster.
- InstanceGroups
-
- Required: Yes
- Type: Array of ClusterInstanceGroupSpecification structures
The instance groups to be created in the SageMaker HyperPod cluster.
- Tags
-
- Type: Array of Tag structures
Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.
- VpcConfig
-
- Type: VpcConfig structure
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
Result Syntax
[
'ClusterArn' => '<string>',
]
Result Details
Members
- ClusterArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the cluster.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateCodeRepository
$result = $client->createCodeRepository([/* ... */]); $promise = $client->createCodeRepositoryAsync([/* ... */]);
Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
Parameter Syntax
$result = $client->createCodeRepository([
'CodeRepositoryName' => '<string>', // REQUIRED
'GitConfig' => [ // REQUIRED
'Branch' => '<string>',
'RepositoryUrl' => '<string>', // REQUIRED
'SecretArn' => '<string>',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- CodeRepositoryName
-
- Required: Yes
- Type: string
The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
- GitConfig
-
- Required: Yes
- Type: GitConfig structure
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
- Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'CodeRepositoryArn' => '<string>',
]
Result Details
Members
- CodeRepositoryArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the new repository.
Errors
There are no errors described for this operation.
CreateCompilationJob
$result = $client->createCompilationJob([/* ... */]); $promise = $client->createCompilationJobAsync([/* ... */]);
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
-
A name for the compilation job
-
Information about the input model artifacts
-
The output location for the compiled model and the device (target) that the model runs on
-
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
Parameter Syntax
$result = $client->createCompilationJob([
'CompilationJobName' => '<string>', // REQUIRED
'InputConfig' => [
'DataInputConfig' => '<string>',
'Framework' => 'TENSORFLOW|KERAS|MXNET|ONNX|PYTORCH|XGBOOST|TFLITE|DARKNET|SKLEARN', // REQUIRED
'FrameworkVersion' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'ModelPackageVersionArn' => '<string>',
'OutputConfig' => [ // REQUIRED
'CompilerOptions' => '<string>',
'KmsKeyId' => '<string>',
'S3OutputLocation' => '<string>', // REQUIRED
'TargetDevice' => 'lambda|ml_m4|ml_m5|ml_c4|ml_c5|ml_p2|ml_p3|ml_g4dn|ml_inf1|ml_inf2|ml_trn1|ml_eia2|jetson_tx1|jetson_tx2|jetson_nano|jetson_xavier|rasp3b|imx8qm|deeplens|rk3399|rk3288|aisage|sbe_c|qcs605|qcs603|sitara_am57x|amba_cv2|amba_cv22|amba_cv25|x86_win32|x86_win64|coreml|jacinto_tda4vm|imx8mplus',
'TargetPlatform' => [
'Accelerator' => 'INTEL_GRAPHICS|MALI|NVIDIA|NNA',
'Arch' => 'X86_64|X86|ARM64|ARM_EABI|ARM_EABIHF', // REQUIRED
'Os' => 'ANDROID|LINUX', // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [ // REQUIRED
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
]);
Parameter Details
Members
- CompilationJobName
-
- Required: Yes
- Type: string
A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
- InputConfig
-
- Type: InputConfig structure
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.
- ModelPackageVersionArn
-
- Type: string
The Amazon Resource Name (ARN) of a versioned model package. Provide either a
ModelPackageVersionArnor anInputConfigobject in the request syntax. The presence of both objects in theCreateCompilationJobrequest will return an exception. - OutputConfig
-
- Required: Yes
- Type: OutputConfig structure
Provides information about the output location for the compiled model and the target device the model runs on.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the
iam:PassRolepermission. For more information, see Amazon SageMaker Roles. - StoppingCondition
-
- Required: Yes
- Type: StoppingCondition structure
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
- Tags
-
- Type: Array of Tag structures
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.
- VpcConfig
-
- Type: NeoVpcConfig structure
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.
Result Syntax
[
'CompilationJobArn' => '<string>',
]
Result Details
Members
- CompilationJobArn
-
- Required: Yes
- Type: string
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
-
CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateContext
$result = $client->createContext([/* ... */]); $promise = $client->createContextAsync([/* ... */]);
Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
Parameter Syntax
$result = $client->createContext([
'ContextName' => '<string>', // REQUIRED
'ContextType' => '<string>', // REQUIRED
'Description' => '<string>',
'Properties' => ['<string>', ...],
'Source' => [ // REQUIRED
'SourceId' => '<string>',
'SourceType' => '<string>',
'SourceUri' => '<string>', // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ContextName
-
- Required: Yes
- Type: string
The name of the context. Must be unique to your account in an Amazon Web Services Region.
- ContextType
-
- Required: Yes
- Type: string
The context type.
- Description
-
- Type: string
The description of the context.
- Properties
-
- Type: Associative array of custom strings keys (StringParameterValue) to strings
A list of properties to add to the context.
- Source
-
- Required: Yes
- Type: ContextSource structure
The source type, ID, and URI.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the context.
Result Syntax
[
'ContextArn' => '<string>',
]
Result Details
Members
- ContextArn
-
- Type: string
The Amazon Resource Name (ARN) of the context.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateDataQualityJobDefinition
$result = $client->createDataQualityJobDefinition([/* ... */]); $promise = $client->createDataQualityJobDefinitionAsync([/* ... */]);
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Parameter Syntax
$result = $client->createDataQualityJobDefinition([
'DataQualityAppSpecification' => [ // REQUIRED
'ContainerArguments' => ['<string>', ...],
'ContainerEntrypoint' => ['<string>', ...],
'Environment' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
'PostAnalyticsProcessorSourceUri' => '<string>',
'RecordPreprocessorSourceUri' => '<string>',
],
'DataQualityBaselineConfig' => [
'BaseliningJobName' => '<string>',
'ConstraintsResource' => [
'S3Uri' => '<string>',
],
'StatisticsResource' => [
'S3Uri' => '<string>',
],
],
'DataQualityJobInput' => [ // REQUIRED
'BatchTransformInput' => [
'DataCapturedDestinationS3Uri' => '<string>', // REQUIRED
'DatasetFormat' => [ // REQUIRED
'Csv' => [
'Header' => true || false,
],
'Json' => [
'Line' => true || false,
],
'Parquet' => [
],
],
'EndTimeOffset' => '<string>',
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'EndpointInput' => [
'EndTimeOffset' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
],
'DataQualityJobOutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'MonitoringOutputs' => [ // REQUIRED
[
'S3Output' => [ // REQUIRED
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'JobDefinitionName' => '<string>', // REQUIRED
'JobResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- DataQualityAppSpecification
-
- Required: Yes
- Type: DataQualityAppSpecification structure
Specifies the container that runs the monitoring job.
- DataQualityBaselineConfig
-
- Type: DataQualityBaselineConfig structure
Configures the constraints and baselines for the monitoring job.
- DataQualityJobInput
-
- Required: Yes
- Type: DataQualityJobInput structure
A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
- DataQualityJobOutputConfig
-
- Required: Yes
- Type: MonitoringOutputConfig structure
The output configuration for monitoring jobs.
- JobDefinitionName
-
- Required: Yes
- Type: string
The name for the monitoring job definition.
- JobResources
-
- Required: Yes
- Type: MonitoringResources structure
Identifies the resources to deploy for a monitoring job.
- NetworkConfig
-
- Type: MonitoringNetworkConfig structure
Specifies networking configuration for the monitoring job.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- StoppingCondition
-
- Type: MonitoringStoppingCondition structure
A time limit for how long the monitoring job is allowed to run before stopping.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'JobDefinitionArn' => '<string>',
]
Result Details
Members
- JobDefinitionArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the job definition.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateDeviceFleet
$result = $client->createDeviceFleet([/* ... */]); $promise = $client->createDeviceFleetAsync([/* ... */]);
Creates a device fleet.
Parameter Syntax
$result = $client->createDeviceFleet([
'Description' => '<string>',
'DeviceFleetName' => '<string>', // REQUIRED
'EnableIotRoleAlias' => true || false,
'OutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'PresetDeploymentConfig' => '<string>',
'PresetDeploymentType' => 'GreengrassV2Component',
'S3OutputLocation' => '<string>', // REQUIRED
],
'RoleArn' => '<string>',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- Description
-
- Type: string
A description of the fleet.
- DeviceFleetName
-
- Required: Yes
- Type: string
The name of the fleet that the device belongs to.
- EnableIotRoleAlias
-
- Type: boolean
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".
- OutputConfig
-
- Required: Yes
- Type: EdgeOutputConfig structure
The output configuration for storing sample data collected by the fleet.
- RoleArn
-
- Type: string
The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
- Tags
-
- Type: Array of Tag structures
Creates tags for the specified fleet.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateDomain
$result = $client->createDomain([/* ... */]); $promise = $client->createDomainAsync([/* ... */]);
Creates a Domain. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:
-
PublicInternetOnly- Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value. -
VpcOnly- All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
Parameter Syntax
$result = $client->createDomain([
'AppNetworkAccessType' => 'PublicInternetOnly|VpcOnly',
'AppSecurityGroupManagement' => 'Service|Customer',
'AuthMode' => 'SSO|IAM', // REQUIRED
'DefaultSpaceSettings' => [
'ExecutionRole' => '<string>',
'JupyterServerAppSettings' => [
'CodeRepositories' => [
[
'RepositoryUrl' => '<string>', // REQUIRED
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'KernelGatewayAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'SecurityGroups' => ['<string>', ...],
],
'DefaultUserSettings' => [ // REQUIRED
'CanvasAppSettings' => [
'DirectDeploySettings' => [
'Status' => 'ENABLED|DISABLED',
],
'IdentityProviderOAuthSettings' => [
[
'DataSourceName' => 'SalesforceGenie|Snowflake',
'SecretArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
// ...
],
'KendraSettings' => [
'Status' => 'ENABLED|DISABLED',
],
'ModelRegisterSettings' => [
'CrossAccountModelRegisterRoleArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
'TimeSeriesForecastingSettings' => [
'AmazonForecastRoleArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
'WorkspaceSettings' => [
'S3ArtifactPath' => '<string>',
'S3KmsKeyId' => '<string>',
],
],
'DefaultLandingUri' => '<string>',
'ExecutionRole' => '<string>',
'JupyterServerAppSettings' => [
'CodeRepositories' => [
[
'RepositoryUrl' => '<string>', // REQUIRED
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'KernelGatewayAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'RSessionAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
],
'RStudioServerProAppSettings' => [
'AccessStatus' => 'ENABLED|DISABLED',
'UserGroup' => 'R_STUDIO_ADMIN|R_STUDIO_USER',
],
'SecurityGroups' => ['<string>', ...],
'SharingSettings' => [
'NotebookOutputOption' => 'Allowed|Disabled',
'S3KmsKeyId' => '<string>',
'S3OutputPath' => '<string>',
],
'StudioWebPortal' => 'ENABLED|DISABLED',
'TensorBoardAppSettings' => [
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
],
],
'DomainName' => '<string>', // REQUIRED
'DomainSettings' => [
'ExecutionRoleIdentityConfig' => 'USER_PROFILE_NAME|DISABLED',
'RStudioServerProDomainSettings' => [
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'DomainExecutionRoleArn' => '<string>', // REQUIRED
'RStudioConnectUrl' => '<string>',
'RStudioPackageManagerUrl' => '<string>',
],
'SecurityGroupIds' => ['<string>', ...],
],
'HomeEfsFileSystemKmsKeyId' => '<string>',
'KmsKeyId' => '<string>',
'SubnetIds' => ['<string>', ...], // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VpcId' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AppNetworkAccessType
-
- Type: string
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 traffic is through the specified VPC and subnets
- AppSecurityGroupManagement
-
- Type: string
The entity that creates and manages the required security groups for inter-app communication in
VPCOnlymode. Required whenCreateDomain.AppNetworkAccessTypeisVPCOnlyandDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArnis provided. If setting up the domain for use with RStudio, this value must be set toService. - AuthMode
-
- Required: Yes
- Type: string
The mode of authentication that members use to access the domain.
- DefaultSpaceSettings
-
- Type: DefaultSpaceSettings structure
The default settings used to create a space.
- DefaultUserSettings
-
- Required: Yes
- Type: UserSettings structure
The default settings to use to create a user profile when
UserSettingsisn't specified in the call to theCreateUserProfileAPI.SecurityGroupsis aggregated when specified in both calls. For all other settings inUserSettings, the values specified inCreateUserProfiletake precedence over those specified inCreateDomain. - DomainName
-
- Required: Yes
- Type: string
A name for the domain.
- DomainSettings
-
- Type: DomainSettings structure
A collection of
Domainsettings. - HomeEfsFileSystemKmsKeyId
-
- Type: string
Use
KmsKeyId. - KmsKeyId
-
- Type: string
SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
- SubnetIds
-
- Required: Yes
- Type: Array of strings
The VPC subnets that the domain uses for communication.
- Tags
-
- Type: Array of Tag structures
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
SearchAPI.Tags that you specify for the Domain are also added to all Apps that the Domain launches.
- VpcId
-
- Required: Yes
- Type: string
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Result Syntax
[
'DomainArn' => '<string>',
'Url' => '<string>',
]
Result Details
Members
- DomainArn
-
- Type: string
The Amazon Resource Name (ARN) of the created domain.
- Url
-
- Type: string
The URL to the created domain.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateEdgeDeploymentPlan
$result = $client->createEdgeDeploymentPlan([/* ... */]); $promise = $client->createEdgeDeploymentPlanAsync([/* ... */]);
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
Parameter Syntax
$result = $client->createEdgeDeploymentPlan([
'DeviceFleetName' => '<string>', // REQUIRED
'EdgeDeploymentPlanName' => '<string>', // REQUIRED
'ModelConfigs' => [ // REQUIRED
[
'EdgePackagingJobName' => '<string>', // REQUIRED
'ModelHandle' => '<string>', // REQUIRED
],
// ...
],
'Stages' => [
[
'DeploymentConfig' => [
'FailureHandlingPolicy' => 'ROLLBACK_ON_FAILURE|DO_NOTHING', // REQUIRED
],
'DeviceSelectionConfig' => [ // REQUIRED
'DeviceNameContains' => '<string>',
'DeviceNames' => ['<string>', ...],
'DeviceSubsetType' => 'PERCENTAGE|SELECTION|NAMECONTAINS', // REQUIRED
'Percentage' => <integer>,
],
'StageName' => '<string>', // REQUIRED
],
// ...
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- DeviceFleetName
-
- Required: Yes
- Type: string
The device fleet used for this edge deployment plan.
- EdgeDeploymentPlanName
-
- Required: Yes
- Type: string
The name of the edge deployment plan.
- ModelConfigs
-
- Required: Yes
- Type: Array of EdgeDeploymentModelConfig structures
List of models associated with the edge deployment plan.
- Stages
-
- Type: Array of DeploymentStage structures
List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.
- Tags
-
- Type: Array of Tag structures
List of tags with which to tag the edge deployment plan.
Result Syntax
[
'EdgeDeploymentPlanArn' => '<string>',
]
Result Details
Members
- EdgeDeploymentPlanArn
-
- Required: Yes
- Type: string
The ARN of the edge deployment plan.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateEdgeDeploymentStage
$result = $client->createEdgeDeploymentStage([/* ... */]); $promise = $client->createEdgeDeploymentStageAsync([/* ... */]);
Creates a new stage in an existing edge deployment plan.
Parameter Syntax
$result = $client->createEdgeDeploymentStage([
'EdgeDeploymentPlanName' => '<string>', // REQUIRED
'Stages' => [ // REQUIRED
[
'DeploymentConfig' => [
'FailureHandlingPolicy' => 'ROLLBACK_ON_FAILURE|DO_NOTHING', // REQUIRED
],
'DeviceSelectionConfig' => [ // REQUIRED
'DeviceNameContains' => '<string>',
'DeviceNames' => ['<string>', ...],
'DeviceSubsetType' => 'PERCENTAGE|SELECTION|NAMECONTAINS', // REQUIRED
'Percentage' => <integer>,
],
'StageName' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- EdgeDeploymentPlanName
-
- Required: Yes
- Type: string
The name of the edge deployment plan.
- Stages
-
- Required: Yes
- Type: Array of DeploymentStage structures
List of stages to be added to the edge deployment plan.
Result Syntax
[]
Result Details
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateEdgePackagingJob
$result = $client->createEdgePackagingJob([/* ... */]); $promise = $client->createEdgePackagingJobAsync([/* ... */]);
Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
Parameter Syntax
$result = $client->createEdgePackagingJob([
'CompilationJobName' => '<string>', // REQUIRED
'EdgePackagingJobName' => '<string>', // REQUIRED
'ModelName' => '<string>', // REQUIRED
'ModelVersion' => '<string>', // REQUIRED
'OutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'PresetDeploymentConfig' => '<string>',
'PresetDeploymentType' => 'GreengrassV2Component',
'S3OutputLocation' => '<string>', // REQUIRED
],
'ResourceKey' => '<string>',
'RoleArn' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- CompilationJobName
-
- Required: Yes
- Type: string
The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
- EdgePackagingJobName
-
- Required: Yes
- Type: string
The name of the edge packaging job.
- ModelName
-
- Required: Yes
- Type: string
The name of the model.
- ModelVersion
-
- Required: Yes
- Type: string
The version of the model.
- OutputConfig
-
- Required: Yes
- Type: EdgeOutputConfig structure
Provides information about the output location for the packaged model.
- ResourceKey
-
- Type: string
The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
- Tags
-
- Type: Array of Tag structures
Creates tags for the packaging job.
Result Syntax
[]
Result Details
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateEndpoint
$result = $client->createEndpoint([/* ... */]); $promise = $client->createEndpointAsync([/* ... */]);
Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
-
Option 1: For a full SageMaker access, search and attach the
AmazonSageMakerFullAccesspolicy. -
Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]"Resource": ["arn:aws:sagemaker:region:account-id:endpoint/endpointName""arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"]For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
Parameter Syntax
$result = $client->createEndpoint([
'DeploymentConfig' => [
'AutoRollbackConfiguration' => [
'Alarms' => [
[
'AlarmName' => '<string>',
],
// ...
],
],
'BlueGreenUpdatePolicy' => [
'MaximumExecutionTimeoutInSeconds' => <integer>,
'TerminationWaitInSeconds' => <integer>,
'TrafficRoutingConfiguration' => [ // REQUIRED
'CanarySize' => [
'Type' => 'INSTANCE_COUNT|CAPACITY_PERCENT', // REQUIRED
'Value' => <integer>, // REQUIRED
],
'LinearStepSize' => [
'Type' => 'INSTANCE_COUNT|CAPACITY_PERCENT', // REQUIRED
'Value' => <integer>, // REQUIRED
],
'Type' => 'ALL_AT_ONCE|CANARY|LINEAR', // REQUIRED
'WaitIntervalInSeconds' => <integer>, // REQUIRED
],
],
'RollingUpdatePolicy' => [
'MaximumBatchSize' => [ // REQUIRED
'Type' => 'INSTANCE_COUNT|CAPACITY_PERCENT', // REQUIRED
'Value' => <integer>, // REQUIRED
],
'MaximumExecutionTimeoutInSeconds' => <integer>,
'RollbackMaximumBatchSize' => [
'Type' => 'INSTANCE_COUNT|CAPACITY_PERCENT', // REQUIRED
'Value' => <integer>, // REQUIRED
],
'WaitIntervalInSeconds' => <integer>, // REQUIRED
],
],
'EndpointConfigName' => '<string>', // REQUIRED
'EndpointName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- DeploymentConfig
-
- Type: DeploymentConfig structure
The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
- EndpointConfigName
-
- Required: Yes
- Type: string
The name of an endpoint configuration. For more information, see CreateEndpointConfig.
- EndpointName
-
- Required: Yes
- Type: string
The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in
CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint. - Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'EndpointArn' => '<string>',
]
Result Details
Members
- EndpointArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the endpoint.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateEndpointConfig
$result = $client->createEndpointConfig([/* ... */]); $promise = $client->createEndpointConfigAsync([/* ... */]);
Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
Parameter Syntax
$result = $client->createEndpointConfig([
'AsyncInferenceConfig' => [
'ClientConfig' => [
'MaxConcurrentInvocationsPerInstance' => <integer>,
],
'OutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'NotificationConfig' => [
'ErrorTopic' => '<string>',
'IncludeInferenceResponseIn' => ['<string>', ...],
'SuccessTopic' => '<string>',
],
'S3FailurePath' => '<string>',
'S3OutputPath' => '<string>',
],
],
'DataCaptureConfig' => [
'CaptureContentTypeHeader' => [
'CsvContentTypes' => ['<string>', ...],
'JsonContentTypes' => ['<string>', ...],
],
'CaptureOptions' => [ // REQUIRED
[
'CaptureMode' => 'Input|Output', // REQUIRED
],
// ...
],
'DestinationS3Uri' => '<string>', // REQUIRED
'EnableCapture' => true || false,
'InitialSamplingPercentage' => <integer>, // REQUIRED
'KmsKeyId' => '<string>',
],
'EnableNetworkIsolation' => true || false,
'EndpointConfigName' => '<string>', // REQUIRED
'ExecutionRoleArn' => '<string>',
'ExplainerConfig' => [
'ClarifyExplainerConfig' => [
'EnableExplanations' => '<string>',
'InferenceConfig' => [
'ContentTemplate' => '<string>',
'FeatureHeaders' => ['<string>', ...],
'FeatureTypes' => ['<string>', ...],
'FeaturesAttribute' => '<string>',
'LabelAttribute' => '<string>',
'LabelHeaders' => ['<string>', ...],
'LabelIndex' => <integer>,
'MaxPayloadInMB' => <integer>,
'MaxRecordCount' => <integer>,
'ProbabilityAttribute' => '<string>',
'ProbabilityIndex' => <integer>,
],
'ShapConfig' => [ // REQUIRED
'NumberOfSamples' => <integer>,
'Seed' => <integer>,
'ShapBaselineConfig' => [ // REQUIRED
'MimeType' => '<string>',
'ShapBaseline' => '<string>',
'ShapBaselineUri' => '<string>',
],
'TextConfig' => [
'Granularity' => 'token|sentence|paragraph', // REQUIRED
'Language' => 'af|sq|ar|hy|eu|bn|bg|ca|zh|hr|cs|da|nl|en|et|fi|fr|de|el|gu|he|hi|hu|is|id|ga|it|kn|ky|lv|lt|lb|mk|ml|mr|ne|nb|fa|pl|pt|ro|ru|sa|sr|tn|si|sk|sl|es|sv|tl|ta|tt|te|tr|uk|ur|yo|lij|xx', // REQUIRED
],
'UseLogit' => true || false,
],
],
],
'KmsKeyId' => '<string>',
'ProductionVariants' => [ // REQUIRED
[
'AcceleratorType' => 'ml.eia1.medium|ml.eia1.large|ml.eia1.xlarge|ml.eia2.medium|ml.eia2.large|ml.eia2.xlarge',
'ContainerStartupHealthCheckTimeoutInSeconds' => <integer>,
'CoreDumpConfig' => [
'DestinationS3Uri' => '<string>', // REQUIRED
'KmsKeyId' => '<string>',
],
'EnableSSMAccess' => true || false,
'InitialInstanceCount' => <integer>,
'InitialVariantWeight' => <float>,
'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.12xlarge|ml.m5d.24xlarge|ml.c4.large|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5d.large|ml.c5d.xlarge|ml.c5d.2xlarge|ml.c5d.4xlarge|ml.c5d.9xlarge|ml.c5d.18xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.12xlarge|ml.r5.24xlarge|ml.r5d.large|ml.r5d.xlarge|ml.r5d.2xlarge|ml.r5d.4xlarge|ml.r5d.12xlarge|ml.r5d.24xlarge|ml.inf1.xlarge|ml.inf1.2xlarge|ml.inf1.6xlarge|ml.inf1.24xlarge|ml.c6i.large|ml.c6i.xlarge|ml.c6i.2xlarge|ml.c6i.4xlarge|ml.c6i.8xlarge|ml.c6i.12xlarge|ml.c6i.16xlarge|ml.c6i.24xlarge|ml.c6i.32xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.12xlarge|ml.g5.16xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.p4d.24xlarge|ml.c7g.large|ml.c7g.xlarge|ml.c7g.2xlarge|ml.c7g.4xlarge|ml.c7g.8xlarge|ml.c7g.12xlarge|ml.c7g.16xlarge|ml.m6g.large|ml.m6g.xlarge|ml.m6g.2xlarge|ml.m6g.4xlarge|ml.m6g.8xlarge|ml.m6g.12xlarge|ml.m6g.16xlarge|ml.m6gd.large|ml.m6gd.xlarge|ml.m6gd.2xlarge|ml.m6gd.4xlarge|ml.m6gd.8xlarge|ml.m6gd.12xlarge|ml.m6gd.16xlarge|ml.c6g.large|ml.c6g.xlarge|ml.c6g.2xlarge|ml.c6g.4xlarge|ml.c6g.8xlarge|ml.c6g.12xlarge|ml.c6g.16xlarge|ml.c6gd.large|ml.c6gd.xlarge|ml.c6gd.2xlarge|ml.c6gd.4xlarge|ml.c6gd.8xlarge|ml.c6gd.12xlarge|ml.c6gd.16xlarge|ml.c6gn.large|ml.c6gn.xlarge|ml.c6gn.2xlarge|ml.c6gn.4xlarge|ml.c6gn.8xlarge|ml.c6gn.12xlarge|ml.c6gn.16xlarge|ml.r6g.large|ml.r6g.xlarge|ml.r6g.2xlarge|ml.r6g.4xlarge|ml.r6g.8xlarge|ml.r6g.12xlarge|ml.r6g.16xlarge|ml.r6gd.large|ml.r6gd.xlarge|ml.r6gd.2xlarge|ml.r6gd.4xlarge|ml.r6gd.8xlarge|ml.r6gd.12xlarge|ml.r6gd.16xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.inf2.xlarge|ml.inf2.8xlarge|ml.inf2.24xlarge|ml.inf2.48xlarge|ml.p5.48xlarge',
'ManagedInstanceScaling' => [
'MaxInstanceCount' => <integer>,
'MinInstanceCount' => <integer>,
'Status' => 'ENABLED|DISABLED',
],
'ModelDataDownloadTimeoutInSeconds' => <integer>,
'ModelName' => '<string>',
'RoutingConfig' => [
'RoutingStrategy' => 'LEAST_OUTSTANDING_REQUESTS|RANDOM', // REQUIRED
],
'ServerlessConfig' => [
'MaxConcurrency' => <integer>, // REQUIRED
'MemorySizeInMB' => <integer>, // REQUIRED
'ProvisionedConcurrency' => <integer>,
],
'VariantName' => '<string>', // REQUIRED
'VolumeSizeInGB' => <integer>,
],
// ...
],
'ShadowProductionVariants' => [
[
'AcceleratorType' => 'ml.eia1.medium|ml.eia1.large|ml.eia1.xlarge|ml.eia2.medium|ml.eia2.large|ml.eia2.xlarge',
'ContainerStartupHealthCheckTimeoutInSeconds' => <integer>,
'CoreDumpConfig' => [
'DestinationS3Uri' => '<string>', // REQUIRED
'KmsKeyId' => '<string>',
],
'EnableSSMAccess' => true || false,
'InitialInstanceCount' => <integer>,
'InitialVariantWeight' => <float>,
'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.12xlarge|ml.m5d.24xlarge|ml.c4.large|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5d.large|ml.c5d.xlarge|ml.c5d.2xlarge|ml.c5d.4xlarge|ml.c5d.9xlarge|ml.c5d.18xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.12xlarge|ml.r5.24xlarge|ml.r5d.large|ml.r5d.xlarge|ml.r5d.2xlarge|ml.r5d.4xlarge|ml.r5d.12xlarge|ml.r5d.24xlarge|ml.inf1.xlarge|ml.inf1.2xlarge|ml.inf1.6xlarge|ml.inf1.24xlarge|ml.c6i.large|ml.c6i.xlarge|ml.c6i.2xlarge|ml.c6i.4xlarge|ml.c6i.8xlarge|ml.c6i.12xlarge|ml.c6i.16xlarge|ml.c6i.24xlarge|ml.c6i.32xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.12xlarge|ml.g5.16xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.p4d.24xlarge|ml.c7g.large|ml.c7g.xlarge|ml.c7g.2xlarge|ml.c7g.4xlarge|ml.c7g.8xlarge|ml.c7g.12xlarge|ml.c7g.16xlarge|ml.m6g.large|ml.m6g.xlarge|ml.m6g.2xlarge|ml.m6g.4xlarge|ml.m6g.8xlarge|ml.m6g.12xlarge|ml.m6g.16xlarge|ml.m6gd.large|ml.m6gd.xlarge|ml.m6gd.2xlarge|ml.m6gd.4xlarge|ml.m6gd.8xlarge|ml.m6gd.12xlarge|ml.m6gd.16xlarge|ml.c6g.large|ml.c6g.xlarge|ml.c6g.2xlarge|ml.c6g.4xlarge|ml.c6g.8xlarge|ml.c6g.12xlarge|ml.c6g.16xlarge|ml.c6gd.large|ml.c6gd.xlarge|ml.c6gd.2xlarge|ml.c6gd.4xlarge|ml.c6gd.8xlarge|ml.c6gd.12xlarge|ml.c6gd.16xlarge|ml.c6gn.large|ml.c6gn.xlarge|ml.c6gn.2xlarge|ml.c6gn.4xlarge|ml.c6gn.8xlarge|ml.c6gn.12xlarge|ml.c6gn.16xlarge|ml.r6g.large|ml.r6g.xlarge|ml.r6g.2xlarge|ml.r6g.4xlarge|ml.r6g.8xlarge|ml.r6g.12xlarge|ml.r6g.16xlarge|ml.r6gd.large|ml.r6gd.xlarge|ml.r6gd.2xlarge|ml.r6gd.4xlarge|ml.r6gd.8xlarge|ml.r6gd.12xlarge|ml.r6gd.16xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.inf2.xlarge|ml.inf2.8xlarge|ml.inf2.24xlarge|ml.inf2.48xlarge|ml.p5.48xlarge',
'ManagedInstanceScaling' => [
'MaxInstanceCount' => <integer>,
'MinInstanceCount' => <integer>,
'Status' => 'ENABLED|DISABLED',
],
'ModelDataDownloadTimeoutInSeconds' => <integer>,
'ModelName' => '<string>',
'RoutingConfig' => [
'RoutingStrategy' => 'LEAST_OUTSTANDING_REQUESTS|RANDOM', // REQUIRED
],
'ServerlessConfig' => [
'MaxConcurrency' => <integer>, // REQUIRED
'MemorySizeInMB' => <integer>, // REQUIRED
'ProvisionedConcurrency' => <integer>,
],
'VariantName' => '<string>', // REQUIRED
'VolumeSizeInGB' => <integer>,
],
// ...
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
]);
Parameter Details
Members
- AsyncInferenceConfig
-
- Type: AsyncInferenceConfig structure
Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
- DataCaptureConfig
-
- Type: DataCaptureConfig structure
Configuration to control how SageMaker captures inference data.
- EnableNetworkIsolation
-
- Type: boolean
Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
- EndpointConfigName
-
- Required: Yes
- Type: string
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
- ExecutionRoleArn
-
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this action must have the
iam:PassRolepermission. - ExplainerConfig
-
- Type: ExplainerConfig structure
A member of
CreateEndpointConfigthat enables explainers. - KmsKeyId
-
- Type: string
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab -
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab -
Alias name:
alias/ExampleAlias -
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint,UpdateEndpointrequests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMSCertain 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
KmsKeyIdwhen using an instance type with local storage. If any of the models that you specify in theProductionVariantsparameter use nitro-based instances with local storage, do not specify a value for theKmsKeyIdparameter. If you specify a value forKmsKeyIdwhen using any nitro-based instances with local storage, the call toCreateEndpointConfigfails.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.
- ProductionVariants
-
- Required: Yes
- Type: Array of ProductionVariant structures
An array of
ProductionVariantobjects, one for each model that you want to host at this endpoint. - ShadowProductionVariants
-
- Type: Array of ProductionVariant structures
An array of
ProductionVariantobjects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified onProductionVariants. If you use this field, you can only specify one variant forProductionVariantsand one variant forShadowProductionVariants. - Tags
-
- Type: Array of Tag structures
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.
- VpcConfig
-
- Type: VpcConfig structure
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
Result Syntax
[
'EndpointConfigArn' => '<string>',
]
Result Details
Members
- EndpointConfigArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the endpoint configuration.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateExperiment
$result = $client->createExperiment([/* ... */]); $promise = $client->createExperimentAsync([/* ... */]);
Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
Parameter Syntax
$result = $client->createExperiment([
'Description' => '<string>',
'DisplayName' => '<string>',
'ExperimentName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- Description
-
- Type: string
The description of the experiment.
- DisplayName
-
- Type: string
The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify
DisplayName, the value inExperimentNameis displayed. - ExperimentName
-
- Required: Yes
- Type: string
The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
- Tags
-
- Type: Array of Tag structures
A list of tags to associate with the experiment. You can use Search API to search on the tags.
Result Syntax
[
'ExperimentArn' => '<string>',
]
Result Details
Members
- ExperimentArn
-
- Type: string
The Amazon Resource Name (ARN) of the experiment.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateFeatureGroup
$result = $client->createFeatureGroup([/* ... */]); $promise = $client->createFeatureGroupAsync([/* ... */]);
Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.
The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType.
You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.
Parameter Syntax
$result = $client->createFeatureGroup([
'Description' => '<string>',
'EventTimeFeatureName' => '<string>', // REQUIRED
'FeatureDefinitions' => [ // REQUIRED
[
'CollectionConfig' => [
'VectorConfig' => [
'Dimension' => <integer>, // REQUIRED
],
],
'CollectionType' => 'List|Set|Vector',
'FeatureName' => '<string>',
'FeatureType' => 'Integral|Fractional|String',
],
// ...
],
'FeatureGroupName' => '<string>', // REQUIRED
'OfflineStoreConfig' => [
'DataCatalogConfig' => [
'Catalog' => '<string>', // REQUIRED
'Database' => '<string>', // REQUIRED
'TableName' => '<string>', // REQUIRED
],
'DisableGlueTableCreation' => true || false,
'S3StorageConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'ResolvedOutputS3Uri' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'TableFormat' => 'Glue|Iceberg',
],
'OnlineStoreConfig' => [
'EnableOnlineStore' => true || false,
'SecurityConfig' => [
'KmsKeyId' => '<string>',
],
'StorageType' => 'Standard|InMemory',
'TtlDuration' => [
'Unit' => 'Seconds|Minutes|Hours|Days|Weeks',
'Value' => <integer>,
],
],
'RecordIdentifierFeatureName' => '<string>', // REQUIRED
'RoleArn' => '<string>',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- Description
-
- Type: string
A free-form description of a
FeatureGroup. - EventTimeFeatureName
-
- Required: Yes
- Type: string
The name of the feature that stores the
EventTimeof aRecordin aFeatureGroup.An
EventTimeis a point in time when a new event occurs that corresponds to the creation or update of aRecordin aFeatureGroup. AllRecordsin theFeatureGroupmust have a correspondingEventTime.An
EventTimecan be aStringorFractional.-
Fractional:EventTimefeature values must be a Unix timestamp in seconds. -
String:EventTimefeature values must be an ISO-8601 string in the format. The following formats are supportedyyyy-MM-dd'T'HH:mm:ssZandyyyy-MM-dd'T'HH:mm:ss.SSSZwhereyyyy,MM, andddrepresent the year, month, and day respectively andHH,mm,ss, and if applicable,SSSrepresent the hour, month, second and milliseconds respsectively.'T'andZare constants.
- FeatureDefinitions
-
- Required: Yes
- Type: Array of FeatureDefinition structures
A list of
Featurenames and types.NameandTypeis compulsory perFeature.Valid feature
FeatureTypes areIntegral,FractionalandString.FeatureNames cannot be any of the following:is_deleted,write_time,api_invocation_timeYou can create up to 2,500
FeatureDefinitions perFeatureGroup. - FeatureGroupName
-
- Required: Yes
- Type: string
The name of the
FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:-
Must start and end with an alphanumeric character.
-
Can only contain alphanumeric character and hyphens. Spaces are not allowed.
- OfflineStoreConfig
-
- Type: OfflineStoreConfig structure
Use this to configure an
OfflineFeatureStore. This parameter allows you to specify:-
The Amazon Simple Storage Service (Amazon S3) location of an
OfflineStore. -
A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
-
An KMS encryption key to encrypt the Amazon S3 location used for
OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent. -
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
To learn more about this parameter, see OfflineStoreConfig.
- OnlineStoreConfig
-
- Type: OnlineStoreConfig structure
You can turn the
OnlineStoreon or off by specifyingTruefor theEnableOnlineStoreflag inOnlineStoreConfig.You can also include an Amazon Web Services KMS key ID (
KMSKeyId) for at-rest encryption of theOnlineStore.The default value is
False. - RecordIdentifierFeatureName
-
- Required: Yes
- Type: string
The name of the
Featurewhose value uniquely identifies aRecorddefined in theFeatureStore. Only the latest record per identifier value will be stored in theOnlineStore.RecordIdentifierFeatureNamemust be one of feature definitions' names.You use the
RecordIdentifierFeatureNameto access data in aFeatureStore.This name:
-
Must start and end with an alphanumeric character.
-
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
- RoleArn
-
- Type: string
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the
OfflineStoreif anOfflineStoreConfigis provided. - Tags
-
- Type: Array of Tag structures
Tags used to identify
Featuresin eachFeatureGroup.
Result Syntax
[
'FeatureGroupArn' => '<string>',
]
Result Details
Members
- FeatureGroupArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the
FeatureGroup. This is a unique identifier for the feature group.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateFlowDefinition
$result = $client->createFlowDefinition([/* ... */]); $promise = $client->createFlowDefinitionAsync([/* ... */]);
Creates a flow definition.
Parameter Syntax
$result = $client->createFlowDefinition([
'FlowDefinitionName' => '<string>', // REQUIRED
'HumanLoopActivationConfig' => [
'HumanLoopActivationConditionsConfig' => [ // REQUIRED
'HumanLoopActivationConditions' => '<string>', // REQUIRED
],
],
'HumanLoopConfig' => [ // REQUIRED
'HumanTaskUiArn' => '<string>', // REQUIRED
'PublicWorkforceTaskPrice' => [
'AmountInUsd' => [
'Cents' => <integer>,
'Dollars' => <integer>,
'TenthFractionsOfACent' => <integer>,
],
],
'TaskAvailabilityLifetimeInSeconds' => <integer>,
'TaskCount' => <integer>, // REQUIRED
'TaskDescription' => '<string>', // REQUIRED
'TaskKeywords' => ['<string>', ...],
'TaskTimeLimitInSeconds' => <integer>,
'TaskTitle' => '<string>', // REQUIRED
'WorkteamArn' => '<string>', // REQUIRED
],
'HumanLoopRequestSource' => [
'AwsManagedHumanLoopRequestSource' => 'AWS/Rekognition/DetectModerationLabels/Image/V3|AWS/Textract/AnalyzeDocument/Forms/V1', // REQUIRED
],
'OutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'RoleArn' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- FlowDefinitionName
-
- Required: Yes
- Type: string
The name of your flow definition.
- HumanLoopActivationConfig
-
- Type: HumanLoopActivationConfig structure
An object containing information about the events that trigger a human workflow.
- HumanLoopConfig
-
- Required: Yes
- Type: HumanLoopConfig structure
An object containing information about the tasks the human reviewers will perform.
- HumanLoopRequestSource
-
- Type: HumanLoopRequestSource structure
Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
- OutputConfig
-
- Required: Yes
- Type: FlowDefinitionOutputConfig structure
An object containing information about where the human review results will be uploaded.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example,
arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298. - Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'FlowDefinitionArn' => '<string>',
]
Result Details
Members
- FlowDefinitionArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the flow definition you create.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateHub
$result = $client->createHub([/* ... */]); $promise = $client->createHubAsync([/* ... */]);
Create a hub.
Hub APIs are only callable through SageMaker Studio.
Parameter Syntax
$result = $client->createHub([
'HubDescription' => '<string>', // REQUIRED
'HubDisplayName' => '<string>',
'HubName' => '<string>', // REQUIRED
'HubSearchKeywords' => ['<string>', ...],
'S3StorageConfig' => [
'S3OutputPath' => '<string>',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- HubDescription
-
- Required: Yes
- Type: string
A description of the hub.
- HubDisplayName
-
- Type: string
The display name of the hub.
- HubName
-
- Required: Yes
- Type: string
The name of the hub to create.
- HubSearchKeywords
-
- Type: Array of strings
The searchable keywords for the hub.
- S3StorageConfig
-
- Type: HubS3StorageConfig structure
The Amazon S3 storage configuration for the hub.
- Tags
-
- Type: Array of Tag structures
Any tags to associate with the hub.
Result Syntax
[
'HubArn' => '<string>',
]
Result Details
Members
- HubArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the hub.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateHumanTaskUi
$result = $client->createHumanTaskUi([/* ... */]); $promise = $client->createHumanTaskUiAsync([/* ... */]);
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
Parameter Syntax
$result = $client->createHumanTaskUi([
'HumanTaskUiName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'UiTemplate' => [ // REQUIRED
'Content' => '<string>', // REQUIRED
],
]);
Parameter Details
Members
- HumanTaskUiName
-
- Required: Yes
- Type: string
The name of the user interface you are creating.
- Tags
-
- Type: Array of Tag structures
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.
- UiTemplate
-
- Required: Yes
- Type: UiTemplate structure
The Liquid template for the worker user interface.
Result Syntax
[
'HumanTaskUiArn' => '<string>',
]
Result Details
Members
- HumanTaskUiArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the human review workflow user interface you create.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateHyperParameterTuningJob
$result = $client->createHyperParameterTuningJob([/* ... */]); $promise = $client->createHyperParameterTuningJobAsync([/* ... */]);
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
Parameter Syntax
$result = $client->createHyperParameterTuningJob([
'Autotune' => [
'Mode' => 'Enabled', // REQUIRED
],
'HyperParameterTuningJobConfig' => [ // REQUIRED
'HyperParameterTuningJobObjective' => [
'MetricName' => '<string>', // REQUIRED
'Type' => 'Maximize|Minimize', // REQUIRED
],
'ParameterRanges' => [
'AutoParameters' => [
[
'Name' => '<string>', // REQUIRED
'ValueHint' => '<string>', // REQUIRED
],
// ...
],
'CategoricalParameterRanges' => [
[
'Name' => '<string>', // REQUIRED
'Values' => ['<string>', ...], // REQUIRED
],
// ...
],
'ContinuousParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
'IntegerParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
],
'RandomSeed' => <integer>,
'ResourceLimits' => [ // REQUIRED
'MaxNumberOfTrainingJobs' => <integer>,
'MaxParallelTrainingJobs' => <integer>, // REQUIRED
'MaxRuntimeInSeconds' => <integer>,
],
'Strategy' => 'Bayesian|Random|Hyperband|Grid', // REQUIRED
'StrategyConfig' => [
'HyperbandStrategyConfig' => [
'MaxResource' => <integer>,
'MinResource' => <integer>,
],
],
'TrainingJobEarlyStoppingType' => 'Off|Auto',
'TuningJobCompletionCriteria' => [
'BestObjectiveNotImproving' => [
'MaxNumberOfTrainingJobsNotImproving' => <integer>,
],
'ConvergenceDetected' => [
'CompleteOnConvergence' => 'Disabled|Enabled',
],
'TargetObjectiveMetricValue' => <float>,
],
],
'HyperParameterTuningJobName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TrainingJobDefinition' => [
'AlgorithmSpecification' => [ // REQUIRED
'AlgorithmName' => '<string>',
'MetricDefinitions' => [
[
'Name' => '<string>', // REQUIRED
'Regex' => '<string>', // REQUIRED
],
// ...
],
'TrainingImage' => '<string>',
'TrainingInputMode' => 'Pipe|File|FastFile', // REQUIRED
],
'CheckpointConfig' => [
'LocalPath' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'DefinitionName' => '<string>',
'EnableInterContainerTrafficEncryption' => true || false,
'EnableManagedSpotTraining' => true || false,
'EnableNetworkIsolation' => true || false,
'Environment' => ['<string>', ...],
'HyperParameterRanges' => [
'AutoParameters' => [
[
'Name' => '<string>', // REQUIRED
'ValueHint' => '<string>', // REQUIRED
],
// ...
],
'CategoricalParameterRanges' => [
[
'Name' => '<string>', // REQUIRED
'Values' => ['<string>', ...], // REQUIRED
],
// ...
],
'ContinuousParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
'IntegerParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
],
'HyperParameterTuningResourceConfig' => [
'AllocationStrategy' => 'Prioritized',
'InstanceConfigs' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
'VolumeSizeInGB' => <integer>, // REQUIRED
],
// ...
],
'InstanceCount' => <integer>,
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>,
],
'InputDataConfig' => [
[
'ChannelName' => '<string>', // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'FileSystemDataSource' => [
'DirectoryPath' => '<string>', // REQUIRED
'FileSystemAccessMode' => 'rw|ro', // REQUIRED
'FileSystemId' => '<string>', // REQUIRED
'FileSystemType' => 'EFS|FSxLustre', // REQUIRED
],
'S3DataSource' => [
'AttributeNames' => ['<string>', ...],
'InstanceGroupNames' => ['<string>', ...],
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'InputMode' => 'Pipe|File|FastFile',
'RecordWrapperType' => 'None|RecordIO',
'ShuffleConfig' => [
'Seed' => <integer>, // REQUIRED
],
],
// ...
],
'OutputDataConfig' => [ // REQUIRED
'CompressionType' => 'GZIP|NONE',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'ResourceConfig' => [
'InstanceCount' => <integer>,
'InstanceGroups' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceGroupName' => '<string>', // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
],
// ...
],
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'KeepAlivePeriodInSeconds' => <integer>,
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
'RetryStrategy' => [
'MaximumRetryAttempts' => <integer>, // REQUIRED
],
'RoleArn' => '<string>', // REQUIRED
'StaticHyperParameters' => ['<string>', ...],
'StoppingCondition' => [ // REQUIRED
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'TuningObjective' => [
'MetricName' => '<string>', // REQUIRED
'Type' => 'Maximize|Minimize', // REQUIRED
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'TrainingJobDefinitions' => [
[
'AlgorithmSpecification' => [ // REQUIRED
'AlgorithmName' => '<string>',
'MetricDefinitions' => [
[
'Name' => '<string>', // REQUIRED
'Regex' => '<string>', // REQUIRED
],
// ...
],
'TrainingImage' => '<string>',
'TrainingInputMode' => 'Pipe|File|FastFile', // REQUIRED
],
'CheckpointConfig' => [
'LocalPath' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'DefinitionName' => '<string>',
'EnableInterContainerTrafficEncryption' => true || false,
'EnableManagedSpotTraining' => true || false,
'EnableNetworkIsolation' => true || false,
'Environment' => ['<string>', ...],
'HyperParameterRanges' => [
'AutoParameters' => [
[
'Name' => '<string>', // REQUIRED
'ValueHint' => '<string>', // REQUIRED
],
// ...
],
'CategoricalParameterRanges' => [
[
'Name' => '<string>', // REQUIRED
'Values' => ['<string>', ...], // REQUIRED
],
// ...
],
'ContinuousParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
'IntegerParameterRanges' => [
[
'MaxValue' => '<string>', // REQUIRED
'MinValue' => '<string>', // REQUIRED
'Name' => '<string>', // REQUIRED
'ScalingType' => 'Auto|Linear|Logarithmic|ReverseLogarithmic',
],
// ...
],
],
'HyperParameterTuningResourceConfig' => [
'AllocationStrategy' => 'Prioritized',
'InstanceConfigs' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
'VolumeSizeInGB' => <integer>, // REQUIRED
],
// ...
],
'InstanceCount' => <integer>,
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>,
],
'InputDataConfig' => [
[
'ChannelName' => '<string>', // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'FileSystemDataSource' => [
'DirectoryPath' => '<string>', // REQUIRED
'FileSystemAccessMode' => 'rw|ro', // REQUIRED
'FileSystemId' => '<string>', // REQUIRED
'FileSystemType' => 'EFS|FSxLustre', // REQUIRED
],
'S3DataSource' => [
'AttributeNames' => ['<string>', ...],
'InstanceGroupNames' => ['<string>', ...],
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'InputMode' => 'Pipe|File|FastFile',
'RecordWrapperType' => 'None|RecordIO',
'ShuffleConfig' => [
'Seed' => <integer>, // REQUIRED
],
],
// ...
],
'OutputDataConfig' => [ // REQUIRED
'CompressionType' => 'GZIP|NONE',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'ResourceConfig' => [
'InstanceCount' => <integer>,
'InstanceGroups' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceGroupName' => '<string>', // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
],
// ...
],
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'KeepAlivePeriodInSeconds' => <integer>,
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
'RetryStrategy' => [
'MaximumRetryAttempts' => <integer>, // REQUIRED
],
'RoleArn' => '<string>', // REQUIRED
'StaticHyperParameters' => ['<string>', ...],
'StoppingCondition' => [ // REQUIRED
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'TuningObjective' => [
'MetricName' => '<string>', // REQUIRED
'Type' => 'Maximize|Minimize', // REQUIRED
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
// ...
],
'WarmStartConfig' => [
'ParentHyperParameterTuningJobs' => [ // REQUIRED
[
'HyperParameterTuningJobName' => '<string>',
],
// ...
],
'WarmStartType' => 'IdenticalDataAndAlgorithm|TransferLearning', // REQUIRED
],
]);
Parameter Details
Members
- Autotune
-
- Type: Autotune structure
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
-
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
-
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
-
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
-
RetryStrategy: The number of times to retry a training job.
-
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
-
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
- HyperParameterTuningJobConfig
-
- Required: Yes
- Type: HyperParameterTuningJobConfig structure
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.
- HyperParameterTuningJobName
-
- Required: Yes
- Type: string
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
- Tags
-
- Type: Array of Tag structures
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.
- TrainingJobDefinition
-
- Type: HyperParameterTrainingJobDefinition structure
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.
- TrainingJobDefinitions
-
- Type: Array of HyperParameterTrainingJobDefinition structures
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
- WarmStartConfig
-
- Type: HyperParameterTuningJobWarmStartConfig structure
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_ALGORITHMas theWarmStartTypevalue 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.
Result Syntax
[
'HyperParameterTuningJobArn' => '<string>',
]
Result Details
Members
- HyperParameterTuningJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateImage
$result = $client->createImage([/* ... */]); $promise = $client->createImageAsync([/* ... */]);
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image.
Parameter Syntax
$result = $client->createImage([
'Description' => '<string>',
'DisplayName' => '<string>',
'ImageName' => '<string>', // REQUIRED
'RoleArn' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- Description
-
- Type: string
The description of the image.
- DisplayName
-
- Type: string
The display name of the image. If not provided,
ImageNameis displayed. - ImageName
-
- Required: Yes
- Type: string
The name of the image. Must be unique to your account.
- RoleArn
-
- Required: Yes
- Type: string
The ARN of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the image.
Result Syntax
[
'ImageArn' => '<string>',
]
Result Details
Members
- ImageArn
-
- Type: string
The ARN of the image.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateImageVersion
$result = $client->createImageVersion([/* ... */]); $promise = $client->createImageVersionAsync([/* ... */]);
Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.
Parameter Syntax
$result = $client->createImageVersion([
'Aliases' => ['<string>', ...],
'BaseImage' => '<string>', // REQUIRED
'ClientToken' => '<string>', // REQUIRED
'Horovod' => true || false,
'ImageName' => '<string>', // REQUIRED
'JobType' => 'TRAINING|INFERENCE|NOTEBOOK_KERNEL',
'MLFramework' => '<string>',
'Processor' => 'CPU|GPU',
'ProgrammingLang' => '<string>',
'ReleaseNotes' => '<string>',
'VendorGuidance' => 'NOT_PROVIDED|STABLE|TO_BE_ARCHIVED|ARCHIVED',
]);
Parameter Details
Members
- Aliases
-
- Type: Array of strings
A list of aliases created with the image version.
- BaseImage
-
- Required: Yes
- Type: string
The registry path of the container image to use as the starting point for this version. The path is an Amazon Elastic Container Registry (ECR) URI in the following format:
<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]> - ClientToken
-
- Required: Yes
- Type: string
A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
- Horovod
-
- Type: boolean
Indicates Horovod compatibility.
- ImageName
-
- Required: Yes
- Type: string
The
ImageNameof theImageto create a version of. - JobType
-
- Type: string
Indicates SageMaker job type compatibility.
-
TRAINING: The image version is compatible with SageMaker training jobs. -
INFERENCE: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.
- MLFramework
-
- Type: string
The machine learning framework vended in the image version.
- Processor
-
- Type: string
Indicates CPU or GPU compatibility.
-
CPU: The image version is compatible with CPU. -
GPU: The image version is compatible with GPU.
- ProgrammingLang
-
- Type: string
The supported programming language and its version.
- ReleaseNotes
-
- Type: string
The maintainer description of the image version.
- VendorGuidance
-
- Type: string
The stability of the image version, specified by the maintainer.
-
NOT_PROVIDED: The maintainers did not provide a status for image version stability. -
STABLE: The image version is stable. -
TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
Result Syntax
[
'ImageVersionArn' => '<string>',
]
Result Details
Members
- ImageVersionArn
-
- Type: string
The ARN of the image version.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being access is not found.
CreateInferenceComponent
$result = $client->createInferenceComponent([/* ... */]); $promise = $client->createInferenceComponentAsync([/* ... */]);
Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
Parameter Syntax
$result = $client->createInferenceComponent([
'EndpointName' => '<string>', // REQUIRED
'InferenceComponentName' => '<string>', // REQUIRED
'RuntimeConfig' => [ // REQUIRED
'CopyCount' => <integer>, // REQUIRED
],
'Specification' => [ // REQUIRED
'ComputeResourceRequirements' => [ // REQUIRED
'MaxMemoryRequiredInMb' => <integer>,
'MinMemoryRequiredInMb' => <integer>, // REQUIRED
'NumberOfAcceleratorDevicesRequired' => <float>,
'NumberOfCpuCoresRequired' => <float>,
],
'Container' => [
'ArtifactUrl' => '<string>',
'Environment' => ['<string>', ...],
'Image' => '<string>',
],
'ModelName' => '<string>',
'StartupParameters' => [
'ContainerStartupHealthCheckTimeoutInSeconds' => <integer>,
'ModelDataDownloadTimeoutInSeconds' => <integer>,
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VariantName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- EndpointName
-
- Required: Yes
- Type: string
The name of an existing endpoint where you host the inference component.
- InferenceComponentName
-
- Required: Yes
- Type: string
A unique name to assign to the inference component.
- RuntimeConfig
-
- Required: Yes
- Type: InferenceComponentRuntimeConfig structure
Runtime settings for a model that is deployed with an inference component.
- Specification
-
- Required: Yes
- Type: InferenceComponentSpecification structure
Details about the resources to deploy with this inference component, including the model, container, and compute resources.
- Tags
-
- Type: Array of Tag structures
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.
- VariantName
-
- Required: Yes
- Type: string
The name of an existing production variant where you host the inference component.
Result Syntax
[
'InferenceComponentArn' => '<string>',
]
Result Details
Members
- InferenceComponentArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the inference component.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateInferenceExperiment
$result = $client->createInferenceExperiment([/* ... */]); $promise = $client->createInferenceExperimentAsync([/* ... */]);
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
Parameter Syntax
$result = $client->createInferenceExperiment([
'DataStorageConfig' => [
'ContentType' => [
'CsvContentTypes' => ['<string>', ...],
'JsonContentTypes' => ['<string>', ...],
],
'Destination' => '<string>', // REQUIRED
'KmsKey' => '<string>',
],
'Description' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'KmsKey' => '<string>',
'ModelVariants' => [ // REQUIRED
[
'InfrastructureConfig' => [ // REQUIRED
'InfrastructureType' => 'RealTimeInference', // REQUIRED
'RealTimeInferenceConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5d.xlarge|ml.c5d.2xlarge|ml.c5d.4xlarge|ml.c5d.9xlarge|ml.c5d.18xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.inf1.xlarge|ml.inf1.2xlarge|ml.inf1.6xlarge|ml.inf1.24xlarge|ml.p4d.24xlarge|ml.p4de.24xlarge', // REQUIRED
],
],
'ModelName' => '<string>', // REQUIRED
'VariantName' => '<string>', // REQUIRED
],
// ...
],
'Name' => '<string>', // REQUIRED
'RoleArn' => '<string>', // REQUIRED
'Schedule' => [
'EndTime' => <integer || string || DateTime>,
'StartTime' => <integer || string || DateTime>,
],
'ShadowModeConfig' => [ // REQUIRED
'ShadowModelVariants' => [ // REQUIRED
[
'SamplingPercentage' => <integer>, // REQUIRED
'ShadowModelVariantName' => '<string>', // REQUIRED
],
// ...
],
'SourceModelVariantName' => '<string>', // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'Type' => 'ShadowMode', // REQUIRED
]);
Parameter Details
Members
- DataStorageConfig
-
- Type: InferenceExperimentDataStorageConfig structure
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
- Description
-
- Type: string
A description for the inference experiment.
- EndpointName
-
- Required: Yes
- Type: string
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
- KmsKey
-
- Type: string
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKeycan 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 forOutputDataConfig. If you use a bucket policy with ans3:PutObjectpermission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryptionto"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
CreateEndpointandUpdateEndpointrequests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide. - ModelVariants
-
- Required: Yes
- Type: Array of ModelVariantConfig structures
An array of
ModelVariantConfigobjects. There is one for each variant in the inference experiment. EachModelVariantConfigobject in the array describes the infrastructure configuration for the corresponding variant. - Name
-
- Required: Yes
- Type: string
The name for the inference experiment.
- RoleArn
-
- Required: Yes
- Type: string
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
- Schedule
-
- Type: InferenceExperimentSchedule structure
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
- ShadowModeConfig
-
- Required: Yes
- Type: ShadowModeConfig structure
The configuration of
ShadowModeinference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates. - Tags
-
- Type: Array of Tag structures
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
- Type
-
- Required: Yes
- Type: string
The type of the inference experiment that you want to run. The following types of experiments are possible:
-
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
Result Syntax
[
'InferenceExperimentArn' => '<string>',
]
Result Details
Members
- InferenceExperimentArn
-
- Required: Yes
- Type: string
The ARN for your inference experiment.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateInferenceRecommendationsJob
$result = $client->createInferenceRecommendationsJob([/* ... */]); $promise = $client->createInferenceRecommendationsJobAsync([/* ... */]);
Starts a recommendation job. You can create either an instance recommendation or load test job.
Parameter Syntax
$result = $client->createInferenceRecommendationsJob([
'InputConfig' => [ // REQUIRED
'ContainerConfig' => [
'DataInputConfig' => '<string>',
'Domain' => '<string>',
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'NearestModelName' => '<string>',
'PayloadConfig' => [
'SamplePayloadUrl' => '<string>',
'SupportedContentTypes' => ['<string>', ...],
],
'SupportedEndpointType' => 'RealTime|Serverless',
'SupportedInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'Task' => '<string>',
],
'EndpointConfigurations' => [
[
'EnvironmentParameterRanges' => [
'CategoricalParameterRanges' => [
[
'Name' => '<string>', // REQUIRED
'Value' => ['<string>', ...], // REQUIRED
],
// ...
],
],
'InferenceSpecificationName' => '<string>',
'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.12xlarge|ml.m5d.24xlarge|ml.c4.large|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5d.large|ml.c5d.xlarge|ml.c5d.2xlarge|ml.c5d.4xlarge|ml.c5d.9xlarge|ml.c5d.18xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.12xlarge|ml.r5.24xlarge|ml.r5d.large|ml.r5d.xlarge|ml.r5d.2xlarge|ml.r5d.4xlarge|ml.r5d.12xlarge|ml.r5d.24xlarge|ml.inf1.xlarge|ml.inf1.2xlarge|ml.inf1.6xlarge|ml.inf1.24xlarge|ml.c6i.large|ml.c6i.xlarge|ml.c6i.2xlarge|ml.c6i.4xlarge|ml.c6i.8xlarge|ml.c6i.12xlarge|ml.c6i.16xlarge|ml.c6i.24xlarge|ml.c6i.32xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.12xlarge|ml.g5.16xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.p4d.24xlarge|ml.c7g.large|ml.c7g.xlarge|ml.c7g.2xlarge|ml.c7g.4xlarge|ml.c7g.8xlarge|ml.c7g.12xlarge|ml.c7g.16xlarge|ml.m6g.large|ml.m6g.xlarge|ml.m6g.2xlarge|ml.m6g.4xlarge|ml.m6g.8xlarge|ml.m6g.12xlarge|ml.m6g.16xlarge|ml.m6gd.large|ml.m6gd.xlarge|ml.m6gd.2xlarge|ml.m6gd.4xlarge|ml.m6gd.8xlarge|ml.m6gd.12xlarge|ml.m6gd.16xlarge|ml.c6g.large|ml.c6g.xlarge|ml.c6g.2xlarge|ml.c6g.4xlarge|ml.c6g.8xlarge|ml.c6g.12xlarge|ml.c6g.16xlarge|ml.c6gd.large|ml.c6gd.xlarge|ml.c6gd.2xlarge|ml.c6gd.4xlarge|ml.c6gd.8xlarge|ml.c6gd.12xlarge|ml.c6gd.16xlarge|ml.c6gn.large|ml.c6gn.xlarge|ml.c6gn.2xlarge|ml.c6gn.4xlarge|ml.c6gn.8xlarge|ml.c6gn.12xlarge|ml.c6gn.16xlarge|ml.r6g.large|ml.r6g.xlarge|ml.r6g.2xlarge|ml.r6g.4xlarge|ml.r6g.8xlarge|ml.r6g.12xlarge|ml.r6g.16xlarge|ml.r6gd.large|ml.r6gd.xlarge|ml.r6gd.2xlarge|ml.r6gd.4xlarge|ml.r6gd.8xlarge|ml.r6gd.12xlarge|ml.r6gd.16xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.inf2.xlarge|ml.inf2.8xlarge|ml.inf2.24xlarge|ml.inf2.48xlarge|ml.p5.48xlarge',
'ServerlessConfig' => [
'MaxConcurrency' => <integer>, // REQUIRED
'MemorySizeInMB' => <integer>, // REQUIRED
'ProvisionedConcurrency' => <integer>,
],
],
// ...
],
'Endpoints' => [
[
'EndpointName' => '<string>', // REQUIRED
],
// ...
],
'JobDurationInSeconds' => <integer>,
'ModelName' => '<string>',
'ModelPackageVersionArn' => '<string>',
'ResourceLimit' => [
'MaxNumberOfTests' => <integer>,
'MaxParallelOfTests' => <integer>,
],
'TrafficPattern' => [
'Phases' => [
[
'DurationInSeconds' => <integer>,
'InitialNumberOfUsers' => <integer>,
'SpawnRate' => <integer>,
],
// ...
],
'Stairs' => [
'DurationInSeconds' => <integer>,
'NumberOfSteps' => <integer>,
'UsersPerStep' => <integer>,
],
'TrafficType' => 'PHASES|STAIRS',
],
'VolumeKmsKeyId' => '<string>',
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'JobDescription' => '<string>',
'JobName' => '<string>', // REQUIRED
'JobType' => 'Default|Advanced', // REQUIRED
'OutputConfig' => [
'CompiledOutputConfig' => [
'S3OutputUri' => '<string>',
],
'KmsKeyId' => '<string>',
],
'RoleArn' => '<string>', // REQUIRED
'StoppingConditions' => [
'FlatInvocations' => 'Continue|Stop',
'MaxInvocations' => <integer>,
'ModelLatencyThresholds' => [
[
'Percentile' => '<string>',
'ValueInMilliseconds' => <integer>,
],
// ...
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- InputConfig
-
- Required: Yes
- Type: RecommendationJobInputConfig structure
Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
- JobDescription
-
- Type: string
Description of the recommendation job.
- JobName
-
- Required: Yes
- Type: string
A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.
- JobType
-
- Required: Yes
- Type: string
Defines the type of recommendation job. Specify
Defaultto initiate an instance recommendation andAdvancedto initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT) job. - OutputConfig
-
- Type: RecommendationJobOutputConfig structure
Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
- StoppingConditions
-
- Type: RecommendationJobStoppingConditions structure
A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
- Tags
-
- Type: Array of Tag structures
The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
Result Syntax
[
'JobArn' => '<string>',
]
Result Details
Members
- JobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the recommendation job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateLabelingJob
$result = $client->createLabelingJob([/* ... */]); $promise = $client->createLabelingJobAsync([/* ... */]);
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
-
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
-
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
-
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
Parameter Syntax
$result = $client->createLabelingJob([
'HumanTaskConfig' => [ // REQUIRED
'AnnotationConsolidationConfig' => [ // REQUIRED
'AnnotationConsolidationLambdaArn' => '<string>', // REQUIRED
],
'MaxConcurrentTaskCount' => <integer>,
'NumberOfHumanWorkersPerDataObject' => <integer>, // REQUIRED
'PreHumanTaskLambdaArn' => '<string>', // REQUIRED
'PublicWorkforceTaskPrice' => [
'AmountInUsd' => [
'Cents' => <integer>,
'Dollars' => <integer>,
'TenthFractionsOfACent' => <integer>,
],
],
'TaskAvailabilityLifetimeInSeconds' => <integer>,
'TaskDescription' => '<string>', // REQUIRED
'TaskKeywords' => ['<string>', ...],
'TaskTimeLimitInSeconds' => <integer>, // REQUIRED
'TaskTitle' => '<string>', // REQUIRED
'UiConfig' => [ // REQUIRED
'HumanTaskUiArn' => '<string>',
'UiTemplateS3Uri' => '<string>',
],
'WorkteamArn' => '<string>', // REQUIRED
],
'InputConfig' => [ // REQUIRED
'DataAttributes' => [
'ContentClassifiers' => ['<string>', ...],
],
'DataSource' => [ // REQUIRED
'S3DataSource' => [
'ManifestS3Uri' => '<string>', // REQUIRED
],
'SnsDataSource' => [
'SnsTopicArn' => '<string>', // REQUIRED
],
],
],
'LabelAttributeName' => '<string>', // REQUIRED
'LabelCategoryConfigS3Uri' => '<string>',
'LabelingJobAlgorithmsConfig' => [
'InitialActiveLearningModelArn' => '<string>',
'LabelingJobAlgorithmSpecificationArn' => '<string>', // REQUIRED
'LabelingJobResourceConfig' => [
'VolumeKmsKeyId' => '<string>',
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
],
'LabelingJobName' => '<string>', // REQUIRED
'OutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
'SnsTopicArn' => '<string>',
],
'RoleArn' => '<string>', // REQUIRED
'StoppingConditions' => [
'MaxHumanLabeledObjectCount' => <integer>,
'MaxPercentageOfInputDatasetLabeled' => <integer>,
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- HumanTaskConfig
-
- Required: Yes
- Type: HumanTaskConfig structure
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
- InputConfig
-
- Required: Yes
- Type: LabelingJobInputConfig structure
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:
S3DataSourceorSnsDataSource.-
Use
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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
ContentClassifiersto specify that your data is free of personally identifiable information and adult content. - LabelAttributeName
-
- Required: Yes
- Type: string
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The
LabelAttributeNamemust 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
LabelAttributeNamethan 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. - LabelCategoryConfigS3Uri
-
- Type: string
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to
"labels", you must provide worker instructions in the label category configuration file using the"instructions"parameter:"instructions": {"shortInstruction":"<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_nwith 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
- LabelingJobAlgorithmsConfig
-
- Type: LabelingJobAlgorithmsConfig structure
Configures the information required to perform automated data labeling.
- LabelingJobName
-
- Required: Yes
- Type: string
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region.
LabelingJobNameis not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth. - OutputConfig
-
- Required: Yes
- Type: LabelingJobOutputConfig structure
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.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
- StoppingConditions
-
- Type: LabelingJobStoppingConditions structure
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.
- Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'LabelingJobArn' => '<string>',
]
Result Details
Members
- LabelingJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateModel
$result = $client->createModel([/* ... */]); $promise = $client->createModelAsync([/* ... */]);
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
Parameter Syntax
$result = $client->createModel([
'Containers' => [
[
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Image' => '<string>',
'ImageConfig' => [
'RepositoryAccessMode' => 'Platform|Vpc', // REQUIRED
'RepositoryAuthConfig' => [
'RepositoryCredentialsProviderArn' => '<string>', // REQUIRED
],
],
'InferenceSpecificationName' => '<string>',
'Mode' => 'SingleModel|MultiModel',
'ModelDataSource' => [
'S3DataSource' => [ // REQUIRED
'CompressionType' => 'None|Gzip', // REQUIRED
'ModelAccessConfig' => [
'AcceptEula' => true || false, // REQUIRED
],
'S3DataType' => 'S3Prefix|S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelDataUrl' => '<string>',
'ModelPackageName' => '<string>',
'MultiModelConfig' => [
'ModelCacheSetting' => 'Enabled|Disabled',
],
],
// ...
],
'EnableNetworkIsolation' => true || false,
'ExecutionRoleArn' => '<string>',
'InferenceExecutionConfig' => [
'Mode' => 'Serial|Direct', // REQUIRED
],
'ModelName' => '<string>', // REQUIRED
'PrimaryContainer' => [
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Image' => '<string>',
'ImageConfig' => [
'RepositoryAccessMode' => 'Platform|Vpc', // REQUIRED
'RepositoryAuthConfig' => [
'RepositoryCredentialsProviderArn' => '<string>', // REQUIRED
],
],
'InferenceSpecificationName' => '<string>',
'Mode' => 'SingleModel|MultiModel',
'ModelDataSource' => [
'S3DataSource' => [ // REQUIRED
'CompressionType' => 'None|Gzip', // REQUIRED
'ModelAccessConfig' => [
'AcceptEula' => true || false, // REQUIRED
],
'S3DataType' => 'S3Prefix|S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelDataUrl' => '<string>',
'ModelPackageName' => '<string>',
'MultiModelConfig' => [
'ModelCacheSetting' => 'Enabled|Disabled',
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
]);
Parameter Details
Members
- Containers
-
- Type: Array of ContainerDefinition structures
Specifies the containers in the inference pipeline.
- EnableNetworkIsolation
-
- Type: boolean
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
- ExecutionRoleArn
-
- Type: string
The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the
iam:PassRolepermission. - InferenceExecutionConfig
-
- Type: InferenceExecutionConfig structure
Specifies details of how containers in a multi-container endpoint are called.
- ModelName
-
- Required: Yes
- Type: string
The name of the new model.
- PrimaryContainer
-
- Type: ContainerDefinition structure
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.
- Tags
-
- Type: Array of Tag structures
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.
- VpcConfig
-
- Type: VpcConfig structure
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.
VpcConfigis 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.
Result Syntax
[
'ModelArn' => '<string>',
]
Result Details
Members
- ModelArn
-
- Required: Yes
- Type: string
The ARN of the model created in SageMaker.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateModelBiasJobDefinition
$result = $client->createModelBiasJobDefinition([/* ... */]); $promise = $client->createModelBiasJobDefinitionAsync([/* ... */]);
Creates the definition for a model bias job.
Parameter Syntax
$result = $client->createModelBiasJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
'JobResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'ModelBiasAppSpecification' => [ // REQUIRED
'ConfigUri' => '<string>', // REQUIRED
'Environment' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
],
'ModelBiasBaselineConfig' => [
'BaseliningJobName' => '<string>',
'ConstraintsResource' => [
'S3Uri' => '<string>',
],
],
'ModelBiasJobInput' => [ // REQUIRED
'BatchTransformInput' => [
'DataCapturedDestinationS3Uri' => '<string>', // REQUIRED
'DatasetFormat' => [ // REQUIRED
'Csv' => [
'Header' => true || false,
],
'Json' => [
'Line' => true || false,
],
'Parquet' => [
],
],
'EndTimeOffset' => '<string>',
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'EndpointInput' => [
'EndTimeOffset' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'GroundTruthS3Input' => [ // REQUIRED
'S3Uri' => '<string>',
],
],
'ModelBiasJobOutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'MonitoringOutputs' => [ // REQUIRED
[
'S3Output' => [ // REQUIRED
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
- JobResources
-
- Required: Yes
- Type: MonitoringResources structure
Identifies the resources to deploy for a monitoring job.
- ModelBiasAppSpecification
-
- Required: Yes
- Type: ModelBiasAppSpecification structure
Configures the model bias job to run a specified Docker container image.
- ModelBiasBaselineConfig
-
- Type: ModelBiasBaselineConfig structure
The baseline configuration for a model bias job.
- ModelBiasJobInput
-
- Required: Yes
- Type: ModelBiasJobInput structure
Inputs for the model bias job.
- ModelBiasJobOutputConfig
-
- Required: Yes
- Type: MonitoringOutputConfig structure
The output configuration for monitoring jobs.
- NetworkConfig
-
- Type: MonitoringNetworkConfig structure
Networking options for a model bias job.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- StoppingCondition
-
- Type: MonitoringStoppingCondition structure
A time limit for how long the monitoring job is allowed to run before stopping.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'JobDefinitionArn' => '<string>',
]
Result Details
Members
- JobDefinitionArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the model bias job.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateModelCard
$result = $client->createModelCard([/* ... */]); $promise = $client->createModelCardAsync([/* ... */]);
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
Parameter Syntax
$result = $client->createModelCard([
'Content' => '<string>', // REQUIRED
'ModelCardName' => '<string>', // REQUIRED
'ModelCardStatus' => 'Draft|PendingReview|Approved|Archived', // REQUIRED
'SecurityConfig' => [
'KmsKeyId' => '<string>',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- Content
-
- Required: Yes
- Type: string
The content of the model card. Content must be in model card JSON schema and provided as a string.
- ModelCardName
-
- Required: Yes
- Type: string
The unique name of the model card.
- ModelCardStatus
-
- Required: Yes
- Type: string
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft: The model card is a work in progress. -
PendingReview: The model card is pending review. -
Approved: The model card is approved. -
Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.
- SecurityConfig
-
- Type: ModelCardSecurityConfig structure
An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.
- Tags
-
- Type: Array of Tag structures
Key-value pairs used to manage metadata for model cards.
Result Syntax
[
'ModelCardArn' => '<string>',
]
Result Details
Members
- ModelCardArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the successfully created model card.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
CreateModelCardExportJob
$result = $client->createModelCardExportJob([/* ... */]); $promise = $client->createModelCardExportJobAsync([/* ... */]);
Creates an Amazon SageMaker Model Card export job.
Parameter Syntax
$result = $client->createModelCardExportJob([
'ModelCardExportJobName' => '<string>', // REQUIRED
'ModelCardName' => '<string>', // REQUIRED
'ModelCardVersion' => <integer>,
'OutputConfig' => [ // REQUIRED
'S3OutputPath' => '<string>', // REQUIRED
],
]);
Parameter Details
Members
- ModelCardExportJobName
-
- Required: Yes
- Type: string
The name of the model card export job.
- ModelCardName
-
- Required: Yes
- Type: string
The name or Amazon Resource Name (ARN) of the model card to export.
- ModelCardVersion
-
- Type: int
The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.
- OutputConfig
-
- Required: Yes
- Type: ModelCardExportOutputConfig structure
The model card output configuration that specifies the Amazon S3 path for exporting.
Result Syntax
[
'ModelCardExportJobArn' => '<string>',
]
Result Details
Members
- ModelCardExportJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the model card export job.
Errors
-
Resource being access is not found.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
CreateModelExplainabilityJobDefinition
$result = $client->createModelExplainabilityJobDefinition([/* ... */]); $promise = $client->createModelExplainabilityJobDefinitionAsync([/* ... */]);
Creates the definition for a model explainability job.
Parameter Syntax
$result = $client->createModelExplainabilityJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
'JobResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'ModelExplainabilityAppSpecification' => [ // REQUIRED
'ConfigUri' => '<string>', // REQUIRED
'Environment' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
],
'ModelExplainabilityBaselineConfig' => [
'BaseliningJobName' => '<string>',
'ConstraintsResource' => [
'S3Uri' => '<string>',
],
],
'ModelExplainabilityJobInput' => [ // REQUIRED
'BatchTransformInput' => [
'DataCapturedDestinationS3Uri' => '<string>', // REQUIRED
'DatasetFormat' => [ // REQUIRED
'Csv' => [
'Header' => true || false,
],
'Json' => [
'Line' => true || false,
],
'Parquet' => [
],
],
'EndTimeOffset' => '<string>',
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'EndpointInput' => [
'EndTimeOffset' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
],
'ModelExplainabilityJobOutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'MonitoringOutputs' => [ // REQUIRED
[
'S3Output' => [ // REQUIRED
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
- JobResources
-
- Required: Yes
- Type: MonitoringResources structure
Identifies the resources to deploy for a monitoring job.
- ModelExplainabilityAppSpecification
-
- Required: Yes
- Type: ModelExplainabilityAppSpecification structure
Configures the model explainability job to run a specified Docker container image.
- ModelExplainabilityBaselineConfig
-
- Type: ModelExplainabilityBaselineConfig structure
The baseline configuration for a model explainability job.
- ModelExplainabilityJobInput
-
- Required: Yes
- Type: ModelExplainabilityJobInput structure
Inputs for the model explainability job.
- ModelExplainabilityJobOutputConfig
-
- Required: Yes
- Type: MonitoringOutputConfig structure
The output configuration for monitoring jobs.
- NetworkConfig
-
- Type: MonitoringNetworkConfig structure
Networking options for a model explainability job.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- StoppingCondition
-
- Type: MonitoringStoppingCondition structure
A time limit for how long the monitoring job is allowed to run before stopping.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'JobDefinitionArn' => '<string>',
]
Result Details
Members
- JobDefinitionArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the model explainability job.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateModelPackage
$result = $client->createModelPackage([/* ... */]); $promise = $client->createModelPackageAsync([/* ... */]);
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.
There are two types of model packages:
-
Versioned - a model that is part of a model group in the model registry.
-
Unversioned - a model package that is not part of a model group.
Parameter Syntax
$result = $client->createModelPackage([
'AdditionalInferenceSpecifications' => [
[
'Containers' => [ // REQUIRED
[
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'Image' => '<string>', // REQUIRED
'ImageDigest' => '<string>',
'ModelDataUrl' => '<string>',
'ModelInput' => [
'DataInputConfig' => '<string>', // REQUIRED
],
'NearestModelName' => '<string>',
'ProductId' => '<string>',
],
// ...
],
'Description' => '<string>',
'Name' => '<string>', // REQUIRED
'SupportedContentTypes' => ['<string>', ...],
'SupportedRealtimeInferenceInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'SupportedTransformInstanceTypes' => ['<string>', ...],
],
// ...
],
'CertifyForMarketplace' => true || false,
'ClientToken' => '<string>',
'CustomerMetadataProperties' => ['<string>', ...],
'Domain' => '<string>',
'DriftCheckBaselines' => [
'Bias' => [
'ConfigFile' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'PostTrainingConstraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'PreTrainingConstraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'Explainability' => [
'ConfigFile' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'Constraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelDataQuality' => [
'Constraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'Statistics' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelQuality' => [
'Constraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'Statistics' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
],
'InferenceSpecification' => [
'Containers' => [ // REQUIRED
[
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'Image' => '<string>', // REQUIRED
'ImageDigest' => '<string>',
'ModelDataUrl' => '<string>',
'ModelInput' => [
'DataInputConfig' => '<string>', // REQUIRED
],
'NearestModelName' => '<string>',
'ProductId' => '<string>',
],
// ...
],
'SupportedContentTypes' => ['<string>', ...],
'SupportedRealtimeInferenceInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'SupportedTransformInstanceTypes' => ['<string>', ...],
],
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'ModelApprovalStatus' => 'Approved|Rejected|PendingManualApproval',
'ModelMetrics' => [
'Bias' => [
'PostTrainingReport' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'PreTrainingReport' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'Report' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'Explainability' => [
'Report' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelDataQuality' => [
'Constraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'Statistics' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'ModelQuality' => [
'Constraints' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
'Statistics' => [
'ContentDigest' => '<string>',
'ContentType' => '<string>', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
],
'ModelPackageDescription' => '<string>',
'ModelPackageGroupName' => '<string>',
'ModelPackageName' => '<string>',
'SamplePayloadUrl' => '<string>',
'SkipModelValidation' => 'All|None',
'SourceAlgorithmSpecification' => [
'SourceAlgorithms' => [ // REQUIRED
[
'AlgorithmName' => '<string>', // REQUIRED
'ModelDataUrl' => '<string>',
],
// ...
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'Task' => '<string>',
'ValidationSpecification' => [
'ValidationProfiles' => [ // REQUIRED
[
'ProfileName' => '<string>', // REQUIRED
'TransformJobDefinition' => [ // REQUIRED
'BatchStrategy' => 'MultiRecord|SingleRecord',
'Environment' => ['<string>', ...],
'MaxConcurrentTransforms' => <integer>,
'MaxPayloadInMB' => <integer>,
'TransformInput' => [ // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'S3DataSource' => [ // REQUIRED
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'SplitType' => 'None|Line|RecordIO|TFRecord',
],
'TransformOutput' => [ // REQUIRED
'Accept' => '<string>',
'AssembleWith' => 'None|Line',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'TransformResources' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
],
],
],
// ...
],
'ValidationRole' => '<string>', // REQUIRED
],
]);
Parameter Details
Members
- AdditionalInferenceSpecifications
-
- Type: Array of AdditionalInferenceSpecificationDefinition structures
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
- CertifyForMarketplace
-
- Type: boolean
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.
- ClientToken
-
- Type: string
A unique token that guarantees that the call to this API is idempotent.
- CustomerMetadataProperties
-
- Type: Associative array of custom strings keys (CustomerMetadataKey) to strings
The metadata properties associated with the model package versions.
- Domain
-
- Type: string
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
- DriftCheckBaselines
-
- Type: DriftCheckBaselines structure
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
- InferenceSpecification
-
- Type: InferenceSpecification structure
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.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- ModelApprovalStatus
-
- Type: string
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
Approvedto deploy the model. - ModelMetrics
-
- Type: ModelMetrics structure
A structure that contains model metrics reports.
- ModelPackageDescription
-
- Type: string
A description of the model package.
- ModelPackageGroupName
-
- Type: string
The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
- ModelPackageName
-
- Type: string
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
- SamplePayloadUrl
-
- Type: string
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
- SkipModelValidation
-
- Type: string
Indicates if you want to skip model validation.
- SourceAlgorithmSpecification
-
- Type: SourceAlgorithmSpecification structure
Details about the algorithm that was used to create the model package.
- Tags
-
- Type: Array of Tag structures
A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
If you supply
ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply atagargument. - Task
-
- Type: string
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender:
"IMAGE_CLASSIFICATION"|"OBJECT_DETECTION"|"TEXT_GENERATION"|"IMAGE_SEGMENTATION"|"FILL_MASK"|"CLASSIFICATION"|"REGRESSION"|"OTHER".Specify "OTHER" if none of the tasks listed fit your use case.
- ValidationSpecification
-
- Type: ModelPackageValidationSpecification structure
Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
Result Syntax
[
'ModelPackageArn' => '<string>',
]
Result Details
Members
- ModelPackageArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the new model package.
Errors
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact. -
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateModelPackageGroup
$result = $client->createModelPackageGroup([/* ... */]); $promise = $client->createModelPackageGroupAsync([/* ... */]);
Creates a model group. A model group contains a group of model versions.
Parameter Syntax
$result = $client->createModelPackageGroup([
'ModelPackageGroupDescription' => '<string>',
'ModelPackageGroupName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ModelPackageGroupDescription
-
- Type: string
A description for the model group.
- ModelPackageGroupName
-
- Required: Yes
- Type: string
The name of the model group.
- Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'ModelPackageGroupArn' => '<string>',
]
Result Details
Members
- ModelPackageGroupArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the model group.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateModelQualityJobDefinition
$result = $client->createModelQualityJobDefinition([/* ... */]); $promise = $client->createModelQualityJobDefinitionAsync([/* ... */]);
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Parameter Syntax
$result = $client->createModelQualityJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
'JobResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'ModelQualityAppSpecification' => [ // REQUIRED
'ContainerArguments' => ['<string>', ...],
'ContainerEntrypoint' => ['<string>', ...],
'Environment' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
'PostAnalyticsProcessorSourceUri' => '<string>',
'ProblemType' => 'BinaryClassification|MulticlassClassification|Regression',
'RecordPreprocessorSourceUri' => '<string>',
],
'ModelQualityBaselineConfig' => [
'BaseliningJobName' => '<string>',
'ConstraintsResource' => [
'S3Uri' => '<string>',
],
],
'ModelQualityJobInput' => [ // REQUIRED
'BatchTransformInput' => [
'DataCapturedDestinationS3Uri' => '<string>', // REQUIRED
'DatasetFormat' => [ // REQUIRED
'Csv' => [
'Header' => true || false,
],
'Json' => [
'Line' => true || false,
],
'Parquet' => [
],
],
'EndTimeOffset' => '<string>',
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'EndpointInput' => [
'EndTimeOffset' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'GroundTruthS3Input' => [ // REQUIRED
'S3Uri' => '<string>',
],
],
'ModelQualityJobOutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'MonitoringOutputs' => [ // REQUIRED
[
'S3Output' => [ // REQUIRED
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the monitoring job definition.
- JobResources
-
- Required: Yes
- Type: MonitoringResources structure
Identifies the resources to deploy for a monitoring job.
- ModelQualityAppSpecification
-
- Required: Yes
- Type: ModelQualityAppSpecification structure
The container that runs the monitoring job.
- ModelQualityBaselineConfig
-
- Type: ModelQualityBaselineConfig structure
Specifies the constraints and baselines for the monitoring job.
- ModelQualityJobInput
-
- Required: Yes
- Type: ModelQualityJobInput structure
A list of the inputs that are monitored. Currently endpoints are supported.
- ModelQualityJobOutputConfig
-
- Required: Yes
- Type: MonitoringOutputConfig structure
The output configuration for monitoring jobs.
- NetworkConfig
-
- Type: MonitoringNetworkConfig structure
Specifies the network configuration for the monitoring job.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- StoppingCondition
-
- Type: MonitoringStoppingCondition structure
A time limit for how long the monitoring job is allowed to run before stopping.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'JobDefinitionArn' => '<string>',
]
Result Details
Members
- JobDefinitionArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the model quality monitoring job.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateMonitoringSchedule
$result = $client->createMonitoringSchedule([/* ... */]); $promise = $client->createMonitoringScheduleAsync([/* ... */]);
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
Parameter Syntax
$result = $client->createMonitoringSchedule([
'MonitoringScheduleConfig' => [ // REQUIRED
'MonitoringJobDefinition' => [
'BaselineConfig' => [
'BaseliningJobName' => '<string>',
'ConstraintsResource' => [
'S3Uri' => '<string>',
],
'StatisticsResource' => [
'S3Uri' => '<string>',
],
],
'Environment' => ['<string>', ...],
'MonitoringAppSpecification' => [ // REQUIRED
'ContainerArguments' => ['<string>', ...],
'ContainerEntrypoint' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
'PostAnalyticsProcessorSourceUri' => '<string>',
'RecordPreprocessorSourceUri' => '<string>',
],
'MonitoringInputs' => [ // REQUIRED
[
'BatchTransformInput' => [
'DataCapturedDestinationS3Uri' => '<string>', // REQUIRED
'DatasetFormat' => [ // REQUIRED
'Csv' => [
'Header' => true || false,
],
'Json' => [
'Line' => true || false,
],
'Parquet' => [
],
],
'EndTimeOffset' => '<string>',
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
'EndpointInput' => [
'EndTimeOffset' => '<string>',
'EndpointName' => '<string>', // REQUIRED
'ExcludeFeaturesAttribute' => '<string>',
'FeaturesAttribute' => '<string>',
'InferenceAttribute' => '<string>',
'LocalPath' => '<string>', // REQUIRED
'ProbabilityAttribute' => '<string>',
'ProbabilityThresholdAttribute' => <float>,
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3InputMode' => 'Pipe|File',
'StartTimeOffset' => '<string>',
],
],
// ...
],
'MonitoringOutputConfig' => [ // REQUIRED
'KmsKeyId' => '<string>',
'MonitoringOutputs' => [ // REQUIRED
[
'S3Output' => [ // REQUIRED
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'MonitoringResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
],
'MonitoringJobDefinitionName' => '<string>',
'MonitoringType' => 'DataQuality|ModelQuality|ModelBias|ModelExplainability',
'ScheduleConfig' => [
'DataAnalysisEndTime' => '<string>',
'DataAnalysisStartTime' => '<string>',
'ScheduleExpression' => '<string>', // REQUIRED
],
],
'MonitoringScheduleName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- MonitoringScheduleConfig
-
- Required: Yes
- Type: MonitoringScheduleConfig structure
The configuration object that specifies the monitoring schedule and defines the monitoring job.
- MonitoringScheduleName
-
- Required: Yes
- Type: string
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'MonitoringScheduleArn' => '<string>',
]
Result Details
Members
- MonitoringScheduleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the monitoring schedule.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateNotebookInstance
$result = $client->createNotebookInstance([/* ... */]); $promise = $client->createNotebookInstanceAsync([/* ... */]);
Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker does the following:
-
Creates a network interface in the SageMaker VPC.
-
(Option) If you specified
SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC. -
Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified
SubnetIdof your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
Parameter Syntax
$result = $client->createNotebookInstance([
'AcceleratorTypes' => ['<string>', ...],
'AdditionalCodeRepositories' => ['<string>', ...],
'DefaultCodeRepository' => '<string>',
'DirectInternetAccess' => 'Enabled|Disabled',
'InstanceMetadataServiceConfiguration' => [
'MinimumInstanceMetadataServiceVersion' => '<string>', // REQUIRED
],
'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5d.xlarge|ml.c5d.2xlarge|ml.c5d.4xlarge|ml.c5d.9xlarge|ml.c5d.18xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.inf1.xlarge|ml.inf1.2xlarge|ml.inf1.6xlarge|ml.inf1.24xlarge|ml.p4d.24xlarge|ml.p4de.24xlarge', // REQUIRED
'KmsKeyId' => '<string>',
'LifecycleConfigName' => '<string>',
'NotebookInstanceName' => '<string>', // REQUIRED
'PlatformIdentifier' => '<string>',
'RoleArn' => '<string>', // REQUIRED
'RootAccess' => 'Enabled|Disabled',
'SecurityGroupIds' => ['<string>', ...],
'SubnetId' => '<string>',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'VolumeSizeInGB' => <integer>,
]);
Parameter Details
Members
- AcceleratorTypes
-
- Type: Array of strings
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.
- AdditionalCodeRepositories
-
- Type: Array of strings
An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
- DefaultCodeRepository
-
- Type: string
A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
- DirectInternetAccess
-
- Type: string
Sets whether SageMaker provides internet access to the notebook instance. If you set this to
Disabledthis notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to
Disabledonly if you set a value for theSubnetIdparameter. - InstanceMetadataServiceConfiguration
-
- Type: InstanceMetadataServiceConfiguration structure
Information on the IMDS configuration of the notebook instance
- InstanceType
-
- Required: Yes
- Type: string
The type of ML compute instance to launch for the notebook instance.
- KmsKeyId
-
- Type: string
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
- LifecycleConfigName
-
- Type: string
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
- NotebookInstanceName
-
- Required: Yes
- Type: string
The name of the new notebook instance.
- PlatformIdentifier
-
- Type: string
The platform identifier of the notebook instance runtime environment.
- RoleArn
-
- Required: Yes
- Type: string
When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker can perform these tasks. The policy must allow the SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the
iam:PassRolepermission. - RootAccess
-
- Type: string
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.
- SecurityGroupIds
-
- Type: Array of strings
The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
- SubnetId
-
- Type: string
The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
- Tags
-
- Type: Array of Tag structures
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.
- VolumeSizeInGB
-
- Type: int
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
Result Syntax
[
'NotebookInstanceArn' => '<string>',
]
Result Details
Members
- NotebookInstanceArn
-
- Type: string
The Amazon Resource Name (ARN) of the notebook instance.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateNotebookInstanceLifecycleConfig
$result = $client->createNotebookInstanceLifecycleConfig([/* ... */]); $promise = $client->createNotebookInstanceLifecycleConfigAsync([/* ... */]);
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Parameter Syntax
$result = $client->createNotebookInstanceLifecycleConfig([
'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
'OnCreate' => [
[
'Content' => '<string>',
],
// ...
],
'OnStart' => [
[
'Content' => '<string>',
],
// ...
],
]);
Parameter Details
Members
- NotebookInstanceLifecycleConfigName
-
- Required: Yes
- Type: string
The name of the lifecycle configuration.
- OnCreate
-
- Type: Array of NotebookInstanceLifecycleHook structures
A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
- OnStart
-
- Type: Array of NotebookInstanceLifecycleHook structures
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.
Result Syntax
[
'NotebookInstanceLifecycleConfigArn' => '<string>',
]
Result Details
Members
- NotebookInstanceLifecycleConfigArn
-
- Type: string
The Amazon Resource Name (ARN) of the lifecycle configuration.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreatePipeline
$result = $client->createPipeline([/* ... */]); $promise = $client->createPipelineAsync([/* ... */]);
Creates a pipeline using a JSON pipeline definition.
Parameter Syntax
$result = $client->createPipeline([
'ClientRequestToken' => '<string>', // REQUIRED
'ParallelismConfiguration' => [
'MaxParallelExecutionSteps' => <integer>, // REQUIRED
],
'PipelineDefinition' => '<string>',
'PipelineDefinitionS3Location' => [
'Bucket' => '<string>', // REQUIRED
'ObjectKey' => '<string>', // REQUIRED
'VersionId' => '<string>',
],
'PipelineDescription' => '<string>',
'PipelineDisplayName' => '<string>',
'PipelineName' => '<string>', // REQUIRED
'RoleArn' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ClientRequestToken
-
- Required: Yes
- Type: string
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
- ParallelismConfiguration
-
- Type: ParallelismConfiguration structure
This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
- PipelineDefinition
-
- Type: string
The JSON pipeline definition of the pipeline.
- PipelineDefinitionS3Location
-
- Type: PipelineDefinitionS3Location structure
The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
- PipelineDescription
-
- Type: string
A description of the pipeline.
- PipelineDisplayName
-
- Type: string
The display name of the pipeline.
- PipelineName
-
- Required: Yes
- Type: string
The name of the pipeline.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
- Tags
-
- Type: Array of Tag structures
A list of tags to apply to the created pipeline.
Result Syntax
[
'PipelineArn' => '<string>',
]
Result Details
Members
- PipelineArn
-
- Type: string
The Amazon Resource Name (ARN) of the created pipeline.
Errors
-
Resource being access is not found.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreatePresignedDomainUrl
$result = $client->createPresignedDomainUrl([/* ... */]); $promise = $client->createPresignedDomainUrlAsync([/* ... */]);
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .
The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
Parameter Syntax
$result = $client->createPresignedDomainUrl([
'DomainId' => '<string>', // REQUIRED
'ExpiresInSeconds' => <integer>,
'LandingUri' => '<string>',
'SessionExpirationDurationInSeconds' => <integer>,
'SpaceName' => '<string>',
'UserProfileName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- ExpiresInSeconds
-
- Type: int
The number of seconds until the pre-signed URL expires. This value defaults to 300.
- LandingUri
-
- Type: string
The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:
-
studio::relative/path: Directs users to the relative path in Studio. -
app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application. -
app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application. -
app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application. -
app:Canvas:relative/path: Directs users to the relative path in the Canvas application.
- SessionExpirationDurationInSeconds
-
- Type: int
The session expiration duration in seconds. This value defaults to 43200.
- SpaceName
-
- Type: string
The name of the space.
- UserProfileName
-
- Required: Yes
- Type: string
The name of the UserProfile to sign-in as.
Result Syntax
[
'AuthorizedUrl' => '<string>',
]
Result Details
Members
- AuthorizedUrl
-
- Type: string
The presigned URL.
Errors
-
Resource being access is not found.
CreatePresignedNotebookInstanceUrl
$result = $client->createPresignedNotebookInstanceUrl([/* ... */]); $promise = $client->createPresignedNotebookInstanceUrlAsync([/* ... */]);
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
Parameter Syntax
$result = $client->createPresignedNotebookInstanceUrl([
'NotebookInstanceName' => '<string>', // REQUIRED
'SessionExpirationDurationInSeconds' => <integer>,
]);
Parameter Details
Members
- NotebookInstanceName
-
- Required: Yes
- Type: string
The name of the notebook instance.
- SessionExpirationDurationInSeconds
-
- Type: int
The duration of the session, in seconds. The default is 12 hours.
Result Syntax
[
'AuthorizedUrl' => '<string>',
]
Result Details
Members
- AuthorizedUrl
-
- Type: string
A JSON object that contains the URL string.
Errors
There are no errors described for this operation.
CreateProcessingJob
$result = $client->createProcessingJob([/* ... */]); $promise = $client->createProcessingJobAsync([/* ... */]);
Creates a processing job.
Parameter Syntax
$result = $client->createProcessingJob([
'AppSpecification' => [ // REQUIRED
'ContainerArguments' => ['<string>', ...],
'ContainerEntrypoint' => ['<string>', ...],
'ImageUri' => '<string>', // REQUIRED
],
'Environment' => ['<string>', ...],
'ExperimentConfig' => [
'ExperimentName' => '<string>',
'RunName' => '<string>',
'TrialComponentDisplayName' => '<string>',
'TrialName' => '<string>',
],
'NetworkConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'EnableNetworkIsolation' => true || false,
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
],
'ProcessingInputs' => [
[
'AppManaged' => true || false,
'DatasetDefinition' => [
'AthenaDatasetDefinition' => [
'Catalog' => '<string>', // REQUIRED
'Database' => '<string>', // REQUIRED
'KmsKeyId' => '<string>',
'OutputCompression' => 'GZIP|SNAPPY|ZLIB',
'OutputFormat' => 'PARQUET|ORC|AVRO|JSON|TEXTFILE', // REQUIRED
'OutputS3Uri' => '<string>', // REQUIRED
'QueryString' => '<string>', // REQUIRED
'WorkGroup' => '<string>',
],
'DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'InputMode' => 'Pipe|File',
'LocalPath' => '<string>',
'RedshiftDatasetDefinition' => [
'ClusterId' => '<string>', // REQUIRED
'ClusterRoleArn' => '<string>', // REQUIRED
'Database' => '<string>', // REQUIRED
'DbUser' => '<string>', // REQUIRED
'KmsKeyId' => '<string>',
'OutputCompression' => 'None|GZIP|BZIP2|ZSTD|SNAPPY',
'OutputFormat' => 'PARQUET|CSV', // REQUIRED
'OutputS3Uri' => '<string>', // REQUIRED
'QueryString' => '<string>', // REQUIRED
],
],
'InputName' => '<string>', // REQUIRED
'S3Input' => [
'LocalPath' => '<string>',
'S3CompressionType' => 'None|Gzip',
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix', // REQUIRED
'S3InputMode' => 'Pipe|File',
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
'ProcessingJobName' => '<string>', // REQUIRED
'ProcessingOutputConfig' => [
'KmsKeyId' => '<string>',
'Outputs' => [ // REQUIRED
[
'AppManaged' => true || false,
'FeatureStoreOutput' => [
'FeatureGroupName' => '<string>', // REQUIRED
],
'OutputName' => '<string>', // REQUIRED
'S3Output' => [
'LocalPath' => '<string>', // REQUIRED
'S3UploadMode' => 'Continuous|EndOfJob', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
// ...
],
],
'ProcessingResources' => [ // REQUIRED
'ClusterConfig' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [
'MaxRuntimeInSeconds' => <integer>, // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- AppSpecification
-
- Required: Yes
- Type: AppSpecification structure
Configures the processing job to run a specified Docker container image.
- Environment
-
- Type: Associative array of custom strings keys (ProcessingEnvironmentKey) to strings
The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
- ExperimentConfig
-
- Type: ExperimentConfig structure
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
- NetworkConfig
-
- Type: NetworkConfig structure
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.
- ProcessingInputs
-
- Type: Array of ProcessingInput structures
An array of inputs configuring the data to download into the processing container.
- ProcessingJobName
-
- Required: Yes
- Type: string
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
- ProcessingOutputConfig
-
- Type: ProcessingOutputConfig structure
Output configuration for the processing job.
- ProcessingResources
-
- Required: Yes
- Type: ProcessingResources structure
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.
- RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
- StoppingCondition
-
- Type: ProcessingStoppingCondition structure
The time limit for how long the processing job is allowed to run.
- Tags
-
- Type: Array of Tag structures
(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.
Result Syntax
[
'ProcessingJobArn' => '<string>',
]
Result Details
Members
- ProcessingJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the processing job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being access is not found.
CreateProject
$result = $client->createProject([/* ... */]); $promise = $client->createProjectAsync([/* ... */]);
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
Parameter Syntax
$result = $client->createProject([
'ProjectDescription' => '<string>',
'ProjectName' => '<string>', // REQUIRED
'ServiceCatalogProvisioningDetails' => [ // REQUIRED
'PathId' => '<string>',
'ProductId' => '<string>', // REQUIRED
'ProvisioningArtifactId' => '<string>',
'ProvisioningParameters' => [
[
'Key' => '<string>',
'Value' => '<string>',
],
// ...
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- ProjectDescription
-
- Type: string
A description for the project.
- ProjectName
-
- Required: Yes
- Type: string
The name of the project.
- ServiceCatalogProvisioningDetails
-
- Required: Yes
- Type: ServiceCatalogProvisioningDetails structure
The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
- Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'ProjectArn' => '<string>',
'ProjectId' => '<string>',
]
Result Details
Members
- ProjectArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the project.
- ProjectId
-
- Required: Yes
- Type: string
The ID of the new project.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateSpace
$result = $client->createSpace([/* ... */]); $promise = $client->createSpaceAsync([/* ... */]);
Creates a space used for real time collaboration in a Domain.
Parameter Syntax
$result = $client->createSpace([
'DomainId' => '<string>', // REQUIRED
'SpaceName' => '<string>', // REQUIRED
'SpaceSettings' => [
'JupyterServerAppSettings' => [
'CodeRepositories' => [
[
'RepositoryUrl' => '<string>', // REQUIRED
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'KernelGatewayAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The ID of the associated Domain.
- SpaceName
-
- Required: Yes
- Type: string
The name of the space.
- SpaceSettings
-
- Type: SpaceSettings structure
A collection of space settings.
- Tags
-
- Type: Array of Tag structures
Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the
SearchAPI.
Result Syntax
[
'SpaceArn' => '<string>',
]
Result Details
Members
- SpaceArn
-
- Type: string
The space's Amazon Resource Name (ARN).
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateStudioLifecycleConfig
$result = $client->createStudioLifecycleConfig([/* ... */]); $promise = $client->createStudioLifecycleConfigAsync([/* ... */]);
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
Parameter Syntax
$result = $client->createStudioLifecycleConfig([
'StudioLifecycleConfigAppType' => 'JupyterServer|KernelGateway', // REQUIRED
'StudioLifecycleConfigContent' => '<string>', // REQUIRED
'StudioLifecycleConfigName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
]);
Parameter Details
Members
- StudioLifecycleConfigAppType
-
- Required: Yes
- Type: string
The App type that the Lifecycle Configuration is attached to.
- StudioLifecycleConfigContent
-
- Required: Yes
- Type: string
The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.
- StudioLifecycleConfigName
-
- Required: Yes
- Type: string
The name of the Amazon SageMaker Studio Lifecycle Configuration to create.
- Tags
-
- Type: Array of Tag structures
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.
Result Syntax
[
'StudioLifecycleConfigArn' => '<string>',
]
Result Details
Members
- StudioLifecycleConfigArn
-
- Type: string
The ARN of your created Lifecycle Configuration.
Errors
-
Resource being accessed is in use.
CreateTrainingJob
$result = $client->createTrainingJob([/* ... */]); $promise = $client->createTrainingJobAsync([/* ... */]);
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
-
AlgorithmSpecification- Identifies the training algorithm to use. -
HyperParameters- Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
-
InputDataConfig- Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. -
OutputDataConfig- Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training. -
ResourceConfig- Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. -
EnableManagedSpotTraining- Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. -
RoleArn- The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. -
StoppingCondition- To help cap training costs, useMaxRuntimeInSecondsto set a time limit for training. UseMaxWaitTimeInSecondsto specify how long a managed spot training job has to complete. -
Environment- The environment variables to set in the Docker container. -
RetryStrategy- The number of times to retry the job when the job fails due to anInternalServerError.
For more information about SageMaker, see How It Works.
Parameter Syntax
$result = $client->createTrainingJob([
'AlgorithmSpecification' => [ // REQUIRED
'AlgorithmName' => '<string>',
'ContainerArguments' => ['<string>', ...],
'ContainerEntrypoint' => ['<string>', ...],
'EnableSageMakerMetricsTimeSeries' => true || false,
'MetricDefinitions' => [
[
'Name' => '<string>', // REQUIRED
'Regex' => '<string>', // REQUIRED
],
// ...
],
'TrainingImage' => '<string>',
'TrainingImageConfig' => [
'TrainingRepositoryAccessMode' => 'Platform|Vpc', // REQUIRED
'TrainingRepositoryAuthConfig' => [
'TrainingRepositoryCredentialsProviderArn' => '<string>', // REQUIRED
],
],
'TrainingInputMode' => 'Pipe|File|FastFile', // REQUIRED
],
'CheckpointConfig' => [
'LocalPath' => '<string>',
'S3Uri' => '<string>', // REQUIRED
],
'DebugHookConfig' => [
'CollectionConfigurations' => [
[
'CollectionName' => '<string>',
'CollectionParameters' => ['<string>', ...],
],
// ...
],
'HookParameters' => ['<string>', ...],
'LocalPath' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'DebugRuleConfigurations' => [
[
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge',
'LocalPath' => '<string>',
'RuleConfigurationName' => '<string>', // REQUIRED
'RuleEvaluatorImage' => '<string>', // REQUIRED
'RuleParameters' => ['<string>', ...],
'S3OutputPath' => '<string>',
'VolumeSizeInGB' => <integer>,
],
// ...
],
'EnableInterContainerTrafficEncryption' => true || false,
'EnableManagedSpotTraining' => true || false,
'EnableNetworkIsolation' => true || false,
'Environment' => ['<string>', ...],
'ExperimentConfig' => [
'ExperimentName' => '<string>',
'RunName' => '<string>',
'TrialComponentDisplayName' => '<string>',
'TrialName' => '<string>',
],
'HyperParameters' => ['<string>', ...],
'InfraCheckConfig' => [
'EnableInfraCheck' => true || false,
],
'InputDataConfig' => [
[
'ChannelName' => '<string>', // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'FileSystemDataSource' => [
'DirectoryPath' => '<string>', // REQUIRED
'FileSystemAccessMode' => 'rw|ro', // REQUIRED
'FileSystemId' => '<string>', // REQUIRED
'FileSystemType' => 'EFS|FSxLustre', // REQUIRED
],
'S3DataSource' => [
'AttributeNames' => ['<string>', ...],
'InstanceGroupNames' => ['<string>', ...],
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'InputMode' => 'Pipe|File|FastFile',
'RecordWrapperType' => 'None|RecordIO',
'ShuffleConfig' => [
'Seed' => <integer>, // REQUIRED
],
],
// ...
],
'OutputDataConfig' => [ // REQUIRED
'CompressionType' => 'GZIP|NONE',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'ProfilerConfig' => [
'DisableProfiler' => true || false,
'ProfilingIntervalInMilliseconds' => <integer>,
'ProfilingParameters' => ['<string>', ...],
'S3OutputPath' => '<string>',
],
'ProfilerRuleConfigurations' => [
[
'InstanceType' => 'ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge',
'LocalPath' => '<string>',
'RuleConfigurationName' => '<string>', // REQUIRED
'RuleEvaluatorImage' => '<string>', // REQUIRED
'RuleParameters' => ['<string>', ...],
'S3OutputPath' => '<string>',
'VolumeSizeInGB' => <integer>,
],
// ...
],
'ResourceConfig' => [ // REQUIRED
'InstanceCount' => <integer>,
'InstanceGroups' => [
[
'InstanceCount' => <integer>, // REQUIRED
'InstanceGroupName' => '<string>', // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge', // REQUIRED
],
// ...
],
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'KeepAlivePeriodInSeconds' => <integer>,
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>, // REQUIRED
],
'RetryStrategy' => [
'MaximumRetryAttempts' => <integer>, // REQUIRED
],
'RoleArn' => '<string>', // REQUIRED
'StoppingCondition' => [ // REQUIRED
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TensorBoardOutputConfig' => [
'LocalPath' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'TrainingJobName' => '<string>', // REQUIRED
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...], // REQUIRED
'Subnets' => ['<string>', ...], // REQUIRED
],
]);
Parameter Details
Members
- AlgorithmSpecification
-
- Required: Yes
- Type: AlgorithmSpecification structure
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
- CheckpointConfig
-
- Type: CheckpointConfig structure
Contains information about the output location for managed spot training checkpoint data.
- DebugHookConfig
-
- Type: DebugHookConfig structure
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfigparameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. - DebugRuleConfigurations
-
- Type: Array of DebugRuleConfiguration structures
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
- EnableInterContainerTrafficEncryption
-
- Type: boolean
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. - EnableManagedSpotTraining
-
- Type: boolean
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.
- EnableNetworkIsolation
-
- Type: boolean
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
- Environment
-
- Type: Associative array of custom strings keys (TrainingEnvironmentKey) to strings
The environment variables to set in the Docker container.
- ExperimentConfig
-
- Type: ExperimentConfig structure
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
- HyperParameters
-
- Type: Associative array of custom strings keys (HyperParameterKey) to strings
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the
Length Constraint.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
- InfraCheckConfig
-
- Type: InfraCheckConfig structure
Contains information about the infrastructure health check configuration for the training job.
- InputDataConfig
-
- Type: Array of Channel structures
An array of
Channelobjects. Each channel is a named input source.InputDataConfigdescribes 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_dataandvalidation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
- OutputDataConfig
-
- Required: Yes
- Type: OutputDataConfig structure
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
- ProfilerConfig
-
- Type: ProfilerConfig structure
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
- ProfilerRuleConfigurations
-
- Type: Array of ProfilerRuleConfiguration structures
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
- ResourceConfig
-
- Required: Yes
- Type: ResourceConfig structure
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
Fileas theTrainingInputModein the algorithm specification. For distributed training algorithms, specify an instance count greater than 1. - RetryStrategy
-
- Type: RetryStrategy structure
The number of times to retry the job when the job fails due to an
InternalServerError. - RoleArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the
iam:PassRolepermission. - StoppingCondition
-
- Required: Yes
- Type: StoppingCondition structure
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERMsignal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. - Tags
-
- Type: Array of Tag structures
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.
- TensorBoardOutputConfig
-
- Type: TensorBoardOutputConfig structure
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
- TrainingJobName
-
- Required: Yes
- Type: string
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
- VpcConfig
-
- Type: VpcConfig structure
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.
Result Syntax
[
'TrainingJobArn' => '<string>',
]
Result Details
Members
- TrainingJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the training job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being access is not found.
CreateTransformJob
$result = $client->createTransformJob([/* ... */]); $promise = $client->createTransformJobAsync([/* ... */]);
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
-
TransformJobName- Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. -
ModelName- Identifies the model to use.ModelNamemust be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. -
TransformInput- Describes the dataset to be transformed and the Amazon S3 location where it is stored. -
TransformOutput- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. -
TransformResources- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
Parameter Syntax
$result = $client->createTransformJob([
'BatchStrategy' => 'MultiRecord|SingleRecord',
'DataCaptureConfig' => [
'DestinationS3Uri' => '<string>', // REQUIRED
'GenerateInferenceId' => true || false,
'KmsKeyId' => '<string>',
],
'DataProcessing' => [
'InputFilter' => '<string>',
'JoinSource' => 'Input|None',
'OutputFilter' => '<string>',
],
'Environment' => ['<string>', ...],
'ExperimentConfig' => [
'ExperimentName' => '<string>',
'RunName' => '<string>',
'TrialComponentDisplayName' => '<string>',
'TrialName' => '<string>',
],
'MaxConcurrentTransforms' => <integer>,
'MaxPayloadInMB' => <integer>,
'ModelClientConfig' => [
'InvocationsMaxRetries' => <integer>,
'InvocationsTimeoutInSeconds' => <integer>,
],
'ModelName' => '<string>', // REQUIRED
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TransformInput' => [ // REQUIRED
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [ // REQUIRED
'S3DataSource' => [ // REQUIRED
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile', // REQUIRED
'S3Uri' => '<string>', // REQUIRED
],
],
'SplitType' => 'None|Line|RecordIO|TFRecord',
],
'TransformJobName' => '<string>', // REQUIRED
'TransformOutput' => [ // REQUIRED
'Accept' => '<string>',
'AssembleWith' => 'None|Line',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>', // REQUIRED
],
'TransformResources' => [ // REQUIRED
'InstanceCount' => <integer>, // REQUIRED
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge', // REQUIRED
'VolumeKmsKeyId' => '<string>',
],
]);
Parameter Details
Members
- BatchStrategy
-
- Type: string
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
SplitTypeproperty toLine,RecordIO, orTFRecord.To use only one record when making an HTTP invocation request to a container, set
BatchStrategytoSingleRecordandSplitTypetoLine.To fit as many records in a mini-batch as can fit within the
MaxPayloadInMBlimit, setBatchStrategytoMultiRecordandSplitTypetoLine. - DataCaptureConfig
-
- Type: BatchDataCaptureConfig structure
Configuration to control how SageMaker captures inference data.
- DataProcessing
-
- Type: DataProcessing structure
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.
- Environment
-
- Type: Associative array of custom strings keys (TransformEnvironmentKey) to strings
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
- ExperimentConfig
-
- Type: ExperimentConfig structure
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
- MaxConcurrentTransforms
-
- Type: int
The maximum number of parallel requests that can be sent to each instance in a transform job. If
MaxConcurrentTransformsis set to0or 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 is1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value forMaxConcurrentTransforms. - MaxPayloadInMB
-
- Type: int
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in
MaxPayloadInMBmust 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 is6MB.The value of
MaxPayloadInMBcannot be greater than 100 MB. If you specify theMaxConcurrentTransformsparameter, the value of(MaxConcurrentTransforms * MaxPayloadInMB)also cannot exceed 100 MB.For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to
0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding. - ModelClientConfig
-
- Type: ModelClientConfig structure
Configures the timeout and maximum number of retries for processing a transform job invocation.
- ModelName
-
- Required: Yes
- Type: string
The name of the model that you want to use for the transform job.
ModelNamemust be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account. - Tags
-
- Type: Array of Tag structures
(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.
- TransformInput
-
- Required: Yes
- Type: TransformInput structure
Describes the input source and the way the transform job consumes it.
- TransformJobName
-
- Required: Yes
- Type: string
The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
- TransformOutput
-
- Required: Yes
- Type: TransformOutput structure
Describes the results of the transform job.
- TransformResources
-
- Required: Yes
- Type: TransformResources structure
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
Result Syntax
[
'TransformJobArn' => '<string>',
]
Result Details
Members
- TransformJobArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the transform job.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being access is not found.
CreateTrial
$result = $client->createTrial([/* ... */]); $promise = $client->createTrialAsync([/* ... */]);
Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
Parameter Syntax
$result = $client->createTrial([
'DisplayName' => '<string>',
'ExperimentName' => '<string>', // REQUIRED
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TrialName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DisplayName
-
- Type: string
The name of the trial as displayed. The name doesn't need to be unique. If
DisplayNameisn't specified,TrialNameis displayed. - ExperimentName
-
- Required: Yes
- Type: string
The name of the experiment to associate the trial with.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- Tags
-
- Type: Array of Tag structures
A list of tags to associate with the trial. You can use Search API to search on the tags.
- TrialName
-
- Required: Yes
- Type: string
The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
Result Syntax
[
'TrialArn' => '<string>',
]
Result Details
Members
- TrialArn
-
- Type: string
The Amazon Resource Name (ARN) of the trial.
Errors
-
Resource being access is not found.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateTrialComponent
$result = $client->createTrialComponent([/* ... */]); $promise = $client->createTrialComponentAsync([/* ... */]);
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
Parameter Syntax
$result = $client->createTrialComponent([
'DisplayName' => '<string>',
'EndTime' => <integer || string || DateTime>,
'InputArtifacts' => [
'<TrialComponentKey64>' => [
'MediaType' => '<string>',
'Value' => '<string>', // REQUIRED
],
// ...
],
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'OutputArtifacts' => [
'<TrialComponentKey64>' => [
'MediaType' => '<string>',
'Value' => '<string>', // REQUIRED
],
// ...
],
'Parameters' => [
'<TrialComponentKey256>' => [
'NumberValue' => <float>,
'StringValue' => '<string>',
],
// ...
],
'StartTime' => <integer || string || DateTime>,
'Status' => [
'Message' => '<string>',
'PrimaryStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'TrialComponentName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DisplayName
-
- Type: string
The name of the component as displayed. The name doesn't need to be unique. If
DisplayNameisn't specified,TrialComponentNameis displayed. - EndTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the component ended.
- InputArtifacts
-
- Type: Associative array of custom strings keys (TrialComponentKey64) to TrialComponentArtifact structures
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- OutputArtifacts
-
- Type: Associative array of custom strings keys (TrialComponentKey64) to TrialComponentArtifact structures
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
- Parameters
-
- Type: Associative array of custom strings keys (TrialComponentKey256) to TrialComponentParameterValue structures
The hyperparameters for the component.
- StartTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the component started.
- Status
-
- Type: TrialComponentStatus structure
The status of the component. States include:
-
InProgress
-
Completed
-
Failed
- Tags
-
- Type: Array of Tag structures
A list of tags to associate with the component. You can use Search API to search on the tags.
- TrialComponentName
-
- Required: Yes
- Type: string
The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
Result Syntax
[
'TrialComponentArn' => '<string>',
]
Result Details
Members
- TrialComponentArn
-
- Type: string
The Amazon Resource Name (ARN) of the trial component.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
CreateUserProfile
$result = $client->createUserProfile([/* ... */]); $promise = $client->createUserProfileAsync([/* ... */]);
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
Parameter Syntax
$result = $client->createUserProfile([
'DomainId' => '<string>', // REQUIRED
'SingleSignOnUserIdentifier' => '<string>',
'SingleSignOnUserValue' => '<string>',
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'UserProfileName' => '<string>', // REQUIRED
'UserSettings' => [
'CanvasAppSettings' => [
'DirectDeploySettings' => [
'Status' => 'ENABLED|DISABLED',
],
'IdentityProviderOAuthSettings' => [
[
'DataSourceName' => 'SalesforceGenie|Snowflake',
'SecretArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
// ...
],
'KendraSettings' => [
'Status' => 'ENABLED|DISABLED',
],
'ModelRegisterSettings' => [
'CrossAccountModelRegisterRoleArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
'TimeSeriesForecastingSettings' => [
'AmazonForecastRoleArn' => '<string>',
'Status' => 'ENABLED|DISABLED',
],
'WorkspaceSettings' => [
'S3ArtifactPath' => '<string>',
'S3KmsKeyId' => '<string>',
],
],
'DefaultLandingUri' => '<string>',
'ExecutionRole' => '<string>',
'JupyterServerAppSettings' => [
'CodeRepositories' => [
[
'RepositoryUrl' => '<string>', // REQUIRED
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'KernelGatewayAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'LifecycleConfigArns' => ['<string>', ...],
],
'RSessionAppSettings' => [
'CustomImages' => [
[
'AppImageConfigName' => '<string>', // REQUIRED
'ImageName' => '<string>', // REQUIRED
'ImageVersionNumber' => <integer>,
],
// ...
],
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
],
'RStudioServerProAppSettings' => [
'AccessStatus' => 'ENABLED|DISABLED',
'UserGroup' => 'R_STUDIO_ADMIN|R_STUDIO_USER',
],
'SecurityGroups' => ['<string>', ...],
'SharingSettings' => [
'NotebookOutputOption' => 'Allowed|Disabled',
'S3KmsKeyId' => '<string>',
'S3OutputPath' => '<string>',
],
'StudioWebPortal' => 'ENABLED|DISABLED',
'TensorBoardAppSettings' => [
'DefaultResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
],
],
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The ID of the associated Domain.
- SingleSignOnUserIdentifier
-
- Type: string
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
- SingleSignOnUserValue
-
- Type: string
The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
- Tags
-
- Type: Array of Tag structures
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.
- UserProfileName
-
- Required: Yes
- Type: string
A name for the UserProfile. This value is not case sensitive.
- UserSettings
-
- Type: UserSettings structure
A collection of settings.
Result Syntax
[
'UserProfileArn' => '<string>',
]
Result Details
Members
- UserProfileArn
-
- Type: string
The user profile Amazon Resource Name (ARN).
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
-
Resource being accessed is in use.
CreateWorkforce
$result = $client->createWorkforce([/* ... */]); $promise = $client->createWorkforceAsync([/* ... */]);
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
Parameter Syntax
$result = $client->createWorkforce([
'CognitoConfig' => [
'ClientId' => '<string>', // REQUIRED
'UserPool' => '<string>', // REQUIRED
],
'OidcConfig' => [
'AuthorizationEndpoint' => '<string>', // REQUIRED
'ClientId' => '<string>', // REQUIRED
'ClientSecret' => '<string>', // REQUIRED
'Issuer' => '<string>', // REQUIRED
'JwksUri' => '<string>', // REQUIRED
'LogoutEndpoint' => '<string>', // REQUIRED
'TokenEndpoint' => '<string>', // REQUIRED
'UserInfoEndpoint' => '<string>', // REQUIRED
],
'SourceIpConfig' => [
'Cidrs' => ['<string>', ...], // REQUIRED
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'WorkforceName' => '<string>', // REQUIRED
'WorkforceVpcConfig' => [
'SecurityGroupIds' => ['<string>', ...],
'Subnets' => ['<string>', ...],
'VpcId' => '<string>',
],
]);
Parameter Details
Members
- CognitoConfig
-
- Type: CognitoConfig structure
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
OidcConfigif you specify values forCognitoConfig. - OidcConfig
-
- Type: OidcConfig structure
Use this parameter to configure a private workforce using your own OIDC Identity Provider.
Do not use
CognitoConfigif you specify values forOidcConfig. - SourceIpConfig
-
- Type: SourceIpConfig structure
A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
- Tags
-
- Type: Array of Tag structures
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.
- WorkforceName
-
- Required: Yes
- Type: string
The name of the private workforce.
- WorkforceVpcConfig
-
- Type: WorkforceVpcConfigRequest structure
Use this parameter to configure a workforce using VPC.
Result Syntax
[
'WorkforceArn' => '<string>',
]
Result Details
Members
- WorkforceArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the workforce.
Errors
There are no errors described for this operation.
CreateWorkteam
$result = $client->createWorkteam([/* ... */]); $promise = $client->createWorkteamAsync([/* ... */]);
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
Parameter Syntax
$result = $client->createWorkteam([
'Description' => '<string>', // REQUIRED
'MemberDefinitions' => [ // REQUIRED
[
'CognitoMemberDefinition' => [
'ClientId' => '<string>', // REQUIRED
'UserGroup' => '<string>', // REQUIRED
'UserPool' => '<string>', // REQUIRED
],
'OidcMemberDefinition' => [
'Groups' => ['<string>', ...], // REQUIRED
],
],
// ...
],
'NotificationConfiguration' => [
'NotificationTopicArn' => '<string>',
],
'Tags' => [
[
'Key' => '<string>', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'WorkforceName' => '<string>',
'WorkteamName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- Description
-
- Required: Yes
- Type: string
A description of the work team.
- MemberDefinitions
-
- Required: Yes
- Type: Array of MemberDefinition structures
A list of
MemberDefinitionobjects 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) useOidcMemberDefinition. 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
CognitoMemberDefinitionobjects that make up the member definition must have the sameClientIdandUserPoolvalues. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in
OidcMemberDefinitionby listing those groups inGroups. - NotificationConfiguration
-
- Type: NotificationConfiguration structure
Configures notification of workers regarding available or expiring work items.
- Tags
-
- Type: Array of Tag structures
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.
- WorkforceName
-
- Type: string
The name of the workforce.
- WorkteamName
-
- Required: Yes
- Type: string
The name of the work team. Use this name to identify the work team.
Result Syntax
[
'WorkteamArn' => '<string>',
]
Result Details
Members
- WorkteamArn
-
- Type: string
The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
Errors
-
Resource being accessed is in use.
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
DeleteAction
$result = $client->deleteAction([/* ... */]); $promise = $client->deleteActionAsync([/* ... */]);
Deletes an action.
Parameter Syntax
$result = $client->deleteAction([
'ActionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ActionName
-
- Required: Yes
- Type: string
The name of the action to delete.
Result Syntax
[
'ActionArn' => '<string>',
]
Result Details
Members
- ActionArn
-
- Type: string
The Amazon Resource Name (ARN) of the action.
Errors
-
Resource being access is not found.
DeleteAlgorithm
$result = $client->deleteAlgorithm([/* ... */]); $promise = $client->deleteAlgorithmAsync([/* ... */]);
Removes the specified algorithm from your account.
Parameter Syntax
$result = $client->deleteAlgorithm([
'AlgorithmName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AlgorithmName
-
- Required: Yes
- Type: string
The name of the algorithm to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteApp
$result = $client->deleteApp([/* ... */]); $promise = $client->deleteAppAsync([/* ... */]);
Used to stop and delete an app.
Parameter Syntax
$result = $client->deleteApp([
'AppName' => '<string>', // REQUIRED
'AppType' => 'JupyterServer|KernelGateway|TensorBoard|RStudioServerPro|RSessionGateway', // REQUIRED
'DomainId' => '<string>', // REQUIRED
'SpaceName' => '<string>',
'UserProfileName' => '<string>',
]);
Parameter Details
Members
- AppName
-
- Required: Yes
- Type: string
The name of the app.
- AppType
-
- Required: Yes
- Type: string
The type of app.
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- SpaceName
-
- Type: string
The name of the space. If this value is not set, then
UserProfileNamemust be set. - UserProfileName
-
- Type: string
The user profile name. If this value is not set, then
SpaceNamemust be set.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteAppImageConfig
$result = $client->deleteAppImageConfig([/* ... */]); $promise = $client->deleteAppImageConfigAsync([/* ... */]);
Deletes an AppImageConfig.
Parameter Syntax
$result = $client->deleteAppImageConfig([
'AppImageConfigName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AppImageConfigName
-
- Required: Yes
- Type: string
The name of the AppImageConfig to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteArtifact
$result = $client->deleteArtifact([/* ... */]); $promise = $client->deleteArtifactAsync([/* ... */]);
Deletes an artifact. Either ArtifactArn or Source must be specified.
Parameter Syntax
$result = $client->deleteArtifact([
'ArtifactArn' => '<string>',
'Source' => [
'SourceTypes' => [
[
'SourceIdType' => 'MD5Hash|S3ETag|S3Version|Custom', // REQUIRED
'Value' => '<string>', // REQUIRED
],
// ...
],
'SourceUri' => '<string>', // REQUIRED
],
]);
Parameter Details
Members
- ArtifactArn
-
- Type: string
The Amazon Resource Name (ARN) of the artifact to delete.
- Source
-
- Type: ArtifactSource structure
The URI of the source.
Result Syntax
[
'ArtifactArn' => '<string>',
]
Result Details
Members
- ArtifactArn
-
- Type: string
The Amazon Resource Name (ARN) of the artifact.
Errors
-
Resource being access is not found.
DeleteAssociation
$result = $client->deleteAssociation([/* ... */]); $promise = $client->deleteAssociationAsync([/* ... */]);
Deletes an association.
Parameter Syntax
$result = $client->deleteAssociation([
'DestinationArn' => '<string>', // REQUIRED
'SourceArn' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DestinationArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the destination.
- SourceArn
-
- Required: Yes
- Type: string
The ARN of the source.
Result Syntax
[
'DestinationArn' => '<string>',
'SourceArn' => '<string>',
]
Result Details
Members
- DestinationArn
-
- Type: string
The Amazon Resource Name (ARN) of the destination.
- SourceArn
-
- Type: string
The ARN of the source.
Errors
-
Resource being access is not found.
DeleteCluster
$result = $client->deleteCluster([/* ... */]); $promise = $client->deleteClusterAsync([/* ... */]);
Delete a SageMaker HyperPod cluster.
Parameter Syntax
$result = $client->deleteCluster([
'ClusterName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ClusterName
-
- Required: Yes
- Type: string
The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
Result Syntax
[
'ClusterArn' => '<string>',
]
Result Details
Members
- ClusterArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
Errors
-
Resource being access is not found.
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
DeleteCodeRepository
$result = $client->deleteCodeRepository([/* ... */]); $promise = $client->deleteCodeRepositoryAsync([/* ... */]);
Deletes the specified Git repository from your account.
Parameter Syntax
$result = $client->deleteCodeRepository([
'CodeRepositoryName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- CodeRepositoryName
-
- Required: Yes
- Type: string
The name of the Git repository to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteContext
$result = $client->deleteContext([/* ... */]); $promise = $client->deleteContextAsync([/* ... */]);
Deletes an context.
Parameter Syntax
$result = $client->deleteContext([
'ContextName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ContextName
-
- Required: Yes
- Type: string
The name of the context to delete.
Result Syntax
[
'ContextArn' => '<string>',
]
Result Details
Members
- ContextArn
-
- Type: string
The Amazon Resource Name (ARN) of the context.
Errors
-
Resource being access is not found.
DeleteDataQualityJobDefinition
$result = $client->deleteDataQualityJobDefinition([/* ... */]); $promise = $client->deleteDataQualityJobDefinitionAsync([/* ... */]);
Deletes a data quality monitoring job definition.
Parameter Syntax
$result = $client->deleteDataQualityJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the data quality monitoring job definition to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteDeviceFleet
$result = $client->deleteDeviceFleet([/* ... */]); $promise = $client->deleteDeviceFleetAsync([/* ... */]);
Deletes a fleet.
Parameter Syntax
$result = $client->deleteDeviceFleet([
'DeviceFleetName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DeviceFleetName
-
- Required: Yes
- Type: string
The name of the fleet to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
DeleteDomain
$result = $client->deleteDomain([/* ... */]); $promise = $client->deleteDomainAsync([/* ... */]);
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
Parameter Syntax
$result = $client->deleteDomain([
'DomainId' => '<string>', // REQUIRED
'RetentionPolicy' => [
'HomeEfsFileSystem' => 'Retain|Delete',
],
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- RetentionPolicy
-
- Type: RetentionPolicy structure
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).
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteEdgeDeploymentPlan
$result = $client->deleteEdgeDeploymentPlan([/* ... */]); $promise = $client->deleteEdgeDeploymentPlanAsync([/* ... */]);
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
Parameter Syntax
$result = $client->deleteEdgeDeploymentPlan([
'EdgeDeploymentPlanName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- EdgeDeploymentPlanName
-
- Required: Yes
- Type: string
The name of the edge deployment plan to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
DeleteEdgeDeploymentStage
$result = $client->deleteEdgeDeploymentStage([/* ... */]); $promise = $client->deleteEdgeDeploymentStageAsync([/* ... */]);
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
Parameter Syntax
$result = $client->deleteEdgeDeploymentStage([
'EdgeDeploymentPlanName' => '<string>', // REQUIRED
'StageName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- EdgeDeploymentPlanName
-
- Required: Yes
- Type: string
The name of the edge deployment plan from which the stage will be deleted.
- StageName
-
- Required: Yes
- Type: string
The name of the stage.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
DeleteEndpoint
$result = $client->deleteEndpoint([/* ... */]); $promise = $client->deleteEndpointAsync([/* ... */]);
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ExecutionRoleArn , otherwise SageMaker cannot delete these resources.
Parameter Syntax
$result = $client->deleteEndpoint([
'EndpointName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- EndpointName
-
- Required: Yes
- Type: string
The name of the endpoint that you want to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteEndpointConfig
$result = $client->deleteEndpointConfig([/* ... */]); $promise = $client->deleteEndpointConfigAsync([/* ... */]);
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
Parameter Syntax
$result = $client->deleteEndpointConfig([
'EndpointConfigName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- EndpointConfigName
-
- Required: Yes
- Type: string
The name of the endpoint configuration that you want to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteExperiment
$result = $client->deleteExperiment([/* ... */]); $promise = $client->deleteExperimentAsync([/* ... */]);
Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
Parameter Syntax
$result = $client->deleteExperiment([
'ExperimentName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ExperimentName
-
- Required: Yes
- Type: string
The name of the experiment to delete.
Result Syntax
[
'ExperimentArn' => '<string>',
]
Result Details
Members
- ExperimentArn
-
- Type: string
The Amazon Resource Name (ARN) of the experiment that is being deleted.
Errors
-
Resource being access is not found.
DeleteFeatureGroup
$result = $client->deleteFeatureGroup([/* ... */]); $promise = $client->deleteFeatureGroupAsync([/* ... */]);
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.
Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.
Parameter Syntax
$result = $client->deleteFeatureGroup([
'FeatureGroupName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- FeatureGroupName
-
- Required: Yes
- Type: string
The name of the
FeatureGroupyou want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteFlowDefinition
$result = $client->deleteFlowDefinition([/* ... */]); $promise = $client->deleteFlowDefinitionAsync([/* ... */]);
Deletes the specified flow definition.
Parameter Syntax
$result = $client->deleteFlowDefinition([
'FlowDefinitionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- FlowDefinitionName
-
- Required: Yes
- Type: string
The name of the flow definition you are deleting.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteHub
$result = $client->deleteHub([/* ... */]); $promise = $client->deleteHubAsync([/* ... */]);
Delete a hub.
Hub APIs are only callable through SageMaker Studio.
Parameter Syntax
$result = $client->deleteHub([
'HubName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- HubName
-
- Required: Yes
- Type: string
The name of the hub to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteHubContent
$result = $client->deleteHubContent([/* ... */]); $promise = $client->deleteHubContentAsync([/* ... */]);
Delete the contents of a hub.
Hub APIs are only callable through SageMaker Studio.
Parameter Syntax
$result = $client->deleteHubContent([
'HubContentName' => '<string>', // REQUIRED
'HubContentType' => 'Model|Notebook', // REQUIRED
'HubContentVersion' => '<string>', // REQUIRED
'HubName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- HubContentName
-
- Required: Yes
- Type: string
The name of the content that you want to delete from a hub.
- HubContentType
-
- Required: Yes
- Type: string
The type of content that you want to delete from a hub.
- HubContentVersion
-
- Required: Yes
- Type: string
The version of the content that you want to delete from a hub.
- HubName
-
- Required: Yes
- Type: string
The name of the hub that you want to delete content in.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteHumanTaskUi
$result = $client->deleteHumanTaskUi([/* ... */]); $promise = $client->deleteHumanTaskUiAsync([/* ... */]);
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
Parameter Syntax
$result = $client->deleteHumanTaskUi([
'HumanTaskUiName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- HumanTaskUiName
-
- Required: Yes
- Type: string
The name of the human task user interface (work task template) you want to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteImage
$result = $client->deleteImage([/* ... */]); $promise = $client->deleteImageAsync([/* ... */]);
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
Parameter Syntax
$result = $client->deleteImage([
'ImageName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ImageName
-
- Required: Yes
- Type: string
The name of the image to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteImageVersion
$result = $client->deleteImageVersion([/* ... */]); $promise = $client->deleteImageVersionAsync([/* ... */]);
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
Parameter Syntax
$result = $client->deleteImageVersion([
'Alias' => '<string>',
'ImageName' => '<string>', // REQUIRED
'Version' => <integer>,
]);
Parameter Details
Members
- Alias
-
- Type: string
The alias of the image to delete.
- ImageName
-
- Required: Yes
- Type: string
The name of the image to delete.
- Version
-
- Type: int
The version to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteInferenceComponent
$result = $client->deleteInferenceComponent([/* ... */]); $promise = $client->deleteInferenceComponentAsync([/* ... */]);
Deletes an inference component.
Parameter Syntax
$result = $client->deleteInferenceComponent([
'InferenceComponentName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- InferenceComponentName
-
- Required: Yes
- Type: string
The name of the inference component to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteInferenceExperiment
$result = $client->deleteInferenceExperiment([/* ... */]); $promise = $client->deleteInferenceExperimentAsync([/* ... */]);
Deletes an inference experiment.
This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.
Parameter Syntax
$result = $client->deleteInferenceExperiment([
'Name' => '<string>', // REQUIRED
]);
Parameter Details
Members
- Name
-
- Required: Yes
- Type: string
The name of the inference experiment you want to delete.
Result Syntax
[
'InferenceExperimentArn' => '<string>',
]
Result Details
Members
- InferenceExperimentArn
-
- Required: Yes
- Type: string
The ARN of the deleted inference experiment.
Errors
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact. -
Resource being access is not found.
DeleteModel
$result = $client->deleteModel([/* ... */]); $promise = $client->deleteModelAsync([/* ... */]);
Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
Parameter Syntax
$result = $client->deleteModel([
'ModelName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ModelName
-
- Required: Yes
- Type: string
The name of the model to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteModelBiasJobDefinition
$result = $client->deleteModelBiasJobDefinition([/* ... */]); $promise = $client->deleteModelBiasJobDefinitionAsync([/* ... */]);
Deletes an Amazon SageMaker model bias job definition.
Parameter Syntax
$result = $client->deleteModelBiasJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the model bias job definition to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteModelCard
$result = $client->deleteModelCard([/* ... */]); $promise = $client->deleteModelCardAsync([/* ... */]);
Deletes an Amazon SageMaker Model Card.
Parameter Syntax
$result = $client->deleteModelCard([
'ModelCardName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ModelCardName
-
- Required: Yes
- Type: string
The name of the model card to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
DeleteModelExplainabilityJobDefinition
$result = $client->deleteModelExplainabilityJobDefinition([/* ... */]); $promise = $client->deleteModelExplainabilityJobDefinitionAsync([/* ... */]);
Deletes an Amazon SageMaker model explainability job definition.
Parameter Syntax
$result = $client->deleteModelExplainabilityJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the model explainability job definition to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteModelPackage
$result = $client->deleteModelPackage([/* ... */]); $promise = $client->deleteModelPackageAsync([/* ... */]);
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
Parameter Syntax
$result = $client->deleteModelPackage([
'ModelPackageName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ModelPackageName
-
- Required: Yes
- Type: string
The name or Amazon Resource Name (ARN) of the model package to delete.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
Result Syntax
[]
Result Details
Errors
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
DeleteModelPackageGroup
$result = $client->deleteModelPackageGroup([/* ... */]); $promise = $client->deleteModelPackageGroupAsync([/* ... */]);
Deletes the specified model group.
Parameter Syntax
$result = $client->deleteModelPackageGroup([
'ModelPackageGroupName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ModelPackageGroupName
-
- Required: Yes
- Type: string
The name of the model group to delete.
Result Syntax
[]
Result Details
Errors
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
DeleteModelPackageGroupPolicy
$result = $client->deleteModelPackageGroupPolicy([/* ... */]); $promise = $client->deleteModelPackageGroupPolicyAsync([/* ... */]);
Deletes a model group resource policy.
Parameter Syntax
$result = $client->deleteModelPackageGroupPolicy([
'ModelPackageGroupName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ModelPackageGroupName
-
- Required: Yes
- Type: string
The name of the model group for which to delete the policy.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteModelQualityJobDefinition
$result = $client->deleteModelQualityJobDefinition([/* ... */]); $promise = $client->deleteModelQualityJobDefinitionAsync([/* ... */]);
Deletes the secified model quality monitoring job definition.
Parameter Syntax
$result = $client->deleteModelQualityJobDefinition([
'JobDefinitionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- JobDefinitionName
-
- Required: Yes
- Type: string
The name of the model quality monitoring job definition to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteMonitoringSchedule
$result = $client->deleteMonitoringSchedule([/* ... */]); $promise = $client->deleteMonitoringScheduleAsync([/* ... */]);
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
Parameter Syntax
$result = $client->deleteMonitoringSchedule([
'MonitoringScheduleName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- MonitoringScheduleName
-
- Required: Yes
- Type: string
The name of the monitoring schedule to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
DeleteNotebookInstance
$result = $client->deleteNotebookInstance([/* ... */]); $promise = $client->deleteNotebookInstanceAsync([/* ... */]);
Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.
When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
Parameter Syntax
$result = $client->deleteNotebookInstance([
'NotebookInstanceName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- NotebookInstanceName
-
- Required: Yes
- Type: string
The name of the SageMaker notebook instance to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteNotebookInstanceLifecycleConfig
$result = $client->deleteNotebookInstanceLifecycleConfig([/* ... */]); $promise = $client->deleteNotebookInstanceLifecycleConfigAsync([/* ... */]);
Deletes a notebook instance lifecycle configuration.
Parameter Syntax
$result = $client->deleteNotebookInstanceLifecycleConfig([
'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- NotebookInstanceLifecycleConfigName
-
- Required: Yes
- Type: string
The name of the lifecycle configuration to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeletePipeline
$result = $client->deletePipeline([/* ... */]); $promise = $client->deletePipelineAsync([/* ... */]);
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
Parameter Syntax
$result = $client->deletePipeline([
'ClientRequestToken' => '<string>', // REQUIRED
'PipelineName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ClientRequestToken
-
- Required: Yes
- Type: string
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
- PipelineName
-
- Required: Yes
- Type: string
The name of the pipeline to delete.
Result Syntax
[
'PipelineArn' => '<string>',
]
Result Details
Members
- PipelineArn
-
- Type: string
The Amazon Resource Name (ARN) of the pipeline to delete.
Errors
-
Resource being access is not found.
DeleteProject
$result = $client->deleteProject([/* ... */]); $promise = $client->deleteProjectAsync([/* ... */]);
Delete the specified project.
Parameter Syntax
$result = $client->deleteProject([
'ProjectName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ProjectName
-
- Required: Yes
- Type: string
The name of the project to delete.
Result Syntax
[]
Result Details
Errors
-
There was a conflict when you attempted to modify a SageMaker entity such as an
ExperimentorArtifact.
DeleteSpace
$result = $client->deleteSpace([/* ... */]); $promise = $client->deleteSpaceAsync([/* ... */]);
Used to delete a space.
Parameter Syntax
$result = $client->deleteSpace([
'DomainId' => '<string>', // REQUIRED
'SpaceName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The ID of the associated Domain.
- SpaceName
-
- Required: Yes
- Type: string
The name of the space.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteStudioLifecycleConfig
$result = $client->deleteStudioLifecycleConfig([/* ... */]); $promise = $client->deleteStudioLifecycleConfigAsync([/* ... */]);
Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
Parameter Syntax
$result = $client->deleteStudioLifecycleConfig([
'StudioLifecycleConfigName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- StudioLifecycleConfigName
-
- Required: Yes
- Type: string
The name of the Amazon SageMaker Studio Lifecycle Configuration to delete.
Result Syntax
[]
Result Details
Errors
-
Resource being access is not found.
-
Resource being accessed is in use.
DeleteTags
$result = $client->deleteTags([/* ... */]); $promise = $client->deleteTagsAsync([/* ... */]);
Deletes the specified tags from an SageMaker resource.
To list a resource's tags, use the ListTags API.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.
Parameter Syntax
$result = $client->deleteTags([
'ResourceArn' => '<string>', // REQUIRED
'TagKeys' => ['<string>', ...], // REQUIRED
]);
Parameter Details
Members
- ResourceArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the resource whose tags you want to delete.
- TagKeys
-
- Required: Yes
- Type: Array of strings
An array or one or more tag keys to delete.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteTrial
$result = $client->deleteTrial([/* ... */]); $promise = $client->deleteTrialAsync([/* ... */]);
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
Parameter Syntax
$result = $client->deleteTrial([
'TrialName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- TrialName
-
- Required: Yes
- Type: string
The name of the trial to delete.
Result Syntax
[
'TrialArn' => '<string>',
]
Result Details
Members
- TrialArn
-
- Type: string
The Amazon Resource Name (ARN) of the trial that is being deleted.
Errors
-
Resource being access is not found.
DeleteTrialComponent
$result = $client->deleteTrialComponent([/* ... */]); $promise = $client->deleteTrialComponentAsync([/* ... */]);
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Parameter Syntax
$result = $client->deleteTrialComponent([
'TrialComponentName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- TrialComponentName
-
- Required: Yes
- Type: string
The name of the component to delete.
Result Syntax
[
'TrialComponentArn' => '<string>',
]
Result Details
Members
- TrialComponentArn
-
- Type: string
The Amazon Resource Name (ARN) of the component is being deleted.
Errors
-
Resource being access is not found.
DeleteUserProfile
$result = $client->deleteUserProfile([/* ... */]); $promise = $client->deleteUserProfileAsync([/* ... */]);
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
Parameter Syntax
$result = $client->deleteUserProfile([
'DomainId' => '<string>', // REQUIRED
'UserProfileName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- UserProfileName
-
- Required: Yes
- Type: string
The user profile name.
Result Syntax
[]
Result Details
Errors
-
Resource being accessed is in use.
-
Resource being access is not found.
DeleteWorkforce
$result = $client->deleteWorkforce([/* ... */]); $promise = $client->deleteWorkforceAsync([/* ... */]);
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.
Parameter Syntax
$result = $client->deleteWorkforce([
'WorkforceName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- WorkforceName
-
- Required: Yes
- Type: string
The name of the workforce.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DeleteWorkteam
$result = $client->deleteWorkteam([/* ... */]); $promise = $client->deleteWorkteamAsync([/* ... */]);
Deletes an existing work team. This operation can't be undone.
Parameter Syntax
$result = $client->deleteWorkteam([
'WorkteamName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- WorkteamName
-
- Required: Yes
- Type: string
The name of the work team to delete.
Result Syntax
[
'Success' => true || false,
]
Result Details
Members
- Success
-
- Required: Yes
- Type: boolean
Returns
trueif the work team was successfully deleted; otherwise, returnsfalse.
Errors
-
You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
DeregisterDevices
$result = $client->deregisterDevices([/* ... */]); $promise = $client->deregisterDevicesAsync([/* ... */]);
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
Parameter Syntax
$result = $client->deregisterDevices([
'DeviceFleetName' => '<string>', // REQUIRED
'DeviceNames' => ['<string>', ...], // REQUIRED
]);
Parameter Details
Members
- DeviceFleetName
-
- Required: Yes
- Type: string
The name of the fleet the devices belong to.
- DeviceNames
-
- Required: Yes
- Type: Array of strings
The unique IDs of the devices.
Result Syntax
[]
Result Details
Errors
There are no errors described for this operation.
DescribeAction
$result = $client->describeAction([/* ... */]); $promise = $client->describeActionAsync([/* ... */]);
Describes an action.
Parameter Syntax
$result = $client->describeAction([
'ActionName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ActionName
-
- Required: Yes
- Type: string
The name of the action to describe.
Result Syntax
[
'ActionArn' => '<string>',
'ActionName' => '<string>',
'ActionType' => '<string>',
'CreatedBy' => [
'DomainId' => '<string>',
'IamIdentity' => [
'Arn' => '<string>',
'PrincipalId' => '<string>',
'SourceIdentity' => '<string>',
],
'UserProfileArn' => '<string>',
'UserProfileName' => '<string>',
],
'CreationTime' => <DateTime>,
'Description' => '<string>',
'LastModifiedBy' => [
'DomainId' => '<string>',
'IamIdentity' => [
'Arn' => '<string>',
'PrincipalId' => '<string>',
'SourceIdentity' => '<string>',
],
'UserProfileArn' => '<string>',
'UserProfileName' => '<string>',
],
'LastModifiedTime' => <DateTime>,
'LineageGroupArn' => '<string>',
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'Properties' => ['<string>', ...],
'Source' => [
'SourceId' => '<string>',
'SourceType' => '<string>',
'SourceUri' => '<string>',
],
'Status' => 'Unknown|InProgress|Completed|Failed|Stopping|Stopped',
]
Result Details
Members
- ActionArn
-
- Type: string
The Amazon Resource Name (ARN) of the action.
- ActionName
-
- Type: string
The name of the action.
- ActionType
-
- Type: string
The type of the action.
- CreatedBy
-
- Type: UserContext structure
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
- CreationTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the action was created.
- Description
-
- Type: string
The description of the action.
- LastModifiedBy
-
- Type: UserContext structure
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
- LastModifiedTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the action was last modified.
- LineageGroupArn
-
- Type: string
The Amazon Resource Name (ARN) of the lineage group.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- Properties
-
- Type: Associative array of custom strings keys (StringParameterValue) to strings
A list of the action's properties.
- Source
-
- Type: ActionSource structure
The source of the action.
- Status
-
- Type: string
The status of the action.
Errors
-
Resource being access is not found.
DescribeAlgorithm
$result = $client->describeAlgorithm([/* ... */]); $promise = $client->describeAlgorithmAsync([/* ... */]);
Returns a description of the specified algorithm that is in your account.
Parameter Syntax
$result = $client->describeAlgorithm([
'AlgorithmName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AlgorithmName
-
- Required: Yes
- Type: string
The name of the algorithm to describe.
Result Syntax
[
'AlgorithmArn' => '<string>',
'AlgorithmDescription' => '<string>',
'AlgorithmName' => '<string>',
'AlgorithmStatus' => 'Pending|InProgress|Completed|Failed|Deleting',
'AlgorithmStatusDetails' => [
'ImageScanStatuses' => [
[
'FailureReason' => '<string>',
'Name' => '<string>',
'Status' => 'NotStarted|InProgress|Completed|Failed',
],
// ...
],
'ValidationStatuses' => [
[
'FailureReason' => '<string>',
'Name' => '<string>',
'Status' => 'NotStarted|InProgress|Completed|Failed',
],
// ...
],
],
'CertifyForMarketplace' => true || false,
'CreationTime' => <DateTime>,
'InferenceSpecification' => [
'Containers' => [
[
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object',
'S3Uri' => '<string>',
],
'ContainerHostname' => '<string>',
'Environment' => ['<string>', ...],
'Framework' => '<string>',
'FrameworkVersion' => '<string>',
'Image' => '<string>',
'ImageDigest' => '<string>',
'ModelDataUrl' => '<string>',
'ModelInput' => [
'DataInputConfig' => '<string>',
],
'NearestModelName' => '<string>',
'ProductId' => '<string>',
],
// ...
],
'SupportedContentTypes' => ['<string>', ...],
'SupportedRealtimeInferenceInstanceTypes' => ['<string>', ...],
'SupportedResponseMIMETypes' => ['<string>', ...],
'SupportedTransformInstanceTypes' => ['<string>', ...],
],
'ProductId' => '<string>',
'TrainingSpecification' => [
'AdditionalS3DataSource' => [
'CompressionType' => 'None|Gzip',
'S3DataType' => 'S3Object',
'S3Uri' => '<string>',
],
'MetricDefinitions' => [
[
'Name' => '<string>',
'Regex' => '<string>',
],
// ...
],
'SupportedHyperParameters' => [
[
'DefaultValue' => '<string>',
'Description' => '<string>',
'IsRequired' => true || false,
'IsTunable' => true || false,
'Name' => '<string>',
'Range' => [
'CategoricalParameterRangeSpecification' => [
'Values' => ['<string>', ...],
],
'ContinuousParameterRangeSpecification' => [
'MaxValue' => '<string>',
'MinValue' => '<string>',
],
'IntegerParameterRangeSpecification' => [
'MaxValue' => '<string>',
'MinValue' => '<string>',
],
],
'Type' => 'Integer|Continuous|Categorical|FreeText',
],
// ...
],
'SupportedTrainingInstanceTypes' => ['<string>', ...],
'SupportedTuningJobObjectiveMetrics' => [
[
'MetricName' => '<string>',
'Type' => 'Maximize|Minimize',
],
// ...
],
'SupportsDistributedTraining' => true || false,
'TrainingChannels' => [
[
'Description' => '<string>',
'IsRequired' => true || false,
'Name' => '<string>',
'SupportedCompressionTypes' => ['<string>', ...],
'SupportedContentTypes' => ['<string>', ...],
'SupportedInputModes' => ['<string>', ...],
],
// ...
],
'TrainingImage' => '<string>',
'TrainingImageDigest' => '<string>',
],
'ValidationSpecification' => [
'ValidationProfiles' => [
[
'ProfileName' => '<string>',
'TrainingJobDefinition' => [
'HyperParameters' => ['<string>', ...],
'InputDataConfig' => [
[
'ChannelName' => '<string>',
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [
'FileSystemDataSource' => [
'DirectoryPath' => '<string>',
'FileSystemAccessMode' => 'rw|ro',
'FileSystemId' => '<string>',
'FileSystemType' => 'EFS|FSxLustre',
],
'S3DataSource' => [
'AttributeNames' => ['<string>', ...],
'InstanceGroupNames' => ['<string>', ...],
'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile',
'S3Uri' => '<string>',
],
],
'InputMode' => 'Pipe|File|FastFile',
'RecordWrapperType' => 'None|RecordIO',
'ShuffleConfig' => [
'Seed' => <integer>,
],
],
// ...
],
'OutputDataConfig' => [
'CompressionType' => 'GZIP|NONE',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>',
],
'ResourceConfig' => [
'InstanceCount' => <integer>,
'InstanceGroups' => [
[
'InstanceCount' => <integer>,
'InstanceGroupName' => '<string>',
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
],
// ...
],
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.p4d.24xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.c5n.xlarge|ml.c5n.2xlarge|ml.c5n.4xlarge|ml.c5n.9xlarge|ml.c5n.18xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge|ml.p5.48xlarge',
'KeepAlivePeriodInSeconds' => <integer>,
'VolumeKmsKeyId' => '<string>',
'VolumeSizeInGB' => <integer>,
],
'StoppingCondition' => [
'MaxPendingTimeInSeconds' => <integer>,
'MaxRuntimeInSeconds' => <integer>,
'MaxWaitTimeInSeconds' => <integer>,
],
'TrainingInputMode' => 'Pipe|File|FastFile',
],
'TransformJobDefinition' => [
'BatchStrategy' => 'MultiRecord|SingleRecord',
'Environment' => ['<string>', ...],
'MaxConcurrentTransforms' => <integer>,
'MaxPayloadInMB' => <integer>,
'TransformInput' => [
'CompressionType' => 'None|Gzip',
'ContentType' => '<string>',
'DataSource' => [
'S3DataSource' => [
'S3DataType' => 'ManifestFile|S3Prefix|AugmentedManifestFile',
'S3Uri' => '<string>',
],
],
'SplitType' => 'None|Line|RecordIO|TFRecord',
],
'TransformOutput' => [
'Accept' => '<string>',
'AssembleWith' => 'None|Line',
'KmsKeyId' => '<string>',
'S3OutputPath' => '<string>',
],
'TransformResources' => [
'InstanceCount' => <integer>,
'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge',
'VolumeKmsKeyId' => '<string>',
],
],
],
// ...
],
'ValidationRole' => '<string>',
],
]
Result Details
Members
- AlgorithmArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the algorithm.
- AlgorithmDescription
-
- Type: string
A brief summary about the algorithm.
- AlgorithmName
-
- Required: Yes
- Type: string
The name of the algorithm being described.
- AlgorithmStatus
-
- Required: Yes
- Type: string
The current status of the algorithm.
- AlgorithmStatusDetails
-
- Required: Yes
- Type: AlgorithmStatusDetails structure
Details about the current status of the algorithm.
- CertifyForMarketplace
-
- Type: boolean
Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
- CreationTime
-
- Required: Yes
- Type: timestamp (string|DateTime or anything parsable by strtotime)
A timestamp specifying when the algorithm was created.
- InferenceSpecification
-
- Type: InferenceSpecification structure
Details about inference jobs that the algorithm runs.
- ProductId
-
- Type: string
The product identifier of the algorithm.
- TrainingSpecification
-
- Required: Yes
- Type: TrainingSpecification structure
Details about training jobs run by this algorithm.
- ValidationSpecification
-
- Type: AlgorithmValidationSpecification structure
Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
Errors
There are no errors described for this operation.
DescribeApp
$result = $client->describeApp([/* ... */]); $promise = $client->describeAppAsync([/* ... */]);
Describes the app.
Parameter Syntax
$result = $client->describeApp([
'AppName' => '<string>', // REQUIRED
'AppType' => 'JupyterServer|KernelGateway|TensorBoard|RStudioServerPro|RSessionGateway', // REQUIRED
'DomainId' => '<string>', // REQUIRED
'SpaceName' => '<string>',
'UserProfileName' => '<string>',
]);
Parameter Details
Members
- AppName
-
- Required: Yes
- Type: string
The name of the app.
- AppType
-
- Required: Yes
- Type: string
The type of app.
- DomainId
-
- Required: Yes
- Type: string
The domain ID.
- SpaceName
-
- Type: string
The name of the space.
- UserProfileName
-
- Type: string
The user profile name. If this value is not set, then
SpaceNamemust be set.
Result Syntax
[
'AppArn' => '<string>',
'AppName' => '<string>',
'AppType' => 'JupyterServer|KernelGateway|TensorBoard|RStudioServerPro|RSessionGateway',
'CreationTime' => <DateTime>,
'DomainId' => '<string>',
'FailureReason' => '<string>',
'LastHealthCheckTimestamp' => <DateTime>,
'LastUserActivityTimestamp' => <DateTime>,
'ResourceSpec' => [
'InstanceType' => 'system|ml.t3.micro|ml.t3.small|ml.t3.medium|ml.t3.large|ml.t3.xlarge|ml.t3.2xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.8xlarge|ml.m5.12xlarge|ml.m5.16xlarge|ml.m5.24xlarge|ml.m5d.large|ml.m5d.xlarge|ml.m5d.2xlarge|ml.m5d.4xlarge|ml.m5d.8xlarge|ml.m5d.12xlarge|ml.m5d.16xlarge|ml.m5d.24xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.12xlarge|ml.c5.18xlarge|ml.c5.24xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.p3dn.24xlarge|ml.g4dn.xlarge|ml.g4dn.2xlarge|ml.g4dn.4xlarge|ml.g4dn.8xlarge|ml.g4dn.12xlarge|ml.g4dn.16xlarge|ml.r5.large|ml.r5.xlarge|ml.r5.2xlarge|ml.r5.4xlarge|ml.r5.8xlarge|ml.r5.12xlarge|ml.r5.16xlarge|ml.r5.24xlarge|ml.g5.xlarge|ml.g5.2xlarge|ml.g5.4xlarge|ml.g5.8xlarge|ml.g5.16xlarge|ml.g5.12xlarge|ml.g5.24xlarge|ml.g5.48xlarge|ml.geospatial.interactive|ml.p4d.24xlarge|ml.p4de.24xlarge|ml.trn1.2xlarge|ml.trn1.32xlarge|ml.trn1n.32xlarge',
'LifecycleConfigArn' => '<string>',
'SageMakerImageArn' => '<string>',
'SageMakerImageVersionAlias' => '<string>',
'SageMakerImageVersionArn' => '<string>',
],
'SpaceName' => '<string>',
'Status' => 'Deleted|Deleting|Failed|InService|Pending',
'UserProfileName' => '<string>',
]
Result Details
Members
- AppArn
-
- Type: string
The Amazon Resource Name (ARN) of the app.
- AppName
-
- Type: string
The name of the app.
- AppType
-
- Type: string
The type of app.
- CreationTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
The creation time.
- DomainId
-
- Type: string
The domain ID.
- FailureReason
-
- Type: string
The failure reason.
- LastHealthCheckTimestamp
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
The timestamp of the last health check.
- LastUserActivityTimestamp
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
The timestamp of the last user's activity.
LastUserActivityTimestampis also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value asLastHealthCheckTimestamp. - ResourceSpec
-
- Type: ResourceSpec structure
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
- SpaceName
-
- Type: string
The name of the space. If this value is not set, then
UserProfileNamemust be set. - Status
-
- Type: string
The status.
- UserProfileName
-
- Type: string
The user profile name.
Errors
-
Resource being access is not found.
DescribeAppImageConfig
$result = $client->describeAppImageConfig([/* ... */]); $promise = $client->describeAppImageConfigAsync([/* ... */]);
Describes an AppImageConfig.
Parameter Syntax
$result = $client->describeAppImageConfig([
'AppImageConfigName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AppImageConfigName
-
- Required: Yes
- Type: string
The name of the AppImageConfig to describe.
Result Syntax
[
'AppImageConfigArn' => '<string>',
'AppImageConfigName' => '<string>',
'CreationTime' => <DateTime>,
'KernelGatewayImageConfig' => [
'FileSystemConfig' => [
'DefaultGid' => <integer>,
'DefaultUid' => <integer>,
'MountPath' => '<string>',
],
'KernelSpecs' => [
[
'DisplayName' => '<string>',
'Name' => '<string>',
],
// ...
],
],
'LastModifiedTime' => <DateTime>,
]
Result Details
Members
- AppImageConfigArn
-
- Type: string
The Amazon Resource Name (ARN) of the AppImageConfig.
- AppImageConfigName
-
- Type: string
The name of the AppImageConfig.
- CreationTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the AppImageConfig was created.
- KernelGatewayImageConfig
-
- Type: KernelGatewayImageConfig structure
The configuration of a KernelGateway app.
- LastModifiedTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the AppImageConfig was last modified.
Errors
-
Resource being access is not found.
DescribeArtifact
$result = $client->describeArtifact([/* ... */]); $promise = $client->describeArtifactAsync([/* ... */]);
Describes an artifact.
Parameter Syntax
$result = $client->describeArtifact([
'ArtifactArn' => '<string>', // REQUIRED
]);
Parameter Details
Members
- ArtifactArn
-
- Required: Yes
- Type: string
The Amazon Resource Name (ARN) of the artifact to describe.
Result Syntax
[
'ArtifactArn' => '<string>',
'ArtifactName' => '<string>',
'ArtifactType' => '<string>',
'CreatedBy' => [
'DomainId' => '<string>',
'IamIdentity' => [
'Arn' => '<string>',
'PrincipalId' => '<string>',
'SourceIdentity' => '<string>',
],
'UserProfileArn' => '<string>',
'UserProfileName' => '<string>',
],
'CreationTime' => <DateTime>,
'LastModifiedBy' => [
'DomainId' => '<string>',
'IamIdentity' => [
'Arn' => '<string>',
'PrincipalId' => '<string>',
'SourceIdentity' => '<string>',
],
'UserProfileArn' => '<string>',
'UserProfileName' => '<string>',
],
'LastModifiedTime' => <DateTime>,
'LineageGroupArn' => '<string>',
'MetadataProperties' => [
'CommitId' => '<string>',
'GeneratedBy' => '<string>',
'ProjectId' => '<string>',
'Repository' => '<string>',
],
'Properties' => ['<string>', ...],
'Source' => [
'SourceTypes' => [
[
'SourceIdType' => 'MD5Hash|S3ETag|S3Version|Custom',
'Value' => '<string>',
],
// ...
],
'SourceUri' => '<string>',
],
]
Result Details
Members
- ArtifactArn
-
- Type: string
The Amazon Resource Name (ARN) of the artifact.
- ArtifactName
-
- Type: string
The name of the artifact.
- ArtifactType
-
- Type: string
The type of the artifact.
- CreatedBy
-
- Type: UserContext structure
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
- CreationTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the artifact was created.
- LastModifiedBy
-
- Type: UserContext structure
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
- LastModifiedTime
-
- Type: timestamp (string|DateTime or anything parsable by strtotime)
When the artifact was last modified.
- LineageGroupArn
-
- Type: string
The Amazon Resource Name (ARN) of the lineage group.
- MetadataProperties
-
- Type: MetadataProperties structure
Metadata properties of the tracking entity, trial, or trial component.
- Properties
-
- Type: Associative array of custom strings keys (StringParameterValue) to strings
A list of the artifact's properties.
- Source
-
- Type: ArtifactSource structure
The source of the artifact.
Errors
-
Resource being access is not found.
DescribeAutoMLJob
$result = $client->describeAutoMLJob([/* ... */]); $promise = $client->describeAutoMLJobAsync([/* ... */]);
Returns information about an AutoML job created by calling CreateAutoMLJob.
AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
Parameter Syntax
$result = $client->describeAutoMLJob([
'AutoMLJobName' => '<string>', // REQUIRED
]);
Parameter Details
Members
- AutoMLJobName
-
- Required: Yes
- Type: string
Requests information about an AutoML job using its unique name.
Result Syntax
[
'AutoMLJobArn' => '<string>',
'AutoMLJobArtifacts' => [
'CandidateDefinitionNotebookLocation' => '<string>',
'DataExplorationNotebookLocation' => '<string>',
],
'AutoMLJobConfig' => [
'CandidateGenerationConfig' => [
'AlgorithmsConfig' => [
[
'AutoMLAlgorithms' => ['<string>', ...],
],
// ...
],
'FeatureSpecificationS3Uri' => '<string>',
],
'CompletionCriteria' => [
'MaxAutoMLJobRuntimeInSeconds' => <integer>,
'MaxCandidates' => <integer>,
'MaxRuntimePerTrainingJobInSeconds' => <integer>,
],
'DataSplitConfig' => [
'ValidationFraction' => <float>,
],
'Mode' => 'AUTO|ENSEMBLING|HYPERPARAMETER_TUNING',
'SecurityConfig' => [
'EnableInterContainerTrafficEncryption' => true || false,
'VolumeKmsKeyId' => '<string>',
'VpcConfig' => [
'SecurityGroupIds' => ['<string>', ...],
'Subnets' => ['<string>', ...],
],
],
],
'AutoMLJobName' => '<string>',
'AutoMLJobObjective' => [
'MetricName' => 'Accuracy|MSE|F1|F1macro|AUC|RMSE|MAE|R2|BalancedAccuracy|Precision|PrecisionMacro|Recall|RecallMacro|MAPE|MASE|WAPE|AverageWeightedQuantileLoss',
],
'AutoMLJobSecondaryStatus' => 'Starting|AnalyzingData|FeatureEngineering|ModelTuning|MaxCandidatesReached|Failed|Stopped|MaxAutoMLJobRuntimeReached|Stopping|CandidateDefinitionsGenerated|GeneratingExplainabilityReport|Completed|ExplainabilityError|DeployingModel|ModelDeploymentError|GeneratingModelInsightsReport|Model